#manual vs automated AI text conversion

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Difference Between Manually Humanizing AI Text vs Using an AI To Human Text Converter

AI-generated content has become increasingly prevalent. Whether for academic writing, blog posts, or content marketing, AI-generated text can serve as a quick starting point for various writing tasks. However, there’s a clear distinction between manually humanizing AI text and using a reliable AI to human text converter. AI To Human Text Converter offers an efficient, free tool to convert AI-generated text into human-like content without compromising meaning, making it a must-have resource for content creators, students, and professionals.

What is AI to Human Text Conversion?

AI to human text conversion refers to transforming machine-generated content into natural, human-readable text. AI-generated text is often monotonous, lacks engagement, and reads robotically, making it necessary to humanize it for professional use.

When it comes to humanizing AI text, there are two approaches:

Manual Humanization: Manually refining the content by rewriting it, adjusting tone, and improving readability.

Using an AI To Human Text Converter: Automating the process with a specialized tool that instantly converts AI-generated text into a more natural-sounding format.

Let’s explore the differences between these two methods.

Manual Humanization of AI Text

Manually humanizing AI text can be effective but comes with its own set of challenges.

Speed: Manually editing and rewriting AI-generated content is time-consuming. It requires a keen understanding of tone, context, and structure to make the text flow naturally.

Complexity: Manually humanizing text demands a high level of effort and expertise. You need to rephrase sentences, break down robotic language, and ensure that the text makes sense while retaining its original meaning.

Security Risks: When manually humanizing AI text, especially if it's sensitive content, the risk of human error increases. There’s always a chance that important details could be misrepresented or misunderstood.

Limitations: Manually refining large volumes of AI-generated text is often overwhelming. This method is not ideal for those who need to process substantial content within tight deadlines.

Using an AI To Human Text Converter

Conversely, an AI To Human Text Converter automates the entire process and offers several benefits over manual humanization.

Speed: Converting AI-generated text into human-readable content is nearly instantaneous with a tool like AI To Human Text Converter. Instead of spending hours manually editing content, users can generate human-like text in seconds.

Simplicity: The tool’s user interface is straightforward, requiring no prior knowledge or expertise to use effectively. Just paste the AI-generated text, click convert, and you’re done!

Security: Since the conversion happens without the need for third-party apps or plugins, security risks are minimized. The tool allows users to safely convert any content while ensuring confidentiality.

No Limitations: With AI To Human Text Converter, there are no usage limits. You can convert as much AI text as needed without worrying about word counts, logins, or subscription fees.

Completely Free: Unlike many online tools that charge for premium access or impose restrictions on free versions, AI To Human Text Converter is entirely free with unlimited usage. No login, signup, or subscription is required.

Key Features Comparison

FeaturesManual Humanize AI TextAI To Human Text ConverterConversion of AI-Generated ContentAvailableAvailableUser InterfaceNot ApplicableStraightforwardSpeedSlowerExtremely FastSecurity RiskRisk-proneSecureUsage LimitationsLimitedUnlimited

Why Use AI To Human Text Converter?

If you’re tired of spending hours trying to manually humanize AI-generated content or need a quick, efficient way to transform robotic text into something engaging and natural, AI To Human Text Converter is the perfect solution. Here’s why it’s the best choice for you:

Free of Cost: The tool is 100% free with no hidden charges. Convert as much text as you need without worrying about subscriptions or limits.

No Authentication Required: There’s no need to sign up or log in. The tool is ready for immediate use.

Unlimited Usage: Whether you’re working on a single article or hundreds of pages, AI To Human Text Converter allows you to convert unlimited content.

Quick and Accurate: Get your AI-generated content humanized in seconds, with natural flow and readability.

Ideal for Multiple Applications: Whether it’s for assignments, essays, marketing materials, or blog posts, this tool ensures your text is suitable for any professional setting.

Why Manual Humanization is Not Always Practical

While manually humanizing AI-generated text can yield good results, it’s often impractical. Here’s why:

Time-Consuming: If you have large volumes of text, manually refining it will take too long. For students or professionals working on tight deadlines, this isn’t a viable solution.

Inconsistency: Manually humanizing content introduces the risk of inconsistencies. What may seem like a minor adjustment to one part of the text might lead to inconsistencies in tone or style.

Increased Effort: Editing AI content manually requires considerable effort, especially for non-professionals who may struggle with rewriting sections effectively.

Choosing between manually humanizing AI text and using an AI to human text converter depends on your needs, time constraints, and the volume of content you’re working with. For anyone looking to save time and effort, AI To Human Text Converter is an invaluable tool. It simplifies the process, delivers human-like text instantly, and comes with no cost or limitations.

Try AI To Human Text Converter today, and discover how easy it is to humanize AI-generated text with just a few clicks!

[seotraininginahmedabad]

Digital Marketing Course in New Chandkheda

1. Digital Marketing Course in New Chandkheda Ahmedabad Overview

2. Personal Digital Marketing Course in New Chandkheda – Search Engine Optimization (SEO)

What are Search Engines and Basics?

HTML Basics.

On Page Optimization.

Off Page Optimization.

Essentials of good website designing & Much More.

3. Content Marketing

Content Marketing Overview and Strategy

Content Marketing Channels

Creating Content

Content Strategy & Challenges

Image Marketing

Video Marketing

Measuring Results

4. Website Structuring

What is Website?- Understanding website

How to register Site & Hosting of site?

Domain Extensions

5. Website Creation Using WordPress

Web Page Creation

WordPress Themes, Widgets, Plugins

Contact Forms, Sliders, Elementor

6. Blog Writing

Blogs Vs Website

How to write blogs for website

How to select topics for blog writing

AI tools for Blog writing

7. Google Analytics

Introduction

Navigating Google Analytics

Sessions

Users

Traffic Source

Content

Real Time Visitors

Bounce Rate%

Customization

Reports

Actionable Insights

Making Better Decisions

8. Understand Acquisition & Conversion

Traffic Reports

Events Tracking

Customization Reports

Actionable Insights

Making Better Decisions

Comparision Reports

9. Google Search Console

Website Performance

Url Inspection

Accelerated Mobile Pages

Google index

Crawl

Security issues

Search Analytics

Links to your Site

Internal Links

Manual Actions

10. Voice Search Optimization

What is voice engine optimization?

How do you implement voice search optimization?

Why you should optimize your website for voice search?

11. E Commerce SEO

Introduction to E commerce SEO

What is e-commerce SEO?

How Online Stores Can Drive Organic Traffic

12. Google My Business: Local Listings

What is Local SEO

Importance of Local SEO

Submission to Google My Business

Completing the Profile

Local SEO Ranking Signals

Local SEO Negative Signals

Citations and Local

Submissions

13. Social Media Optimization

What is Social Media?

How social media help Business?

Establishing your online identity.

Engaging your Audience.

How to use Groups, Forums, etc.

14. Facebook Organic

How can Facebook be used to aid my business?

Developing a useful Company / fan Page

Establishing your online identity.

Engaging your Audience, Types of posts, post scheduling

How to create & use Groups

Importance of Hashtags & how to use them

15. Twitter Organic

Basic concepts – from setting-up optimally, creating a Twitter business existence, to advanced marketing procedures and strategies.

How to use Twitter

What are hashtags, Lists

Twitter Tools

Popular Twitter Campiagns

16. LinkedIn Organic

Your Profile: Building quality connections & getting recommendations from others

How to use Groups-drive traffic with news & discussions

How to create LinkedIn Company Page & Groups

Engaging your Audience.

17. YouTube Organic

How to create YouTube channel

Youtube Keyword Research

Publish a High Retention Video

YouTube ranking factors

YouTube Video Optimization

Promote Your Video

Use of playlists

18. Video SEO

YouTube Keyword Research

Publish a High Retention Video

YouTube Ranking Factors

YouTube Video Optimization

19. YouTube Monetization

YouTube channel monetization policies

How Does YouTube Monetization Work?

YouTube monetization requirements

20. Social Media Tools

What are the main types of social media tools?

Top Social Media Tools You Need to Use

Tools used for Social Media Management

21. Social Media Automation

What is Social Media Automation?

Social Media Automation/ Management Tool

Buffer/ Hootsuite/ Postcron

Setup Connection with Facebook, Twitter, Linkedin, Instagram, Etc.

Add/ Remove Profiles in Tools

Post Scheduling in Tools

Performance Analysis

22. Facebook Ads

How to create Business Manager Accounts

What is Account, Campaign, Ad Sets, Ad Copy

How to Create Campaigns on Facebook

What is Budget & Bidding

Difference Between Reach & Impressions

Facebook Retargeting

23. Instagram Ads

Text Ads and Guidelines

Image Ad Formats and Guidelines

Landing Page Optimization

Performance Metrics: CTR, Avg. Position, Search Term

Report, Segment Data Analysis, Impression Shares

AdWords Policies, Ad Extensions

24. LinkedIn Ads

How to create Campaign Manager Account

What is Account, Campaign Groups, Campaigns

Objectives for Campaigns

Bidding Strategies

Detail Targeting

25. YouTube Advertising

How to run Video Ads?

Types of Video Ads:

Skippable in Stream Ads

Non Skippable in stream Ads

Bumper Ads

Bidding Strategies for Video Ads

26. Google PPC

Ad-Words Account Setup

Creating Ad-Words Account

Ad-Words Dash Board

Billing in Ad-Words

Creating First Campaign

Understanding purpose of Campaign

Account Limits in Ad-Words

Location and Language Settings

Networks and Devices

Bidding and Budget

Schedule: Start date, end date, ad scheduling

Ad delivery: Ad rotation, frequency capping

Ad groups and Keywords

27. Search Ads/ Text Ads

Text Ads and Guidelines

Landing Page Optimization

Performance Metrics: CTR, Avg. Position, Search Term

Report, Segment Data Analysis, Impression Shares

AdWords Policies, Ad Extensions

CPC bidding

Types of Keywords: Exact, Broad, Phrase

Bids & Budget

How to create Text ads

28. Image Ads

Image Ad Formats and Guidelines

Targeting Methods: Keywords, Topics, Placement Targeting

Performance Metrics: CPM, vCPM, Budget

Report, Segment Data Analysis, Impression Shares

Frequency Capping

Automated rules

Target Audience Strategies

29. Video Ads

How to Video Ads

Types of Video Ads

Skippable in stream ads

Non-skippable in stream ads

Bumper Ads

How to link Google AdWords Account to YouTube Channel

30. Discovery Ads

What are Discovery Ads

How to Create Discovery Ads

Bidding Strategies

How to track conversions

31. Bidding Strategies in Google Ads

Different Bidding Strategies in Google AdWords

CPC bidding, CPM bidding, CPV bidding

How to calculate CTR

What are impressions, impression shares

32. Performance Planner

33. Lead Generation for Business

Why Lead Generation Is Important?

Understanding the Landing Page

Understanding Thank You Page

Landing Page Vs. Website

Best Practices to Create Landing Page

Best Practices to Create Thank You Page

What Is A/B Testing?

How to Do A/B Testing?

Converting Leads into Sale

Understanding Lead Funnel

34. Conversion Tracking Tool

Introduction to Conversion Optimization

Conversion Planning

Landing Page Optimization

35. Remarketing and Conversion

What is conversion

Implementing conversion tracking

Conversion tracking

Remarketing in adwords

Benefits of remarketing strategy

Building remarketing list & custom targets

Creating remarketing campaign

36. Quora Marketing

How to Use Quora for Marketing

Quora Marketing Strategy for Your Business

37. Growth Hacking Topic

Growth Hacking Basics

Role of Growth Hacker

Growth Hacking Case Studies

38. Introduction to Affiliate Marketing

Understanding Affiliate Marketing

Sources to Make money online

Applying for an Affiliate

Payments & Payouts

Blogging

39. Introduction to Google AdSense

Basics of Google Adsense

Adsense code installation

Different types of Ads

Increasing your profitability through Adsense

Effective tips in placing video, image and text ads into your website correctly

40. Google Tag Manager

Adding GTM to your website

Configuring trigger & variables

Set up AdWords conversion tracking

Set up Google Analytics

Set up Google Remarketing

Set up LinkedIn Code

41. Email Marketing

Introduction to Email Marketing basic.

How does Email Marketing Works.

Building an Email List.

Creating Email Content.

Optimising Email Campaign.

CAN SPAM Act

Email Marketing Best Practices

42. SMS Marketing

Setting up account for Bulk SMS

Naming the Campaign & SMS

SMS Content

Character limits

SMS Scheduling

43. Media Buying

Advertising: Principles, Concepts and Management

Media Planning

44. What’s App Marketing

Whatsapp Marketing Strategies

Whatsapp Business Features

Business Profile Setup

Auto Replies

45. Influencer Marketing

Major topics covered are, identifying the influencers, measuring them, and establishing a relationship with the influencer. A go through the influencer marketing case studies.

46. Freelancing Projects

How to work as a freelancer

Different websites for getting projects on Digital Marketing

47. Online Reputation Management

What Is ORM?

Why We Need ORM

Examples of ORM

Case Study

48. Resume Building

How to build resume for different job profiles

Platforms for resume building

Which points you should add in Digital Marketing Resume

49. Interview Preparation

Dos and Don’t for Your First Job Interview

How to prepare for interview

Commonly asked interview question & answers

50. Client Pitch

How to send quotation to the clients

How to decide budget for campaign

Quotation formats

51. Graphic Designing: Canva

How to create images using tools like Canva 

How to add effects to images

52. Analysis of Other Website

Post navigatio

[softwaredevelopusa]

Voice & Sentiment Analysis in Customer Feedback Platforms

The digital age has fundamentally reshaped the relationship between businesses and their customers. With every click, comment, call, and review, customers are generating an unprecedented volume of feedback. This rich tapestry of information, often unstructured and voluminous, holds the key to understanding customer satisfaction, identifying pain points, and driving product and service improvements. However, manually sifting through thousands, or even millions, of interactions is an impossible task. This is where the power of Voice and Sentiment Analysis in Customer Feedback Platforms becomes transformative.

No longer limited to simple surveys, modern Customer Feedback platforms leverage advanced Artificial Intelligence (AI) and Machine Learning (ML) to listen, comprehend, and quantify the emotions and intent behind what customers say and write. By analyzing both the content of spoken and written words, as well as the nuances of tone and behavior, these platforms provide businesses with unparalleled, real-time insights into the true "Voice of the Customer" (VoC). This capability allows organizations to move from reactive problem-solving to proactive customer engagement, fostering deeper loyalty and competitive advantage.

The Evolution of Customer Feedback Analysis

Traditional methods of collecting and analyzing Customer Feedback have inherent limitations:

Surveys (NPS, CSAT, CES): While structured and quantifiable, surveys often suffer from low response rates, selection bias, and the inability to capture the full context or underlying emotion. They provide "what" but rarely "why."

Manual Review of Text Feedback: Analysts manually read through emails, chat logs, and written reviews. This is time-consuming, prone to human bias, and not scalable for large volumes of data.

Ad-hoc Listening to Call Recordings: Listening to a small sample of calls offers anecdotal insights but lacks comprehensive, objective analysis across all interactions.

Voice and Sentiment Analysis, powered by Natural Language Processing (NLP) and Machine Learning, represent a paradigm shift. They enable automated, scalable, and objective analysis of unstructured Customer Feedback, allowing businesses to extract richer, deeper insights.

Demystifying Voice Analysis in Customer Feedback Platforms

Voice analysis, often referred to as Speech Analytics, goes beyond mere transcription to understand the nuances of spoken communication. It's particularly crucial for contact centers and any business that interacts with customers over the phone.

How Voice Analysis Works:

Audio Capture & Transcription (Speech-to-Text):

The first step involves capturing audio from customer calls, voicemails, or recorded interactions.

Advanced Automatic Speech Recognition (ASR) technology, often powered by deep learning, then transcribes these spoken words into text. Modern ASR models are highly accurate, even handling different accents, dialects, and speaking speeds.

Speaker Diarization:

Identifies and separates different speakers in a conversation (e.g., customer vs. agent). This allows for separate analysis of each participant's speech and sentiment.

Acoustic Feature Extraction:

Beyond words, voice analysis extracts acoustic features that convey emotion and meaning. These include:

Pitch: The perceived highness or lowness of a voice.

Volume/Loudness: Indicates intensity or stress.

Speaking Rate: Speed of speech, which can indicate urgency or frustration.

Vocal Energy: Reflects excitement or fatigue.

Pauses and Fillers: (e.g., "um," "uh") can signal hesitation or discomfort.

Tone of Voice: Overall emotional quality.

Language and Contextual Analysis:

The transcribed text, combined with acoustic features, is then subjected to NLP techniques to understand the linguistic content.

This includes identifying keywords, phrases, topics, and intents.

For example, identifying keywords like "billing issue," "product defect," or "account setup" to categorize the reason for the call.

Demystifying Sentiment Analysis in Customer Feedback Platforms

Sentiment analysis, also known as Opinion Mining, is the computational process of determining the emotional tone behind a piece of text or speech – whether it's positive, negative, or neutral. When combined with voice analysis, it paints a truly comprehensive picture.

How Sentiment Analysis Works:

Text Pre-processing:

The transcribed text (from voice calls, or direct text input like emails, chats, reviews, social media) is cleaned and normalized. This involves removing punctuation, converting text to lowercase, stemming/lemmatization (reducing words to their root form), and removing stop words (common words like "the," "is").

Feature Extraction:

This involves identifying features from the text that indicate sentiment. Common approaches include:

Lexicon-Based: Using pre-defined dictionaries of words associated with positive or negative sentiment (e.g., "amazing" = positive, "frustrating" = negative). Each word might have a sentiment score.

Rule-Based: Applying linguistic rules to analyze sentence structure, presence of negations (e.g., "not good" vs. "good"), intensifiers (e.g., "very good" vs. "good"), or emojis.

Machine Learning Model Application:

ML algorithms (e.g., Support Vector Machines, Naive Bayes, Recurrent Neural Networks, Transformer models) are trained on vast datasets of human-labeled text to learn patterns between words, phrases, and their associated sentiment.

Fine-Grained Sentiment: Beyond just positive/negative/neutral, advanced models can identify nuanced sentiments like "very positive," "slightly negative," or even specific emotions (joy, anger, sadness, surprise, frustration).

Aspect-Based Sentiment: This advanced technique identifies the sentiment towards specific aspects of a product or service. For example, in "The camera is excellent, but the battery life is terrible," the sentiment is positive towards "camera" and negative towards "battery life."

Emotion Detection (Beyond Sentiment):

Some advanced platforms go beyond general sentiment to detect specific human emotions from both textual and vocal cues. This often involves more sophisticated deep learning models trained on highly annotated datasets.

Behavioral Signals Integration:

Modern sentiment analysis often integrates behavioral data from digital interactions (e.g., website clicks, scroll patterns, time spent on pages, form abandonment, rage clicks) to provide a more complete understanding of customer frustration or engagement. A customer might not explicitly say "I'm frustrated," but repeated clicks on an error message or rapid scrolling could indicate negative sentiment.

Benefits of Voice & Sentiment Analysis in Customer Feedback Platforms

Implementing these advanced analytics capabilities offers a multitude of benefits for businesses:

Real-time Problem Detection and Resolution:

Monitor live calls and chats to detect rising frustration or anger, allowing agents or supervisors to intervene immediately. This can prevent customer churn before it escalates. Real-time sentiment analysis can improve first-call resolution rates by up to 20% (Gartner, estimated).

Quickly identify emerging issues with products or services as customers discuss them, enabling proactive fixes.

Enhanced Customer Experience (CX) and Personalization:

Agents can adapt their tone and approach based on real-time sentiment cues from the customer, leading to more empathetic and effective interactions.

Personalize follow-up actions: A highly frustrated customer might receive a call from a manager, while a highly satisfied one might be prompted for a review.

Customer satisfaction (CSAT) can increase by 15-20% when voice and sentiment analysis are effectively utilized to improve support interactions.

Deeper Customer Insights and Root Cause Analysis:

Uncover the underlying "why" behind customer satisfaction or dissatisfaction, going beyond what surveys reveal.

Automatically identify recurring pain points, common reasons for complaints, and trending topics across thousands of interactions. This helps product development, marketing, and operations teams prioritize improvements.

For example, if sentiment analysis reveals consistent negativity around "billing errors" or "delivery times," the business knows exactly where to focus its efforts.

Improved Agent Performance and Coaching:

Automatically identify calls where agents struggled with unhappy customers or where positive sentiment was successfully cultivated.

Provide targeted coaching based on specific emotional cues or conversation patterns (e.g., "agent needs training on handling frustrated customers," "agent excels at empathy").

Automated Quality Assurance (QA): Instead of manually reviewing a small sample of calls, AI can analyze 100% of interactions, offering objective performance insights and reducing manual QA efforts by 50-70%.

Proactive Churn Prevention:

By continuously monitoring sentiment across all touchpoints, businesses can identify customers at risk of churning early (e.g., sustained negative sentiment, repeated complaints about key features) and initiate proactive retention strategies.

Early churn detection can reduce customer attrition by 10-15% (industry average for effective predictive analytics).

Optimized Marketing and Product Development:

Uncover the emotional language customers use when they are excited or frustrated about specific product features or marketing campaigns.

Inform product roadmaps by understanding what features are highly valued and what areas need improvement based on aggregated sentiment.

Tailor marketing messages to resonate with customer emotions by identifying keywords and themes that evoke positive sentiment.

Sales Optimization:

In sales calls, sentiment analysis can help identify buyer hesitation, interest, or urgency in real-time, allowing sales reps to adjust their pitch or close more effectively.

Post-sale, analyze sentiment to identify upsell or cross-sell opportunities with highly satisfied customers.

Enhanced Brand Reputation:

Monitor public sentiment on social media, review sites, and forums to quickly address negative feedback before it escalates into a crisis.

Identify brand advocates and leverage positive sentiment for testimonials and marketing.

Challenges in Implementing Voice & Sentiment Analysis

Despite the immense benefits, implementing robust voice and sentiment analysis in Customer Feedback platforms comes with its own set of challenges:

Accuracy and Nuance:

Sarcasm and Irony: A major hurdle for AI. "Oh, great service!" can mean the opposite. AI struggles with contextual cues that humans easily pick up.

Context Dependency: The meaning and sentiment of words can change drastically based on context. "Sick" can mean ill or excellent depending on the phrase.

Domain Specificity: A general sentiment model might misinterpret industry-specific jargon or slang. Custom models often need training on domain-specific data.

Subjectivity: Distinguishing objective statements from subjective opinions can be difficult.

Data Quality and Volume:

Noisy Audio: Background noise, poor microphone quality, or overlapping speech can significantly reduce ASR accuracy, impacting subsequent sentiment analysis.

Volume and Storage: Capturing and processing vast amounts of audio and text data requires significant storage and computational resources.

Data Imbalance: In some cases, genuine emotional expressions might be rarer than neutral conversations, creating imbalanced datasets for training.

Language and Cultural Differences:

Multilingual Support: Building and maintaining accurate models for multiple languages is complex due to different linguistic structures, idioms, and emotional expressions.

Cultural Nuances: What is considered positive or negative sentiment can vary across cultures.

Privacy and Ethical Concerns:

Consent: Ensuring explicit consent for recording and analyzing customer voice data is crucial, especially under regulations like GDPR.

Data Security: Protecting sensitive customer conversations and personal data is paramount.

Bias in Algorithms: If training data is biased, the sentiment analysis model might inadvertently perpetuate stereotypes or misinterpret emotions from certain demographic groups.

Integration Complexity:

Integrating voice and sentiment analysis platforms with existing CRM systems, contact center software, and other business intelligence tools can be technically challenging.

Actionability Gap:

Generating insights is one thing; acting on them effectively is another. Organizations need robust workflows to translate sentiment insights into actionable improvements.

Ensuring that insights reach the right teams (product, marketing, support) in a timely and understandable format.

Future of Voice & Sentiment Analysis in Customer Feedback Platforms

The future of voice and sentiment analysis is characterized by increasing sophistication, deeper integration, and greater personalization:

Multimodal Sentiment Analysis: Combining insights from voice (tone, pitch), text (words, phrases), and visual cues (facial expressions in video calls) for a truly holistic understanding of emotion.

Generative AI for Personalized Responses: Beyond just identifying sentiment, AI will increasingly assist agents in crafting empathetic and highly personalized responses in real-time, even suggesting next best actions. 63% of service professionals believe generative AI is their ticket to faster, smarter support (Forbes, 2024).

Predictive Customer Behavior: More advanced models will move beyond current sentiment to predict future customer behavior, such as churn risk, likelihood to purchase, or propensity to escalate.

Hyper-Personalized Self-Service: Chatbots and virtual assistants will leverage voice and sentiment analysis to provide more emotionally intelligent and adaptive self-service options, guiding customers more effectively based on their emotional state.

Emotional AI and Empathy-as-a-Service: The ability of AI to understand and even simulate empathy will lead to more nuanced and human-like interactions in automated systems.

Ethical AI by Design: Greater emphasis on bias detection and mitigation, ensuring fairness, privacy, and transparency in sentiment analysis models. Regulations like the EU AI Act will drive this.

Deeper Integration with CRM and ERP: Seamless flow of sentiment insights directly into customer profiles within CRM systems, providing a 360-degree view of the customer and enabling enterprise-wide action.

Proactive Issue Resolution: Systems will automatically detect early signs of frustration and trigger interventions (e.g., a proactive call from an agent, a personalized offer) before a customer explicitly complains.

Leading Customer Feedback Platforms with Voice & Sentiment Analysis Capabilities

The market for Customer Feedback platforms leveraging voice and sentiment analysis is rapidly expanding. Key players and solution types include:

Unified CX Platforms:

Qualtrics XM: Offers comprehensive experience management, including Text iQ for advanced sentiment analysis across various feedback channels.

Medallia: A leading experience management platform that aggregates feedback from numerous touchpoints, providing deep insights with advanced sentiment analysis.

InMoment (Lexalytics): Utilizes AI to analyze text from multiple sources, translating unstructured feedback into actionable insights.

Speech Analytics & Contact Center AI:

CallMiner Eureka: Specializes in advanced speech analytics, providing real-time call monitoring, sentiment, and topic discovery within voice interactions.

NICE CXone: Offers comprehensive contact center solutions with integrated AI for speech and text analytics, sentiment analysis, and agent performance management.

Verint: Provides Voice of the Customer software with capabilities including speech and text analytics, and sentiment analysis for omni-channel interactions.

Calabrio ONE: A unified workforce engagement and customer experience intelligence platform with robust VoC analytics, including speech and text analytics.

Observe.AI: Focuses on contact center AI, providing real-time agent assist, sentiment analysis, and automated quality assurance from voice interactions.

SentiSum: AI-powered customer experience analytics platform with distinct offerings in support ticket, customer feedback, and customer review monitoring, including voice call sentiment analysis.

Text Analysis & Social Listening Tools:

MonkeyLearn: An AI tool specifically for analyzing customer sentiment from social media texts and other qualitative data.

Brandwatch: A comprehensive social listening and analytics platform that helps businesses understand online conversations and brand sentiment.

Sprout Social: A social media management software with AI that monitors user sentiment across various social platforms.

Zonka Feedback: Offers AI-powered Sentiment Analysis to gauge mood and emotions from feedback.

HubSpot Service Hub: Integrates communication tools with Customer Feedback analytics, including sentiment analysis.

Conclusion

In the hyper-competitive market of today, understanding and responding to the Customer Feedback is paramount for survival and growth. Voice and Sentiment Analysis in Customer Feedback Platforms are no longer just an advantage; they are becoming a necessity. By automatically transforming raw, unstructured interactions into quantifiable insights about customer emotions, intent, and pain points, these technologies empower businesses to:

Act quickly to resolve issues,

Personalize customer journeys,

Optimize product and service offerings,

Enhance agent performance, and

Ultimately build stronger, more loyal customer relationships.

While challenges related to accuracy, data quality, and ethical considerations persist, ongoing advancements in AI and ML are continuously refining these capabilities. Embracing Voice and Sentiment Analysis is a strategic investment that enables businesses to truly listen to, understand, and engage with the authentic Voice of the Customer, driving superior experiences and sustained success.

[aihumanizerpro]

Use AI Humanizer to Successfully Bypass Turnitin Checks

Have you ever used ChatGPT to finish a paper only to have it flagged as "AI-generated" by Turnitin? You are not by yourself. Although busy content creators, researchers, and students rely on AI for speed, new Turnitin algorithms now look for the minute patterns that reveal machine text. The good news is that you can still benefit from AI's convenience by using AI Humanizer to edit your work. By altering tone, rhythm, and vocabulary, this tool helps bypass AI detectors by making the writing sound human rather than automated.

Why Raw AI Content Is Seen on Turnitin

Turnitin is now more than just a plagiarism detector. Its AI detector looks for recurring sentence patterns, excessively formal language, and predictable transitions—all of which are indicators of output from large language models. Recent updates highlight text that has been subjected to basic grammar or paraphrasing tools, and even minor manual edits can preserve these fingerprints. According to a campus study, half of the assignments that were only corrected with grammar checkers still resulted in the "likely AI" warning.

Human Writing vs. AI Text

People blend short and long sentences, add idioms, questions, and emotion, and switch between formal and informal tones with ease. To add personality to our writing, we include brief anecdotes, such as "Have you ever noticed how a quick walk clears your head?" AI, on the other hand, is reliable, sometimes in a painful way. It seldom deviates from a formal, neutral voice, remains literal, and ignores cultural context. Turnitin takes advantage of that consistency.

How Humanizer AI Fills the Gap

AI Humanizer rewrites at the idea level rather than changing synonyms like a simple spinner would:

Sentence mixing: This technique mimics natural speech by varying sentence length, beginnings, and rhythm.

Contextual adaptation modifies vocabulary and voice to fit your audience, topic, and goal.

Flow fixes include the removal of abrupt transitions, the addition of informal connectors, and the use of mild rhetorical questions.

Grammar Variation: For authenticity, use everyday contractions, informal language, and the odd idiom.

The expression only changes, but your original meaning remains the same. The outcome is friendly, conversational, and above all more difficult for Turnitin's AI filter to identify.

What Sets AI Humanizer Pro Apart

Many of the "AI detection bypass" tools available on the market just juggle words and obliterate clarity. AI Humanizer Pro adopts a more comprehensive strategy:

In order to capture real style shifts, advanced algorithms were trained on large human-written corpora

One-Click Simplicity—paste, click, finish. No complicated settings to adjust, no coding.

Speed—creates a polished version in a matter of seconds rather than minutes.

Broad Coverage: tested with high success rates against major detectors such as GPTZero and Turnitin.

Real-World Benefits

AI Humanizer makes sure that the machine-generated text blends in perfectly with your voice if you only use AI for brainstorming or outlining. When you use AI to draft whole essays, the tool adds the erratic rhythm and emotional depth that a detector looks for in human writers. You save time without sacrificing quality, and professors appreciate a well-written, unique submission.

In conclusion,

While the emergence of AI writing has made academic integrity more difficult, it has also led to more intelligent solutions. AI Humanizer enables you to take advantage of machine speed without coming across as robotic by converting AI text to human text. Put your thoughts first and don't worry about Turnitin flags. Try AI Humanizer Pro to make your next project both clearly yours and AI-assisted.

[inovartech123]

End-to-End Automation Made Easy with Agentic AI Services

Introduction to Agentic AI Services

Welcome to the future, where automation isn’t just smart, it’s agentic. In a world increasingly run by artificial intelligence, Agentic AI services are leading a revolution in how businesses operate, scale, and serve customers. Please explain what agentic AI is and why it is increasingly becoming the preferred solution for companies globally.

What Is Agentic AI?

Agentic AI refers to autonomous software agents capable of making decisions, adapting in real-time, and performing tasks with minimal human intervention. Unlike traditional automation, these agents act independently, analyze complex situations, and take appropriate actions on their own.

Imagine empowering your AI with a driver's license and placing your trust in it not to crash.

The Rise of Agentic AI Services

From chatbots to workflow automation, businesses have dabbled in AI for years. However, agentic AI services redefine the industry by providing comprehensive automation across departments without the need for micromanagement.

Why now? Simple. The tech has caught up, and the demand for efficiency is skyrocketing.

Agentic AI vs Generative AI

Definitions and Core Differences:

Here’s a common confusion: agentic AI vs. generative AI. Let's clarify this matter.

Generative AI creates content—text, images, and code. Think ChatGPT or Midjourney.

Agentic AI takes actions—managing systems, making decisions, and executing strategies.

Both are smart. But one’s your brainstorming buddy, and the other’s your reliable project manager.

Use Cases of Agentic AI and Generative AI

Generative AI: content marketing, creative writing, art generation.

Agentic AI: Automating IT workflows, handling customer service, and managing operations.

Why Choose Agentic AI for Automation?

Agentic AI goes beyond mere thought. It acts—intelligently and autonomously.

Core Features of Agentic AI Services

Autonomous Decision-Making Capabilities:

Agentic systems don’t wait for you to press “Go.” They identify problems, analyze options, and move forward based on logic, context, and data.

Context Awareness and Adaptability:

These agents aren’t rigid bots. They’re adaptive—learning from their environment and changing behavior accordingly. That’s next-level intelligence.

Real-Time Learning and Optimization:

Imagine an employee who continuously learns, never sleeps, and consistently improves. That’s what you get with agentive AI.

Agentive AI in the Real World

Use of Agentive AI in Enterprise Solutions:

Whether you're running a global enterprise or a growing startup, agentive AI brings personalized, scalable solutions—cutting manual work and boosting performance.

Here are some examples of companies that provide Agentic AI services and their success stories:

Companies like UiPath, Replikant, and Adept AI are setting standards in agentic AI services, offering frameworks that empower autonomous agents across industries.

AI Agent Development Companies Driving Innovation

Top AI Agent Development Company Trends:

Modern AI agent development companies are blending machine learning, NLP, and decision science to craft next-gen solutions.

Custom Agentic AI Solution Development:

Need something specific? Custom-built Agentic AI solutions ensure your automation aligns perfectly with business goals.

Agentic AI in Sales and Marketing

Agentic AI Sales Automation:

Agentic AI in sales means leads get nurtured, follow-ups are instant, and deals close faster—all while your team focuses on strategy, not spreadsheets.

Boosting Customer Engagement and Conversion Rates:

Imagine a sales rep that knows every client’s preferences, history, and needs—available 24/7. That’s the power of agentic AI sales tools.

Agentic AI in Service and Support

Agentic Technology in Service Workflows:

Forget clunky ticket systems. Agentic technology in service allows real-time routing, prioritization, and resolution.

Using Agentic AI for Service Personalization:

Each customer feels heard. That’s because Agentic AI for service adapts its responses based on user sentiment, history, and intent.

Agentic AI Copilot for Customer Support Teams

Support teams love their Agentic AI copilot—an assistant that suggests solutions, flags issues, and learns from each interaction.

Vision AI Systems for Manufacturing

Vision AI solutions for manufacturing bring visual intelligence to production lines—detecting flaws, tracking components, and predicting failures before they happen.

AI Vision System in Quality Control and Efficiency

Say goodbye to human error. An AI vision system ensures consistent quality, optimized processes, and massive efficiency gains.

Benefits of End-to-End Automation with Agentic AI

Enhanced Productivity and Scalability:

From HR to logistics, end-to-end automation with Agentic AI services scales effortlessly—without expanding your workforce.

Cost Reduction and ROI

Fewer errors, faster workflows, and leaner teams = major cost savings. The ROI on Agentic AI solutions is evident.

Human-AI Collaboration

Humans plus agentive AI equals magic. It’s not about replacing jobs—it’s about upgrading them.

Challenges and Considerations

Data Privacy and Security:

With great power comes enormous responsibility. Protecting user data in Agentic AI services is non-negotiable.

Ethical Implications of Autonomous Agents

What happens when AI makes a disastrous decision? Designing ethical boundaries is key to responsible AI agent development.

Future of Agentic AI:

Trends to Watch

Expect smarter, more specialized agents. Integration with IoT, blockchain, and vision AI systems will reshape automation.

What’s Next for AI Agent Development Companies?

The competition is underway. Leading AI agent development companies are building the foundation for AI-driven enterprises of tomorrow.

Conclusion:

Agentic AI isn’t just another tech trend—it’s the future of intelligent automation. Whether you're running a sales team, a support desk, or a factory floor, Agentic AI services can simplify operations, boost efficiency, and unlock massive value.

FAQs:

1. What is the difference between agentic AI and generative AI? Agentic AI acts autonomously, while generative AI creates content. One type of AI makes decisions, while the other type creates content.

2. Can small businesses use agentic AI? Absolutely. Scalable solutions exist for companies of all sizes—from startups to enterprises.

3. How secure are Agentic AI services? With proper implementation, agentic AI can meet top-tier data security standards, including encryption and compliance protocols.

4. What industries benefit most from agentic AI solutions? Sales, customer service, manufacturing, logistics, and healthcare are seeing massive gains.

5. How do I choose the right AI agent development company? Search for experience, scalability, industry fit, and customization options in their portfolio.

[precallai]

Integrating AI Call Transcription into Your VoIP or CRM System

In today’s hyper-connected business environment, customer communication is one of the most valuable assets a company possesses. Every sales call, support ticket, or service request contains rich data that can improve business processes—if captured and analyzed properly. This is where AI call transcription becomes a game changer. By converting voice conversations into searchable, structured text, businesses can unlock powerful insights. The real value, however, comes when these capabilities are integrated directly into VoIP and CRM systems, streamlining operations and enhancing customer experiences.

Why AI Call Transcription Matters

AI call transcription leverages advanced technologies such as Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) to convert real-time or recorded voice conversations into text. These transcripts can then be used for:

Compliance and auditing

Agent performance evaluation

Customer sentiment analysis

CRM data enrichment

Automated note-taking

Keyword tracking and lead scoring

Traditionally, analyzing calls was a manual and time-consuming task. AI makes this process scalable and real-time.

Key Components of AI Call Transcription Systems

Before diving into integration, it’s essential to understand the key components of an AI transcription pipeline:

Speech-to-Text Engine (ASR): Converts audio to raw text.

Speaker Diarization: Identifies and separates different speakers.

Timestamping: Tags text with time information for playback syncing.

Language Modeling: Uses NLP to enhance context, punctuation, and accuracy.

Post-processing Modules: Cleans up the transcript for readability.

APIs/SDKs: Interface for integration with external systems like CRMs or VoIP platforms.

Common Use Cases for VoIP + CRM + AI Transcription

The integration of AI transcription with VoIP and CRM platforms opens up a wide range of operational enhancements:

Sales teams: Automatically log conversations, extract deal-related data, and trigger follow-up tasks.

Customer support: Analyze tone, keywords, and escalation patterns for better agent training.

Compliance teams: Use searchable transcripts to verify adherence to legal and regulatory requirements.

Marketing teams: Mine conversation data for campaign insights, objections, and buying signals.

Step-by-Step: Integrating AI Call Transcription into VoIP Systems

Step 1: Capture the Audio Stream

Most modern VoIP systems like Twilio, RingCentral, Zoom Phone, or Aircall provide APIs or webhooks that allow you to:

Record calls in real time

Access audio streams post-call

Configure cloud storage for call files (MP3, WAV)

Ensure that you're adhering to legal and privacy regulations such as GDPR or HIPAA when capturing and storing call data.

Step 2: Choose an AI Transcription Provider

Several commercial and open-source options exist, including:

Google Speech-to-Text

AWS Transcribe

Microsoft Azure Speech

AssemblyAI

Deepgram

Whisper by OpenAI (open-source)

When selecting a provider, evaluate:

Language support

Real-time vs. batch processing capabilities

Accuracy in noisy environments

Speaker diarization support

API response latency

Security/compliance features

Step 3: Transcribe the Audio

Using the API of your chosen ASR provider, submit the call recording. Many platforms allow streaming input for real-time use cases, or you can upload an audio file for asynchronous transcription.

Here’s a basic flow using an API:

python

CopyEdit

import requests

response = requests.post(

    "https://api.transcriptionprovider.com/v1/transcribe",

    headers={"Authorization": "Bearer YOUR_API_KEY"},

    json={"audio_url": "https://storage.yourvoip.com/call123.wav"}

)

transcript = response.json()

The returned transcript typically includes speaker turns, timestamps, and a confidence score.

Step-by-Step: Integrating Transcription with CRM Systems

Once you’ve obtained the transcription, you can inject it into your CRM platform (e.g., Salesforce, HubSpot, Zoho, GoHighLevel) using their APIs.

Step 4: Map Transcripts to CRM Records

You’ll need to determine where and how transcripts should appear in your CRM:

Contact record timeline

Activity or task notes

Custom transcription field

Opportunity or deal notes

For example, in HubSpot:

python

CopyEdit

requests.post(

    "https://api.hubapi.com/engagements/v1/engagements",

    headers={"Authorization": "Bearer YOUR_HUBSPOT_TOKEN"},

    json={

        "engagement": {"active": True, "type": "NOTE"},

        "associations": {"contactIds": [contact_id]},

        "metadata": {"body": transcript_text}

    }

)

Step 5: Automate Trigger-Based Actions

You can automate workflows based on keywords or intent in the transcript, such as:

Create follow-up tasks if "schedule demo" is mentioned

Alert a manager if "cancel account" is detected

Move deal stage if certain intent phrases are spoken

This is where NLP tagging or intent classification models can add value.

Advanced Features and Enhancements

1. Sentiment Analysis

Apply sentiment models to gauge caller mood and flag negative experiences for review.

2. Custom Vocabulary

Teach the transcription engine brand-specific terms, product names, or industry jargon for better accuracy.

3. Voice Biometrics

Authenticate speakers based on voiceprints for added security.

4. Real-Time Transcription

Show live captions during calls or video meetings for accessibility and note-taking.

Challenges to Consider

Privacy & Consent: Ensure callers are aware that calls are recorded and transcribed.

Data Storage: Securely store transcripts, especially when handling sensitive data.

Accuracy Limitations: Background noise, accents, or low-quality audio can degrade results.

System Compatibility: Some CRMs may require custom middleware or third-party plugins for integration.

Tools That Make It Easy

Zapier/Integromat: For non-developers to connect transcription services with CRMs.

Webhooks: Trigger events based on call status or new transcriptions.

CRM Plugins: Some platforms offer native transcription integrations.

Final Thoughts

Integrating AI call transcription into your VoIP and CRM systems can significantly boost your team’s productivity, improve customer relationships, and offer new layers of business intelligence. As the technology matures and becomes more accessible, now is the right time to embrace it.

With the right strategy and tools in place, what used to be fleeting conversations can now become a core part of your data-driven decision-making process.

[forenerblog]

Is Lemlist Worth It? A Complete Breakdown of Pricing, Features, and Benefits

If you’re looking for an email outreach tool, chances are you’ve heard of Lemlist. It promises higher engagement, better deliverability, and automated follow-ups—but is it really worth the price? Or are there better options available?

In this guide, we’ll break down Lemlist’s pricing, features, and benefits to help you decide if it’s the right tool for your business.

1. What is Lemlist?

Lemlist is an email outreach and sales engagement platform that helps businesses send personalized cold emails, automate follow-ups, and improve deliverability. Unlike traditional email marketing tools, Lemlist focuses on cold email outreach—meaning it’s designed for reaching out to new leads rather than sending newsletters to existing contacts.

2. Who is Lemlist For?

Lemlist is ideal for: ✔ Sales teams looking to book more meetings ✔ Marketers who want to generate leads ✔ Recruiters searching for top talent ✔ Agencies running cold outreach campaigns

If your goal is to convert cold leads into warm conversations, Lemlist could be a great choice.

3. Key Features of Lemlist

Lemlist stands out from other tools because of its personalization, automation, and multi-channel outreach. Here are some of its standout features:

✔ Email Personalization

Lemlist allows you to add custom images, dynamic text, and even videos in your emails. This makes messages feel more personal and engaging.

✔ Automated Follow-Ups

Instead of manually tracking responses, Lemlist automatically sends follow-ups based on recipient actions.

✔ Multi-Channel Outreach

Lemlist doesn’t stop at email. You can integrate LinkedIn and phone calls to create a more comprehensive outreach strategy.

✔ Email Deliverability Booster (Lemwarm)

One of Lemlist’s biggest selling points is Lemwarm, a tool that warms up your email domain to prevent emails from landing in spam.

4. Lemlist Pricing Plans

Lemlist offers three pricing plans: PlanPriceBest ForEmail Outreach$39/monthBasic cold email outreachSales Engagement$69/monthMulti-channel outreach (email + LinkedIn)AgencyCustomLarge teams and agencies

While Lemlist isn’t the cheapest tool, its unique features might justify the cost.

5. Lemwarm: Does It Really Improve Deliverability?

Lemwarm is an AI-driven email warm-up tool that gradually builds your sender reputation. It does this by automatically sending and engaging with emails from different accounts, signaling to email providers that your domain is trustworthy.

📌 Does it work? Yes! Many users report fewer emails going to spam after using Lemwarm for a few weeks.

6. Lemlist’s Personalization Features

Unlike most email tools that use basic templates, Lemlist lets you: ✔ Insert dynamic text (e.g., first name, company name) ✔ Add personalized images with text overlays ✔ Embed custom videos for a more human touch

This makes your emails stand out in crowded inboxes, increasing the chances of replies.

7. Ease of Use: How Beginner-Friendly is Lemlist?

Lemlist has a modern interface, but it does come with a learning curve. Beginners might need some time to get used to features like automation sequences and personalization options. However, once you get the hang of it, it’s a powerful and intuitive tool.

8. Lemlist vs Competitors: How Does It Compare?

Let’s see how Lemlist stacks up against Mailshake, Reply.io, and GMass:

| Feature | Lemlist | Mailshake | Reply.io | GMass | |---------|--------|----------|---------| | Personalization | ✅ Advanced | ✅ Basic | ✅ AI-driven | ❌ No | | Automated Follow-ups | ✅ Yes | ✅ Yes | ✅ Yes | ✅ Yes | | Multi-Channel Outreach | ✅ Yes | ❌ No | ✅ Yes | ❌ No | | Email Warm-up | ✅ Yes | ❌ No | ❌ No | ❌ No | | Pricing (Starting) | $39/month | $44/month | $49/month | $19.95/month |

📌 Verdict: If personalization and deliverability are your top priorities, Lemlist is the best choice. However, if you want a cheaper alternative, GMass or Mailshake might be better.

9. Pros and Cons of Lemlist

✔ Highly personalized email campaigns ✔ Automated follow-ups for better engagement ✔ Lemwarm improves email deliverability ✔ Multi-channel outreach (email + LinkedIn + calls) ✘ Higher learning curve for beginners ✘ More expensive than some competitors

10. Best Alternatives to Lemlist

Mailshake – Best for beginners

Reply.io – Best for AI-powered automation

GMass – Best for budget users

Woodpecker – Best for B2B outreach

11. Who Should and Shouldn’t Use Lemlist?

✔ Use Lemlist if:

You need highly personalized emails

You struggle with email deliverability

You want a multi-channel outreach tool

✘ Avoid Lemlist if:

You’re looking for a cheap tool

You need a simple email marketing tool instead of cold outreach

12. Final Verdict: Is Lemlist Worth It?

Lemlist is one of the best cold email outreach tools available today. Its personalization, automation, and email warm-up features make it a top choice for businesses serious about cold outreach.

[seotraininginahmedabad]

Digital Marketing Course in New CG Road Ahmedabad

1. Digital Marketing Course in New CG Road Ahmedabad Overview

2. Personal Digital Marketing Course in New CG Road Ahmedabad – Search Engine Optimization (SEO)

What are Search Engines and Basics?

HTML Basics.

On Page Optimization.

Off Page Optimization.

Essentials of good website designing & Much More.

3. Content Marketing

Content Marketing Overview and Strategy

Content Marketing Channels

Creating Content

Content Strategy & Challenges

Image Marketing

Video Marketing

Measuring Results

4. Website Structuring

What is Website?- Understanding website

How to register Site & Hosting of site?

Domain Extensions

5. Website Creation Using WordPress

Web Page Creation

WordPress Themes, Widgets, Plugins

Contact Forms, Sliders, Elementor

6. Blog Writing

Blogs Vs Website

How to write blogs for website

How to select topics for blog writing

AI tools for Blog writing

7. Google Analytics

Introduction

Navigating Google Analytics

Sessions

Users

Traffic Source

Content

Real Time Visitors

Bounce Rate%

Customization

Reports

Actionable Insights

Making Better Decisions

8. Understand Acquisition & Conversion

Traffic Reports

Events Tracking

Customization Reports

Actionable Insights

Making Better Decisions

Comparision Reports

9. Google Search Console

Website Performance

Url Inspection

Accelerated Mobile Pages

Google index

Crawl

Security issues

Search Analytics

Links to your Site

Internal Links

Manual Actions

10. Voice Search Optimization

What is voice engine optimization?

How do you implement voice search optimization?

Why you should optimize your website for voice search?

11. E Commerce SEO

Introduction to E commerce SEO

What is e-commerce SEO?

How Online Stores Can Drive Organic Traffic

12. Google My Business: Local Listings

What is Local SEO

Importance of Local SEO

Submission to Google My Business

Completing the Profile

Local SEO Ranking Signals

Local SEO Negative Signals

Citations and Local

Submissions

13. Social Media Optimization

What is Social Media?

How social media help Business?

Establishing your online identity.

Engaging your Audience.

How to use Groups, Forums, etc.

14. Facebook Organic

How can Facebook be used to aid my business?

Developing a useful Company / fan Page

Establishing your online identity.

Engaging your Audience, Types of posts, post scheduling

How to create & use Groups

Importance of Hashtags & how to use them

15. Twitter Organic

Basic concepts – from setting-up optimally, creating a Twitter business existence, to advanced marketing procedures and strategies.

How to use Twitter

What are hashtags, Lists

Twitter Tools

Popular Twitter Campiagns

16. LinkedIn Organic

Your Profile: Building quality connections & getting recommendations from others

How to use Groups-drive traffic with news & discussions

How to create LinkedIn Company Page & Groups

Engaging your Audience.

17. YouTube Organic

How to create YouTube channel

Youtube Keyword Research

Publish a High Retention Video

YouTube ranking factors

YouTube Video Optimization

Promote Your Video

Use of playlists

18. Video SEO

YouTube Keyword Research

Publish a High Retention Video

YouTube Ranking Factors

YouTube Video Optimization

19. YouTube Monetization

YouTube channel monetization policies

How Does YouTube Monetization Work?

YouTube monetization requirements

20. Social Media Tools

What are the main types of social media tools?

Top Social Media Tools You Need to Use

Tools used for Social Media Management

21. Social Media Automation

What is Social Media Automation?

Social Media Automation/ Management Tool

Buffer/ Hootsuite/ Postcron

Setup Connection with Facebook, Twitter, Linkedin, Instagram, Etc.

Add/ Remove Profiles in Tools

Post Scheduling in Tools

Performance Analysis

22. Facebook Ads

How to create Business Manager Accounts

What is Account, Campaign, Ad Sets, Ad Copy

How to Create Campaigns on Facebook

What is Budget & Bidding

Difference Between Reach & Impressions

Facebook Retargeting

23. Instagram Ads

Text Ads and Guidelines

Image Ad Formats and Guidelines

Landing Page Optimization

Performance Metrics: CTR, Avg. Position, Search Term

Report, Segment Data Analysis, Impression Shares

AdWords Policies, Ad Extensions

24. LinkedIn Ads

How to create Campaign Manager Account

What is Account, Campaign Groups, Campaigns

Objectives for Campaigns

Bidding Strategies

Detail Targeting

25. YouTube Advertising

How to run Video Ads?

Types of Video Ads:

Skippable in Stream Ads

Non Skippable in stream Ads

Bumper Ads

Bidding Strategies for Video Ads

26. Google PPC

Ad-Words Account Setup

Creating Ad-Words Account

Ad-Words Dash Board

Billing in Ad-Words

Creating First Campaign

Understanding purpose of Campaign

Account Limits in Ad-Words

Location and Language Settings

Networks and Devices

Bidding and Budget

Schedule: Start date, end date, ad scheduling

Ad delivery: Ad rotation, frequency capping

Ad groups and Keywords

27. Search Ads/ Text Ads

Text Ads and Guidelines

Landing Page Optimization

Performance Metrics: CTR, Avg. Position, Search Term

Report, Segment Data Analysis, Impression Shares

AdWords Policies, Ad Extensions

CPC bidding

Types of Keywords: Exact, Broad, Phrase

Bids & Budget

How to create Text ads

28. Image Ads

Image Ad Formats and Guidelines

Targeting Methods: Keywords, Topics, Placement Targeting

Performance Metrics: CPM, vCPM, Budget

Report, Segment Data Analysis, Impression Shares

Frequency Capping

Automated rules

Target Audience Strategies

29. Video Ads

How to Video Ads

Types of Video Ads

Skippable in stream ads

Non-skippable in stream ads

Bumper Ads

How to link Google AdWords Account to YouTube Channel

30. Discovery Ads

What are Discovery Ads

How to Create Discovery Ads

Bidding Strategies

How to track conversions

31. Bidding Strategies in Google Ads

Different Bidding Strategies in Google AdWords

CPC bidding, CPM bidding, CPV bidding

How to calculate CTR

What are impressions, impression shares

32. Performance Planner

33. Lead Generation for Business

Why Lead Generation Is Important?

Understanding the Landing Page

Understanding Thank You Page

Landing Page Vs. Website

Best Practices to Create Landing Page

Best Practices to Create Thank You Page

What Is A/B Testing?

How to Do A/B Testing?

Converting Leads into Sale

Understanding Lead Funnel

34. Conversion Tracking Tool

Introduction to Conversion Optimization

Conversion Planning

Landing Page Optimization

35. Remarketing and Conversion

What is conversion

Implementing conversion tracking

Conversion tracking

Remarketing in adwords

Benefits of remarketing strategy

Building remarketing list & custom targets

Creating remarketing campaign

36. Quora Marketing

How to Use Quora for Marketing

Quora Marketing Strategy for Your Business

37. Growth Hacking Topic

Growth Hacking Basics

Role of Growth Hacker

Growth Hacking Case Studies

38. Introduction to Affiliate Marketing

Understanding Affiliate Marketing

Sources to Make money online

Applying for an Affiliate

Payments & Payouts

Blogging

39. Introduction to Google AdSense

Basics of Google Adsense

Adsense code installation

Different types of Ads

Increasing your profitability through Adsense

Effective tips in placing video, image and text ads into your website correctly

40. Google Tag Manager

Adding GTM to your website

Configuring trigger & variables

Set up AdWords conversion tracking

Set up Google Analytics

Set up Google Remarketing

Set up LinkedIn Code

41. Email Marketing

Introduction to Email Marketing basic.

How does Email Marketing Works.

Building an Email List.

Creating Email Content.

Optimising Email Campaign.

CAN SPAM Act

Email Marketing Best Practices

42. SMS Marketing

Setting up account for Bulk SMS

Naming the Campaign & SMS

SMS Content

Character limits

SMS Scheduling

43. Media Buying

Advertising: Principles, Concepts and Management

Media Planning

44. What’s App Marketing

Whatsapp Marketing Strategies

Whatsapp Business Features

Business Profile Setup

Auto Replies

45. Influencer Marketing

Major topics covered are, identifying the influencers, measuring them, and establishing a relationship with the influencer. A go through the influencer marketing case studies.

46. Freelancing Projects

How to work as a freelancer

Different websites for getting projects on Digital Marketing

47. Online Reputation Management

What Is ORM?

Why We Need ORM

Examples of ORM

Case Study

48. Resume Building

How to build resume for different job profiles

Platforms for resume building

Which points you should add in Digital Marketing Resume

49. Interview Preparation

Dos and Don’t for Your First Job Interview

How to prepare for interview

Commonly asked interview question & answers

50. Client Pitch

How to send quotation to the clients

How to decide budget for campaign

Quotation formats

51. Graphic Designing: Canva

How to create images using tools like Canva 

How to add effects to images

52. Analysis of Other Website

https://seotrainingahmedabad.com/digital-marketing-course-in-new-cg-road-ahmedabad/

[bestlanguagetranslationservices]

The Process of Professional Thai Transcription Services

Thai transcription services involve the process of converting spoken Thai language from audio or video recordings into written text. This service is crucial for businesses, content creators, legal professionals, healthcare providers, and other industries requiring accurate and reliable transcriptions of conversations, interviews, meetings, and other spoken material in Thai.

These services cater to a variety of needs, from converting podcasts and webinars into readable formats to transcribing court hearings or medical consultations. Thai transcription requires a high level of proficiency in the language, including understanding regional accents, cultural nuances, and industry-specific terminology.

There are two primary methods for Thai transcription: manual transcription, where skilled human transcribers listen to the audio and transcribe it word-for-word, and automated transcription, which relies on software and AI algorithms. While automated tools can offer speed, human transcriptionists ensure higher accuracy, especially for complex or noisy recordings.

Overall, professional Thai transcription services provide invaluable support for businesses looking to enhance accessibility, improve SEO through transcribed content, and ensure legal and medical accuracy in their records.

Step 1: Receiving the Audio or Video File

File Submission: Clients submit their audio or video recordings in various formats, such as MP3, WAV, or MP4. Files can be uploaded via secure portals or emailed, depending on the service provider's preferences.

File Requirements: Clients specify any special instructions, such as whether the file includes multiple speakers, background noise, or if timestamps are needed.

Step 2: Choosing the Right Transcriptionist

Expertise Matching: The transcription provider selects a native Thai speaker with expertise in the relevant industry (e.g., legal, medical, or business) to ensure that specialized terminology is accurately transcribed.

Human vs. AI: While AI transcription tools may be used in some cases for faster turnaround, human transcriptionists are preferred for complex projects, especially for content that requires contextual understanding or nuanced language.

Step 3: Transcription Process

Listening & Understanding: The transcriptionist listens carefully to the audio or video file, paying attention to accents, background noises, and dialects. Thai transcriptionists must be familiar with regional variations in pronunciation and vocabulary.

Word-by-Word Transcription: The transcriptionist types out every spoken word in Thai, ensuring accuracy and coherence. In cases of unclear speech or difficult accents, the transcriber may pause, replay, or research terms to ensure the best transcription.

Handling Multiple Speakers: If the recording includes multiple speakers, the transcriptionist distinguishes between them, marking each speaker’s dialogue clearly.

Step 4: Quality Control and Editing

Proofreading: Once the initial transcript is completed, it undergoes a thorough proofreading process. The transcriptionist reviews the text for spelling, grammar, and punctuation errors.

Verifying Accuracy: The transcript is checked against the original audio to ensure no words, phrases, or important details are missed. The transcriptionist cross-references challenging parts of the audio for clarity.

Ensuring Consistency: Industry-specific terms, jargon, and proper nouns are verified for consistency and accuracy.

Step 5: Final Review and Formatting

Formatting for Readability: The transcript is formatted to ensure it is easy to read. Common formats include standard text documents (Word or PDF) or more structured formats with timestamps for video or audio recordings.

Timestamps (if required): If the transcript needs to be time-coded, timestamps are added at specific intervals, such as every time a new speaker begins or at regular time markers (e.g., every minute).

Client Specifications: The transcript is customized according to the client's specific needs, whether it’s for subtitles, legal documentation, or content marketing.

Step 6: Delivery and Client Review

Delivery of Transcripts: The final transcript is sent to the client through their preferred method, typically via email or a secure file-sharing platform.

Client Feedback: Clients review the transcript to ensure that it meets their expectations. If there are any issues or revisions needed, the client can provide feedback.

Revisions: If required, the transcription provider makes any necessary revisions based on the client’s feedback and sends the updated document.

Step 7: Final Approval

Client Satisfaction: Once the client is satisfied with the final transcript, the service is marked as complete.

Archiving: The final version is stored for future reference or can be deleted upon the client’s request for privacy and security reasons.

Why Professional Thai Transcription Services

Explore the importance of accuracy in transcriptions and how professionals ensure that no detail is missed.

Discuss the benefits, such as high-quality results, confidentiality, and time efficiency.

Illustrate how outsourcing transcription helps businesses and individuals focus on core tasks.

Examine why professionals in these industries require precise transcription for records and communication.

Highlight the limitations of automated transcription software and the advantages of human transcriptionists.

Explain how transcriptions make audio or video content accessible to a wider audience, including those with hearing impairments.

Show how transcriptions can improve SEO, engagement, and the reach of online content.

Discuss why companies prefer to outsource transcription for high-quality and reliable results.

Focus on how accurate transcription ensures that content stays consistent and professional across channels.

Explore how accurate transcription supports companies working in global markets with Thai-speaking customers or partners.

Discuss how transcriptions play a crucial role in research, interviews, and academic documentation.

Examine the need for professional transcription to create precise subtitles or captions for films, videos, and webinars.

Highlight the security measures that professional services take to ensure sensitive information is kept confidential.

Discuss how accurate transcription prevents costly errors and the need for revisions.

Explain how understanding the cultural context of the Thai language helps transcribers capture the full meaning of the conversation.

[commercepulseenterprise]

AI Meeting Notes: Transforming the Way We Capture Meeting Insights.

Business operations rely on meetings, but keeping track of notes has been considered a tedious chore. AI meeting notes come into play there to help us capture, organize, and use the information from discussions in a way that is easier to understand. Fortunately, with automation in control, these tools minimize the variables and prevent any of this from getting lost in translation. In this article, let’s start with how AI meeting notes are transforming our working environment.

Understanding AI Meeting Notes

What Are AI Meeting Notes?

You may refer to AI meeting notes as meeting summaries or even detailed transcriptions created through artificial intelligence during a meeting. These tools are powered by technologies such as voice recognition, natural language processing (NLP), and machine learning, enabling them to listen, transcribe, and format conversations in real time.

The ability to capture key points of a conversation, highlight action items, and even assign responsibilities to enhance accountability makes AI tools an indispensable asset for any modern team during meetings.

How Do AI Tools Capture and Organize Notes?

Basically, AI tools use algorithms that convert audio into text. They identify speakers, summarize discussion, and categorize the content into concrete action items. Even some tools are integrated with platforms such as Zoom, Google Meet, or Microsoft Teams and work seamlessly.

The Benefits of Using AI for Meeting Notes

1. Improved Accuracy and Efficiency

Humans are prone to error, especially in fast-paced meetings. AI eliminates the risk of missing critical information, providing precise and structured notes.

2. Real-Time Transcription and Organization

AI tools offer real-time transcription, so team members can follow along as the meeting progresses. This feature ensures immediate access to notes without any delays.

3. Accessibility and Collaboration

With cloud storage and integration capabilities, AI meeting notes are easily accessible to all team members. This ensures better collaboration, especially for remote or hybrid teams.

Key Features of AI-Powered Meeting Note Tools

Voice Recognition and NLP

The backbone of AI meeting tools lies in their ability to recognize different voices and languages while analyzing context. This ensures accurate summaries.

Summarization Capabilities

AI tools don’t just transcribe; they summarize. By focusing on key points, action items, and takeaways, these tools save users from sifting through pages of raw text.

Industries Leveraging AI Meeting Notes

1. Corporate Environments

From board meetings to team huddles, AI meeting notes help executives stay focused on strategy rather than scribbling notes.

2. Education and Training

Professors and trainers use AI tools to record lectures or training sessions, providing accurate transcripts for later reference.

3. Healthcare and Legal Fields

Doctors and lawyers rely on AI meeting notes to record patient interactions or case discussions while focusing on client care.

How AI Meeting Notes Work

Step-by-Step Process of Note Generation

Audio Capture: The AI listens to the conversation.

Transcription: It converts speech to text in real time.

Analysis: Context and sentiment are analyzed for clarity.

Output: A structured summary with key points is delivered.

The Role of Machine Learning

Machine learning algorithms adapt over time, learning from repeated usage to improve accuracy and relevance in note-taking.

Comparison Between Manual and AI Meeting Notes

Manual Methods vs. AI-Driven Solutions

Manual note-taking has been the norm for decades, requiring intense focus and multitasking. This traditional approach often leads to incomplete notes or missed information. In contrast, AI meeting notes offer automated transcription and real-time summaries, significantly reducing the workload for participants.

Time, Effort, and Quality Metrics

AI tools excel in saving time by providing instant documentation and eliminating the need for post-meeting revisions. Quality also improves as AI systems ensure consistent and precise records, free from human biases or errors.

How AI Meeting Notes Impact Productivity

Saving Time for High-Priority Tasks

By automating the note-taking process, employees can focus on brainstorming and strategic discussions, driving innovation.

Streamlining Workflows

AI tools bridge gaps between meetings and execution by directly exporting action items to project management systems.

Common Misconceptions About AI Meeting Notes

1. AI Will Replace Human Judgment

While AI tools assist with documentation, human oversight remains essential to interpret and apply meeting insights effectively.

2. AI Tools Are Hard to Use

Contrary to belief, most AI meeting note solutions offer intuitive interfaces and require minimal technical expertise.

Conclusion

The most prominent: AI meeting notes are democratizing how we do meeting documentation and the movement is only changing what and how others document. If you are a small business or a large enterprise, these tools are going to save you time, increase productivity and facilitate collaboration. But with increased advancement of AI technology, the workplace will also be influenced, making better decisions and being more efficient.

[concettolabs]

Microsoft Rebrands Flow Service to Power Automate in Ignite 2019

In this digital era, you will find several cloud services or applications to do just about everything. But what’s the actual point, if we can’t couple it together and work?

Disconnected applications and services are much like a cell phone device that has no internet accessibility. In this state, you cannot make any progress. But, Microsoft Power Automate allows your apps, services, and employees to work together seamlessly. If technology and your team are coupled together and started working together, then you can work smarter, work less, and do more.

What is Microsoft Power Automate?

Microsoft Power Automate, formerly known as Microsoft Flow or MS Flow. It is Microsoft Power Platform’s one of the newest services. With Power Automate, you can streamline time-consuming tasks and paperless processes. Automate is a workflow automation engine that focuses on Business Process Management (BPM). It aims to optimize, enhance, and automate your business processes.

Why Did Microsoft Rebrands Flow?

At the Ignite 2019, Microsoft announced the rebranding of Microsoft Rebrands Flow as Power Automate, the latest additions, and features to its Power Platform family. It brings in line with the rest of the platforms.

As a part of Power Platform alongside Power BI and PowerApps, the rebranding aligns with those other services. Moreover, Microsoft is adding support for RPA – Robotic Process Automation. With the integration of RAP, the app can support UI flows and become an end-to-end automation solution. It allows you to do both API-based automation and UI-based automation.

In the case of what Power Automate offers there are a few changes from the overall Microsoft Rebrands Flow experience. With this service, users can create automated workflows across applications. It’s known as an enterprise-oriented IFTTT competitor. Users can gather data collected, file synchronization, and notifications using automated chains.

With the help of AI and bots, RPA automates business processes, repetitive tasks, and freeing up staff to be more productive. You can easily create UI flows in Power Automate, which requires minimal coding knowledge. This means anyone from your organization can be a developer now.

Power Automate vs Flow

Microsoft Rebrands Flow always had limitations such as starting a second flow as a continuation of the primary one, not capable of reordering the steps in each flow, the complexity of new flow recreation, or reconnecting it to new lists, approved email formation, and so on.

Now, Microsoft has eliminated these limitations and added new features that empower business through its new capabilities – Power Automate Platform.

Before we proceed further, let us tell you a quick note on rebranding. Microsoft has to rebrand one of its products every quarter or it’s not allowed to call itself Microsoft. Microsoft Flow launched out of preview in October 2016. The competitor, IFTTT was always more about automation than workflows – it just took Microsoft three years to realize it.

Other Features by MicrosoftPower Automate goes RPA (Robotic Process Automation)

Having said earlier, Microsoft revealed a new feature RAP in Power Automate, which is known as UI flows. UI flows are now available in public preview.

The RPA eliminates the manual processes involved in automated workflows that record and playback human-driven interaction with software systems that don’t support API automation. Since Power Automate comes up with pre-built connectors for more than 275 apps and services that do support API automation, Microsoft claims it now has an end-to-end automation platform that can reinvent business processes for workload across industries.

Power Virtual Agents and AI Builder

If the requirement of needing some coding experience for UI flows concerns you, you will be relieved to hear about the Power Virtual Agents. It’s now available in public preview.

Power Virtual Agents allow the different functions in your company such as customer service, sales, marketing, finance, HR, and so on, to create bots with a guided, no-code graphical interface. Think of it as democratizing bot creation. There is no need for any data scientists, developers, coding, or AI expertise. Because Power Virtual Agents are part of the Power Platform. You can easily use the pre-built connectors to allow your bot to communicate with your backend or other systems or call and API.

As a matter of fact, Microsoft has revealed more no-code options in the form of AI Builder on Power Platform. Additionally, the Power Platform also has new prebuilt models in public preview:

Key Phrase Extraction – analyzes the main talking points from your text.

Language Detection – analyzes the predominant language from your text.

Text recognition – extracts embedded printed and handwritten text from images into machine-readable character streams.

Sentiment Analysis – detects positive, negative, neutral, or mixed sentiment in social media, customer reviews, or any text data.

With AI Builder, organizations can tailor AI scenarios for their specific business.

Security Improvements and Teams Integration

As per the above statement, Power BI is getting more security features with the following new capabilities.

Able to analyze and secure user activity on sensitive data in real-time with alerts, session monitoring, and risk remediation through Microsoft Cloud App security.

Allow security administrators who are using data protection reports and security investigation capabilities to leverage Microsoft Cloud App Security to enhance organizational oversight.

Moreover, with Power Platform, Microsoft is trying to collaborate its workplace tools and Microsoft Teams. Now, Power Platform dashboards, app & Power automation are available within Teams. And bots can be accessed through conversations. Additionally, PowerApps developers can also publish their apps directly into their company’s app library within Teams. New Power Automate triggers and actions are now available for common team and personal tasks.

Impact of Power Automate on Your Business

Using Power Automate in your business will surely take it to the next level. It will change the way you interact with your business.

Let’s go through some of the advantages of how your business can benefit from Power Platform.

Enhances Productivity

Your team members can create time-saving workflows for everything while using hundreds of pre-built connectors. This workflow includes individual tasks to scalable systems You can save time, improve your organization, and work more efficiently by automating repetitive tasks.

Streamline Repetitive Tasks

Power Automate notifies you if you receive any high-prioritized email or tweet or message from the receiver. It also sends a template to those you don’t need to review. This ensures that you never miss any single update and stay in touch with your clients while you are engaged in your personal life.

Easy and Quick Data Sharing

By connecting apps and creating flows to copy data from one folder to another, you can easily share files. Even, if you integrate Power Automate with Common Data Services, it will help you store and manage data used by business applications.

How Can You Reap the Benefits?

If you are implementing Power Automate, there are endless possibilities for your business. To reap the benefits, you must create the necessary workflows that keep the track of email attachments, get action items approved quickly, stay on top of relevant emails, and monitor brand-related posts and events on social media.

So, if you are also planning to transform your business with the help of Microsoft Power Platforms such as Power BI, PowerApps, and Flow, then hire PowerApps developers from us. Contact us and will discuss your specific business needs.

[alexjosephalex-blog]

Customer Service Automation: Human-Robot Collaboration Best Practices

Zinnov sits down with Scott Merritt of Jacada for an insightful, two-part interview on the current state, and future of RPA.

Did you know that enterprises spent more than $2.3 billion on RPA in FY19? And that this number is expected to grow exponentially by 35-40%, to reach more than $11 billion by 2024?

RPA and Intelligent Automation have not only become mainstream conversations but are being implemented across enterprises at a rapid pace. Further, with attended and unattended scenarios making visible inroads in enterprises’ automation journeys, there is a renewed focus on Intelligent Automation. Based on Zinnov’s research and analysis, we estimate that attended RPA (bots working together with humans) accounts for approximately 30% of the market share and is growing at a steady pace.

Given the attention that Intelligent Automation is receiving across companies, and irrespective of industry vertical, Nischay Mittal, Engagement Manager at Zinnov caught up with Scott Merritt, Vice President, Global Head of Automation and Marketing at Jacada, to gather his perspectives on the key trends within the RPA space, especially the growing focus on attended RPA and Customer Service Automation (CSA).

Here’s an excerpt from the interview.

Zinnov: The RPA space has been growing at a rapid pace over the last 2 years. What are the top 2-3 trends you are witnessing within RPA?

Merritt: One of the biggest trends that we are witnessing is customers struggling to understand what or where to automate next. Customers often get through the proof of concept (POC) stage, start accepting the technology as a viable part of their architecture, but they fail to go beyond those initial use cases and pilots. This challenge to scale RPA for early adopters has quickly accelerated the industry from say RPA 1.0 to RPA 2.0, which is bringing to light a much-needed focus on a broader “intelligent automation” toolset rather than just an RPA only bot operation.

As a result, RPA companies are investing in additional capabilities to expand their offerings in order to drive meaningful business outcomes instead of just delivering a somewhat commoditized back-office RPA bot that can navigate a UI on behalf of a user.

Another trend stemming from this scale gap in RPA is the growing focus on Attended RPA. Formerly known as “desktop automation”, Attended RPA is a 10+ year old automation approach that grew out of a need in the contact center to integrate non-API ready applications and automate the many manual microtasks that bog down agents during a call, impacting handle time and customer experience (CX). At Jacada, we often find two dozen or more solutions on the agent desktop and they are a combination of these API-ready and non-API ready legacy applications. Automating for this type of environment, however, requires a different type of discovery approach and supporting RPA framework, one that is built for human collaboration vs the one built for robots (unattended).

Zinnov: We are witnessing immense traction within Attended RPA scenarios (or automation of front office processes) over the past year. Even some of the bigger RPA tool vendors are now increasingly focusing on Attended RPA use cases. What do you think are the key reasons for that?

Merritt: There are many reasons but one I often site is “back office blinders” or too narrow a scope. Let me explain. The original entry point for RPA unattended vendors and SIs was to target high volume / low complexity processes that could be automated end-to-end in back office departments like IT, Finance, Accounting, HR, etc. While these groups do have a need for RPA, they don’t always represent the ideal use case environment for RPA… or at least not after the low hanging fruit is picked off during the first few projects. What you often find here are smaller departments of people working on many disparate, more complex tasks; which often require human discretion at some point in order to complete the process. For example, one regional bank I worked with in the past had 80 back office workers in their card services division working on 160 different exception-based processes in any given month. Going into this environment with an unattended RPA only approach, it’d be difficult to support an ROI that would justify the project but several companies like this bank have moved forward based on limited options and poor guidance. We are now a few years into this approach and over 50% of companies have deployed < 10 robots and haven’t scaled their RPA programs beyond their original list of projects. This represents a flawed RPA strategy at the foundation and unfortunately one that was pushed by many of the new players in the market along with many of the SIs.

To counter this misstep and open up new use cases for RPA, vendors are addressing the more complex back office use cases with add-on capabilities like OCR, ML, Computer Vision, etc. However, to unlock RPA’s full potential, you have to add Attended bot capabilities to your intelligent automation stack allowing you to expand into areas of the organization where a much higher volume of people and tasks exist. A strategy that often brings you to the customer service operations arena.

As some of the larger unattended players realized this new fertile ground, they pivoted strategy and began to market this new type of bot with “attended” and “customer service” banners flying on their websites. These vendors are quickly finding that successfully managing this automation triangle of Customer-Robot-Employee is very different (in approach and supporting RPA technology) than building automation solutions for robots.

Zinnov: Currently, there are a lot of RPA tool vendors in the industry. How does Jacada differentiate itself vis-à-vis some of the other players? Can you elaborate on Jacada’s positioning within RPA? What would you say is your USP?

Merritt: Jacada is a niche player in the RPA space but at the same time, I would argue that all RPA vendors are niche players based on their core use cases and technology strength. Jacada‘s niche targets end to end customer service interactions by focusing on human and robot collaboration use cases in both digital self-service and customer assisted service scenarios. We have a strategy and supporting technology stack that has been transforming customer interactions for almost 30 years now. The space is unique compared to back office RPA as it involves an additional layer of complexity which is that all automations involve real-time interactions with agents and/or customers. As a result, we go to market with a Collaboration-First approach where capabilities like UX design, conversational AI, guidance, and automations have to be designed and orchestrated together in order to deliver a successful solution at scale for both customers and agents. All capabilities that reside within our low code intelligent automation platform, a unique offering compared to other RPA players in this space who often have to bring together multiple technology partners to deliver this kind of end to end customer service value proposition.

Zinnov: Is it safe to say that more complex processes would require more intelligence or leverage of cognitive tech?

Merritt: It all depends on how you define “complexity” quite honestly as process complexity can come in different forms when it comes to RPA.

Sometimes complexity is defined by the complex desktop environment that bots must navigate relative to the automation triangle I mentioned earlier. One of the exciting areas where we use AI to enhance RPA is in real-time speech to intent trigger. For example, when a customer calls a contact center, we leverage AI to power real-time speech analytics to orchestrate different bots throughout the live interaction. A speech bot can listen to the conversation and wait until a trained intent is recognized and then trigger a new guidance flow or bring back the answer to a customer question using Attended RPA without the agent having to touch the keyboard. This is a situation where a combined RPA and AI strategy can deliver on more complex use cases to enhance both the agent’s and customer’s experience.

Process complexity can also be defined by the complexity of the applications or application types that a bot needs to work with. Perhaps the number one challenge we see in the market is a result of a vendor platforms’ inability to integrate and automate with all of the necessary application types needed to automate the desired use cases. Known in the industry as “application coverage”, those vendors who lack broad and deep coverage of applications will require more investment in their RPA tech stack, not necessarily more AI to solve this problem. Unless of course, you include computer vision into the mix but that would be a different conversation altogether.

Another form of process complexity could come from the need to automate processes that involve converting unstructured data from forms or documents as a first step to automate a process end to end. Once again, the vendor would first need to have enhanced capabilities within their RPA platform to support OCR, computer vision, or other methods that can be used to access the unstructured text. When that is achieved, AI/ML can provide a more scalable way to identify and structure this data thereby removing a significant amount of human effort from the process.

Look, the future of AI within automation is exciting and all of us are focusing on R&D to further enhance our platforms with it. However, it is not always the best first choice when trying to solve for process complexity, despite what vendors may say to convince you. Very often I see organizations jumping too quickly into the AI vortex hoping for that quick fix before they have established a proper RPA foundation.

What is your focus on – Intelligent Automation, or leveraging cognitive tech such as AI/ML, NLP, CV, etc.?

Merritt: Our focus is on bringing together the relevant customer service automation capabilities into a common intelligent automation platform in order to automate customer service interactions. This customer outcome-driven approach guides our investment in AI and other cognitive technology. Take Natural Language Processing (NLP/NLG) for example. Our use cases and supporting capabilities for conversational AI focus on leveraging intent and sentiment recognition to understand in real-time a customer request regardless of channel. To support these different use cases, we have developed in-house AI capabilities to enable automation design while offering an intelligent automation and AI hub that allows clients to use their own preferred conversational AI platform whether it’s Google, Facebook, Watson, or other best-in-class providers. This open AI platform approach offers our clients complete flexibility while at the same time provides a plug-and-play environment for them to tie the entire customers’ interactions together in one low code design environment.

This is similar but yet different from the AI investment approach you typically see in back office unattended RPA use cases which often target data structuring to automate things like a document or email processing. The point here is that every RPA platform provider is investing in some form of cognitive/AI/ML technology to expand the number and type of outcomes they can deliver to their customers. As a result, RPA buyers really have to understand what outcomes they are trying to achieve and select the right vendor(s) accordingly – if they are trying to match invoices by leveraging AI/ML, there might be a certain vendor path for them; if they are looking to understand intent and deploy a virtual assistant for employees, that might result in a different vendor selection. Today there is not one vendor who covers use cases across all verticals despite the vendor claims (going back to my point of view that all RPA vendors are niche players right now). I think it’s about finding the vendor who has the relevant expertise and experience to match the outcomes, use cases and application integration requirements as per the customer demand.

Zinnov: Thank you, Scott.

Even though it’s the new-age disruptive technologies that form the spine of any successful automation initiative, organizations often struggle with the implementation aspect of Customer Service Automation. Read the second part of this interview blog to get Scott’s insights on the challenges and key drivers of RPA implementation.

[bishal-06]

12 Best Online Advertising and Content Management Tools for Businesses in 2020

1)      Wask

Wask has created to maximize your ads’ efficiency for you. You can connect all your different accounts into one platform. You can make special ads for your needs with the help of our experienced advisors. Always updated unique AI program that Wask use to manage your ads is provide you to manage your budget and time in the most efficient way. Compare your results, control your ads automatically daily/weekly/monthly, start/stop/delete your ads at any time with Performance Comparison, AutoPilot and Scheduler to increase your efficiency.

https://www.wask.co/

2)      Quuu

Quu is a hand-curated content suggestions platform for social media, Select from over 300 interest categories to receive suggestions that matter to you and your audience, hoose how many suggestions you'd like to send to your social profiles via your Buffer or HubSpot account and Let Quuu handle everything, or manually approve suggestions yourself.

https://www.quuu.co/

3)      Planable

Planable is a platform that allows agencies and social media managers to collaborate with their clients and within marketing teams. We created a tool that speeds up the way social media campaigns are managed and makes planning, visualizing and approving social media posts easy and fun. We are the easiest way to preview your social media content in a simple interface and giving a familiar feel and look for every social media manager, teammate and colleague. After that, you can easily schedule or publish the post immediately to social media knowing that everyone is happy with the campaign. One workspace is one brand or one of your clients you are an agency or You can one page of each social network in one workspace, up to people on the free plan. Click the green button new post and type your content, select the emojis, upload an image or video, select a date and click add button. Now, invite your clients or teammates into the workspace and collaborate on content youve just created. The right side of the post is created to exchange ideas, share a text, upload an image, video or gif, suggest a correct a typo, check for legal issues and just send a high five kudos!

https://planable.io/

4)      Word Swag

Have you wasted time and money hiring a social media graphic design artist, only to notget exactly what you want?Word Swag is an awesome application for your smartphone or tablet that will allow you toeasily create social media graphics. The toolset in Word Swag is amazing because it easily allows you to import some great photos from their library, Pixabay, or from your own photos. You will get an overview of the interface and how the program I will show you how to import images to begin creating right away. After that we will take a look at some of the best text and fontediting tools ever seen in a smartphone application. Finally, I will show you how to use custom colors and filters in your design before exportingit out as a final image. So if you want to start creating amazing images and graphics today, then go ahead and enroll and I will see you in the first lecture.

http://wordswag.co/

5)      Waaffle

New tools have recently emerged to meet the evolving needs of busy social media marketers. In this article, you’ll discover six tools that will improve your social media marketing workflow. Waaffle is an useful social media tool. Create Content Feeds With Waaffle According to Yotpo, ads based on content can get 4x higher rates and a 50% lower cost per click than average. Waaffle simplifies the process by creating aggregate custom feeds based on any account or Waaffle’s current “early bird” pricing starts at per campaign.

https://waaffle.com

6)      Contently

In November Contently was named by Advertising Age as one of the best places to work in media and. Contently has a lot of clients , and we rely on freelancers like yourself to staff projects for them. We have an algorithm that combs through users’ portfolios and pushes forward relevant candidates. So when you’re setting up your portfolio, make sure you focus on the kind of things algorithms like: namely, keywords that highlight your expertise and skills. In the headline section, be sure to specify whether you’re a writer, videographer, designer, and so on. We’d love to provide assignments for everyone who signs up for a Contently unfortunately, we are not an open marketplace and cannot supply work for the roughly 80,000 users now using a portfolio.

https://contently.com/

7)      Keyhole

Keyhole is a real-time hashtag tracker for Twitter, Instagram and Facebook. Its visual dashboard is simple, beautiful and shareable! Keyhole's real-time dashboard shows how many people posted with your hashtag, along with the number of Retweets, Likes and Impressions your campaign is generating. Keyhole tracks the most influential people engaging with your brand. Reach out to them to promote your content and increase your brand's reach.

http://keyhole.co/

8)      Revealbot

RevealBot Paid social media tactics have a much more limited pool of choice for reporting tools. You can automate bid strategies, get alerted to changes in status and review performance directly from the messaging client. Snapchats business model is based around attracting pound campaigns, but its become a popular tool for influencer marketing . It allows users to review the performance of your influencers snaps via open rates, completion rates and estimated reach, which arent accessible through the Snapchat app itself.

https://revealbot.com

9)      Instagram for business

Use relevant, popular hashtags Engage by following others and liking their photos selected images to your Facebook page with a hashtag that aligns with your campaign or brand image to help people who don’t know you’re on Instagram to find you there. Debut Videos Instagram’s recent Video on Instagram has given Twitter’s Vine a serious competitor to contend with. Jordan Crook charts the differences between Instagram and Vine in the image below: Jordan Crook charts Instagram vs. Embed Instagram Video in Your Blog or Website Last month, Instagram released a new embed feature for its desktop web browser version. We’ll talk more about this in Generate a Flexible Posting Plan Carley Keenan offers the following advice on the frequency of sharing on Instagram: “You don’t need to post on Instagram every day. If you start posting a lot, you might saturate your followers’ feeds, and you don’t want to force yourself into the noise too often. There are apps that let users print images, search tags and keywords, subscribe to Instagram profiles via email, download all Instagram photos in a single archive folder, plus many more. Inspire Potential Customers Anna Colibri suggests you post photos that are relevant to your brand and potential customers. Whole Foods Market posts representative photos to promote healthy, wholesome food products, store events, sustainability and their active community of customers and employees. A study conducted by Simply Measured earlier this year found percent of the world’s top brands are now active on Instagram.

https://business.instagram.com/

10)  Statusbrew

Statusbrew is a social media management platform that combines the power of internet and technology to empowers businesses, brands, marketing agencies, and organizations to discover and manage customer experience across various social touch-points and drive growth. With a simple to use interface for the planning of social publishing of marketing and PR campaigns on multiple social networks, Statusbrew is a trusted partner for Teams.

The publishing feature helps you to schedule content across different social platforms. With Engage on Statusbrew, never miss out any conversation about your brand or business on social. Reply to DMs, Mentions, and Comments from all the profiles in a single unified social inbox. With real-time sync, receive and assign replies to specific teammates for them to work on it as soon as a prospect talks about you and never miss out on any lead. Slack Integration will change the way Brands, and Agencies can bring in their teams to collaborate in Social Publishing and Brand Monitoring right from their workspace. When you connect your Statusbrew with one or more Slack channels or workplaces, you will receive instant Slack notifications for all the activities you choose are necessary for your Business. Signup on Statusbrew for a free 14 days trial to gain a competitive edge and build strong social connections! show less

https://statusbrew.com/

[a-breton]

Opportunities for AI in Content Marketing Easily Explained

Until recently, the closest I’ve come to understanding artificial intelligence is knowing that it powered tools in my martech stack (e.g., marketing automation, predictive lead scoring, etc.).

Beyond that, I found the concept hard to grasp until Chris Penn’s presentation at Content Marketing World, How to Use AI to Boost Your Content Marketing Impact.

Chris, co-founder and chief innovator at Trust Insights, covered several real-world applications of AI. His examples helped transform abstract concepts into tangible use cases.

Chris implemented these examples himself via hands-on coding in the R programming language, using a deep understanding of mathematics, data science, and machine learning. But most marketers don’t have data science and computer programming skills. Later in this article, I share Chris’ advice about how marketers can apply these AI concepts.

Here are several of Chris’ experiments.

Driver analysis: What results in profitable action?

When you have a bunch of data but you’re not sure what matters to the outcome you want, driver analysis is an effective tool, Chris says.

Machine learning software excels in this case. You feed in all the data and it tells you what matters in it. Chris explains that the analysis concludes with something like, “Hey, this combination of variables seems to have the strongest mathematical relationship to the objective you want.”

AI in #contentmarketing: Driver analysis to show what factors drive the most leads. @cspenn Click To Tweet

Chris performed driver analysis on the popular PR and marketing blog, Spin Sucks, where the primary business objective is lead generation.

“(It) determined that organic search was the third most powerful driver. The team focused a lot of time and energy on it, and they should, but email was the No. 1 driver,” Chris says.

By understanding better what drives leads, the Spin Sucks team could decide to shift more of their time to email marketing because it was the most effective source.

Whether your objective is page views, social shares, leads, or revenue, a ranked list of drivers can help you plan resources, priorities, and budgets more effectively.

Implementation detail: Chris used the R programing language to implement Markov chain attribution. For a detailed look at one such implementation, read this post by data scientist Sergey Bryl, which will give you a good sense of how much mathematics and data science is involved.

HANDPICKED RELATED CONTENT: Why Marketers Need to Think Like Data Scientists (And How to Do It)

Text mining: Reveal topics, keywords, and hidden problems

Text mining is an application of AI that ingests content (e.g., text) to classify, categorize, and make sense of it.

Chris notes that text mining uses vectorization, which transforms words into numbers. It looks at the mathematical relationship among those numbers and determines how similar those words are. It is a form of deep learning.

Reverse engineer Google to reveal key topics and terms

The Google algorithm, which uses a heavy amount of AI itself, is an example of a deep-learning system. “Google’s search algorithm is so complex now that no one knows how it works, including Google,” Chris said. “They have very little interpretability of their model.”

You can use text mining to reverse engineer the Google algorithm for your targeted topics. “We can deploy our own machine learning models to say, ‘OK, for a search term like content marketing, what words do the top 10 or 20 pages all have in common?’”

AI in #contentmarketing: Find #SEO-friendly #content topics via text mining. @cspenn Click To Tweet

Here’s a sample output from reverse engineering Google:

The resulting lists hint at what words or categories to cover when developing new content around your reverse-engineered keyword. Having this set of common words gives you a higher chance of success with organic search than simply saying, “Let’s write a really good article about content marketing.”

Implementation detail: Chris implemented text mining and topic modeling via the R programming language, extracting related topics from a corpus of text (e.g., the contents of articles found in the search engine results pages).

HANDPICKED RELATED CONTENT: How to Make Your Content Powerful in Eyes of Searchers (and Google)

Extract hidden insights via text mining

In 2014, Darden Restaurants, the parent company of Olive Garden, replaced its board. The new group implemented changes, including enforcing its existing but mostly ignored breadstick policy (serving one per person plus one extra).

As Chris explains, employees then spent their time enforcing the policy by counting the number of breadsticks in the basket based on the number of people at the table.

Chris used text mining on 2,500 publicly available reviews written by the company’s employees on Glassdoor. Here’s a glimpse of the results:

Text mining surfaced breadsticks as a problem. If Olive Garden was looking to repair low employee morale and a poor customer experience, a manual review of its Glassdoor reviews, where the usual restaurant worker complaints like low pay and long hours abound, may have led them down the wrong path.

AI use in #contentmarketing: Use text mining on reviews to reveal hidden problems. @cspenn Click To Tweet

Text mining revealed the breadstick problem. (After intense public pushback, Olive Garden returned to its previous breadstick approach.)

Text mining of unstructured data can be applied in many useful marketing contexts: customer reviews, poor/high performing blog posts, transcripts of customer success phone calls, etc. Extracting that hidden gem of insight can point you to courses of action with a high ROI.

Implementation detail: Similar to the reverse engineering Google example, Chris implemented text mining via the R programming language.

HANDPICKED RELATED CONTENT: Scale Your B2B Content With Artificial Intelligence: Ideas and Tools Marketers Can Try

Time-series forecasting: Analyze competitors’ brand searches

Let’s combine math, statistics, and AI to create a Magic 8-Ball.

“Wouldn’t it be great to know what’s going to happen,” Chris asked. “It would be so much easier to plan, to set budgets, to staff, to have an editorial calendar.”

Chris did an exercise of predictive time-series forecasting for Cleveland hotel search data. He looked at more than 12 months of branded searches — where searchers named specific hotels (e.g., Hilton Cleveland, Holiday Inn Cleveland, Hyatt Cleveland, Marriott Cleveland).

The results predicted when search volumes go up and down for each hotel:

“If you (worked at) the Cleveland Marriott here, you now know that right around the end of September you have more search interests than your competitors. You could be running campaigns against them to take even more market share away from them,” Chris said.

Any brand could benefit from predictive time-series forecasting – analyzing brand searches for your company vs. your competitors. You can search for when your brand underperforms, for example, and use that data to bid on your competitors’ brand names with a relevant content asset or promotional offer.

AI in #contentmarketing: Use time-series forecasting to predict lead-gen and revenue. @cspenn Click To Tweet

“Imagine search topics, conversations, social media. You can forecast more than search volume,” Chris said. “You can forecast lead generation from your marketing automation software. You can forecast revenue from your CRM or your ERP. Anything that is regular data over time you can forecast forward.”

Implementation detail: Chris used R to process five years of Google search data, then implemented a statistical method called autoregressive integrated moving average (ARIMA).

How content marketers can try these AI uses

I know what some of you must be thinking about now:

“Wait, really?”

“The data science and probability are over my head.”

“I’m too busy and can’t possibly learn to do this myself.”

These reactions are understandable. The good news is that you have options. And you don’t need to learn the deep nuts and bolts covered earlier.

Chris offered three recommendations for marketers thinking about approaching AI.

Do it yourself. This approach fits for the small percentage of marketers who have a genuine interest in data science and machine learning. You should be interested in going deep with math, statistics, and probability – and comfortable writing code.

If you decide to go this route, Chris suggests checking out Google’s Machine Learning Crash Course, available free online, which takes you through 40-plus exercises, 25 lessons, real-world case studies, and lectures from Google researchers.

Chris notes that IBM Watson Studio has an intuitive, drag-and-drop user interface. While Watson does enable programmers to write code on its platform, the UI can be useful for marketers who are not inclined to write code.

For those interested in coding, Chris recommends learning the R and Python languages, which form the basis for a lot of AI tools and libraries. Be prepared to spend six to 12 months to learn the programming language and another six to 12 months to learn the data science.

If you’re just getting started with coding, the Dummies franchise has books that may be useful: R for Dummies and Python for Dummies.

Tap your staff data scientist. The second option applies to larger organizations that employ data scientists (e.g., Google, Facebook, and Uber). “Staff with data science skills are quantitatively inclined and know how to use the technology properly, so they can be of great help,” Chris said.

Think back to the use cases I mentioned. For text mining or time-series forecasting, in-house data scientists will understand your objectives and goals, build the right models, then implement the necessary codes.

Outsource. This option works for organizations that don’t have AI and data science talent in-house. The answer is to outsource to the experts: people or agencies with the necessary AI know-how and experience.

Chris puts it this way: “Agencies and consultants can help you use the methodologies. You can do small projects on a one-off basis. If the need is ongoing or more frequent, they can help you build software that runs when you need it to.”

Next steps

No matter which of the three options makes sense for you, there’s one thing I urge all marketers to do: Learn about AI and understand the role it plays in marketing technology.

While you don’t need to understand Markov chain attribution or how to program in R, you need to know enough to determine where and how AI can help your marketing. Basic AI knowledge will also help you better evaluate vendor solutions and claims.

Think about the kind of knowledge you need to buy a computer. You don’t need to be a chip designer, but you need to know the difference between a 32-bit and 64-bit processor and whether a 1.5 GHz processor is better than a 2.7 GHz processor. With AI, when a vendor says, “Our predictive analytics solution uses the latest AI techniques,” you need to know how to question the claim and how to distinguish fluff from reality.

HANDPICKED RELATED CONTENT: Are You Really Smart About How AI Works in Marketing?

Since AI is a topic often covered in business, marketing, and technology publications, I’m soaking up as much as I can. Next, I’ll probably enroll in some free, online courses in machine learning.

What about you? What’s your interest level in AI for marketing and how are you staying informed and educated?

Here’s an excerpt from Chris’ talk:

Further your tech skills in 2019 by attending ContentTECH Summit in April. Register today using code BLOG100 to save $100. 

Cover image by Joseph Kalinowski/Content Marketing Institute

from http://bit.ly/2VHL0sx

[commercepulseenterprise]

AI Meeting Notes: Revolutionizing How We Document Meetings

Business operations rely on meetings, but keeping track of notes has been considered a tedious chore. AI meeting notes come into play there to help us capture, organize, and use the information from discussions in a way that is easier to understand. Fortunately, with automation in control, these tools minimize the variables and prevent any of this from getting lost in translation. In this article, let’s start with how AI meeting notes are transforming our working environment.

Understanding AI Meeting Notes

What Are AI Meeting Notes?

You may refer to AI meeting notes as meeting summaries or even detailed transcriptions created through artificial intelligence during a meeting. These tools are powered by technologies such as voice recognition, natural language processing (NLP), and machine learning, enabling them to listen, transcribe, and format conversations in real time.

The ability to capture key points of a conversation, highlight action items, and even assign responsibilities to enhance accountability makes AI tools an indispensable asset for any modern team during meetings.

How Do AI Tools Capture and Organize Notes?

Basically, AI tools use algorithms that convert audio into text. They identify speakers, summarize discussion, and categorize the content into concrete action items. Even some tools are integrated with platforms such as Zoom, Google Meet, or Microsoft Teams and work seamlessly.

The Benefits of Using AI for Meeting Notes

1. Improved Accuracy and Efficiency

Humans are prone to error, especially in fast-paced meetings. AI eliminates the risk of missing critical information, providing precise and structured notes.

2. Real-Time Transcription and Organization

AI tools offer real-time transcription, so team members can follow along as the meeting progresses. This feature ensures immediate access to notes without any delays.

3. Accessibility and Collaboration

With cloud storage and integration capabilities, AI meeting notes are easily accessible to all team members. This ensures better collaboration, especially for remote or hybrid teams.

Key Features of AI-Powered Meeting Note Tools

Voice Recognition and NLP

The backbone of AI meeting tools lies in their ability to recognize different voices and languages while analyzing context. This ensures accurate summaries.

Summarization Capabilities

AI tools don’t just transcribe; they summarize. By focusing on key points, action items, and takeaways, these tools save users from sifting through pages of raw text.

Industries Leveraging AI Meeting Notes

1. Corporate Environments

From board meetings to team huddles, AI meeting notes help executives stay focused on strategy rather than scribbling notes.

2. Education and Training

Professors and trainers use AI tools to record lectures or training sessions, providing accurate transcripts for later reference.

3. Healthcare and Legal Fields

Doctors and lawyers rely on AI meeting notes to record patient interactions or case discussions while focusing on client care.

How AI Meeting Notes Work

Step-by-Step Process of Note Generation

Audio Capture: The AI listens to the conversation.

Transcription: It converts speech to text in real time.

Analysis: Context and sentiment are analyzed for clarity.

Output: A structured summary with key points is delivered.

The Role of Machine Learning

Machine learning algorithms adapt over time, learning from repeated usage to improve accuracy and relevance in note-taking.

Comparison Between Manual and AI Meeting Notes

Manual Methods vs. AI-Driven Solutions

Manual note-taking has been the norm for decades, requiring intense focus and multitasking. This traditional approach often leads to incomplete notes or missed information. In contrast, AI meeting notes offer automated transcription and real-time summaries, significantly reducing the workload for participants.

Time, Effort, and Quality Metrics

AI tools excel in saving time by providing instant documentation and eliminating the need for post-meeting revisions. Quality also improves as AI systems ensure consistent and precise records, free from human biases or errors.

How AI Meeting Notes Impact Productivity

Saving Time for High-Priority Tasks

By automating the note-taking process, employees can focus on brainstorming and strategic discussions, driving innovation.

Streamlining Workflows

AI tools bridge gaps between meetings and execution by directly exporting action items to project management systems.

Common Misconceptions About AI Meeting Notes

1. AI Will Replace Human Judgment

While AI tools assist with documentation, human oversight remains essential to interpret and apply meeting insights effectively.

2. AI Tools Are Hard to Use

Contrary to belief, most AI meeting note solutions offer intuitive interfaces and require minimal technical expertise.

Conclusion

The most prominent: AI meeting notes are democratizing how we do meeting documentation and the movement is only changing what and how others document. If you are a small business or a large enterprise, these tools are going to save you time, increase productivity and facilitate collaboration. But with increased advancement of AI technology, the workplace will also be influenced, making better decisions and being more efficient.

[lucyariablog]

Opportunities for AI in Content Marketing Easily Explained

Until recently, the closest I’ve come to understanding artificial intelligence is knowing that it powered tools in my martech stack (e.g., marketing automation, predictive lead scoring, etc.).

Beyond that, I found the concept hard to grasp until Chris Penn’s presentation at Content Marketing World, How to Use AI to Boost Your Content Marketing Impact.

Chris, co-founder and chief innovator at Trust Insights, covered several real-world applications of AI. His examples helped transform abstract concepts into tangible use cases.

Chris implemented these examples himself via hands-on coding in the R programming language, using a deep understanding of mathematics, data science, and machine learning. But most marketers don’t have data science and computer programming skills. Later in this article, I share Chris’ advice about how marketers can apply these AI concepts.

Here are several of Chris’ experiments.

Driver analysis: What results in profitable action?

When you have a bunch of data but you’re not sure what matters to the outcome you want, driver analysis is an effective tool, Chris says.

Machine learning software excels in this case. You feed in all the data and it tells you what matters in it. Chris explains that the analysis concludes with something like, “Hey, this combination of variables seems to have the strongest mathematical relationship to the objective you want.”

AI in #contentmarketing: Driver analysis to show what factors drive the most leads. @cspenn Click To Tweet

Chris performed driver analysis on the popular PR and marketing blog, Spin Sucks, where the primary business objective is lead generation.

“(It) determined that organic search was the third most powerful driver. The team focused a lot of time and energy on it, and they should, but email was the No. 1 driver,” Chris says.

By understanding better what drives leads, the Spin Sucks team could decide to shift more of their time to email marketing because it was the most effective source.

Whether your objective is page views, social shares, leads, or revenue, a ranked list of drivers can help you plan resources, priorities, and budgets more effectively.

Implementation detail: Chris used the R programing language to implement Markov chain attribution. For a detailed look at one such implementation, read this post by data scientist Sergey Bryl, which will give you a good sense of how much mathematics and data science is involved.

HANDPICKED RELATED CONTENT: Why Marketers Need to Think Like Data Scientists (And How to Do It)

Text mining: Reveal topics, keywords, and hidden problems

Text mining is an application of AI that ingests content (e.g., text) to classify, categorize, and make sense of it.

Chris notes that text mining uses vectorization, which transforms words into numbers. It looks at the mathematical relationship among those numbers and determines how similar those words are. It is a form of deep learning.

Reverse engineer Google to reveal key topics and terms

The Google algorithm, which uses a heavy amount of AI itself, is an example of a deep-learning system. “Google’s search algorithm is so complex now that no one knows how it works, including Google,” Chris said. “They have very little interpretability of their model.”

You can use text mining to reverse engineer the Google algorithm for your targeted topics. “We can deploy our own machine learning models to say, ‘OK, for a search term like content marketing, what words do the top 10 or 20 pages all have in common?’”

AI in #contentmarketing: Find #SEO-friendly #content topics via text mining. @cspenn Click To Tweet

Here’s a sample output from reverse engineering Google:

The resulting lists hint at what words or categories to cover when developing new content around your reverse-engineered keyword. Having this set of common words gives you a higher chance of success with organic search than simply saying, “Let’s write a really good article about content marketing.”

Implementation detail: Chris implemented text mining and topic modeling via the R programming language, extracting related topics from a corpus of text (e.g., the contents of articles found in the search engine results pages).

HANDPICKED RELATED CONTENT: How to Make Your Content Powerful in Eyes of Searchers (and Google)

Extract hidden insights via text mining

In 2014, Darden Restaurants, the parent company of Olive Garden, replaced its board. The new group implemented changes, including enforcing its existing but mostly ignored breadstick policy (serving one per person plus one extra).

As Chris explains, employees then spent their time enforcing the policy by counting the number of breadsticks in the basket based on the number of people at the table.

Chris used text mining on 2,500 publicly available reviews written by the company’s employees on Glassdoor. Here’s a glimpse of the results:

Text mining surfaced breadsticks as a problem. If Olive Garden was looking to repair low employee morale and a poor customer experience, a manual review of its Glassdoor reviews, where the usual restaurant worker complaints like low pay and long hours abound, may have led them down the wrong path.

AI use in #contentmarketing: Use text mining on reviews to reveal hidden problems. @cspenn Click To Tweet

Text mining revealed the breadstick problem. (After intense public pushback, Olive Garden returned to its previous breadstick approach.)

Text mining of unstructured data can be applied in many useful marketing contexts: customer reviews, poor/high performing blog posts, transcripts of customer success phone calls, etc. Extracting that hidden gem of insight can point you to courses of action with a high ROI.

Implementation detail: Similar to the reverse engineering Google example, Chris implemented text mining via the R programming language.

HANDPICKED RELATED CONTENT: Scale Your B2B Content With Artificial Intelligence: Ideas and Tools Marketers Can Try

Time-series forecasting: Analyze competitors’ brand searches

Let’s combine math, statistics, and AI to create a Magic 8-Ball.

“Wouldn’t it be great to know what’s going to happen,” Chris asked. “It would be so much easier to plan, to set budgets, to staff, to have an editorial calendar.”

Chris did an exercise of predictive time-series forecasting for Cleveland hotel search data. He looked at more than 12 months of branded searches — where searchers named specific hotels (e.g., Hilton Cleveland, Holiday Inn Cleveland, Hyatt Cleveland, Marriott Cleveland).

The results predicted when search volumes go up and down for each hotel:

“If you (worked at) the Cleveland Marriott here, you now know that right around the end of September you have more search interests than your competitors. You could be running campaigns against them to take even more market share away from them,” Chris said.

Any brand could benefit from predictive time-series forecasting – analyzing brand searches for your company vs. your competitors. You can search for when your brand underperforms, for example, and use that data to bid on your competitors’ brand names with a relevant content asset or promotional offer.

AI in #contentmarketing: Use time-series forecasting to predict lead-gen and revenue. @cspenn Click To Tweet

“Imagine search topics, conversations, social media. You can forecast more than search volume,” Chris said. “You can forecast lead generation from your marketing automation software. You can forecast revenue from your CRM or your ERP. Anything that is regular data over time you can forecast forward.”

Implementation detail: Chris used R to process five years of Google search data, then implemented a statistical method called autoregressive integrated moving average (ARIMA).

How content marketers can try these AI uses

I know what some of you must be thinking about now:

“Wait, really?”

“The data science and probability are over my head.”

“I’m too busy and can’t possibly learn to do this myself.”

These reactions are understandable. The good news is that you have options. And you don’t need to learn the deep nuts and bolts covered earlier.

Chris offered three recommendations for marketers thinking about approaching AI.

Do it yourself. This approach fits for the small percentage of marketers who have a genuine interest in data science and machine learning. You should be interested in going deep with math, statistics, and probability – and comfortable writing code.

If you decide to go this route, Chris suggests checking out Google’s Machine Learning Crash Course, available free online, which takes you through 40-plus exercises, 25 lessons, real-world case studies, and lectures from Google researchers.

Chris notes that IBM Watson Studio has an intuitive, drag-and-drop user interface. While Watson does enable programmers to write code on its platform, the UI can be useful for marketers who are not inclined to write code.

For those interested in coding, Chris recommends learning the R and Python languages, which form the basis for a lot of AI tools and libraries. Be prepared to spend six to 12 months to learn the programming language and another six to 12 months to learn the data science.

If you’re just getting started with coding, the Dummies franchise has books that may be useful: R for Dummies and Python for Dummies.

Tap your staff data scientist. The second option applies to larger organizations that employ data scientists (e.g., Google, Facebook, and Uber). “Staff with data science skills are quantitatively inclined and know how to use the technology properly, so they can be of great help,” Chris said.

Think back to the use cases I mentioned. For text mining or time-series forecasting, in-house data scientists will understand your objectives and goals, build the right models, then implement the necessary codes.

Outsource. This option works for organizations that don’t have AI and data science talent in-house. The answer is to outsource to the experts: people or agencies with the necessary AI know-how and experience.

Chris puts it this way: “Agencies and consultants can help you use the methodologies. You can do small projects on a one-off basis. If the need is ongoing or more frequent, they can help you build software that runs when you need it to.”

Next steps

No matter which of the three options makes sense for you, there’s one thing I urge all marketers to do: Learn about AI and understand the role it plays in marketing technology.

While you don’t need to understand Markov chain attribution or how to program in R, you need to know enough to determine where and how AI can help your marketing. Basic AI knowledge will also help you better evaluate vendor solutions and claims.

Think about the kind of knowledge you need to buy a computer. You don’t need to be a chip designer, but you need to know the difference between a 32-bit and 64-bit processor and whether a 1.5 GHz processor is better than a 2.7 GHz processor. With AI, when a vendor says, “Our predictive analytics solution uses the latest AI techniques,” you need to know how to question the claim and how to distinguish fluff from reality.

HANDPICKED RELATED CONTENT: Are You Really Smart About How AI Works in Marketing?

Since AI is a topic often covered in business, marketing, and technology publications, I’m soaking up as much as I can. Next, I’ll probably enroll in some free, online courses in machine learning.

What about you? What’s your interest level in AI for marketing and how are you staying informed and educated?

Here’s an excerpt from Chris’ talk:

Further your tech skills in 2019 by attending ContentTECH Summit in April. Register today using code BLOG100 to save $100. 

Cover image by Joseph Kalinowski/Content Marketing Institute

The post Opportunities for AI in Content Marketing Easily Explained appeared first on Content Marketing Institute.

from https://contentmarketinginstitute.com/2019/01/artificial-intelligence-content-marketing/