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Hallucinating LLMs — How to Prevent them?

As ChatGPT and enterprise applications with Gen AI see rapid adoption, one of the common downside or gotchas commonly expressed by the GenAI (Generative AI) practitioners is to do with the concerns around the LLMs or Large Language Models producing misleading results or what are commonly called as Hallucinations.

A simple example for hallucination is when GenAI responds back with reasonable confidence, an answer that doesn’t align much with reality. With their ability to generate diverse content in text, music and multi-media, the impact of the hallucinated responses can be quite stark based on where the Gen AI results are applied.

This manifestation of hallucinations has garnered substantial interest among the GenAI users due to its potential adverse implications. One good example is the fake citations in legal cases.

Two aspects related to hallucinations are very important.

1) Understanding the underlying causes on what contributes to these hallucinations and

2) How could we be safe and develop effective strategies to be aware, if not prevent them 100%

What causes the LLMs to hallucinate?

While it is a challenge to attribute to the hallucinations to one or few definite reasons, here are few reasons why it happens:

Sparsity of the data. What could be called as the primary reason, the lack of sufficient data causes the models to respond back with incorrect answers. GenAI is only as good as the dataset it is trained on and this limitation includes scope, quality, timeframe, biases and inaccuracies. For example, GPT-4 was trained with data only till 2021 and the model tended to generalize the answers from what it has learnt with that. Perhaps, this scenario could be easier to understand in a human context, where generalizing with half-baked knowledge is very common.

The way it learns. The base methodology used to train the models are ‘Unsupervised’ or datasets that are not labelled. The models tend to pick up random patterns from the diverse text data set that was used to train them, unlike supervised models that are carefully labelled and verified.

In this context, it is very important to know how GenAI models work, which are primarily probabilistic techniques that just predicts the next token or tokens. It just doesn’t use any rational thinking to produce the next token, it just predicts the next possible token or word.

Missing feedback loop. LLMs don’t have a real-time feedback loop to correct from mistakes or regenerate automatically. Also, the model architecture has a fixed-length context or to a very finite set of tokens at any point in time.

What could be some of the effective strategies against hallucinations?

While there is no easy way to guarantee that the LLMs will never hallucinate, you can adopt some effective techniques to reduce them to a major extent.

Domain specific knowledge base. Limit the content to a particular domain related to an industry or a knowledge space. Most of the enterprise implementations are this way and there is very little need to replicate or build something that is closer to a ChatGPT or BARD that can answer questions across any diverse topic on the planet. Keeping it domain-specific also helps us reduce the chances of hallucination by carefully refining the content.

Usage of RAG Models. This is a very common technique used in many enterprise implementations of GenAI. At purpleSlate we do this for all the use cases, starting with knowledge base sourced from PDFs, websites, share point or wikis or even documents. You are basically create content vectors, chunking them and passing it on to a selected LLM to generate the response.

In addition, we also follow a weighted approach to help the model pick topics of most relevance in the response generation process.

Pair them with humans. Always. As a principle AI and more specifically GenAI are here to augment human capabilities, improve productivity and provide efficiency gains. In scenarios where the AI response is customer or business critical, have a human validate or enhance the response.

While there are several easy ways to mitigate and almost completely remove hallucinations if you are working in the Enterprise context, the most profound method could be this.

Unlike a much desired human trait around humility, the GenAI models are not built to say ‘I don’t know’. Sometimes you feel it was as simple as that. Instead they produce the most likely response based on the training data, even if there is a chance of being factually incorrect.

Bottomline, the opportunities with Gen AI are real. And, given the way Gen AI is making its presence felt in diverse fields, it makes it even more important for us to understand the possible downsides.

Knowing that the Gen AI models can hallucinate, trying to understand the reasons for hallucination and some reasonable ways to mitigate those are key to derive success. Knowing the limitations and having sufficient guard rails is paramount to improve trust and reliability of the Gen AI results.

This blog was originally published in: https://www.purpleslate.com/hallucinating-llms-how-to-prevent-them/

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How Generative AI Can Help Your Sales Team Sell Faster

In recent years, the world of sales has been undergoing a remarkable transformation, fueled by advancements in Generative AI. This cutting-edge technology has the potential to revolutionize how sales professionals approach their craft, from lead generation to personalized customer experiences. In this blog, we will explore the concept of generative AI, its applications in the sales domain, and the benefits it brings to businesses in terms of efficiency, productivity, and customer satisfaction.

Why should Businesses think Generative AI?

Generative AI is centered around the creation and generation of fresh content and data, as opposed to merely analyzing pre-existing information. Its versatile applications range from content creation, image manipulation, to even code generation.

This technology finds utility in a wide spectrum of industries, spanning marketing, security, and coding, among others. With the ability to learn from diverse experiences, continuously improve its knowledge, and engage in contextually meaningful interactions with users, Generative AI has proven to be a transformative force within the sales function.

The subsequent section will delve deeper into the applications of Generative AI in the realm of sales.

Revamping Sales with Generative AI

Sales is a versatile function, a boundary role known for its challenges. People play a major role in the sales process. Networking and relationship building are table stakes for any sales leader. There are numerous stories of sales cycle lasting from just hours to months and even years.

Given its highly unstructured nature, technology adoption in sales was always slow and “gut-feelings” were given precedence. But Generative AI can reimagine sales for modern organizations, precisely because of its unstructured approach.

60% of Sales Leaders identify the potential of Generative AI in lead identification– McKinsey

Personalized Customer Interactions: Generative AI can analyze vast amounts of customer data, including purchase history, preferences, and browsing behavior, to create highly personalized interactions. Sales representatives can utilize these insights to offer tailored product recommendations, promotions, and incentives, thereby fostering stronger customer relationships and boosting loyalty.

Dynamic Pricing Strategies: With generative AI, businesses can dynamically adjust pricing based on real-time market conditions, customer demand, and competitor pricing. This enables sales teams to optimize pricing to maximize revenue and stay competitive.

Lead Generation and Scoring: Traditional lead generation processes can be time-consuming and imprecise. Generative AI algorithms can sift through vast databases of potential leads, identifying and prioritizing the most promising prospects based on historical sales data and patterns. This ensures that sales teams focus their efforts on leads with higher conversion potential, leading to improved sales efficiency with generative ai.

Enhanced Sales Presentations: Generative AI can assist sales professionals in creating engaging and visually appealing presentations. By analyzing previous successful sales pitches, the AI can generate content and visuals that resonate with the target audience, increasing the likelihood of closing deals.

Chatbots for Customer Support: Incorporating generative AI into chatbots enables businesses to offer 24/7 customer support. These AI-powered chatbots can understand and respond to customer queries, resolve issues, and even process orders, enhancing customer satisfaction and streamlining sales operations.

Top Benefits of Implementing Generative AI for Sales Teams

As readers, you have witnessed the transformative power of Generative AI for sales teams. But if you still need to zero-in on the why of investing and implementing Generative AI for Sales teams, we can help you there. We have outlined the top benefits of implementing a Generative AI solution for sales teams below.

Increased Efficiency

By automating repetitive tasks and streamlining lead generation, generative AI frees up sales teams to focus on higher-value activities, such as building relationships and closing deals. This boosts overall sales productivity and drives revenue growth.

Data-Driven Decision Making

Generative AI empowers sales managers with real-time data and actionable insights. Data-driven sales strategies making helps businesses identify trends, predict customer behavior, and align their sales strategies accordingly.

Competitive Edge

Embracing generative AI gives businesses a competitive edge. Companies that leverage AI to provide personalized customer experiences and optimize pricing strategies are more likely to stand out in a crowded marketplace.

Enhanced Customer Experience

Generative AI enables businesses to offer personalized, relevant, and timely interactions, resulting in improved customer satisfaction. Satisfied customers are more likely to become repeat buyers and advocates for the brand.

Challenges of Generative AI and How to Overcome them

While the potential of generative AI in sales is immense, there are some challenges of generative ai in sales and ethical considerations that businesses must address.

Privacy and Ethics

Privacy concerns related to customer data collection and usage must be handled responsibly, adhering to data protection regulations. Additionally, businesses should ensure transparency in AI-generated content to maintain trust with customers. Ethical considerations also come into play when using AI for sales. Companies need to be transparent with their customers about the use of AI in their interactions.

Hallucinations of Generative AI

Generative AI has the tendency to suffer from a condition known as hallucinations where it generates false content based on its own understanding of a scenario. The reasons for hallucinations vary from improper training data, especially fitting the model into real-world scenarios. These hallucinations will lead to wrong communications during sales calls.

Overreliance on AI

Although generative AI streamlines sales processes, it should complement human efforts rather than replace them entirely. Human-to-human connections remain vital in sales, as customers still seek genuine interactions and empathy from sales professionals.

Future Prospects of Generative AI in Sales

90 percent of commercial leaders expect to utilize gen AI solutions “often” over the next two years — McKinsey

As generative AI continues to evolve, its potential in the sales domain will expand exponentially. One exciting aspect is the prospect of hyper-personalization. With more sophisticated algorithms and data analysis, businesses can create individualized buying experiences for each customer, leading to higher conversion rates and customer loyalty.

Additionally, advancements in the field will enable Generative and Conversational AI chatbots to have more natural and contextually aware conversations with customers. These chatbots will become even more integral in handling complex queries and facilitating seamless customer interactions.

Summary

Generative AI is transforming the sales landscape, providing businesses with powerful tools to improve efficiency, customer experiences, and revenue generation. Embracing this technology offers a competitive advantage and opens up new possibilities for sales teams to thrive in a rapidly evolving market. As the technology continues to evolve, sales professionals should embrace generative AI as a key ally in driving success and delivering exceptional customer value. The future of sales belongs to those who harness the power of generative AI.

This post was originally published in: https://www.purpleslate.com/how-generative-ai-can-help-your-sales-team-sell-faster/

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The Shiny Object — Navigating the Generative AI Hype

The Shiny Object

For a change, let’s play the devil’s advocate here or take a bold shot to prescribe against a popular technology trend in the industry today.

Generative AI is here.

Very much evident from the fact that overnight many companies became ‘AI First’ or changed their name to have a GPT prefix. It feels so much out of fashion if you are not somethingGPT today.

No question, AI is destined to transform businesses and boost performance across functions such as sales and marketing, customer operations, and software development. In the process of doing this, AI could unlock trillions of dollars in value across industries.

Many are experimenting with the different LLM capabilities, while a large section of the stakeholders are grappling with the technology’s impact on their business trying to figure out the right use case.

Is AI a good thing or bad thing? It’s like asking if money is a good thing or a bad thing. Money is good and essential for survival. But, the question is how much of it and forwhat purpose?

As a product manager, identifying exactly the right business and user problems and applying the right technology including Generative AI is what makes the role exciting. It is not about the fanciest of technologies, but the potential to make an impact.

No technology leader wants to be left behind or miss an opportunity to make things better for their customers, businesses and users. You are caught between the dichotomy of keeping pace and making sense of the technology and the the pressure of FOMO (Fear of Missing Out) and the urge ‘to do something’.

Photo by davisuko on Unsplash

You build something even if it is not aligned with your strategy and goals.

The risks of prioritizing shiny new ideas without deep thought about the broader impact are real — feature bloat, complex user experiences, and wasted resources to name a few.

This does not mean that the hottest new technology or trend does not have a place in your product. It just means that you apply extra skepticism and rigour when evaluating ideas and building new functionality.

How can someone confront the Shiny Object Syndrome (SOS) head-on?

Understand

Many of the elevated expectations and apprehensions related to Generative AI relates to the lack of understanding of the technology. The world has been buzzing over ChatGPT since December of last year and it picked up steam around the February of this year with the release of ChatGPT and no wonder it took the shortest time to hit a million users.

Soon, it gathered momentum among the industry and it seems like every company was talking about rolling out AI in some capacity.

Here lies the very important first step.

While ChatGPT can automatically generate text that predominantly make sense, it is very important to understand what an LLM (Large Language Model) is? How does it work? Why does it work? While the words ‘Large’ and ‘Model’ in that name are quite self-explanatory, what exactly does the word ‘Language’ in the name mean? How is this different to any other Machine Learning algorithms?

So much of literature is readily available for easier understanding and my best recommendation is this long blog from Stephen Wolfram which is now available as a book.

In addition to understanding the underlying technology, ask these simple questions.

Is it necessary for your application to have a Gen AI component? Validate it against the use cases that leveraged Gen AI is persumed to be good at — analyze large corpus of content, summarize, generate answers from a very large content, assist in writing.

To better understand your customers’ needs, think about how they currently use your product and where there are opportunities to optimize their experience with the power of AI.

Start Small

Almost everyone appears confident about the potential of AI and the hype cycle of the technology also adds to that inflated expectations. One simple way to get started is to start small on a particular use case with the greatest relevance. Identify low-hanging fruit and look for simple, high-impact, low-cost and low-risk use cases. Easier said than done.

I have seen a similar scenario at many organizations during the initial Big Data hype cycle. Many of the firms were busy fiddling around with Hadoop related technologies when their total data footprint would not be more than few GBs. A simple database application would have solved their problem.

Read the fine print

Okay; now you have understood the technology and see an opportunity with a right use case. Focus on asking the right questions next.

What exactly is a private LLM? How does my data stay safe? How much of it goes out? Wear the critical lens to weigh the costs and benefits of using an Open AI model.

How do we operationalize? How does this Gen AI use case integrate well with my other enterprise applications? How much does it cost? What is our testing strategy?

LLMs tend to hallucinate? How does this impact your product feature or your application? What is your way to provide a safety net over any incorrect results from the AI feature?

In summary, AI is here for sure. It has already permeated our lives in multiple ways and its progress for enterprise applications is going to mature in the coming days. It may not be one of the routine tasks as write text, music or create digital art that has garnered the excitement so far.

Doing so will allow you to focus on investing in the features that will deliver real value and make the greatest impact.

In the mean time, the boldest move could be not doing something or resisting the temptation to not pursue something because it could be a shiny object.

Sometimes, it is okay not to do something.

Or, at least till we figure out or learn more on what it is.

This post was originally published in: The Shiny Object — Navigating the Generative AI Hype

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Kea Aids Retail Brand Managers improve MRP Sales by Restocking the Right Number of Running Shoes

A brand or sales manager and his/her team managing a big region in the sportswear sector would operate at least 70+ retail outlets including multi-brand stores. Planning for inventory including models starts at least 6–8 months before the start of the season.

Common Challenges Faced by Retailers

What quantity of running shoes should the manager order for the next season?

Some common challenges plaguing the retail sportswear market are discussed below.

Exceeding Order Limits — Without proper information at hand, many sales managers handling sportswear sales for a region pressurize the retail outlets to order more numbers. The lack of research into data sometimes leads to the sportswear lying in the warehouse or eating up display space without any sales.

Unable to Deliver Promised Margins — Since the products occupy shelf space that can be used for newer products, retailers are sometimes forced to sell them at highly discounted prices, affecting their stores’ overall margins.

Missing out on Opportunities — Many sportswear retail sales spike based on sports activities happening around the city. For example, a marathon will increase the number of running shoes in the region. However, sometimes when brands fail to work on market intelligence, they tend to miss out on opportunity costs, In the above example, if less number of running shoes were ordered, then there is a huge loss in terms of potential revenue increases or overachieving targets.

Problem Statement

Rakesh Ramalingam is a brand retail manager tasked with improving his region’s full-price sell-through for running shoes. He handles more than 70 stores, including sports-only department stores, franchise-owned retail outlets, company-owned and company-operated retail outlets, multi-brand retail outlets, and mega discount department stores. Also, Rakesh is responsible for e-commerce and other online channel sales.

Ordering for Autumn Winter or Fall Winter is always a tough task as the brand releases new types of shoes and strives to increase existing sales numbers by 20%. Amidst the pressure from the company to increase targets, Rakesh also faces pushback from the retailers who have seen how the market will perform on the extra targets. Bringing a balance between these two entities is what Rakesh strives for daily whilst increasing retailer margins. The retailer margins increase when most of their products have a full-price sell-through, i.e., selling them with zero discount. Figuring out a near-perfect number to place orders with the organization is mission critical for Rakesh to ensure both his retailers and his organization are happy, while he himself can walk away with a fat commission at the end of the day. A true win-win-win situation.

Rakesh knows that he will be walking into a den of disbelievers when he meets the retailers for order planning. He must be armed to the teeth with data insights. Even then, he would face scenarios where he won’t have the answers because of not having access to the right information. This is where Rakesh turns to Kea to help him out of this predicament.

Meticulous Order Planning for Improving Retailer Margin

The process kickstarts in early January when retailers and their teams are called in for a meeting and the proposed shoe designs for the year are displayed. Rakesh collects the required orders to be handed over to the manufacturing unit. The numbers that need to be identified for placing the orders follow a chronological process, and it can be simplified with a conversational insights platform like Kea.

Scenario — 1: Rakesh wants to know which retail store performs the most in men’s running shoes. With Kea, he can get a chart that shows city-wise and store-wise performance. Rakesh notices a spike in terms of running shoes in Salem. He had already noticed this trend. Rakesh decided to probe further, concluding that there are many educational institutions in and around the city of Salem. The spike occurs Y-o-Y as most of these institutions regularly produce athletes in the 100m dash, 200m dash, and other running events. His brand is well-known in the area, and only one outlet exists.

Comeback: Rakesh asks the franchise owner to increase his order number by 25% as the intercollegiate athletic meet will happen in the next season with many colleges participating. Along with the regular customers, Rakesh is sure that walk-ins will also increase on account of this event.

Scenario — 2: Many times when Rakesh comes in for a discussion with his region’s franchise owners they have two recurring complaints. One is that since the introduction of e-commerce websites, many of their sales have gone down for Rakesh’s brand. This is because most customers prefer buying directly online to get better discounts. The second complaint is that since there are company owned company operated outlets within a 5 Km radius for some major franchisees, their footfall has gone down significantly. Thus they wanted to order 20% less as opposed to the usual. They claim that Rakesh, the brand manager, prefers pushing more customers there. Rakesh wanted to quell the doubts once and for all.

Comeback: Rakesh points out that out of 4 major channels — Dept stores, Distribution (Company owned company operated outlets), Franchise, and E-commerce, Franchise has the highest sales. Because Franchise gives the touch and buys feel that e-commerce lacks, the service is far better than company outlets. Also, the franchise outlets are located in prime areas with more footfall as opposed to company outlets that serve as a secondary stores in case the required product is unavailable in the franchise or as a channel to push End-of-Season or Going-out-of-business style sales. Thus they should not bring their order numbers down.

Scenario — 3: A store wanted to run a specialized campaign for Running in September and wanted to order 15% more shoes than the fixed target. It was confident of the result of the campaign and was almost on the verge of convincing Rakesh. But Rakesh, a data-driven brand manager, quickly checked the sales numbers for September and was astonished to see that only five running shoes were sold that month. The sales had gone down by 44%.

Comeback: Rakesh showed the number to the franchise and explained why they cannot rely on September to increase running shoe sales. September is considered part of an on-and-off monsoon season in that franchise’s region, and the rains, even though sporadic, are heavy. Thus, increasing the order numbers to sell in September will impact their full-price sell-through and will end up as heavily discounted sales. Thus, it’s better to move the campaign to November when a marathon is planned to support cancer awareness.

Scenario — 4: Rakesh’s boss Ranjan was at the meeting where he was talking to his Franchise owners and ordering shoes for FW season. Ranjan wanted to increase the order numbers by another 10% per franchise given the number of activities being planned around the region. He pulled Rakesh aside and told him that he needed this increase in the target number no matter what. Rakesh tried many times to convince Ranjan that it would be futile and that the shoes would go for discount sales. But Ranjan being a bull-headed boss was not at all listening to Rakesh.

Comeback: Rakesh showed the weekly running shoe trend for the last year and talked to Ranjan. He said there was no clear spike in sales throughout the week and this trend was true for the entire second half of the year. This has been the case for a few years now because of various reasons ranging from hostile takeover of franchises by competitors to unforeseen cancellation of running events. Most of them are not in the control of the brand team, and if Ranjan still wants to increase the order number, there is a high chance that franchises will demand a 10% cut down on the order numbers the following year.

Kea — Simplifying Information Access

Kea helped Rakesh instantaneously access information. Previously, Rakesh used to fidget with his spreadsheet to get the same answers, and he had zero time to prepare for a counter strategy. This time was different because Rakesh got all the answers right and his order numbers were as close to perfect as possible. The retailers were happy that Rakesh’s order numbers helped them gain better margins and that Rakesh could expand his network of franchises based on their recommendations.

Imagine as a sports retail brand or sales manager if you’re able to be like Rakesh. Get answers immediately. Plan well in advance for your next meeting with your franchise network owners. And walk back with a fat commission for a successful autumn-winter sale.

This is where Kea’s true power lies. It empowers you to make the right decisions without ever doubting yourself. Simply, you just plan your order numbers and targets to be achieved. Let Kea worry about fetching the right information for you at the right time.

Want to know more about Kea? How about you get in touch with us, and we will let you feel the real power of Kea with a small demo?

This post was originally published in: https://www.purpleslate.com/kea-aids-retail-brand-managers-improve-sales/

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Kea Empowering Car Dealers with Data Insights to Increase Revenue

Disrupting Data Analytics with Kea

Today’s automobile dealers have it tough. The number of challenges is innumerable. The number of challenges increases if the dealer is a multi-brand with more than 500 used car sales per month. Collecting data insights to make data-driven decisions becomes a major challenge but that’s not the end of it. Some common challenges are listed below.

Inventory Delays

The availability of used and new cars, and relevant parts is a significant concern for dealerships. This leads to lost opportunities with customers and excess inventory creating a shortage of floor space and tying up the capital, thereby affecting the margins of these dealerships.

Lowering Dealer Loyalty

Many customers are looking for dealerships that can provide a seamless pick-up and drop option while servicing their vehicle, adequate financing and mortgage options, and, more importantly, health certificates for the used cars they’re buying. Even if the dealership falters in one of the points, customers easily switch the dealership. Not having the best-in-class customer experience is indeed a deal-breaker.

Online Marketplace

The online marketplace has emerged as a strong competitor for used-car dealerships. The sellers can initiate conversations from the comfort of their homes. Prospective customers can select, shortlist, negotiate, and close deals faster. They also eliminate the “middle-man” from the equation, ensuring both parties’ profitability. Dealerships are coming up with their own websites and platforms to counter this and give value-added services for free.

The Predicament at Hand

Jake Hallson, the sales manager was looking to maximize revenue for his car dealership and liquidate his used cars inventory. The dealership is based in a bustling city in the American midwest. A multi-brand showroom will have walk-ins of a variety of people with varied backgrounds and to cater to a wide variety of users, he had to come up with interesting campaigns, understand where to place his ads, and ensure that his team has up-to-date information in terms of the cars in the warehouse, and make sure that his customers walk out of the dealership with a smiling face, assured that they have the best deal.

To ensure he achieves the target and delights his customers, Jake must depend on data and metrics. He must clearly understand the brands that move fast, the transmissions preferred by different customer segments, and other important information such as the power train, engine capacity, fuel type, etc.

However, the dealership doesn’t have the budget to set up a big team of data analysts crunching through the data in hand and producing dashboards. Also, Jake is not a technology expert, nor does he have the luxury of time to learn and prepare such effective visualizations.

Here’s where he turned to Kea — Our Next Gen Virtual Data Analyst capable of mining millions of rows of data in microseconds, and producing relevant actionable insights at a moment’s notice.

Next “Action” Plan — Maximizing Sales & Revenue with CI

The dealership needed to plan for maximizing sales and liquidating existing inventory. An effective campaign plan in line with the cultural aspects of their target audience can turn the tables around for them, especially with the rising loss of customers to the online marketplace. It was imperative for the dealership to come up with personalized campaigns that would attract people to walk into their stores.

Conversational insights come to the rescue here. It is employed by used-car dealers to plan better using data insights having a simple language-driven interface. Jake sorts out the plan with instantaneous insights derived from Kea where he asks simple data questions in their natural language and gets pointed answers with visualizations.

Impact Campaign — Created a social media campaign in alignment with “Blue is not only for boys” with the image of different Audi cars from the warehouse. Erected a hoarding with the message “Prove the boys wrong 2745 times” and improved the footfall of working women in the demographic of 30–35.

Impact Campaign — Executed a “Safety has no Price” campaign with a safety message in and around residential neighborhoods. Demonstrated the safety features of XC90 and XC60 in those neighborhoods and improved the sales of Swedish brand cars.

Impact Campaign — Introduced a “Be the Face of New America” campaign targeting power couples. Rolled out specific feature videos and explained additional value add services that they get from the dealership. This led to clearing out the existing Audi A1 and A8 cars inventory.

Impact Campaign — Planned and executed “Feel right at home in your Lamborghini” targeting bachelors living in the European quarter. Gave the customers tailored experiences and explained how the dealership partners directly with Automobili Lamborghini to get trained on service engineering directly from them thus assuring best-in-class care for their cars. Improved the sales of Lamborghini by roughly 40%.

Impact Campaign — Set up “Make your first car memorable” stalls across different universities. Ensured the students who came to the dealership from the stall references got an extra 5% off, thereby improving budget French car sales amongst students.

Kea — Ensuring Instantaneous Insights!

Kea helped Jake get all the information they needed in a jiffy instead of fiddling around in a cluttered dashboard loaded with information. Time was really on Jake’s side because the insights were instantaneous, and timing is everything in a field like Sales and marketing. His campaigns were successful, ensuring a good flow of revenue to the dealership.

Jake depended on data insights and did not get stuck in the usual rut of collecting data, preparing dashboards, deriving insights, and then executing the campaigns. Frankly, Jake is not supposed to do that because he is a salesman, and his role is to improve sales. However, in many organizations, many salesmen are still trying to make sense of their data spread across multiple spreadsheets.

Now, I leave the decision to you, the reader. Do you want to empower your sales team with a state-of-the-art next-gen conversational insights platform like Kea? Or, do you want them to be stuck in the vicious cycle of data collection and dashboard preparation?

Head over to our webinar to learn more about how conversational insights simplify information access and what happens behind the scenes.

This post was originally published in: https://www.purpleslate.com/kea-empowering-car-dealers-with-data-insights-to-increase-revenue/

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What is Data Mining?

We are currently in a data-driven world, rich in information. Knowing there is superfluous and readily available information can feel comforting. However, the more there is available knowledge, the more challenging it becomes for you to get helpful insights. In that case, we will explore data mining aspects. We will discuss data mining, its use, history, how it works, techniques, use cases, and examples. Let’s understand what is data mining and then handle the other techniques and concepts.

What is Data Mining

Data mining is the process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories. Data mining employs pattern recognition technologies, as well as statistical and mathematical techniques — Gartner

It also incorporates searching for wide data sets to establish correlations, anomalies, and patterns that result in actionable information. For instance, weather forecasting requires analysis of historical data to discover patterns and forecast weather conditions depending on climate, time of the years, and other aspects.

The Need for Data Mining

Data mining provides marketers with helpful customer insights about their preferences and behaviors. With such knowledge, they can design targeted advertising and marketing campaigns. Additionally, its results are essential to the sales team in rate enhancement of the lead conversations. Therefore, they can sell more products and services to their clients.

Also, it offers numerous opportunities for entities as it has descriptive and predictive powers. The technology enables the business to forecast the future and make it more profitable. For example, a retail industry can access and analyze past sales patterns and customer behavior which helps in business decision-making.

History of Data Mining

You may be tempted to assume data mining has begun recently since it is often related to new technology. Nevertheless, we are looking at more than a century old rich legacy. The discipline started with Regression Analysis and Bayes’ Theorem used in identifying data patterns.

Increasing technological power and data set complexity has resulted in evolutions of data mining from disks and tapes to massive databases and improved algorithms. By the late 80s, management information system communities, data analysts, and statisticians depended on it.

In the 1990s, mining was identified with the process of Knowledge Discovery in Databases as a step or sub-process. In that case, its popularity grew with the increasing technology and computer capability to store numerous data. Organizations could also store data and process information in computer-readable forms.

Data mining was now a well-understood technique by the end 1990s. An organization could now record customer data and purchases. The mining of the resulting data provided knowledge of clients’ purchasing patterns. Over the last decades, data mining’s popularity has grown continually.

Breaking Down Data Mining Step by Step

Understanding the process will give you a deeper knowledge on its workings.

Collection: Data is gathered, organized, and moved in the data warehouse. It is then stored and managed in the cloud or in-house servers.

Understanding: Data scientists and business analysts examine the data properties and have in-depth analysis from the business’s problem statement perspective. It is addressed through visualization, reporting, and querying.

Preparation: After confirmation of the existing data sources, they are formatted, constructed, and cleaned as desired.

Modeling: Datasets are organized systematically for accurate storage and retrieval from the database.

Evaluation: Based on the business objectives, the results are evaluated, and if there are new patterns, new business requirements are created.

Data Mining Techniques

Businesses can employ various data mining techniques to transform raw data into valuable insights.

Clustering: The clustering technique depends on visual methods of understanding data. This mechanism uses colors and graphs to show data distribution to the various types of metrics.

Sequential Patterns: Sequential pattern technique works on identifying a sequential series of events. The understanding of such patterns is helpful to the organization in recommending other items to customers, boosting its sales.

Prediction: The prediction technique employs current and historical data patterns to forecast the future. In that case, organizations gain helpful insights into future trends that will take place in their data.

The Industries Benefiting from Data Mining

The predictive power of data mining has resulted in significant changes in business strategy designs. In today’s world, organizations can understand the present to make future predictions. Here are a few examples of current industries using data mining:

Telecommunication, Media & Technology: These systems gather and analyze anonymous data from programming, broadcasts, and channel views. Through data mining, networks can create personalized content for television viewers and radio listeners. Also, they can understand their behaviors better and gather information on their activities and interests in real-time. Through determining the relationship between customer aspects like tastes, gender, and age, one can predict their behavior and design personalized campaigns.

Education: Data mining in education is used to categorize and predict teachers’ and students’ performance and dropouts. Educators can keep track of academic progress and enhance their teaching techniques. On the other hand, data mining helps promote effectiveness and efficiency in education management, making it easier for students to select courses.

Banking: Banks can understand market risks better through data mining. Usually, intelligent anti-fraud systems and credit ratings help analyze the client’s financial data, purchasing patterns, card transactions, and other transactions. Through data mining, banks gain a deeper understanding of customers’ online habits or preferences for regulatory compliance obligation management, assess sales channel performances, or optimize marketing campaign returns.

Insurance: Data mining techniques help insurance industries determine future claim amounts in medical coverage and property. It helps them promote effectiveness in planning, preventing fraud activities, and paying incorrect claims.

Manufacturing: Using sensory data, manufacturers can forecast machine failures before they happen. Data mining also helps in identifying commonalities and anomalies in production systems. Therefore, manufacturers can optimize manufacturing capacity and identify patterns that help enhance the quality of all products.

Retail: Data mining helps accurately predict sales volume at particular retail locations to discover the exact inventory levels. It also helps predict future product consumption rates depending on environmental and seasonal conditions. Retail stores can also assess the various product relationships to enhance their layout and maximize sales promotions.

E-Commerce: Data mining is used for up-sells and cross-sells in e-commerce websites to gain more customers. For example, Amazon employs data mining to recommend products based on customer data, enhancing customer experience.

Final Thoughts

Data mining is used across different fields like education, banking, insurance, retail, and manufacturing. When properly used, it offers businesses useful insights into customers and helps develop effective marketing strategies that give them a competitive advantage. Explore the various data mining techniques and employ one of the methods for better decision-making.

This blog was originally published in: https://www.purpleslate.com/what-is-data-mining/

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Driving The Next Generation Of Customer Experience With Conversational AI

Prologue – Conversational AI Is Now In Demand More Than Ever

“The global conversational AI market size is expected to reach USD 41.39 billion by 2030, according to a new report. It is expected to expand at a CAGR of 23.6% from 2022 to 2030.” – Research and Markets 

Surprised? Don’t be. Pandemic was a watershed moment for industries and leaders worldwide on many fronts. Businesses shifted to a digital-first attitude overnight and hyper-personalization came to the forefront for driving exceptional customer experience.

Many organizations pivoted their customer service practices to ensure they stay ahead of the curve by offering their customers highly contextual conversations and enhanced value. In this article, we touch upon what is conversational AI, how it is different from the traditional chatbot and why it has gained prominence over the years. We also brief upon the three core tenets leaders must not forget when implementing Conversational AI.

Conversational AI – The Protagonist Of This Story

Deloitte defines conversational AI as “A programmatic and intelligent way of offering a conversational experience to mimic conversations with real people, through digital and telecommunication technologies.”

When we are speaking about conversational AI, it’s impossible to proceed with the story without showing noticeable differences between a traditional chatbot and a conversational chatbot, which is covered in the next section.

Conversational AI – The Differentiator In A Cluttered World Of Traditional Chatbots

Traditional script-based chatbots require pre-populated answers to the questions a customer ‘may’ ask. These are written by the company’s customer service agents and loaded onto the chatbot. Whenever a customer asks a question to the bot, it checks for matching keywords and pulls the most relevant answer from the script. However, the moment the query steps out of the rule, the bot fails. In the world of cut-throat customer experience engines, that is a big mistake. This is where Conversational AI comes into the picture.

We believe this can be explained better with an example. Imagine that you would like to know about your current refund status to a cancelled order. If you’re chatting with a traditional chatbot, the conversation may go like the illustration on the left. However, even if one of the words in the response do not match with a keyword, the output will go for a 360 turnaround as illustrated on the right.

Apart from the above scenario, there are more differences between a scripted chatbot and a conversational chatbot.

Traditional chatbots

Keyword driven with stringent rule based responses

Bot will ‘break’ when the query steps out of the rule

Spelling mistakes and short forms cause a nightmare

Inability to scale the bot as further responses require re-training with an additional cost factor

Conversational chatbots

Learns from historical data and tailors the response

Previous interactions help in building contextual responses

Powered by entity recognition to understand abstract inputs

Easily scalable as the bot learns and updates its responses to real-world scenarios

Traditional chatbots have a history of botching up customer experience, precisely because of its stringent rule based responses. So it should come as a no-brainer that there was a negative impact on the purchase behavior of 79.7% customers when they knew that they were communicating with a non-human conversational partner. According to them, the bots are less-knowledgable and less-empathetic. Now, the question arises. How can conversational AI plug the obvious gaps in the current rule-driven chatbot environment? The next chapter outlines the benefits of conversational AI for the context driven millennial customer.

Conversational AI – The Win-Win Solution For Better Customer Engagements

Conversational AI for customer engagement? Yes. Conversational AI has a multitude of advantages in the current world where business leaders need to have that extra edge when serving their end-customers and stakeholders. Let’s see the benefits of conversational AI from two perspectives. One from the perspective of the end-user and the other from the perspective of the business leader.

Perspective of the end-user

Hyper-personalized experiences

The technology has the ability to provide tailored recommendations, personalized content, and information relevant to the unique needs of the customer.

All around the clock service

In a very traditional customer service set-up following the sun-staffing model, many a time the end-user after spending precious time on the phone finds himself/herself with a customer agent situated at the farthest corner of the world. This is because humans work for 8 hours a day. However, a chatbot is readily available 24/7, 365 days a year with an immediate resolution to their queries.

Context, now more than ever

People find it absolutely tiring to explain their predicament all over again, just because the human agent decided to transfer the call to his manager or the rule-based chatbot, unfortunately, cannot comprehend the query. Conversational AI-powered chatbots understand the context from past conversations and then proceed to answer the customer query.

Emotions matter

Conversational AI bots are built with the ability to understand the emotion behind each word by analyzing key triggers, connotations, and placement of the word within the sentence.

Multilingual support

Conversational AI can be trained to offer support in multiple languages. This positively impacts the customer experience as customers from non-native English speaking countries can have their queries solved while the business, they have a single end-to-end platform addressing all the problems.

Omnichannel experience

The days are long gone when systems and people used to operate in siloed channels to address customer queries. Conversational AI chatbots are truly omnipresent, enabled to connect through multiple channels for the customers to get their problems resolved while the business gathers data from all these channels in a single shot.

Perspective of the business

Enhanced customer acquisition

With customized recommendations, personalized offers, and ease of access to find content and information regarding the product, conversational AI drives better customer acquisition.

Enhanced customer loyalty

Immediate support to resolve high-ticket issues increases customer satisfaction. The data gathered from such interactions helps analysts understand customer pain points and execute the most effective plan of action to reduce churn.

Enhanced employee satisfaction

Automating low-value routine tasks frees up a lot of time on the analyst’s plate thereby giving them the opportunity to do high-value strategic tasks. The virtual agent goes the extra mile by recommending leads to the analysts and helping them achieve their sales targets.

Enhanced scalability

The bots can handle multiple queries at the same time without the business leader needing to expand their team and increase headcount. If there is a sudden surge in queries, small teams of agents can face challenges in reaching out to individual customers.

Reduced operating costs

Low integration costs and low maintenance costs coupled with a high potential for faster ROI help business leaders see their overheads reducing in the long run.

Generally, a topic about conversational AI covers how to approach the implementation. We would like to change the narrative a little bit and showcase to you the important requirements when approaching conversational AI projects as illustrated in the chapter below.

Conversational AI – The Core Tenets That Business Leaders Must Not Ignore 

Have a strategy in place!

Having an absolute clarity of what is expected out of implementing a conversational AI chatbot is the most important requirement. Is the chatbot for improving customer engagements? Enhancing brand awareness? Bringing in more leads or a combination of all? The digital stewards of the organization must have answers to this question which can then define the process and create actionable KPIs.

Buy, don’t build!

We understand the innate pull to convert this to an internal IT project. It influences both project governance and cost reduction. However, the leaders have to understand that their IT teams lack the required domain knowledge, and they definitely do not have access to proper training data to train the AI algorithm. Instead find expert partners in the field who have proven use cases.

Find a partner, not a vendor!

Do your due diligence before zeroing in on a partner. Vendors who say yes to every small bit of customizations, will drag this into a science project with long implementation timelines.

Instead, find partners who commit to bringing the best results as they will have skin in the game and will provide you with the best value in a shorter turnaround time.

Epilogue – Conversational AI Will Define The Future Of A 10X Economy

Conversational AI is here to stay and rock the world of customer experience. Many businesses are investing in conversational AI programs to improve their brand awareness, marketing strategy, customer support and overall customer experience.

With 75% of customers demanding service within five minutes of being online according to a McKinsey study, staying hyper-relevant in conversations will catapult businesses into next gen customer engagement programs. The key to unlocking success with conversational AI is to partner with the right solution provider who will help the CXOs achieve the best value out of their digital transformation initiatives in the customer department.

The question is, are you ready to join the bandwagon of digital stewards who are changing the world of customer experience?

This blog was originally published in: https://www.purpleslate.com/conversational-ai-for-customer-engagement-soon-to-become-the-new-normal/

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How different Industries use Conversational AI

Conversational AI has seeped into almost every industry today and it is changing how businesses and individuals interact and perform everyday tasks. Customers and businesses alike have joined this AI revolution over the last few years. For customers, conversational AI solutions means quicker responses, better customer service, easier order fulfillment, and a more personal connection with the brands they do business with while for businesses it means cheaper customer support costs, higher conversion rates, boosted customer loyalty, accelerated sales cycles, and increased lead generation.

Here are the top industries that have adopted conversational AI, how it is being used across sectors and how they can be customized to suit business needs:

Conversational AI for Insurance

According to research, 87% of insurance brands invested over $5 million in AI-related technologies each year. InsurTech has become a powerful driver of change in the insurance sector. There’s a lot of potential for AI technology to transform this industry and automate repetitive and time-consuming tasks. A smart conversational AI assistant can act as a digital agent for an insurer’s customers, be available to them at all times and provide a seamless experience every time they interact with them.

In information-heavy businesses such as insurance, conversational AI has been proven to be advantageous in bridging knowledge gaps and in creating an environment of transparency and clarity around insurance. Insurance providers can steer conversational AI to go directly to the customers and help policyholders to go through the entirely digital insurance purchase journey by themselves.

For example, a buyer looking to buy car insurance can explore the various products available, compare their prices and premiums, find the plan that suits their needs best and proceed to purchase — all by asking questions to the conversational AI assistant. This way, businesses can benefit from enabling self-service for end-user without having to connect to an agent and thereby accelerating the buyer’s journey. It is also important to note that conversational AI has opened up new channels of selling for insurance firms. Customers today can connect with AI virtual assistants through any of their desired channels such as Whatsapp, Facebook, Instagram, and so on through voice or text or even through IVR/telephony and have human-like conversations with AI assistants.

Conversational AI for Healthcare

Being a healthcare professional is difficult as it is. The absolute focus and attention to detail that is medical profession demands take a toll on doctors, nurses, and employees of the medical industry alike. A study shows that one-sixth of an average physician’s working hours are spent on administrative tasks.

In the healthcare industry, conversational AI services has the potential to revolutionize the way healthcare professionals interact with patients, streamline administrative processes, and ease patient treatment experiences.

Conversational AI virtual assistants allow patients to ask questions, receive the right information, and schedule appointments through a conversational interface, such as a chat window or voice assistant. AI assistants also allow patients to easily access information and resources without having to navigate complex websites or wait on hold for a customer service representative. This can be especially useful for patients who have mobility issues or live in remote areas, as they can easily access healthcare information from the comfort of their own homes.

In addition to this, conversational AI can also be used to streamline administrative processes in healthcare by integrating information sources and creating a single system of records. They can also be used to automatically schedule appointments, refill prescriptions, or process insurance claims. This can help to reduce the workload for healthcare professionals, freeing them up to focus on more complex tasks and improving the overall efficiency of the system.

Conversational AI for E-Commerce

The global eCommerce market is growing rapidly. The total value of retail eCommerce sales in 2017 alone was $2.3 trillion. And by 2025, it is expected to have doubled to $7.39 trillion. Due to this exponential growth, traditional customer service and sales mechanisms including phone calls, emails, and social media have collapsed simply because there aren’t enough support representatives to cater to customer needs.

With conversational AI, e-commerce businesses can answer up to 80% of customer queries and provide detailed product descriptions about features and specifications, helping customers to make informed purchasing decisions all through real-time conversations.

Brands that offer personalized shopping experiences can win customer loyalty and encourage shoppers to come back to them. One of the primary use cases of conversational AI for e-commerce is exactly that. Conversational AI Assistant can be used to provide personalized product recommendations based on a customer’s previous purchases or browsing history, helping to increase sales and customer satisfaction. In addition to this, conversational AI can also be used for upselling and cross-selling strategies by suggesting products that are similar or complementary to existing products bought by the customers.

Virtual AI assistants can guide customers through the checkout process, helping to reduce abandoned cart rates and increase conversion rates. They can also be leveraged to automatically process orders and provide updates on delivery status, reducing the need for manual input and freeing up time for employees for other tasks. Here’s an interesting read on eCommerce powered by conversational AI also known as conversational commerce.

Conversational AI for Manufacturing

Conversational AI is a great tool to use in manufacturing because it can automate manual tasks and help improve productivity. Worker safety is one of the indispensable divisions that often requires the most attention in a manufacturing setting that involves heavy machinery. One of the key benefits of conversational assistants in manufacturing is that they can improve safety on the job. In a manufacturing setting, workers often have to multitask and juggle multiple responsibilities, which can lead to distractions and accidents. With a voice-enabled AI assistant that can transcript, workers can keep their hands and eyes free while documenting crucial data and save time, and also significantly reduce the risk of accidents.

Conversational AI for manufacturing can come in handy during several other tasks such as inventory management, machinery maintenance, training and so much more. For example, through conversational AI, companies will be able to track their inventory levels more efficiently and keep themselves from running out of stock before its time. Similarly, when it comes to maintenance, an AI assistant can be integrated with machinery to monitor their performance and alert maintenance staff to potential issues before they become serious problems. This can help to reduce downtime and improve the overall efficiency of the manufacturing process.

Conclusion

Conversational AI is an exciting technology that has several use cases for many different industries. Conversational AI is already being used by insurance companies to help customers understand their coverage options and reduce fraud risk. Similarly, it has the potential to improve customer experience, increase efficiency, and open doors to new opportunities across industries. To learn how companies of various industries are using conversational AI and to create an AI assistant that is tailored to your unique business needs, get in touch with purpleSlate today.

This blog was originally published in: https://www.purpleslate.com/how-different-industries-use-conversational-ai/

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Conversational AI for Personalization and Automation of the Order Management Process in Retail

Before learning about the personalization and automation capabilities of Conversational AI in Retail sector, one must know about the major channels used by retailers.

Amazon, SnapDeal, Flipkart

Teleshopping channels

Catalogs, newspapers, and magazines

Social media like Facebook, Instagram

E-commerce sites managed by self and 3rd party retailers

These are some of the channels, but not an exhaustive list of the channels available for buying retail products. Expectation from the customer is to fulfill their orders on time, irrespective of the channel they have ordered the product on. It is right for the customer to expect and it is the responsibility of the enterprises to fulfill the order on time through their order management system.

The Process

The order management system kicks in when the customer places orders and ends with order fulfillment. At times, it extends to return and cancellation. For effective customer service, a continuum of people, systems, and suppliers is essential.

The efficacy of the order management processes has become a more substantial challenge for firms as the number of transactions has increased. Companies must operate error-free from order receipt to delivery to satisfy customers with on-time deliveries. As the number of orders and customer expectations are getting higher, companies are implementing conversational AI to automate and expedite the orders. Conversational AI for retail can help manage the creation of new orders, check the status of orders in progress, cancelled orders, delayed orders, and a lot more.

There is also a very definite trend towards further individualization and customization that drives the strong growth of and constant changes in the product portfolio. The competition is driven by online transparency and simple access to a plethora of options of where to purchase and what to buy. To build on these trends and cope with the changed requirements, the order management system needs to become much faster, more granular, and much more precise.

Below is a list of the CAI application across the Order Management System(OMS) value chain.

Placement: Conversational AI chatbots enable customers to place orders across a range of platforms, such as websites, WhatsApp, social media, and more in a Conversational manner.

Receipt confirmation: Distribute confirmation receipts with accurate order details automatically to customers of any size.

Tracking: Assist consumers in tracking the status of their orders during shipping to cut down on the amount of time spent on the phone, via chat, and text.

Notification: Customers can receive customized, timely delivery/payment reminders and order notifications.

Repeat orders: Easy and conversational way for customers to repeat their orders after regular time intervals.

Cancellation and Return: Faster and automated processing of order returns and cancellation requests from customers.

Various repetitive tasks associated with order management can be automated with AI chatbots and smart virtual assistants. Company’s ERP and/or CRM are integrated with the power of machine learning (ML) and natural language processing (NLP). This allows CAI to capture and automatically utilize product names, and order numbers, give order status, send receipts, provide confirmations, and more.

1. Enhanced Customer Experience

Customer support is a big part of any business and Customer satisfaction is vital to OMS from order to delivery. CAI redefines the CX and it is used to interact with customers to offer assistance or answer questions about products or services. It is also helpful when providing feedback on products and services, which can help businesses to improve their product quality and customer experience.

Conversational AI in retail is used to automatically gather customer details with their interactions. This helps to better understand customer demands and plan customer journeys accordingly. An OMS with AI capabilities may locate and utilize useful consumer information like buying patterns, routines, complaints, and sales cycles.

2. Error-free order management

AI bots save from the chances of errors in both customer and product data collection associated with orders and enable accurate data collection. CAI either prevents (in the case of self-service) or reduces (in the case of document data extraction) mistyping and duplication errors during customer data collection processes. It also understands duplication errors and mistyped information and fixes them before collecting the data. This helps to reduce order processing delays.

A Conversational AI platform reduces the likelihood of any order being delivered to the incorrect location by tracking all orders concurrently and keeping order details in the OMS. By tracking orders in real-time and keeping details in the system, order management tools significantly reduce wrong order deliveries.

3. Faster order processes

Order management automation with CAI adds a faster pace to every associated process, which allows organizations to manage more activities within the same time frame. Without taking any breaks, a CAI-enabled OMS can handle repetitive processes such as entering order information, managing distribution processes, and other similar tasks. It also enables companies to free up human resources to handle more critical tasks in the same time frame.

4. Real-time order tracking

When a customer places an order, we need to track the status of that order and notify them when it ships or if any additional steps are required. Conversational AI in retail can track orders constantly from its packaging to delivery to the customers. The OMS safeguards all information, and clients continue to receive proactive updates regarding their orders on demand.

AI-powered chatbots app with voice recognition capabilities can respond automatically based on queries related to order tracking.

5. On-Time Delivery

To satisfy their customers, businesses must deliver correct orders on time (or, better yet, ahead of schedule) and in neat packaging. On the other hand, one incorrect order could result in lost business. Order management technologies deliver orders on schedule and offer a good client experience by tracking orders in real-time and storing details in the system.

6. Automated notifications

Customers obtain a smoother ordering experience when CAI automatically retrieves order and payment data and sends relevant and timely notifications. Notifying customers at each stage of order fulfillment will enhance customer satisfaction and increase loyalty.

7. Automates Repetitive Tasks

Users can interact with technology in a contextual conversation that resembles human interaction thanks to conversational AI, which removes the complex user interface. The user thereby receives customized answers to their inquiries. The capacity to quickly find insightful information is layered onto this streamlined engagement with data.

8. Increase Productivity and Efficiency

AI is used to automate processes within your company’s OMS. For example, Conversational AI is used in order fulfilment centers. Smart Virtual Assistants assimilate information from various data sources and coordinate tasks on order status and escalate to humans for any deviations. The other benefits of CAI in increasing productivity are listed below.

· Reduce live agent handling time and costs

· Upskill live agents to handle higher-value items that necessitate the human touch.

· Validate different types of information fast and effectively.

· Allow customers to self-service any order management request.

· It is compatible with IVR, contact centers, and data analytics.

CAI implementations are designed with customer experience in mind, so they feel, sound, and speak like humans. Smart virtual assistant enable organizations to provide a superhuman customer experience with 24/7 availability, no wait times, and exhaustive data.

Conversational AI Can Expand Your OMS Capabilities!

When the number of customers, orders, and consumer data increases, so does the size of the business. This surge is easily manageable thanks to the integration of conversational AI with order management systems.

AI-powered virtual agents use conversational AI services to automate the routine conversations traditionally handled by live agents. Organizations typically start in voice, where the ROI is the greatest, then scale the same experience to chat or text for a complete omnichannel self-service strategy.

This blog was originally published in: https://www.purpleslate.com/conversational-ai-for-personalization-and-automation-of-the-order-management-process-in-retail/

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Coming of Age: How Gen Z is Transforming Insurance

As a generation, Gen Z is different from any that have come before and their life journeys have not followed the traditional path set by older generations. They are digital natives who have grown up in a world of instant gratification and constant change. And when it comes to their insurance needs, they are no different.

The Gen Z population desires a holistic customer experience — where digital offerings bring together products and services to help the customer manage their lives. When insurers can’t deliver that view, a gap arises between customer expectations and what insurers are delivering. Insurers need to prioritize the needs of this generation to establish themselves as trusted insurers to an aging Gen Z in the future.

Changing Insurance Landscape

From new insurance products to evolving customer needs, the insurance landscape is constantly changing. And as the needs of Gen Z customers change, so too will the insurance landscape.

Here are some of the ways that the insurance landscape is changing:

1. New Insurance Products: As customer needs change, so do the products that they demand. insurers are always looking to develop new products that meet the changing needs of their customers. For example, high percentages of Gen Z are single and living with pets. A vast majority of them are looking for products that allow them to insure their pets today and so other products such as family insurance plans are irrelevant to them.

2. Evolving Customer Needs: The needs of Gen Z customers are constantly evolving. As such, insurers must be prepared to adapt their products and services to meet these changing needs. They look for plans that specifically address their individual needs and are also ready to share personal data that support these plans. For example, a Gen Z customer investing in health insurance wants to be able to eliminate coverage they do not need and be able to reduce the cost of their premiums overall.

3. Technology: Technology is playing an increasingly important role in the insurance landscape. From online quoting and policy management to telematics and drones, insurers are using technology to improve their operations and better serve their customers.

What does Gen Z look for in insurance?

As the first generation to grow up with the internet, Gen Z has a different perspective on insurance than their previous generations. Gen Z’s life journeys have not followed the traditional path set by older generations. They have made other significant lifestyle shifts from older generations that result in different risk needs that require different insurance needs.

Gen Z insurance companies to develop in a direction that is favorable to them, aiding them in supporting the challenges of life — both individual and global. Maintaining relevance with this critical consumer group requires insurance companies to deliver comprehensive added value within their policies. Adapting tech-dependent business models is a necessity for insurers if this generation’s desire for digital individuality and flexibility is to be met, particularly during a time when younger people tend to lose interest in insurance.

Here are some of the expectations younger generations have from their insurers:

Digital Experience

As the first generation to grow up with digital technology, Gen Z has high expectations for the digital experiences they have with brands. A study by Accenture found that 78% of Gen Z consumers said they would leave a brand they love after just one bad experience.

This generation expects to be able to purchase insurance online and have 24/7 access to their account information. They also want to be able to contact their insurer through digital channels such as chat, email, or social media.

To meet the needs of this tech-savvy generation, insurers need to provide a seamless, omnichannel customer experience. This means being available across all channels, from social media to chatbots to in-person customer service.

Hyper-personalization

Accenture found that 62% of Gen Zers said they were more likely to buy insurance from a company that offers personalized services and prices. So, in addition to providing a great digital experience, insurers need to ensure they are tailoring their products and services to meet the unique needs of this generation.

One of the many reasons younger generations cite for being uninsured is cost. Traditional policies often include coverage that doesn’t apply to the customer’s personal situation and on top of that, are often costlier.

Hyper-personalization is the process of using data to create highly personalized experiences for customers. With this approach, insurers can personalize all insurance processes from customizing an insurance policy to fit an individual’s needs to provide real-time updates on claims status.

Hyper personalization in insurance is becoming increasingly important, especially for Gen Zers who have grown up with technology. According to a recent study, 78% of Gen Zers say they’re more likely to purchase from a company that offers a personalized experience.

There are a few things insurers can do to provide a more personalized experience for their Gen-Z customers:

1. Use data to understand customer needs: Insurers should analyze data on their customers in order to better understand their needs. This data can be used to develop products and services that meet customer needs and expectations.

2. Personalize Communications: Insurers should use customer data to personalize communications and interactions. For example, if an insurer knows that a customer has recently had a baby, they could send them information on life insurance policies that would benefit their family.

3. Offer real-time updates: Customers appreciate being kept in the loop, especially when it comes to their insurance claims. By offering real-time updates on claim status, insurers can make customers feel like they are part of the insurance journey and ensure transparency.

Self-service with Conversational AI

Conversational AI is revolutionizing the way insurance companies interact with their customers. By providing a self-service option that can handle simple tasks, Conversational AI frees up customer service representatives to handle more complex inquiries. This results in shorter wait times and happier customers.

Insurance companies are able to provide a better customer experience by using Conversational AI to handle routine tasks such as answering FAQs, scheduling appointments, and even processing claims. This allows customer service representatives to focus on more complicated issues, resulting in shorter wait times for customers. In addition, Conversational AI can provide a more personalized experience by engaging with customers in natural, human-like conversations.

By offering a self-service option with Conversational AI in insurance, companies have the opportunity to improve the customer experience while reducing costs. This technology is revolutionizing the way insurance companies interact with their customers and is poised to change the industry for years to come.

Product Knowledge Base

With a product knowledge repository, insurers can allow consumers to make informed decisions about the products they purchase and use. The insurance industry has been notoriously opaque, making it difficult for consumers to comparison-shop and understand what they’re buying. But that’s starting to change, thanks in part to digital tools like an online knowledge base that makes it easier for companies to be transparent about their products.

With the recent advancements in InsurTech, insurers can provide online customer portals that show consumers different coverage options and prices side-by-side and technology that provides customers with real-time insights into their claims history or the status of their policy.

As the insurance industry continues to evolve, product transparency will become increasingly important. Insurers that embrace transparency will be better positioned to win over price-conscious consumers who want to know exactly what they’re getting for their money.

Faster Claims Processing

As the world becomes increasingly digitized, customers are demanding faster claims processing from their insurance providers. And rightfully so — when you’ve been in an accident or your home has been damaged, the last thing you want is to wait for weeks or even months to settle your claim.

Fortunately, many insurance companies are starting to catch on to this customer need and are working to speed up their claims process. For example, by accessing an active customer portal through a mobile app, customers can upload photos and start their claims right from their phones and also help process claims faster with the help of AI and ML models.

Conclusion

Regardless of whether an insurer is traditional or considered InsurTech, those that make Gen Z feel safe in a world of risk will win over the world’s first generation of digital natives. Ultimately, Gen Z will be drawn to companies that evoke transparency and authenticity. This is an exciting time for insurers to build new relationships and it is imperative to exploit these opportunities with the help of smart tools and stay ahead of the competition. Get in touch with our experts at purpleSlate today to evaluate your InsurTech needs and proactively shift with these ever-evolving advancements.

This blog was originally published in: https://www.purpleslate.com/coming-of-age-how-gen-z-is-transforming-insurance/

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The Future Vision of Data Analytics (Infographic)

The future of data analytics, data science, and data management holds immense intrigue as it evolves rapidly in a dynamic and fast-paced environment. Advancements in these fields are occurring at unprecedented speed and scale, shaping the way businesses operate and innovate. To remain ahead in this transformative landscape, it is imperative for business leaders to stay informed about the latest trends and developments in the sector.

To shed light on the upcoming changes, we have curated predictions from Gartner in the data space in the form of an infographic. These insights will revolutionize how data is perceived, utilized, and harnessed, unlocking untapped potential and driving growth and success in various industries.

The future vision of data analytics holds tremendous potential as businesses harness the power of data to drive innovation and make informed decisions. With the exponential growth of data, advanced analytics technologies, such as AI and machine learning, are becoming indispensable tools in extracting valuable insights from vast datasets. Real-time analytics will enable organizations to respond swiftly to dynamic market changes, optimizing processes and enhancing customer experiences. Additionally, predictive analytics will revolutionize industries by forecasting trends, improving supply chain management, and identifying potential risks before they manifest. Moreover, data-driven strategies will steer businesses towards greater personalization, delivering tailored products and services that resonate with customers. As data privacy and security remain paramount concerns, the future of data analytics will also prioritize robust cybersecurity measures.

In this ever-evolving landscape, edge computing emerges as a transformative force in data analytics. Edge computing allows data processing and analysis to occur closer to the data source, reducing latency and enhancing response times.

This blog was originally published in: https://www.purpleslate.com/future-vision-of-data-analytics/

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How different Industries use Conversational AI

Conversational AI has seeped into almost every industry today and it is changing how businesses and individuals interact and perform everyday tasks. Customers and businesses alike have joined this AI revolution over the last few years. For customers, conversational AI solutions means quicker responses, better customer service, easier order fulfillment, and a more personal connection with the brands they do business with while for businesses it means cheaper customer support costs, higher conversion rates, boosted customer loyalty, accelerated sales cycles, and increased lead generation.

Here are the top industries that have adopted conversational AI, how it is being used across sectors and how they can be customized to suit business needs:

Conversational AI for Insurance

According to research, 87% of insurance brands invested over $5 million in AI-related technologies each year. InsurTech has become a powerful driver of change in the insurance sector. There’s a lot of potential for AI technology to transform this industry and automate repetitive and time-consuming tasks. A smart conversational AI assistant can act as a digital agent for an insurer’s customers, be available to them at all times and provide a seamless experience every time they interact with them.

In information-heavy businesses such as insurance, conversational AI has been proven to be advantageous in bridging knowledge gaps and in creating an environment of transparency and clarity around insurance. Insurance providers can steer conversational AI to go directly to the customers and help policyholders to go through the entirely digital insurance purchase journey by themselves.

For example, a buyer looking to buy car insurance can explore the various products available, compare their prices and premiums, find the plan that suits their needs best and proceed to purchase — all by asking questions to the conversational AI assistant. This way, businesses can benefit from enabling self-service for end-user without having to connect to an agent and thereby accelerating the buyer’s journey. It is also important to note that conversational AI has opened up new channels of selling for insurance firms. Customers today can connect with AI virtual assistants through any of their desired channels such as Whatsapp, Facebook, Instagram, and so on through voice or text or even through IVR/telephony and have human-like conversations with AI assistants.

Conversational AI for Healthcare

Being a healthcare professional is difficult as it is. The absolute focus and attention to detail that is medical profession demands take a toll on doctors, nurses, and employees of the medical industry alike. A study shows that one-sixth of an average physician’s working hours are spent on administrative tasks.

In the healthcare industry, conversational AI services has the potential to revolutionize the way healthcare professionals interact with patients, streamline administrative processes, and ease patient treatment experiences.

Conversational AI virtual assistants allow patients to ask questions, receive the right information, and schedule appointments through a conversational interface, such as a chat window or voice assistant. AI assistants also allow patients to easily access information and resources without having to navigate complex websites or wait on hold for a customer service representative. This can be especially useful for patients who have mobility issues or live in remote areas, as they can easily access healthcare information from the comfort of their own homes.

In addition to this, conversational AI can also be used to streamline administrative processes in healthcare by integrating information sources and creating a single system of records. They can also be used to automatically schedule appointments, refill prescriptions, or process insurance claims. This can help to reduce the workload for healthcare professionals, freeing them up to focus on more complex tasks and improving the overall efficiency of the system.

Conversational AI for E-Commerce

The global eCommerce market is growing rapidly. The total value of retail eCommerce sales in 2017 alone was $2.3 trillion. And by 2025, it is expected to have doubled to $7.39 trillion. Due to this exponential growth, traditional customer service and sales mechanisms including phone calls, emails, and social media have collapsed simply because there aren’t enough support representatives to cater to customer needs.

With conversational AI, e-commerce businesses can answer up to 80% of customer queries and provide detailed product descriptions about features and specifications, helping customers to make informed purchasing decisions all through real-time conversations.

Brands that offer personalized shopping experiences can win customer loyalty and encourage shoppers to come back to them. One of the primary use cases of conversational AI for e-commerce is exactly that. Conversational AI Assistant can be used to provide personalized product recommendations based on a customer’s previous purchases or browsing history, helping to increase sales and customer satisfaction. In addition to this, conversational AI can also be used for upselling and cross-selling strategies by suggesting products that are similar or complementary to existing products bought by the customers.

Virtual AI assistants can guide customers through the checkout process, helping to reduce abandoned cart rates and increase conversion rates. They can also be leveraged to automatically process orders and provide updates on delivery status, reducing the need for manual input and freeing up time for employees for other tasks. Here’s an interesting read on eCommerce powered by conversational AI also known as conversational commerce.

Conversational AI for Manufacturing

Conversational AI is a great tool to use in manufacturing because it can automate manual tasks and help improve productivity. Worker safety is one of the indispensable divisions that often requires the most attention in a manufacturing setting that involves heavy machinery. One of the key benefits of conversational assistants in manufacturing is that they can improve safety on the job. In a manufacturing setting, workers often have to multitask and juggle multiple responsibilities, which can lead to distractions and accidents. With a voice-enabled AI assistant that can transcript, workers can keep their hands and eyes free while documenting crucial data and save time, and also significantly reduce the risk of accidents.

Conversational AI for manufacturing can come in handy during several other tasks such as inventory management, machinery maintenance, training and so much more. For example, through conversational AI, companies will be able to track their inventory levels more efficiently and keep themselves from running out of stock before its time. Similarly, when it comes to maintenance, an AI assistant can be integrated with machinery to monitor their performance and alert maintenance staff to potential issues before they become serious problems. This can help to reduce downtime and improve the overall efficiency of the manufacturing process.

Conclusion

Conversational AI is an exciting technology that has several use cases for many different industries. Conversational AI is already being used by insurance companies to help customers understand their coverage options and reduce fraud risk. Similarly, it has the potential to improve customer experience, increase efficiency, and open doors to new opportunities across industries. To learn how companies of various industries are using conversational AI and to create an AI assistant that is tailored to your unique business needs, get in touch with purpleSlate today.

This blog was originally published in: https://www.purpleslate.com/how-different-industries-use-conversational-ai/

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What is Conversational AI and Why your Business needs it?

Artificial Intelligence is everywhere today. From little kids calling out for “Alexa” to play their favorite rhymes and the facial recognition we use to open our phones to cab drivers relying on Google maps to take us to our workplace every morning, AI is everywhere and it has made a huge impact on our everyday lives. So much so that the Conversational AI market size is forecasted to reach USD 41.39 billion by the end of 2030.

What is Conversational AI?

It is difficult for businesses to provide a hyper-personalized customer experience to a huge number of people simultaneously. And Conversational AI enables this. Conversational AI Services is an Artificial Intelligence tool that allows computers to conversate like humans through text/speech with their users. Conversational AI combines technology components like Natural Language Processing (NLP) and Machine Learning (ML) with traditional software like a chatbot and helps it function more effectively in serving its users like understanding keyword triggers and making recommendations. The goal of Conversational AI for business is to provide its users with an experience as similar to humans as possible and thereby automate end-to-end user experiences.

Customer Experience (CX)

Customer Experience or CX is how your consumers perceive your business treats them. CX is related to how customers feel at every stage of the buying process from the awareness stage to the end sale. The long-term success of any business today depends on how remarkable its customer experience is.

According to research done by Khoros, 65% of customers said they have left the brands they loved and moved to their competitors because of poor CX experiences. This solidifies the importance of CX for any business today.

However, what most businesses often overlook is that a business cannot build loyalty just by delighting its customers, Harvard Business Review says. HBR highlights that instead of merely entertaining the consumers, businesses must help their clients reduce their effort in solving a problem that they have. This alone would improve customer experience, reduces customer service costs, and reduces churn. Conversational AI addresses this issue precisely.

Conversational AI for an enhanced CX

Why Conversational AI? A report says that 65% of users seem to not trust traditional chatbots and feel that they don’t fully understand their issues. That is where Conversational AI is transforming Customer Engagement and Experience in numerous ways. The advantages are many such as:

1. A More Personalized Interaction

Businesses can offer their clients a more individualized experience by using conversational AI. This is accomplished by utilizing the data gathered by the chatbot to comprehend the client’s preferences and requirements.

2. Around-the-clock Customer Service

Businesses can provide faster and more efficient customer service as chatbots can handle numerous requests at the same time. Additionally, they can direct customers to appropriate agents and cut down on wait times. They are also available around the clock every day of the year, which is a significant benefit for businesses.

3. Eliminates Language Barriers

AI chatbots can be combined with language translation software allowing interpreting and generating responses in any language efficiently. This makes businesses feel more welcoming and opens them up to a wider range of customers.

4. Helps make Purchase Decisions

AI chatbots can learn to make recommendations based on what is in their cart and the items they have viewed, with the help of Machine Learning (ML), and create a personalized experience for the customers. Thus creating and modifying sales as well.

5. Omni-channel Presence

Conversational AI-powered chatbots are truly omnipresent, can be programmed to offer services, and be made available across multiple platforms, channels, and devices. This way businesses can benefit from a consistent brand presence.

Are Chatbots and Conversational AI the same?

No, they are not. These two terms are often used interchangeably and the line between the both can start to blur. A chatbot is a computer application that offers basic answers and responses to automate simple interactions between businesses and customers. These are also called FAQ bots. Primarily, chatbots follow a rule-based approach where they can address simple queries and fail to manage complex ones. When customers don’t type the exact predetermined keywords, the flow breaks, and the bot gets stuck in a loop.

Meanwhile, Conversational AI is more intuitive and can manage complex dialogues. This technology follows a self-learning approach, learning from past interactions and customizing responses according to specific users.

It aims at providing human-like interactions by deciphering speech and text, recognizing intent, translating different languages, and responding in a way that mimics human interactions. This is achieved by bringing together several technologies like Natural Language Processing, Machine Learning, Automated Speech Recognition (ASR), Contextual Awareness, Dialog Management, etc.

For instance, say a customer is looking to buy shirts on an online retail platform. The customer asks the chatbot to help find a ‘red shirt’ and add it to the cart. Using the keywords ‘red shirt’, and ‘add to cart’ the chatbot helps the customer with the query.

If the customer changes his mind in the middle of the buying journey and asks the chatbot to add a ‘blue shirt’ instead of the red one, the chatbot’s linear flow breaks down. It repeats the same message over and over again until it receives the keyword that it is looking for, frustrating the user.

Meanwhile, a Conversational AI can intuitively understand what the customer wants with the help of Intent Recognition and seamlessly attend to complex customer queries.

Conversational AI Working Principle

How does Conversational AI work? The more data input, the better that AI works. As data input grows, AI can identify patterns more quickly. With a combination of several technologies, a Conversational AI generally functions by following a five-step process

1. Listening

First, the application receives information input from a user in the form of voice or text. With the help of Natural Language Understanding and Automatic Speech Recognition (in the case of a speech input), the application translates the input into a computer-readable format.

2. Comprehending

The machine then uses technology elements like NLU and/or Intent Recognition to extract meaning out of the given input to understand the query.

3. Forming a response

With the help of Dialog Management, the application forms a response to the query based on its comprehension.

4. Offering the response

Dialog Management composes responses and uses Natural Language Generation (NLG) to convert the responses into human-comprehensible formats. The machine then delivers the response in text or converts the response to a speech by using speech Synthesis.

5. Learning

The application learns from its experiences and collects information from its interactions and uses it upon itself to deliver better responses in future interactions.

How can businesses benefit from Conversational AI?

The use of conversational AI in customer experience has numerous advantages. Increased customer satisfaction and loyalty, higher engagement and conversion rates, and saving on resources are some of the most significant advantages. Here’s a deeper look at some of the Advantages

1. Sell Anytime

Conversational AI makes it easy for customers to find information about businesses and buy from them anytime, anywhere. This also reduces response time to customer queries.

2. Cost Reduction

Conversational AI can help businesses to boost productivity and cut costs. According to a study, 62% of consumers prefer using a customer service bot rather than waiting for human agents to answer their queries. This way, Conversational AI enables businesses to manage more queries at a lower cost and still offer excellent customer support while allowing employees to engage in more high-value and meaningful work to boost ROI.

3. Increase Revenue per Customer

AI handles simple, routine client interactions. The analysis of the data collected from these interactions can help businesses to create customized offers, promotions, and products and respond to customer preferences and pain points.

4. Reduce Customer Churn Rate

According to a report by PwC, 32% of customers will leave a brand after just one bad experience. This shows that customer experience is the holy grail of client retention. One of the sure-fire ways to reduce churn is by showing customers that their feedback, input, and time are valued greatly.

Implementing Conversational AI Solutions does exactly that. It makes use of customer data and creates a seamless customer experience and offers them what they want intuitively thus retaining their existing customers.

5. Increase conversions

While it can be challenging for customer service representatives to chase customers to use coupons and digital vouchers, an AI chatbot is quite efficient at this. These chatbots can routinely inform and remind clients about discount codes and other promotional offers. Apart from this, Conversational AI helps with lead generation. It offers 24/7 live support to the prospects and makes a consistent effort to convert the generated leads.

6. Better customer knowledge

The majority of businesses store a staggering amount of consumer data, including details of past contacts, transactions, and even phone and chat transcripts.

Conversational AI has the capability to mine these unstructured data and produce insights about consumer behaviors. It can easily accomplish this by using intent recognition and keyword analysis and predicting what the customer wants. It compares this current data with other information such as past interactions with the same user, how other users have responded in similar situations, and with other data stored in the databases. This enables it to foresee ways to maximize customer satisfaction.

Closing Notes

As a result of the pandemic, many businesses saw a major rise in consumer demands while seeing a decline in the number of employees. Businesses have rapidly embraced new technologies in the last two years, such as chatbots that are AI-powered, in order to meet client expectations for prompt responses to inquiries and problem resolution.

Conversational AI is a promising technology that is being rapidly adopted by every industry to optimize its Customer Experience and transform into more customer-centric and data-driven organizations. To begin your Conversational AI journey, start by finding the areas where chatbots can be most beneficial for your business. Next, select the type of chatbot you wish to use. Envision the experience you want to deliver to your customers and make sure your AI choice can support that vision.

This blog was originally published in: https://www.purpleslate.com/what-is-conversational-ai-and-why-your-business-needs-it/

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Cross Selling Insurance Products with Conversational AI

Acquiring a new customer is the most difficult task in any business. It is considered to be 8 to 10 times more difficult for an insurance agent to onboard a new customer compared to other businesses.

Acquiring new customers is imperative for businesses to grow, but one shouldn’t neglect its existing customer base. Research shows that on average a customer has 1.2 to 1.5 insurance products with the same company. What does it mean? It shows that if a customer buys an insurance product from a company he/she is likely to buy another product from the same company. It could be the result of cross-selling. So, what is cross sell insurance?

Cross-selling is the practice of persuading customers to purchase other products based on their previous purchases. For instance, if one of your clients bought a medical policy for their children, you can use this information to sell to them an accident cover policy.

Studies show cross-selling can increase profits by 30% and sales by 20%. The chances of selling to an existing client range between 60–70%, while trying to sell to a new client range as low as 5–20%.

Is it easy to cross-sell products? Well, it depends. It is easy because they’ve already interacted with your products and can attest to the quality of your services. It is already given that they trust your business. It could be difficult as they already have one insurance product and might not be ready to spend their money on an insurance product again.

Benefits of Cross-selling

● Customer loyalty and growing existing clients.

● Increased revenues

● Generate more leads

● Enhance customer convenience

● Build barriers for other insurance companies in poaching your clients

Timing matters when it comes to cross-selling. We can’t randomly call up a client and give a sales pitch. Instead, we should make cross-selling a natural part of your agents during regular client visits. How to make it sound more natural and organic? The answer to it is conversational AI.

Conversational AI Powered Cross-Selling

Using Conversational AI Services is an innovative way to promote cross-selling to existing customers. It can provide tailored responses to all queries by customers, employees, and partners by using natural language. It employs natural language processing, understanding, and up to a specific level even natural language generation.

It enables increased communication, and deeper insights for the customer regarding specialized insurance products, and aids them with selecting the right insurance. It uses machine learning and artificial intelligence to analyze customer responses and provides instant resolution through conversation AI in an efficient manner.

Omnipresent Conversation AI

Conversational AI can be easily integrated with mostly all social media applications like Facebook, Instagram, and WhatsApp making it omnipresent. Conversational AI’s omnipresence capability facilitates the agents to have more touch points with the customer in their preferred channel, providing ample opportunities for agents to prepare the ground for upselling and closing the sales.

Timing is Everything

Cross selling insurance products is not an easy task. Convincing customers to buy newer insurance is tough and will depend on the level of service provided. However, a key point to note here is that many customers have the habit of missing out on insurance renewal dates. Many clients also need access to key insurance documents at all times. Certain insurance payments are tax-friendly in some countries, hence it’s imperative to have access to insurance payment receipts.

These can be automated using a CAI smart assistant even with subtle nudges in the form of small text chats or pop-ups. Once the renewal or receipt download happens, CAI smart assistants can initiate a conversation for cross-selling a related product or top-up the existing insurance to increase the premium, etc. Many times this has a higher chance of converting into a sale.

Proactive Conversational AI

Having hands-on knowledge about the entire customer experience and the mental state of your buyers and consumers on your insurance products can help plan better campaigns.

Conversational Ads with a blinking icon can be sent to customers through email or social media. When the customer clicks on the icon, chatbots can warmly welcome the customer and provide relevant product details for cross-selling.

There is no doubt that the future of CX excellence is personalization. Conversational AI by opening up the conversation with the customer must provide expert-like guidance, understand the intent behind the previous purchase and help them find exactly their relevant products in a minimum amount of time. This is a future state achieved with regular training on customer data, to let your system slowly become prescriptive by nature in a conversational setting.

The other important CX trend is customer journeys. Providing a great customer experience is the foundation for customer retention and loyalty. CAI will be able to interact with the customers irrespective of the channel, providing relevant product recommendations, guiding, and responding to their queries instantly along the customer journey. It improves cross-selling conversion ratio and supports the customer even after the customer journey is completed.

Automated Conversations, Enhanced CX, and Improved Cross-Selling Figures

Conversational AI reduces human intervention and can be there for your customer 24×7 to facilitate cross-selling. CAI is sophisticated enough to pick up customer signals on when to redirect an ongoing conversation to a new product or service. Its capabilities are increasingly becoming table stakes in every facet of insurance distribution. Insurance companies that successfully combine these qualities with a skilled sales force are frequently better positioned to forge close bonds with their clients and take advantage of opportunities for cross-selling goods and services.

Another important issue is the cart abandonment that we saw in my previous post. Though there are various reasons attributed to this customer behavior, one of the main reasons is insufficient information on the product or service. Customers need more time to be ready to call or message the company for additional information. CAI can pick up this scenario to initiate a conversation with the customer and provide adequate information to complete the online buying process. As a retrospective measure, CAI can send push notifications to customers on the status of the customer journey.

Conversational AI Solutions can re-engage with customers on pending cart reminders, alerts on new products, discounts for existing customers or any festival bundled offers. CAI can add value at critical customer touchpoints leading to improved cross-selling ratio and repeat purchases from the customer.

Closing Thoughts

Cross-selling is always a product of enhanced customer experience. Providing automated customer support is definitely an advantage for organizations, but many miss out on an important part — Humanizing such conversations. However, following customer support teams using follow the sun staffing models has its own problems.

Here we need a solution that can effectively resolve customer queries, 24×7, 365 days and also actively identify, pursue, close cross-selling opportunities, and if required escalate to a human agent. Conversational AI is one such solution that fits the bill of all insurance organizations in this regard and helps both the company and third-party partners such as agents enjoy a win-win situation.

Would you like to witness CAI in action on how to cross sell insurance? Check out this webinar.

This blog was originally published in: https://www.purpleslate.com/cross-selling-insurance-products-with-conversational-ai/

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6 Use Cases of Leveraging Voice AI Assistants for Manufacturing

The manufacturing industry is constantly evolving and seeking new ways to improve efficiency and productivity. In recent years, voice AI assistant, also known as voice recognition or voice-enabled technology, have emerged as powerful tools for achieving these goals. These virtual assistants use natural language processing to understand and respond to spoken commands and requests, making them an effective tool for improving communication and streamlining tasks in manufacturing environments.

Improved Maintenance Efficiency

Employing conversational AI in manufacturing maintenance department can help identify problems in a timely manner. A voice AI assistant can be integrated with machinery to monitor their performance and alert maintenance staff to potential issues before they become serious problems. This can help to reduce downtime and improve the overall efficiency of the manufacturing process. A voice AI assistant can also collect data on the usage and performance of a machine and this information can be used to identify trends or patterns. This can help maintenance staff to better understand the needs of their equipment and prioritize maintenance tasks accordingly.

In addition to identifying problems, voice AI assistants can also be used to provide guidance on how to resolve maintenance issues. For example, a voice AI assistant might be able to provide step-by-step instructions for completing a repair or offer suggestions for troubleshooting problems and help to reduce the need for specialized knowledge or training and enable maintenance staff to complete tasks more efficiently.

Better Inventory Management

By leveraging voice AI assistants, the manufacturing industry can reduce errors and improve accuracy when it comes to inventory management. With a smart voice AI assistant, workers can track the movement of inventory items in real-time, allowing employees to quickly and easily check on the availability of specific items and help to reduce the risk of errors and improve the overall efficiency of the manufacturing process.

A voice AI assistant can also be automated to reorder supplies when inventory levels drop below a certain threshold, reducing the need for manual input and freeing up time for other tasks. They can also be used to track and monitor inventory levels, providing valuable insights into consumption patterns and helping manufacturers to optimize their inventory management strategies.

Improved Communication

Voice artificial intelligence (AI) assistants are revolutionizing the way that communication is carried out in the manufacturing industry. These technologies enable employees to communicate with machines and systems through natural language inputs, making it easier for them to get the information they need and complete tasks more efficiently.

One of the main ways that voice AI assistants are transforming the manufacturing industry is by improving communication. In a manufacturing setting, it can be difficult and time-consuming for workers to communicate with each other or access information. With a voice AI assistant, workers can simply speak their requests or questions and receive a prompt response, saving time and increasing efficiency. For example, a worker on the factory floor can use a voice AI assistant to request a specific information or ask for clarification on a task without having to physically locate someone or search for information on a computer. This not only saves time but also improves the flow of communication and helps to ensure that tasks are completed accurately.

Automating Tasks

Voice AI assistants can be used to automate tasks and processes. This can be especially beneficial in manufacturing settings where workers are often required to perform repetitive or monotonous tasks. By automating these tasks, voice AI assistants can free up workers to focus on more complex or value-added tasks, increasing overall productivity. A worker could use a voice AI assistant to input data into a computer system, schedule maintenance on equipment, or retrieve information from a database and ultimately save time, reduce the risk of errors, and improve overall accuracy.

Safer Workplace

Worker safety is one of the indispensable divisions that often requires the most attention in a manufacturing setting that involves heavy machinery. Another benefit of voice AI assistants in manufacturing is that they can improve safety on the job. In a manufacturing setting, workers often have to multitask and juggle multiple responsibilities, which can lead to distractions and accidents. With a voice AI assistant, workers can keep their hands and eyes free to focus on their work, reducing the risk of accidents.

For example, a worker could use a voice AI assistant to request a safety check or report an issue without having to take their hands off their work or look away from their surroundings. This not only improves safety but also allows workers to focus on their job without having to multitask and work more efficiently.

Training

In addition to these benefits, voice AI assistants can also help to reduce training time and costs. With traditional training methods, it can take time for workers to learn new tasks or processes, and this demands time out of the days of efficient knowledgeable workers. With a voice AI assistant, workers can simply ask for instructions or clarification, allowing them to learn on the job and get up to speed more quickly. While this helps save time, it also reduces training costs and helps to ensure that workers are fully trained and able to complete tasks accurately.

Conclusion

Overall, voice AI solutions are transforming the manufacturing industry by improving communication, automating tasks and processes, improving safety, and reducing training time and costs. As AI virtual assistants become more advanced and widespread, it is likely that they will continue to have a significant impact on the manufacturing industry. In the future, it is possible that voice AI assistants will be integrated into a wide range of manufacturing processes, from assembly line tasks to quality control and beyond. As the manufacturing industry continues to evolve, it is important for manufacturers to leverage smart AI tools and stay ahead of the curve. Get in touch with our experts at purpleSlate today to learn more about how we leverage our smart conversational AI tools in simplifying complex challenges in the manufacturing sector.

This blog was originally published in: https://www.purpleslate.com/6-use-cases-of-leveraging-voice-ai-assistants-for-manufacturing/

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What is Conversational Insights in Under 5 Minutes

What is Conversational Insights?

Conversational insights is a novel approach to analyzing data that uses the natural language of customers, employees, and partners to understand their needs. It allows for better communication, improved insight, and faster decision-making.

Conversational insights is a new way to interact with your business data. It’s more natural and intuitive for users, who can get answers without the added complexities of a query-driven data analytics tool. And it can be used in many industries — from healthcare to manufacturing — to improve productivity and better understand customer needs.

The concept of conversation-driven analytics has been around for some time, but it’s just now starting to gain traction because of its potential as part of the trend toward Natural Language Processing (NLP). This technology is also part of the growing interest in artificial intelligence (AI), which uses computers’ ability to learn from experience or observation rather than being told what to do by programmers or humans telling them how things should work.

Why Do We Need Conversational Insights?

To understand the need for conversational-driven business intelligence platforms, one needs to look at the current suite of self-service analytics tools. They started with the noble intention of enabling everyone to derive contextual stories from data, but have metamorphosed into a form that’s undesirable at large. There are three major shortcomings of the current suite of self-service analytics platforms.

Complexity in Usage: These tools demand a certain degree of expertise that requires training, certifications, and more to use. The difficulty of operating these tools exponentially increases with the amount of data being collected and processed.

Additional Overheads: Specialized teams are employed to create reports when the volume and the level of sophistication surpass the expertise of regular IT teams. This adds to the overheads along with licensing costs.

Time Loss: Even for a seasoned user to create dashboards and reports, will take him or her a specific amount of time. The time loss is directly proportional to the volume of reports.

The impact of shortcomings affects businesses heavily, often resulting in loss of revenue.

Information Overload: An excess of information to make a data-driven decision leads to employee burnout, and failing productivity levels.

Painful Delays in Data Access: Time loss in delivering dashboards coupled with information overload hits the business where it hurts. Taking data-driven time-bound decisions.

Hence it’s imperative to implement a different business intelligence system, one that’s intuitive to how humans access information.

Are there any Benefits in Implementing Conversational Insights?

For decades, the adoption of business intelligence tools has hovered in the range of 20–30% of users in an organization. Business Intelligence systems were used only by a few within the organization and not tapping their full potential. Conversational Insights is designed to improve adoption amongst all data users by encouraging them to access insights in the language they speak.

Introducing intuitive business intelligence platforms to the middle and senior management team or whoever is part of the decision-making, will lead to a manifold increase in the company’s revenue. AI-powered conversational insights enable business users to find information on the go. Ad hoc queries can be resolved quickly by BI teams, taking only a few seconds as opposed to days or weeks. What’s more important is that the system will be able to learn and improve continuously.

Enhanced Returns: Enables business users with actionable insights and allows them to uncover business issues even before they occur

Higher user adoption: A straightforward language-based interface that enables even all users in the organization to use the tools with basic training

Data democratization: Access and understand data without analytical, statistical, or data-handling skills

Improved decision-making: A search-driven analytics platform allows users to dive deeper, discover AI/ML-powered insights, and find the most granular information by allowing them to explore data in any direction

The Future of Business Intelligence will be Conversational

Conversational insights is the future of business intelligence and is here to get the most out of available data and make better decisions. Voice-enabled data analytics help HR managers find the right people, engage with them, and build a relationship before they even decide to hire them. This approach enables sales managers to understand customer emotions and build tailored experiences for them. Supply chain personnel can plan to mitigate the risk of dwindling SKUs and proactively plan effective shipping routes. The applications of a conversational insight tool are endless.

"Intrigued to learn more about conversational insights? Check out our webinar where we discuss the story of how conversational insights is revolutionizing the data analytics industry."

This blog was originally published in: https://www.purpleslate.com/what-is-conversational-insights-in-under-5-minutes/

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Conversational Insights — The Future of Business Intelligence!

The Past Few Years of Business Intelligence

The year is 2002. The CFO of a Fortune 500 company is looking at optimizing his cash flow. Plugging the leak is mission critical, because share prices are plummeting, and shareholders are getting impatient. He’s waiting for that annual spend report to come in. It needs to get through all the red tape because the board wanted a solution yesterday.

And then he gets the news. IT team needs two more days to finish the report because some new “intern” forgot to save the spend analysis dashboard. When the CFO introspects, he understands that he had followed the process to the T.

He submitted the request 5 business days ago as mentioned in the SOP.

He clearly explained the requirement, its objective, business impact, guidelines to follow, and the report format.

He even followed up a couple of times.

So what went wrong? Nothing. The issue was with the entire concept of Traditional Business Intelligence (BI). Traditional business intelligence had many shortcomings but external dependency and complexity to derive insights take the cake. Such events were common in businesses and they sparked the inception of self-service analytics.

The Current Suite of Self-Service Analytical Tools is Selfish

Fast forward to 2022. Self-service analytics have taken precedence over the legacy method of generating business intelligence reports or dashboards. Users are empowered enough to create data visualizations. However, a CFO facing a similar situation still waits for at least 4–5 business days to get that report in hand. The process has become a little bit refined but still, the dependencies exist. Complexities exist. It’s almost as if certain shortcomings in the space of business intelligence are set in stone. The future of business intelligence depends on how we address the following issues.

Difficulty in Information Access

The complexity of operating a self-service tool unfortunately has led to organizations introducing dedicated data analytics teams. This effectively cripples the purpose of self-service by creating dependencies or forcing business leaders to become tech experts.

Increase in Cost

Because a team requires people. Right from the salaries of the members, to footing their bill for additional certifications proves to be a costly affair for the organizations. Factor in the license costs of the tool, and organizations will start paying they hit pay dirt soon with the investments.

Time Loss

Most self-service analytics tools take months and years to master. Even then for a seasoned user to create dashboards and reports, it will take him or her a specific amount of time. For momentary information needs the current suite of self-service tools is not sufficient.

Real-Time Insights is Still a Distant Dream

Most of these tools create insights based on historical data. This implies for every weekly, fortnightly, or monthly meeting needs a fresh set of dashboards or reports to be produced. This will add to the already existing time delay which seriously hinders the decision-making process for business leaders.This is by far the most lengthy problem statement that I’ve given to you - a visionary, a board member, a CXO, a farsighted business leader, and most importantly, my reader.

But I’m a big believer in setting the expectations right and most importantly, making sure you understand the context. Reading through this piece, you would have thought, “That’s me! I go through that daily.” But the solution is right here and of course, nothing is set in stone. Not even the above mentioned issues.

Conversational Insights — The Future of Business Intelligence

Interesting term? And no, this is not analyzing the conversations with the customer to understand their preferences. We are just redefining the term a little bit. Conversational Insights or Conversational BI is using natural language-driven conversations with your data to derive actionable insights. Conversational insights powered by Conversational AI, aim to remove existing complexities in information access by helping you, the data user Talk to Your Data™.

In layman’s terms, ensuring self-service analytics remain truly self-service. This is not a revolutionary ideology that sprung up out of nowhere. It has been in the discussion and works for quite some time now. Celebrated author Nir Eyal discussed this concept way back in 2016 in his blog.

If you ask the question, why conversational insights and more importantly why now, it’s fairly simple. Language is the most seamless and easy-to-use interface for human beings. Imagine involving in a dialogue with your data. You don’t have to click a thousand times and jump between multiple windows to get the information you were seeking. Rather, a simple “What were my sales for 2020 Q4?” should fetch you the relevant results. That’s the power of conversational BI.

Gartner predicts that by 2023, 25 percent of employee interactions with applications will be via voice, up from under 3 percent in 2019.

This change in data analytics can be attributed to three major factors.

Changing Technology Landscape

To explain this point a simple example would be how writing happened — Then vs Now. You write an email. Then you review it at least 3 times and correct it before sharing. The entire exercise takes you a considerable amount of time. Now? AI Tools like Grammarly does it for you and you get an error-free email in less than a minute. Technology has enabled this.

Gravitating Towards Convenience

Technological advancements enabled convenience for humans. Taking a cue from the previous example, we needed to correct our mistakes by typing them. Now, it’s the click of a button. Going forward, I’m sure the entire exercise of typing will be replaced by smart transcription, and Joaquin Phoenix’s movie Her won’t be science fiction anymore. If convenience can exist in a simple exercise like writing an email, why can’t BI tools have it?

Humans Thrive on Momentary Insights

The most important point here. The one that drives the ball home. Humans exhibit definite psychology when they seek information (also read insights). The chronological order starts with one question, then follow-up questions to gain additional insights, and then finally the action element. They do not expect a lot of insights, they just need answers to the momentary data questions they have. In simpler terms, they don’t want their BI tool to beat around the bush.

Conversational Insights Address the Root Cause

Conversational Insights is the remedy for making sure that users are easily able to access data insights that satisfy their momentary questions. The user dependency on other teams is greatly reduced thereby making sure that insights are accessed within the required time frame. Delays in decision-making and their impending effects can be mitigated by investing in conversational insights.

Ease of use: No prior training or technical expertise is required to derive insights. You just need to know how to ask questions to your data. The system makes sure that relevant insights are shown to you in different formats including visualizations.

Multiple input formats: You can converse with your data in multiple formats. You can chat with it using text, voice, or search based on your convenience and preferences.

Omnichannel capabilities: Conversational Insights can make sure insights are delivered to you on the device of your choice in the channel required. Whether it’s collaboration tools like Google Chat, Microsoft Teams, Slack, or Smart Speakers like Alexa.

Insights, anytime: You don’t have to wait around for your data analysts anymore to get that dashboard. You can open your smartphone even in the dead of night and start getting relevant information. In the truest essence, power back to people!

Enhanced Productivity: Across teams, functions, and the organization at large. Majorly because of the timely availability of insights. Every team will overdeliver including the data analytics function as they would be able to concentrate more on their core value-adding tasks.

It’s Going to be a Conversational Insights Driven Decade

The closing notes are pretty simple. The world is increasingly going conversational. Gartner did say that by the end of 2022, 70% of the global workforce will be interacting with conversational platforms daily. The conversational business intelligence function of an organization is also part of this transformation and conversational insights is here to define the future of business intelligence.

This post was originally published in: https://www.purpleslate.com/conversational-insights-the-future-of-business-intelligence/