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Apple Vs Samsung - Digital Marketing Strategy

APPLE

The brand is appreciated for its design, which is the fundamental value of the company. The reputation of Apple is based on its strategy of arousing the desire of the consumer at each product launch. BCG Matrix of Apple is used to pull off different marketing and investment strategies.  It is by cultivating the secret to each new product that the brand maintains the loyalty of its followers.

At the Worldwide Developers Conference  2017, Apple chose to launch a humorous " Appocalypse  " commercial  to motivate application developers to create more. A commercial different from all the brand that had already launched. Indeed the Apple brand has chosen to transmit through its communication campaigns sophistication that promote the simplicity and performance of products ( iPhone X ). Apple still wants to control his image , which explains his absence on social networks.

From the Samsung side  :

In comparison, Samsung's marketing strategy is tough and powerful. Indeed the brand is very present in terms of communication, be it posters or audiovisual campaigns, the brand wants to be seen to establish itself as a  leader . BCG Matrix of Samsung is used to create so- called "cuckoo" marketing because the brand openly nargues its competitor through certain campaigns ( Galaxy S II ).

Samsung continues its quest in the field of health and mutual aid through its various digital operations as the connected banner to alert in case of concussion. Indeed by showing that it is interested in consumers and their health, the brand through its innovations earn points, and shows that it is still present against Apple.

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Positioning strategy of Samsung

Samsung uses a product positioning strategy and especially through imitation. The goal is to proceed in the same way as its main competitor (it is for the most part activities of the company Apple), while adding an added value. This is often only a detail, but it can make all the difference in the choice. 

The consumer will then be interested in this element in addition that is proposed by Samsung. When the company is a challenger, the strategy is called the "fast follower", that is to say, to anticipate and watch over the launches of innovative products competitors, then, depending on sales, to launch or not his own very similar product. On the other hand, in activities where Koreans are leaders, is themselves who will be more likely to be copied. 

The competitors will then use the same strategy to match and see ahead of Samsung. In addition, the company Samsung is very fast vis-à-vis its competitors. Indeed, Koreans have the ability to release a product very quickly after the launch of the leader company. BCG Matrix of Samsung also has different products placed in the right quadrant that also help Samsung in making the right strategy. Rather than innovate and take risks, Samsung wants to copy. The company invests 6% of its turnover in research and development, in order not to be caught by the competitors who follow, or not to fall behind the market leader. 

It also allows the company to have all the new technologies at the same time as the leader, and when it comes out, it will only have to improve it. C ' that's why we can say that Koreans also use a continuity innovation strategy. This one corresponds to a weak degree of innovation, one improves an element of something which already exists. In the article, the Vice President of Samsung France, Philippe Barthelet, speaks of the company saying "we scrutinize the markets to avoid mistakes." The main purpose of the company is therefore not to take risks, to be careful.

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An original but powerful development strategy in the IT sector of the Korean brand

Since entering the tech sector, the Korean brand Samsung has made immediately useful innovation a key piece of its development strategy. It puts at the service of consumers practical and well-thought-out solutions that simplify the everyday use of its products, especially in the PC market. This brand positioning of Samsung also includes ease of interaction between all digital devices in the group.

Samsung, positioning in the most dynamic markets of the PC

The goal of the Korean manufacturer and the main aim of the marketing mix of samsung is to offer simple products to take in hand, while bringing, by advanced performance, a very comfortable work. This approach explains the very frequently revolutionary character of the brand's references, like its Ativ line, which houses mid-laptop and half-touch tablet devices . The group is particularly renowned for its products intended for computing in mobility situation: its "notebooks" Series 9 are distinguished by their very small thickness while displaying a significant power, while its "ultrabooks", barely larger than an A4 sheet, are really getting away everywhere.

Samsung's PC market positioning combined with advanced interaction

Also a major player in consumer electronics and mobile telecommunication devices, the Korean brand offers owners of its PCs to enroll in a broader ecosystem. The latter is characterized by a very strong interaction between all the products of the manufacturer. Several cross-functional applications facilitate the sharing of content between desktop and laptop computers , tablets, phablets, smartphones, capture devices and connected TVs, or speed up the creation and editing of any type of document.

THE KOREAN BRAND, THE MAIN PROMOTER OF "CHROMEBOOKS"

Samsung is one of the world's leading manufacturers of "chromebooks", lightweight computers at a very attractive price, ideal for audiences unfamiliar with microcomputers or the education sector.

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Understanding the customer's voice: why surveys are not enough

Brands know that a quality customer experience that builds long-term revenue and loyalty requires listening to their customers. That's why they invest in Voice of the customer (VoC) programs that try to collect information that will determine their actions. A recent study by Eptica reveals that 70% of brands have already implemented a VoC program, in one form or another.

However, most approaches to collecting the customer's voice are based on surveys to collect feedback. But that can not be the voice of customer tools Whether conducted by telephone, face-to-face or on the Internet, the disadvantages of an inquiry-based approach are numerous:

Customer surveys do not make it possible to listen to everyone Everyone does not have the time or the desire to answer a questionnaire. Respondents often represent the two extremes: either they are very satisfied or they are not at all. The results are therefore biased. It has all happened to us, after a positive experience with a customer service, that an agent insists on filling out a questionnaire, hoping for a better premium. Surveys can also be done weeks or months after an interaction. Even a most obliging customer will have a hard time answering precisely.

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Large amounts of data - an important resource of the present and the future

In the digital age, data analysis is becoming increasingly important. Digitization, big data, data protection and data mining go hand in hand. With programs like Excel and Co. you can not get by today. When it comes to data analysis, one means today data volumes that could not be seen by a human alone, let alone analyzed. From this aspect alone it becomes clear which tasks are to be assigned to companies today.

- Data will become a very important, if not the most important, resource for businesses in the future. - The better and higher quality the data analysis, the more goal-oriented can be made based on these decisions.

More than 60 percent of German companies rely on big data, according to one survey. In most cases, the company leaders promise risk minimization, sales growth and cost reduction. In order to be able to operate data analysis effectively, a strategy is necessary in addition to the corresponding software. So what should be considered here?

The importance of data analysis

Relevant business decisions are being made more and more frequently based on data analysis. Increasingly, data analyzes are also an integral part of the value chain. Simple tools are no longer enough for these analyzes. In the case of large amounts of data, the evaluation of these data is often a problem. Special software supports companies with corresponding analysis and evaluation functions. In addition to purely descriptive analysis methods, there are also forward-looking analyzes that can be used to forecast future developments.

Big Data analytics stands for the ability to analyze large amounts of data from many different sources and sometimes different structures. Modern analyzes profit on the one hand from ever larger data memories and on the other hand from an ever higher computing speed. For companies, therefore, far more approaches are possible than mere customer data analysis, or specification of a product. Modern analysis methods also enable predictions of economic development and more. Top data analytics tools make it possible to bundle information from a wide variety of areas and derive concrete entrepreneurial measures from this information. Under the following link you can find out more about data analysis. Here are backgrounds and methodologies illuminated.

Do not oversleep the trend

Businesses are using the voice of customer tools in order to "understand" customers or their purchase decisions and data is the best way to date. In contrast to classic corporate strategies, a company that relies on data analysis needs to reorient itself and rethink and reorient existing strategies. Only if a targeted data analysis is firmly integrated into the corporate structure, the company can be successful in the long term. However, those who overslept the trend risk being suspended in the long term. In order to make future-proof informed decisions and thus minimize the entrepreneurial risk, it is essential to deal with big data and data analysis too busy. The evolving technologies will further increase the complexity of data analysis in the future. Certainly it is difficult to orientate oneself in this rapidly growing market. It is therefore all the more important to start integrating a corresponding software and overall concept in good time.

Security in handling data

In addition to the pure analysis of data, data security is also becoming increasingly important today. Because data is such an important resource, security is an important sticking point. For example, it would be a disaster if a software developer accidentally destroys data through a programming error or if a hardware defect occurs on the server. Just as relevant is the handling of sensitive personal data of customers and business partners.

Learn more about top 10 free data visualization tools that can be used by businesses to present their findings in a better and understandable format

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Big Data: Data visualization is the key to success

The analysis of big data provides comprehensive and business-relevant results that are essential for the development of organizations.

The demand for free data visualization tools is increasing rapidly. This is partly because companies are trying to gain valuable business insight from big data data analytics initiatives . However, data management experts say that achieving data visualization success requires a fresh new way of thinking about how information is prepared for business users. This is especially true for big data environments.

With data visualization, users can create graphical and often interactive displays of large and small datasets. It can also make a significant contribution to business intelligence (BI) and higher productivity. This is evident from the results of a survey conducted by The Data Warehousing Institute (TDWI) in 2010.

In this online survey of 210 BI professionals and business users, 74 percent of respondents said the importance of data visualization to gain business insight in their organizations was "very high" or "high." By contrast, only 23 percent believed that the technology had only a modest impact on BI processes.

The survey also shows that the use of data visualization tools increases the adoption of BI dashboards. These are the preferred medium for accessing and viewing charts, maps, graphics, and other forms of presentation. More recent TDWI studies also show a combination of software for data visualization and top big data analytics tools

"My own research shows that advanced data visualization is the segment in which users are more likely to purchase and use more tools. This is considered best practice in the context of big data analytics, "said Philip Russom, Head of Management Research at TDWI. "We see a very positive connection between advanced visualization and analytics. People just assume the two belong together. "

Effective data visualization is indispensable for adding value from big data analytics investments. This is also what Stephen McDaniel, co-founder of Freakalytics LLC, a seattle-based visual analytics consultancy, says. However, McDaniel - who recently talked about big data analytics at a TDWI forum - says there are some limitations in practice. Organizations should be aware of these before embarking on the process of visualizing big data analytics results.

Identify the right big data mix for visualization tools

So creating charts with billions of data points is not a very effective way to get started. "The normal monitor that's on your desk can not stand more than 200,000 points," McDaniel explains. "If you buy a much more advanced monitor, you should be able to get around a few million points. But even that does not even come close to billions of values. "

Organizations planning to visualize large amounts of data can overcome such limitations. To do this, they need to aggregate data or eliminate redundant data points based on the specific needs of each audience, McDaniel says.

A good graphics selection and good dashboard design are also important. "You need well-thought-out management dashboards," notes McDaniel. If only a smaller amount of data appears on the screen, executives and other end users should be able to see the underlying details. This helps to make ad hoc queries to find the answers you are looking for.

In addition, Russom recommends that when creating visual representations of big data analytics results, consider twice whether to overlook or eliminate outliers. A good visualization helps you understand the true voice of the customer. Companies are using the voice of customer tools to get the findings of their customer and make informed business decisions. For example, this refers to data points about customers whose purchasing behavior is significantly outside the norm. Observing such small pieces of information can help organizations, for example, solve tasks such as detecting fraud or identifying new customer segments, such as bulk buyers.

"If the data too busy to clean up , it can happen that you lose the tiny, glittering nuggets, after which you are actually looking for," said Russom. In consequence, he notes, one experiences "from an analytical point of view a loss of value".

According to McDaniel, data visualization technology, despite increasing popularity, has some general drawbacks that need to be considered. For one thing, he says, most people have never really learned how to use data and graphics correctly to "tell a story." As a result, diagrams, graphics and other visual media are often created that are difficult to understand and fail to convey the intended points.

The problem of data visualization: too much information

The best way to solve this problem is to focus on the questions that need to be answered and then keep the presentations as simple and clear as possible, McDaniels says. "It will be hard for you to succeed if you have yours Business partners and customers can not present and explain the data, "he says. "And it does not matter how great your ideas are."

Another common mistake, according to McDaniels, is that users of data visualization tools use the wrong types of graphics for their stories. For example, "a multitude of studies" have shown that bar charts can communicate information more effectively than pie graphics - bars could represent significantly more data points without being too complicated. "I've already created bar charts that have a total of 50 bars and can still interpret them correctly," explains McDaniels.

Another mistake reported by McDaniel is overcrowding-the use of superfluous 3D graphics, pop-ups, animations, or other flourishes. This could be distracted from the actual statement of the visual presentation. "Visual analytics is so important because it allows iterative query of data," he says. "And unfortunately, there's now evidence that 3D graphics look cool and interesting, but we're not in the least capable of processing or really understanding them."

For Elaine McDaniel, data visualization consultant and co-founder of Freakalytics, the best way to make effective visual representations of data is clear: the business side and the enterprise IT department need to communicate more intensively.

"Ask people for their opinions. Request feedback. Put their opinions into action and make people work together, "she says. "The biggest complaint of all business analysts we've dealt with over the years has always been that you could not talk to the people in IT."

James MacGillivray, a BI architect with his own company in the agricultural sector, sees a similar situation: it is important to invest enough time in determining the right user requirements before moving on to a data visualization project - or even Big Data. Analytics - get in.

"Too often, attempts are made to just start thinking that everything is going to happen. That's the wrong approach, "says MacGillivray. "You have to make sure that the user drives the project because as an IT person I do not want to push it forward. I'm here to make it happen. "

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Technology trends for a comprehensive data analysis

Gartner's top ten technology trends reflect a rapidly evolving world of technology, where increasing data volumes and greater analysis needs are playing an increasingly important role. Just think of the Internet of Things , which makes business analytics suitable not just for everyone but for everything.

This trend is likely to continue as the amount of data directly available to humans increases exponentially. That alone is an important message for the leaders of today. It should not be overlooked, however, that the amount of data to be handling calls for a culture of analysis. Only then are companies always one step ahead of the competition.

The following items are expected to be crucial this year:

1. Info apps and self-service: advanced, comprehensive analysis in the form of apps for general access to company data

2. The skilled labor shortage in the digital sector

3. Machine learning

4. Master Data Management (MDM)

Info apps and self-service

As data growth continues, business analytics becomes increasingly important to make smart decisions. Each employee of a company has the role of a decision-maker in one way or another. For this reason, employees should be able to access the data relevant to their job and analyze it for their decision-making so as to become more productive day by day.

According to the analyst firm, Gartner Business Intelligence ( BI ) is currently established in less than 30 percent of the surveyed companies. In addition, their use is often limited to professional analysts who use complex tools and spend most of their time analyzing data, as well as managers who read the reports and dashboards. Most other operational staff - as well as customers and partners - do not have direct access to information that enables more informed decisions.

The right tool for every employee

An important step towards achieving widespread commitment is the recognition that there is no universal solution in BI and analytics. It is very important that the right approach is chosen for different users. For example, analysts need to be provided with more complex and in-depth tools, while frontline staff should be given hands-on apps.

Gartner supports this assessment by saying that one of the keys to disseminating BI and analytics is a new way to deliver information - apps. The correct approach to this access to information is to make the analysis process invisible and integrate it into a user-friendly app.

This should follow the trend of "consumerization" and prepare information according to consumer behavior and user expectations. An app provides users with all the information they need to make informed decisions without the need for extensive analysis. At the same time they can flexibly decide on the use of the data and look at the required information in detail.

Each organization benefits from broad BI and analytics adoption as it translates to corporate culture change, where strategy and operations are fully aligned through a common system of fact-based decision making.

Skills shortage in the digital sector

Despite the benefits of BI and top big data analytics tools across the organization, analysts and data scientists continue to play a significant role. The demand in this area is correspondingly high. On the other hand, there is a Europe-wide shortage of skilled workers in the digital sector. According to a representative study by the high-tech association BITKOM , 41,000 IT specialists are missing in Germany in order to fully exploit the economic potential. Companies should therefore invest more in the promotion of young talent in order to prevail in the fight for the best brains.

This year, the role of the Chief Data Officer (CDO) will grow in importance as data is now recognized as the most important asset of a business today. Accordingly, the number of companies relying on data and needing the help of a CDO is growing. In particular, to find ways to make financial use of this data.

It can also be observed that the role of the Chief Analytics Officer (CAO) is becoming increasingly important due to the need for trend analysis. The challenge here is to find people who not only have the necessary technical skills, but also entrepreneurial acumen.

Machine learning

As with any labor shortage, the solution lies in technology; In this case, machine learning is the answer. In the presentation " Analytics Trends 2014 " Deloitte has already pointed to some interesting points. One example is that managers have so far taken away from machine learning in decision-making processes. This was justified by a flawed basis due to missing hypotheses and human explanations. Managers are using the voice of customer tools to understand the findings from machine learning and make decisions that can help businesses. However, data projects are now too dynamic for conventional hypothesis-based analysis. This explains why companies are increasingly adopting machine learning to handle the large volume of multidimensional data available.

Today, data can have many variables-age, education, income, frequency of purchase, and so on. Visualization without machine assistance is difficult because typically only three variables can be considered. Machines, on the other hand, can use mathematical methods to scour entire data mountains to discover patterns that analysts then use to explore trends. This can be translated into business strategies, such as identifying the right audience for specific marketing campaigns to maximize ROI on ad spending.

Master Data Management (MDM)

As analysts face a growing volume of data, the aspect of master data management will also become more important. Analysts must have the freedom to work with data at will. On the other hand, IT must be able to manage this data to ensure that different analysts draw the same conclusion from the information available. This process becomes more complex when multiple sources are merged and may not be adequately described in the metadata.

It has been pointed to data growth, but it's not just the sheer volume of data, it's also the number of data sources. Areas such as social media today provide businesses with a wealth of information analysts want to see in real-time. The key is data governance, which gives central control to IT while giving analysts the flexibility they need. It also covers the organization-wide extension of this function to a "self-service" model, where employees can access corporate data relevant to their role.

Another interesting aspect of this trend is the importance of integration and real-time data processing and analysis. Due to the particular emphasis on context-based systems, the depth and breadth of the context are highly dependent on data collection, data quality, and data integration from multiple systems. Any gaps in these processes can make the overall picture look incomplete or indistinct, so that the context benefits the decision-making process. These gaps can also be an obstacle to automating the decision support that intelligent machines rely on. As a result, the topic of big data again comes on the scene. Machine data and unstructured data, such as social data from customers, should be a high priority for companies - because they can contribute to growth and increase sales.

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The benefits of data mining on sales

Are you satisfied with the turnover of your company? the opening rate of mails from your contact list? Do you know who your customers are? In order to answer these questions, we propose this interesting post about the benefits of data mining

The data mining as a discipline is gaining ground. Companies are beginning to see the benefits. With the democratization of services, less and less expensive, it allows small and medium-sized companies to benefit from an in-depth vision of their data.

Learn more about the top 5 data analytics for 2018

How does data mining increase revenue?

Attracting customers on the Internet, increasing sales, reducing costs associated with marketing initiatives, ensuring customer loyalty is a matter of data mining.  All of these goals are achievable when companies put into practice the most known data mining techniques such as:

Cluster Detection : This is a model recognition mode used to detect clusters with similar characteristics within large data sets. It allows you to organize a large amount of information into categories through the use of emerging models during data analysis . Without it, and unless they are obvious, it would be difficult to identify them.

Regressive analysis: this  is a technique whose purpose is to predict future results from the study of big data variables . It is generally used to know the participation or involvement of users in the future, improve customer loyalty and even set more adequate prices.

According to information obtained through data mining , companies can opt for various lines of action , such as:

In the case of commercial establishments: taking into account the customer's buying habits, they can rely on the most relevant details of the analysis in order to optimize the store design and thereby improve the customer experience and increase their customer experience. profits.

Online shops : e-commerce is a major beneficiary of the use of data mining techniques . On the one hand, data mining makes it possible to increase the efficiency of content published on the Net and the way to do it. In addition, the focus is on how users came to the site, how they surfed and when they decided to leave the page. Data mining helps you understand the voice of the customer, understand what they like, what they don't like and allow them to make a course correction in the future.

Learn more about the voice of customer tools 

With all these data a clearer picture emerges of the information to be shared and how to do it. It's a way to better connect with potential customers, attract new users and guarantee them a satisfying experience in every way.

About marketing: Data mining techniques are used to improve conversations, customer satisfaction and succeed in major advertising campaigns . It can even be used to analyze market needs and create new product lines.

To do this, data mining explores historical sales and compares them with customer and social media data. This creates large-scale forecasting models.

Learn more about the data preparation tools 

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Big data analytics will continue to keep the ball rolling in 2018 as well with business leaders changing their perception towards analytics tools and adopt the technology for better. 

Check out the top 5 big data analytics tools for 2018 that are best bets for 2018. 

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Did you know users by 2020 videos will be the preferred medium for users to consume information ! . The need of the hour to build your buyer persona ,  understand what is content strategy and include it in your marketing business plan

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The top 4 marketing tactics for 2018 that are going to change the marketing landscape this year. 

Check out how to create a marketing plan   and  what is buyer persona

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http://heartofcodes.com/top-4-essential-marketing-tactics-for-2018/

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Understand what is customer persona  and the importance of customer persona   in building a business marketing plan .

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Follow this marketing plan template and learn how to create a marketing plan.

Get the 4P’s of marketing mix right which can help you build you create customer persona for business