7 Real-World Examples Of Data Mining In Business, Marketing, Retail

The importance of big data is unquestionable. And if you wondering why, some real-world data mining examples in business, marketing, and retail, can help you understand its power.

Now, many leading companies successfully use data analytics to convert big data into solid profitable results.

Let’s see how!

On this page:

  • What is data mining?
  • Real life examples of data mining in:
    – improving customer service
    – driving innovations
    – boosting SEO
    – social media optimization
    – defining profitable store locations in retail
    – making sales forecasts
    – Market Basket Analysis
  • Infographics in PDF

What is Data Mining?

Now, there is an enormous amount of data available in the business. But this data is worthless for the management until it is turned into useful information.

And this is where data mining comes to play. It turns the big unstructured data into actionable information that is like a gold mine for successful data-driven decision-making.

So, what is data mining?

Data mining is the process of finding patterns and trends within large data sets to make decisions or to predict outcomes.

How is this possible?

Throughout data analytics software programs. The software uses different ways to collect data and a variety of data analysis methods and techniques.

Basically, data mining uses a range of mathematical and statistical algorithms to segment a large volume of data, to find patterns there and to assess the probability of future events.

Data analytics is one of the best competitive advantage examples that can help your business stand out in the market.

10 Data Mining Examples In Business, Marketing, And Retails

Data mining can help you improve many aspects of your business and marketing. Let’s see how with examples.

1. Improving Customer Service

Ways Data Improve Customer Service - a short infographic

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Now, anyone knows that providing great experiences for customers can dramatically impact business growth. Undeniable, customer service creates and represents your brand and image.

So, how can data mining improve your customer experience?

Simply, by performing deep customer analysis of data that come from various sources of information about the customer journey.

Examples of these sources are customer feedback, surveys, social media comments, business emails, and etc.

Data mining techniques decompose these data to identify moments of dissatisfaction as well as moments of delight.

Thus you can discover insights such as what are the customer needs and preferences, what makes customers happy and loyal, why customers buy from you, etc.

Real-life data mining examples:

Amazon is a is the world’s largest online retailer. Its online marketplace platform leverages big data with a customer-centric approach to improve customer experience and user delights.

Amazon has long been using data analytics to serve better its enormous customer base.


The retailer collects data on each customer while they use the site. The data includes information such as: what pages you look at, your reviews, the time spent browsing each page, your shipping address, and etc.

The company also use of external datasets, such as census data for collecting customer’s demographic details.

The more Amazon knows about you, the better it can understand you, and forecast what you need to purchase.

And, once the retailer has the information about you, it can recommend you various products that suit you instead of making you lose time as search their huge catalog.

2. Innovation And Product Development

Ways Data Help You Drive Innovations - infographic


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One of the most powerful things you can do with data analytics is to utilize it in creating and developing new product and services.

The ability to innovate and redevelop is critical for many businesses worldwide. And data mining is a huge help for them.

Data mining allows companies to understand what motivates customers and how the products can most effectively appeal to them. This is a great basis for successful innovations.

Real-life data mining examples:

Whirlpool Corporation is one of the world’s leading major home appliance companies. Innovation takes formidable priority in the company business and marketing efforts.

Consumer feedback plays a great role in their innovation strategies.

Whirpool has a million reviews being added globally in a month. This data is collected from 40 different websites and six different form factors. The types of data range from emails to different contact forms with sales representatives.

Whirpool uses data mining software that analyzes all this data and answers vital questions such as how are the products performing in the market, which features are consumers like the most, how is the customer purchasing experience, and etc.

In addition to these insights, data analytics also helps the entire organization speak a common language and perform a comprehensive competitive product analysis to see where they are in terms of competition.

Converting this data to insights allows the company to find different paths and ideas for innovations.

3. Social Media Optimization 

Ways Data Help Social Media Optimization - a short infographic

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Today, social media is a key weapon in the marketing arsenal of almost any company.

Data mining collects and processes a great volume of unstructured information (such as comments, posts, tweets, images) shared on networks like Facebook, Linkedin, and Twitter.

Then, this information is used to identify important social media trends and signals and transform them into a steady stream of valuable insights about customers.

This allows businesses and marketing intelligence departments to analyze the information about how people perceive the brand, what kind of products consumers like and dislike and generally how your digital marketing efforts should be improved.

The information is also used for web competitive intelligence as well as for more comprehensive competitive market research.

Real-life data mining examples:

McDonald’s Corporation is a world-famous fast-food company that is also very discussed across the most popular social media channels – Facebook and Twitter.

The company has a glamorous amount of social media commentary which is a great foundation for uncovering valuable insights. McDonald’s uses social data mining as a basis for many management decisions such as the breakfast menu solutions.

For example, the fast-food chain brought back Szechuan sauce and launched evening breakfasts in response to online feedback.

Social media monitoring and analysis can reveal also surprising insights. For instance, it may reveal that Happy Meal toys, rather than Happy Meals, drive customer engagement.

The social media mining is also very helpful for fast reactions to the negative comments. McDonald’s is able to react very quickly to problems such as mentions of broken ice cream machine, for example.

Its goal is to solve the problem as quickly as possible and make sure negative conversations don’t spread further.

4. Boosting SEO (Search Engine Optimization) of Your Website

Ways data boost the SEO of Your Website - a short infographic

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As we are living in a digital world, data mining can boost your web presence through improved SEO strategies.

Today data is widely used for gaining actionable SEO insights that marketers use to meet customer needs digitally.

There are a vast number of modern and powerful SEO and keyword analysis tools that provides webmasters with great access to analytical data and custom reports.

The tools show you key information such as technical SEO audit, positioning tracking, semantic core collection, backing analytics, as well as ideas for gaining more organic traffic.

The data collected also can show you which topics and pages of your website engage your audience, what are the most relevant industry blogs for distribution, what is the impact of your content in terms of social shares and referral traffic, and etc.

All of these insights can help you uncover the most effective digital marketing strategies for your business and to spot new opportunities for growth.

Real-life data mining examples:

Booking.com is one of the largest travel e-commerce companies in the world. They are the 3rd biggest e-commerce website today that get millions of users every day.

Booking.com is leveraging the power of SEO through data analytics to discover valuable information and to expand its international market presence.

One of the first discoveries the company made was that the solid imbalance between their focus on Europe and the US. For example, for the same keyword, their visibility in the US was much bigger than in the UK.

Data mining also facilitates new ideas generation in the company. For example, when estimating an idea to create a Romantic Hotels in London landing page, the company looked at data for search volume and competitiveness of the keyword to validate the idea.

The results that Booking has achieved through data mining also include top ranking in Google for some of the hottest industry keywords such as ‘hotels in London’.

Additionally, data analytics has helped Booking.com to build a brand outside Europe and to get important partnerships there.

5. Defining Profitable Store Locations

Ways Data Help You Define Profitable Store Locations - a short infographic

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Data intelligence has the power to help retailers make better decisions about where to open new stores.

Retailers can identify new locations for expansion and work out the sales estimates for these places through deep analysis of socio-economic data.

By using different GIS tools, many companies are able to understand the expectations of their target audience within certain locations as well as to reveal the demographic characteristics of the areas.

With data, businesses can analyze a problematic location, such as a store that is not profiting well.

For example, they can realize that the target audience traffic is very low for the area or the people visiting that area are not their target marketing segment.

On the other side, data mining can show you locations with high potential that you’re not exploring.

Real-life data mining examples:

Starbucks Corporation is an American coffee company and world-famous coffeehouse chain. The right location is one of the essential reasons for its tremendous success.

The company uses a software solution for visualizing data in the form of maps that helping it fine-tune how to choose a store location in order to drive more traffic and increase sales.

In 2007 and 2008, Starbucks needed to close hundreds of stores and rethink the company’s strategic plan for growth.

As a result, Starbucks has implemented a data-driven approach to store openings with the help of mapping software that is capable of analyzing enormous amounts of data about planned store openings.

Through analyzing location-based data and demographics, the software helps Starbucks to identify the best locations to open stores without hurting sales at other Starbucks locations.

This data mining software tool even can predict the impact to other Starbucks places in the area if a new store were to open.

6. Marketing And Sales Forecasts

Ways Data Help You Forecast Sales - a short infographic

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Big data has changed the way businesses sell to customers, which helps companies increase their performance and profits.

There are many examples of how companies use data to predict and boost sales. Data analytics allow businesses to predict products that customers may want to purchase, to influence the customer’s behavior, to forecast trends, and to optimize the sales funnel and marketing campaigns.

The results of sales predictions can be used for planning. You can plan your demand or supply actions. It helps to see where to invest more.

Real-life data mining examples:

One of the most compelling data mining examples for analytics predictions can be seen on the world-famous retail company Walmart.

They use data in multiple ways and for many purposes.

Walmart is utilizing predictive analytics to forecast the customer demand at specific hours and thus to define the number of associates needed at specific counters.

The company also make sales predictions based on their historical data of stores from different regions.

Each store has multiple departments and the retailer uses data mining to predict the sales for each department in the store.

7. Market Basket Analysis (MBA)

Download the above infographic in PDF

When it comes to classical data mining examples, Market Basket Analysis has a top place.

Market Basket Analysis is one of the key data mining techniques widely used by retailers to boost business as predicting what items customers buy together or what goods are placed in the same basket by customers.

For example: if someone buys a sandwich, they are more likely to buy a drink than someone who did not buy a sandwich.

Another example – when a person buys a particular model of smartphone in an online store, the platform may recommend other products such as screen protectors or memory cards for that specific phone.

The technique works via examining the combinations of items that appear together frequently in transactions. It allows retailers to identify different relationships between the items that people purchase.

The results are used to increase profitability through marketing activities such as cross-selling in online stores, recommendations, promotions, catalog design, and etc.

Real-life data mining examples:

Amazon.com is the most famous example of how Market Basket Analysis is used successfully for boosting online sales.

If you are shopping on Amazon, you know that the retailer platform delivers to you the right product recommendation at the right moment.

But besides Amazon nowadays many other web retailers are able to benefit from the power of the MBA.

Through complex data analysis, these companies predict what you’ll want to see next. The analysis is based on the history of the products that similar customers have purchased in the past.

MBA give customers exactly the item they want before they even know they want it.

Download the bellow infographic in PDF

7 ways data mining help businesses growth - infographic


The above data mining examples show how successful business leaders rely on data to help them make decisions.

The best-managed companies are data-driven and this sets them apart from their competition. Data empower business leaders worldwide to make decisions based on trends, facts, and statistical numbers.

With the latest data analytics software solutions with powerful data dashboards, businesses have a wonderful opportunity in their hands. Just need to use them.

The data software can process enormous datasets and show you a complete picture of the environment in your company as well of the world outside it.

The true value of data hides on what you do with it. And some data management best practices and strategies can help you unlock the data potential for your organization.

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