Data Mining Application – Real Life Applications of Data Mining in Sales, Marketing, NLP, Text Mining etc.

This article is about “Data Mining Application – Real Life Applications of Data Mining in Sales, Marketing, NLP, Text Mining etc.”, hope you will like the information. If yes please do share it with others. What are the real life application areas of data mining like in marketing, sales, medical science etc.? Business Analytics, sales analutics, purchase behviour analytics, product analytics, route optimisation, medical science etc.

Data Mining Application:

Data mining has applications in every field be it IT, finance, marketing, sales or whatever you can think of.

It is the process or method of finding valuable insights (using sql, python, R, microsoft excel etc.) using data (can be extracted from databases using sql) that can help in taking effective actions for improving business profits or already existing methods in any field. For e.g.

  • Optimisation of sales funnel:
    • For e.g. seeing region wise how many leads have been generated, out of that leads how many leads already crossed the demo stage and out of that to how many quote has been shared helps in finding out the sales funnel conversion numbers for different regions.
  • Optimisation of customer journey on website to Increase paid conversions
    • Finding page level conversion: By tracking how many visitors are coming to any webpage of your website and out of that how many are clicking on CTAs available on that page like submitting contacts and how many out of that are finally purchasing your product.
    • Finding best pages on website: By doing tracking as mentioned in above example one can find out which webpages are performing best and on that pages which CTA is most effective in bringing final conversion, hence one can replicate the same for more conversion or if there are any issues with the existing funnel then you can fix that and again try to increase your final conversion.
    • Further Read: Website funnel analysis: using funnel analytics to increase conversions on your website
  • Optimising price on website:
    • One size fits all strategy doesn’t work sometimes and so online businesses have to try with different pricing tiers like selling same product at different prices with different features available in all of them.
    • Finding customer choices: a specific type of customer may like to buy the product annually, while other set want to buy it on monthly basis i.e. they will pay monthly for 12 months, whereas other set it paying annually as they have more frequent needs.
    • So, to take care of both type of customers as mentioned in above example multiple pricing strategies are employed.
    • Also, note the the aim of each pricing strategy is to set the best price for a product or service.
    • Further Read: The Ultimate Guide to Price Optimisation
  • Optimising route between source and destination:
    • Finding best route between point a to point b: Route optimisation is finding the best route between Point-a to Point-b based on distance, time, traffic, road closures and other events that can lead to some hindrance.
    • Google Maps: One of the best example of route optimisation is google maps that based on various parameters shows the best route to the vehicle driver and the route is different for cars, two-wheeler, walking etc., so it optimises and shows the route as per your vehicle type as well.
    • Further Read: What Is Route Optimization?
  • Optimising search queries to show effective results to a query irrespective of spelling mistakes, languages etc.
  • NLP (Natural Language Processing):
    • Extracting useful text information from documents: NLP includes understanding text and extracting meaningful information out of it like extracting information like number of years required for a job application from job description, or extracting in which area the experience is required etc.
    • Another example includes extracting information like number of years of experience a candidate has his/her resume, in which field the candidate has experience etc. but using data science or machine learning algorithm.
    • Further Read: Document Processing Using ML/AI for Airline
  • Classifying Text based on keywords or meaning:
    • E.g. Extracting meaningful information from text like finding all the sentences that are of same type using clustering algorithm of data science, (Note: clustering is an unsupervised algorithm).
  • Business analytics: Explained in detail in later section of this article.
  • Sales analytics: Explained in detail in later section of this article.
  • Analysing product features and engagement on product:
    • Analysis of product features: Product analytics includes in-depth analysis of product features, and how some features are very good for engaging users, while others are important from retention point of view, some features has issues that are not good for customer experience.
    • So, product analytics includes finding answer to various users related issues like finding eureka moment, engagement, retention, churn issues etc. and solving them or using them for enhancing final experience for users.
    • Further read:
  • Other examples:

Data mining also called as data crunching is one of the new field of study which became very popular in last decade or so.

It includes other steps in it as well like data extraction or data collection, data preparation, data crunching etc. to name a few and all this can be achieved using sql or python or excel or combination of them)


What are the real life application areas of data mining like in marketing, sales, medical science etc.?

Some of the applications data mining are:

Applications of data mining.
a. Business Analytics: 

Helping businesses finding the most valuable customers (i.e. customer targeting) who are paying a lot or can pay and so targeting only those kind of customers, hence helping in improving profitability of a business.


Helping in proactively finding customers who are on the verge of churn (churn prediction or improving customer retention), which account management teams can utilise and avoid them from leaving or churning.

Further Read:

b. Sales Analytics: 

Collecting data of how sales representatives are doing, analysing their blockers in terms of closing the deal and devising the strategy based on data for effective results.

Targeting correct customers by identifying them with the help of data like their geography, purchasing capacity, interest etc. helps in decreasing the final closure or TAT (turn around time) of sales cycle.

c. Purchase Behaviour Analytics: 

In e-commerce understanding what kind of products people buy together (using apriori algorithm of data science) and showing those products in recommendations (recommendation engines) for other users searching for the same product for e.g. people buying milk also buys break and eggs, so bundling those products together while a customer is purchasing any one of them.

So, all this is also called as customers purchasing behaviour prediction or customer behavior analytics, where we try to find the way customer is behaving, why he is behaving in that manner and how that can be replicated again and again.

d. Product Analytics: 

In software business or in online services business using data mining to understand which features are most liked by users and developing in same direction for better results.

And if customers are facing certain issue then data is the best approach to prove it, also data can help in identifying exactly with what kind of users this problem is prevailing and so resolving such issues becomes fairly easy with this approach.

e. Route Optimisation: 
Route optimisation in data mining.

In transport and Logistic services to reduce the time of delivery by route optimisation e.g. google maps tells information in advance about the traffic conditions in an area and alternative route for the same. One of the classic example of this Uber whose business depends on it heavily.

f. Medical Science: 

In biology of Medical Science by using the existing data of some existing infection or disease and identifying those proactively in other populations, hence avoiding such situations etc. and so on.


Using the data to understand response of body to certain medicines or stimuli helps in identifying the pattern and modifying the doses accordingly for results or for treatment of disease.

You can also refer youtube to understand data mining more in-depth, although there are not as such any funky video available as such.

Also, if you an data aspirant looking for a job in this field, want to understand and learn more about data then online courses will be of big help to you.

So, this is all about “Data Mining Application”, do let us know in comment section what else you want to read about or some other information you require in the current topic i.e. “How to freeze a row in google sheets”, we are more than happy to help you.

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FAQs on Data Mining Application:

Q. Why data mining is required?

Ans. There are many ways to this answer i.e. why to use data mining, i tried to summarise this in broad buckets:
Extracting valuable insights: To find valuable insights or information out of the data,
To solve complex problems: To solve some real world problem,
To optimise existing processes: To optimise any existing process which otherwise it not possible.
So, by looking at the data one gets 100% of what will be the outcome if they make some changes in existing system or process.

Q. How long does data mining take?

Ans. There is no straight forward answer to this question as:
– If dataset is small enough then the data processing will happen in seconds,
– If dataset is not small not very big but you are using good machine with adequate processing capacity then again this will happen quickly may be in minutes or even in seconds,
– If dataset is too large or if you are working on big data where users exact activity data is present it can millions of rows and then in that case case it may range from minutes to even hours depending upon complexity of calculation as well as machine configuration.

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