• Claire Matuka

Common Applications of Data Science in Marketing



We are currently living in a data-driven world. How businesses use data to derive insights and make decisions can be what sets them apart from their competition and gives them an edge.


Nearly every business is offering some kind of product or service. For example; Shein sells clothes (product) while Uber offers rides and transportation (service). But how do we make sure that the right audience comes across these products and services? One answer - Marketing.


What is marketing?

Marketing involves various activities that promote and sell products and services to consumers. Some of the activities include: advertising, communication, events, good packaging, product design, sales and promotions. These activities are not only used to acquire new customers, but also to retain existing ones.


Benefits of marketing

Through marketing, a business is able to get matched to its target market. Some of the benefits include:

  • Increase in sales

  • Better understanding of the consumers

  • Better decision making


Applications of data science in marketing


Data science can be used to combat several challenges in marketing.


1. Market basket analysis

Suppose that at my local coffee shop, whenever I buy a cup of coffee, I also often buy a blueberry muffin. Now, let us also say that on a typical day, for every 10 people who buy a cup of coffee, 7 of them also buy a blueberry muffin. There appears to be some sort of an association between the two products (coffee and a blueberry muffin). Given this information, the coffee shop owner can choose to run sales on a coffee and blueberry muffin combo, or introduce a new item / package on the menu that consists of both items.


In real life however, identifying associations is not as easy. Especially when there are thousands of products being sold. This is where market basket analysis comes in. It is used to identify associations that exist between several products, based on what customers are buying (customer data). Armed with this information, the marketing team is best placed to come up with suitable packages for their clients.


2. A/B testing

Sometimes when I am shopping online, I will click on a product and it will automatically open in the same tab. Personally, I would have preferred if it opened in a separate tab, just so that I get to compare several items. The back and forth on one tab is exhausting (it also takes long to right click) and can potentially lead to me not buying anything at all. Assume that there are several such people using your site. It may seem like something small, but humans thrive on convenience.


A simple A/B test would allow a business to carry out a random experiment such that, both sites are tested on different users (one that opens a new tab, one that continues on the same tab). Analytics after will reveal which option generated more sales and hence which option is better.


A/B testing is an experiment that is conducted to investigate two versions of a website, webpage or even app so as to determine which performs better. The versions can be in terms of the design of a webpage, whether the text used is bold or not, the color used and even presence or absence of an image. It is key when trying to improve user experience.


3. Customer segmentation

The type of insurance individuals in their 20s are interested in is different from the one individuals in their 30s, 40s or 50s will be interested in. It is therefore important to market the right insurance product to the right individuals.


Customer segmentation is the process by which customers are grouped and divided based on common characteristics. These characteristics can range from age, marital status, income and even their job.


By grouping individuals into these segments, the marketing team is then able to better target individuals and acquire more information on the business brand.


4. Churn prediction

According to Harvard Business review, getting a new customer is 5 to 25 times more expensive than retaining an existing one. In short, keep your current customer happy.


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Churn simply refers to when a customer or client cancels a subscription for a service, or leaves.


Churn prediction is a data science technique that predicts which customer is likely to cancel a subscription or leave. Armed with this information, this group of people can be targeted in a bid to retain them. Popular methods used to achieve this include communication, or even offering sales and discounts.


5. Sentiment analysis

In 2019, Kim Kardashian launched a new shapewear line and named it "Kimono". Unknown to her and her team, a Kimono is actually a traditional Japanese garment. As a result, she received major backlash and was accused of cultural appropriation. Most of the sentiments echoed on social media were negative. Thanks to the sentiments that were shared, she listened and later changed the name to Skims.


It is important to gather sentiments from users especially when launching a new campaign or even introducing a new product. Sentiment analysis can also be used to gather insights on the competition and leverage on their weaknesses.


6. Lead scoring

If you haven't watched the movie, "The Pursuit of Happyness", I suggest that you drop everything and do that immediately after reading this blog post. You are probably wondering what a movie has to do with data science. Stay with me here.


Chris Gardner who is played by Will Smith lands an unpaid internship that could potentially land him the job of a stockbroker. He has to make phone sales calls by working his way up a given list. Problem is, he lacks time. So instead, he opts to reach out to potentially high value customers only. I won't be a spoiler and so I will leave you guessing on whether he actually landed sales.


Businesses often have many potential client leads, however not all of them convert into paying clients. Lead scoring is used to analyze new leads based on several characteristics and then ranks them in terms of likelihood to becoming a paying client. This way, the sales and marketing team can focus their efforts and time on the leads that are most likely to convert into paying clients.


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Conclusion


Good marketing techniques lead to more customers and hence more sales. It is clear from the above article that data science is key in informing these marketing techniques. From understanding current customers (descriptive) to predicting the actions of customers (predictive). Understanding how data science can be applied in marketing, as well as acquiring analytic skills to solve problems in marketing is crucial in today's world.

Have you been able to practically apply any data science and analytic skills in marketing? If so, kindly share what discovery and decision you made.


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