data science apps for e-commerce retention

7 Data Science Applications in eCommerce to Maximize Customer Retention and Conversion

Capturing their audience’s attention and converting them into paying customers is paramount for online businesses in a fiercely competitive e-commerce market. Driven by the need to succeed, more and more businesses are turning to artificial intelligence (AI) and data science applications in e-commerce to maximize customer retention and conversion. 

According to a Gartner report, 30% of all B2B companies will employ AI to augment at least one of their primary sales processes by 2020. Using AI in e-commerce sales brings more efficiency in business processes, which can drive the conversion rates up, as per Gartner.     

What is Data Science?

Data science uses AI in its core operations and is the current reigning technology that has brought about the “Fourth Industrial Revolution”. Data science addresses the growing need of online businesses to rely on data to acquire new customers and retain existing ones. 

Every single click on the internet generates data. Data science techniques help e-commerce businesses make sense of this data with the help of machine learning algorithms. Data science involves proficiently using underlying fields such as statistics, mathematics, and programming to develop an understanding of trends and patterns in structured and unstructured data. This data is deciphered into actions for defining customer acquisition and retention strategies by e-commerce businesses.   

Since increasing conversion propensity is the bottom line for e-commerce businesses, the insights on user behavior provided by customer analytics help target the right set of customers. 
Here are a few key e-commerce data science projects that are enabling e-commerce platforms to deliver unmatched user experience (UX); and increase customer conversion and retention.    

  1. Customer Lifetime Value (CLV) 

    Any sales team works with the intent to attract new customers, retain existing ones, and cut down on the customer acquisition cost (CAC). 

    It is important for e-commerce businesses to figure out how much is a customer worth to them post acquisition as it helps justify sales and marketing budgets. Customer lifetime value (CLV) is the calculation of how much revenue a customer can bring in throughout his/her lifetime. 

    Customer lifetime value is predicted based on the customer’s purchase and transaction history with the e-commerce website or mobile app. Since it is difficult to predict how much a customer will buy in the future by dwelling on past transactional history, data science is used for delivering more accurate results.

    Data science uses statistics to model a customer’s buying pattern to make predictions. These predictions tell you how much you can earn from a customer over his/her lifetime. The key inputs data science uses for making these predictions include size of first purchase, size of repeat orders, the time interval between orders, and not to forget the all-important, discount factor.

    How Much Should You Acquire a Customer For?


    The simple math here is: Customer Acquisition Cost (CAC) < Customer Lifetime Value (CLV)

    CLV is an essential metric that must be taken into account for running a profitable e-commerce store. Your e-commerce portal can only make profits if your CAC is less than the CLV. Online e-commerce stores generally target a customer acquisition cost (CAC) to customer lifetime value ratio between 0.2 and 0.33.

    Customer Acquisition Cost (CAC) = Cost per Click (CPC) / Conversion Rate from Click to Sale


    There are multiple channels e-commerce stores use to acquire customers such as social media platforms like Facebook; Google AdWords, etc. by way of pay per click (PPC). Customer acquisition cost (CAC) is determined by dividing the cost per click by the conversion rate from click to sale.   

    How to Calculate CLV?

    The basic formula for calculating customer lifetime value is:

    {Average Order Value} x {Number of Repeat Orders} x {Average Customer Lifespan}
  2. Churn Rate

    All online e-commerce marketplaces encounter some amount of customer churn. While data analytics provides metrics on conversions and sales, the success of your e-commerce business also depends on your churn rate, which means the rate at which customers leave or cancel subscriptions.

    The key to success for an e-commerce store lies in keeping the churn rate down, as on an average, the cost of attracting new customers is 6-7 times more than retaining existing ones. Once a customer buys on an e-commerce website there’s a possibility that he/she would visit again, if satisfied with your products and service offerings.

    All e-commerce platforms should consider implementing a churn model to add value to their businesses as it is a bare essential component for customer retention. Customer retention is the ability of a company to retain its customers over a specified period of time.

    Although it’s good to acquire new customers, existing customers bring in far more revenue in comparison. Another crucial aspect that underlines the dire need of retaining loyal customers is that they foster conversions by word of mouth publicity and referrals.   

    Churn model is the most commonly used data science technique to identify customers who are most likely to switch loyalties to another e-commerce website. The churn model calculates the churn rate by using different metrics depending on the nature of business. Primarily, there are three main variables that determine the churn rate: a specified period of time (month, quarter, or year), number of the customers at the beginning of the period, number of customers lost during the period. 

    The Basic Churn Rate Formula is:

    Number of Customers Lost During the Period / Number of Customers at Start of the Period

    To give you an example, if an e-commerce website has 100 customers at the start of the month and loses 10 customers by the end of that month, the monthly churn rate would be 10% calculated as:

    10 / 100 = 0.10
  3. Recommendation System

    Over the last few years recommendation engine or a recommendation system has taken over the internet, adding value to e-commerce businesses. On visiting any e-commerce website you are overwhelmed by recommendations and suggestions on what you should buy.

    While shopping on Amazon you are recommended items bought by other customers under the category: “customers who bought this item also bought”. Amazon has been leveraging AI for years, generating more than 35% of its e-commerce revenue through dynamic and personalized product recommendations. 

    According to a research by Barilliance, e-commerce businesses generate more than 31% of their revenue through personalized and relevant recommendations for products. 
    A recommendation system is one of the most successful implementations of AI and machine learning technologies in e-commerce. Machine learning algorithms in a recommendation system filter choices for a particular user on the basis of his/her past searches or purchase data. 

    A recommendation system provides users with a personalized experience while shopping on an e-commerce website for relevant products. For example, if you’re shopping for a new smartphone on Amazon, the recommendation engine would suggest you to buy a cover for your new phone after analyzing your previous purchases or searches data. 

    There are three main classification categories or techniques that machine learning algorithms in recommendation systems are classified into:

    •    Content-based Filtering

    In content-based filtering the recommendations are provided on the basis of users’ profiles and the item description. In this technique the results are based on the past user behavior and purchase history.   

    •    Collaborative Filtering

    Collaborative filtering is the most popular technique with e-commerce businesses. In collaborative filtering automatic predictions are made about the interests of the user on the basis of preferences of many users.  

    •    Hybrid Recommendation Filtering 

    Hybrid recommendation system is a combination of content-based and collaborative filtering and can be used in multiple ways. Predictions can be made using content-based and collaborative filtering separately, the results of which can be later combined or the results from one technique used as input for another technique. Netflix uses a hybrid recommendation system

    A recommendation system is absolutely necessary for an e-commerce website as it helps improve customer retention and conversion by enhancing the customer experience (CX). Just imagine an e-commerce website without a recommendation engine where shoppers have to go through endless options to find what they are looking for. 
  4. AI-powered Intelligent Predictive Search

    As per a research by WebLinc, the users searching for products on-site are 216% more likely to convert than regular users. Furthermore, the users who find their desired products quickly and easily are likely to buy 21% more products on an average.

    The ability of your e-commerce business to acquire new customers and retain existing ones depends a lot on the search options of your website. At times users looking for a product are not entirely sure of its name or description. Since searches are based on keywords, a mismatch might occur between the item name entered by the user and the title of the same product on your website. This might lead a user into believing that the product isn’t available on your e-commerce store. 

    Fortunately, AI can fill this gap with intelligent predictive search by helping find associating keywords and correlate products that match with the user’s search even though the keywords in the search query won’t match. Twiggle is an AI-powered plugin that uses machine learning to develop a deep understanding of customer intent to optimize on-page search results. The plugin scans product descriptions to create a library of relevant keywords that customers would use to search a product. Customer behavior influences the program to make adjustments for continuous improvements. 
  5. AI-based Intelligent Chatbots

    Providing customer service is integral to any e-commerce business. It’s all but natural that a customer would want to clear his/her doubts before buying a product online. For this purpose almost every e-commerce website provides a ‘chat now’ box on their website. While earlier companies used to assign a dedicated person for live chat and customer query resolution, the job has been now taken over by artificial intelligence.

    AI-based chatbots have evolved to the point that modern day e-commerce users don’t even realize that they are actually talking to a machine and not a human being. The advent of AI-powered chatbots has improved the overall quality of live chat by making it more personalized and intelligent leading to greater customer experience (CX).

    Apart from the fact that chatbots don’t need refreshment or lunch breaks and neither go on leave, they can also automate order processing, saving human effort, time, and money. A chabot is an all important tool for e-commerce businesses for providing 24x7x365 support with greater level of efficiency. According to Juniper Research, chatbots will save over $8 billion per year by 2022. 
  6. Fraud Detection

    Fraud is a billion-dollar industry and will remain to grow as long as time-rich and cash-poor fraudsters exist. According to PwC’s 2018 Global Economic Crime and Fraud Survey, 49% of global companies said that they had experienced economic crime in the past two years.  

    Living in a digital world where millions of transactions happen with a single click is laden with risks. Since an unaware and unassuming individual can easily be mugged online, e-commerce businesses are rapidly implementing Online Fraud Detection. 

    There are a number of ways a fraud can happen online including merchant identity fraud, identity theft, affiliate fraud, chargeback fraud, etc. Data science and machine learning techniques are being used by e-commerce businesses to detect online frauds and suspicious behaviors such as multiple orders to the same address with different debit/credit cards, unusually large orders with next day shipping requests, multiple orders of the same item, etc. 

    Some of the common data science/machine learning techniques being used by e-commerce websites for fraud detection includes data mining, time series analysis, among others. 

  7. Customer Sentiment Analysis

    It is imperative for an e-commerce business to provide the best service possible in order to retain loyal customers. Let us look at how data science can help you achieve improved customer service.

    Although almost every online business has a ratings and review section on their website, at times it gets difficult to make sense of some reviews since they are posted by users who aren’t proficient in languages. Such reviews contain spelling mistakes, shorthand or slang words. By deploying data science techniques like Natural Language Processing (NLP) such reviews can be interpreted by data scientists.  

    After retrieving the reviews data scientists further segregate them and conduct a Customer Sentiment Analysis. This helps them to understand why a bad review was given in a particular case. Armed with this information e-commerce businesses can re-think and strategize their product and service offerings for enhancing customer satisfaction. 

    Wrapping It Up

    The real secret behind running a successful e-commerce business is to know what your customers want and how you can provide it to them. Contemporary AI algorithms such as deep learning can help e-commerce businesses understand the patterns in data to drive customer retention and conversion. 

    A key aspect of customer retention strategy is figuring out which customers are at maximum risk of leaving your e-commerce platform. Driven by AI, data science has the perfect arsenal in its armory to help e-commerce businesses tap into customer intent, leading to higher profitability.