How to Retain Customers Using AI in ECommerce

Posted by Monis Khan on Aug 23, 2019 5:43:57 PM

Customer retention is one of the significant causes of concern for ECommerce companies. Most ECommerce companies would invest a considerable amount of money in acquiring new customers only to see them never purchase again. The customer churn is like a hole in the bucket of business. The ECommerce business owners are trying their best to fill the bucket with more and more new customers each day. But the hole at the bottom of the bucket keeps leaking out the customers. Learning customer behavior is vital to understand how to reduce churn. Artificial intelligence technologies can help ECommerce companies learn customer behavior and thereby, ways to reduce customer churn.

How to retain customers in ECommerce using AI

What is Customer Retention in ECommerce

Customer retention is the set of measures that an ECommerce company would employ to drive repeat purchase from an existing customer. Customer retention in ECommerce is vital to sustainable and predictable growth. The actions taken for customer retention are mostly revolving around customer loyalty, brand loyalty, and offers that can bring the customers back to the ECommerce website. In the absence of an understanding of customer retention, the day-to-day business acquisition contributes to accidental growth in ECommerce. To understand customer retention and manage it, ECommerce business owners need to understand what is customer retention rate. 

ECommerce Customer Retention Rate

The customer retention rate is the number of buyers that purchase from you consistently over a particular period as a proportion of an overall number of customers served during the same period. Customer churn is the number of customers who never purchased again from the ECommerce company in a particular period as a proportion of the total number of customers in that period. The customer retention rate is typically the inverse of the customer churn rate in ECommerce. As per the Mixpanel report, average retention rate across industries is below 20%

AI-based Customer Retention App

There are two parts to the overall customer retention strategy and apps. Part one is to understand the set of customers, offers, and set of products that will help you influence better customer retention rate in your ECommerce business. Artificial intelligence and machine learning play a critical role in helping you learn these so that you can focus on the right areas while devising customer retention strategies. Typically the past transaction data, seasonality data, and other external data-points are good enough for you to begin learning these leading indicators of better customer retention. Ready to use AI apps such as Datoin help you know precise parameters to focus on for customer retention.

The second part in the customer retention strategy is the marketing channels that will push the message to the right set of customers so that they are better engaged. 

Here are specific use cases that can be executed with the help of AI. These use cases help you begin your journey in the direction of better customer retention.

AI-based Product Recommendations for Customer Retention

Product recommendations are essential when influencing the content of the basket as well as getting the customers back to their buying journey. Product recommendation is to predict which other product is the customer more likely to buy once they have seen a particular product or has bought it already. Based on the past purchase data, buyer profiles, and other data points an AI customer retention app can provide instant product recommendations. The product recommendations are also useful in retaining customer dropping-off. Product recommendations can help the existing buyer get an intuitive buying experience which can increase their likelihood of buying again.

AI based Buyer Propensity Scoring for Customer Retention

Buyer propensity scoring is to predict how much is the likelihood of each prospective buyer to buy during the session. You could also predict the probability of a potential buyer to drop-off during the session. Both of these are the indicators to further engage with these buyers contextually so that they complete their transaction. The propensity scoring helps retain a purchase that would have otherwise gone-off.

AI based Predictive Pricing for Customer Retention

Predictive pricing is to predict at what price the prospective buyer is more likely to convert into a paying customer. Sometimes also referred to as the personalized pricing, this helps devise dynamic pricing discounts for customers based on their buying journey and buying history. The customers can see that they get a discount offer just as they wanted and that helps them keep coming back to buy more.

There are many ways in which AI and machine learning can help ECommerce companies to retain more customers with the help of their data. The key to customer retention is to keep experimenting with your customer data and launch innovative engagement campaigns based on the insights learned from the data. Cost of the AI and machine learning experiments have gone down significantly. You can master the behavior of your customers individually with the help of ready to use ECommerce AI platforms such as Datoin. If you have data but not using the AI or machine learning on it as of now, then begin customer retention using AI, try Datoin.

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About Datoin

For online businesses, E-Commerce startups Datoin is the best AI platform. Datoin is no code AI platform for business users who understand the domain and are not from tech or data science background. Datoin is designed with a vision that the online businesses of any size need not invest in building data science expertise to begin monetizing the insights from their own data to drive more sales.

Datoin looks at building a community of ECommerce startups, online subscription businesses  using advanced AI signals to sell productively.

ROI Centric Use cases:

  • Product recommendations, Engine 
  • Customer churn prediction
  • Predictive pricing
  • Dynamics discounting
  • Demand forecasting
  • Predicting List of Upsell/ Cross-sell ready customers 

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