How to predict customer churn rate using artificial intelligence in ECommerce

Benefit of ECommerce business is the convenience of online buying and selling. However with this convenience comes the unpredictability of business in ECommerce. To stay relevant in this ever evolving ECommerce business space, the business owners have to employ advanced artificial intelligence solutions in ECommerce to stay in control of customer behavior and use that to generate incremental business. Future of AI in retail is bright considering the ROI driven use cases it serves.

Artificial Intelligence in ECommerce

Artificial intelligence service providers are helping ECommerce companies learn the behavior of the customers who are online shopping online with them. Artificial intelligence is a combination of data science and technology that enables the ECommerce business owners to learn through vast amount of business data and come up with actionable insights and recommendations. ECommerce marketers can incorporate these insights and recommendations in their marketing and sales execution to fetch incremental business. Non-returning customers is one of the biggest issues in the ECommerce business. There could be hundreds of reasons why customers don’t do repeat purchase. For an ECommerce business owner it is critical to understand these reasons and engage with the prospective churning customers in a timely manner. Artificial intelligence (AI) in ECommerce has been creating value precisely in this area.

Customer Churn Rate

Simply put, the customer churn rate formula is to divide the number of customers who stopped buying from you in a specific period of time (say, 1 month or 1 quarter) by the total number of customers you have had at the beginning of that period. Customer churn rate is generally mentioned in terms of percentage. Identifying customer churn rate extremely critical for all kinds of business (more so for ECommerce) because there lies the opportunity to fetch incremental business. Being able to predict the churn rate is an important capability to plug the holes in your marketing and customer service and avoid potential loss of business. 

Predicting churn rate for subscriber driven business is far more important for consistent in month on month business. For non-subscriber based ECommerce it is important to first define the churn and then begin the journey of identifying it. 

Preparing Data for Churn Rate Prediction

To be able to predict customer churn rate you need to have your past sales data. This data should be preferably customer wise sales data. Before you prepare your data, you need to make a choice on churn period time frame that marks customers as churned. For example, assume that you chose 1 month as the churn period. This means you’ll mark all the customers who didn’t make any purchases in last 1 month as churn customers. 

The data points could include any of the following that you may have from your transaction system: 

  1. Customer profile information 
  2. Customer purchase information (products/ services they bought, date/ time, mode of payment, amount, number of purchases)
  3. Seasonality aspects (vacation time, festive season etc) – as a Yes/ No flag for each of these aspects 
  4. Flag whether the customer is churned or not (use the example above to get this)

These are basic data points, you need not worry about having everything in place  before you begin experimenting with your data to predict customer churn rate. These data points will help you train your artificial intelligence churn model to be trained on your specific churn dataset. Most data points relevant for churn prediction come from the subject matter expert in the business who works across the functional units of the ECommerce such as marketing, customer service, sales, delivery.

What if I don’t have data scientists?

This is the case with most ECommerce companies, they don’t have data science team at hand. Machine learning algorithms and artificial intelligence has so well evolved that you aren’t supposed to be worried about this. Now there are ML apps that can learn to use the right algorithms and build ML models automatically based on the data that you provide during the training phase for accurate churn rate prediction. Automated machine learning platforms such as Datoin provide an off-the-shelf churn solution apps for ECommerce companies so that you don’t have to spend time, money and effort in complexities of building data models. 

If you don’t know data science then you can follow a simple 3 step process: 

  1. Train the churn rate prediction model
  2. Building the churn predictor application 
  3. Upload the current data to see the prediction

Finally, iterate through this exercise of 3 steps as long as you can so that your churn rate prediction becomes more and more accurate.

Experimenting with your data is critical to the success of accurately predicting the customer churn and then retaining those customers. As an emerging ECommerce company you might want to try experimenting before you can associate any budget to adopting AI in your ECommerce business. Datoin understands this and therefore offers a free 1 month trial with full data science support to ECommerce companies. Try customer churn prediction app on Datoin today.