Knowing what the customer wants even before the customer buys a product is an imperative to thrive in this digital world. It gets more customer-centric, with organizations wanting to know every bit about the customer, predict everything around the customer and take suitable action to create a swell in the number of loyal customers.

And there is customer behavior data that offers nuggets of customer wisdom. As part of this ‘predicting everything around the customer’ exercise, there is also a growing need to predict customer next day purchase, with the behavioral data rising in relevance to know when customers would buy next.

Returns from ‘proactive’ next purchase day

‘Proactive’ is the byword for companies that are customer-focused and that want to win and keep customers coming back. Being there with the customer always means knowing everything about the customer. When there are customers who have had large delays between purchases and who are likely to churn and bring less revenue, proactive action needs to be taken to keep customers coming back – and this cause of increasing customer loyalty is well supported by predicting the time taken to make another purchase.

Now that we know when customers will make a purchase in the future, promotional mails can be sent accordingly, in order to increase their purchase and frequency of purchase. Predicting next day purchase also works to the advantage of organizations in the way customer loyalty can be nurtured and customer lifetime value can be improved.

Behavioral data – The key to predicting next purchase day

 Customer next purchase day is a popular technique which can be achieved leveraging predictive analytics. What if you know your customer is going to make a purchase in 3 days or 100 days?

One of the key aspects in predicting customer’s next day purchase would be the behavioral data. Many data wrangling and feature engineering techniques are applied to bring out the most from data towards predicting the customer’s next purchase day. Some of the most common features leveraged in predicting the next purchase day would be the RFM metrics. The RFM metrics is nothing but the Recency, Frequency and Monetary metrics. Bringing these features in would tell how often customers purchased, how recently they had purchased and how much revenue they have brought in so far.

Using an ML approach helps bring in a lot of contributing factors that would help make the next purchase day more accurate. The ‘date of customer purchase’ then becomes the prime feature for predicting the next purchase day. With advanced techniques applied on the dataset at hand and with predictions on customer next purchase day, retailers turn proactive in meeting customer requirements.

Leveraging customer segmentation to support next purchase day

Customer segmentation, for instance segmenting customers based on their behavior, offers invaluable support to take suitable action once next purchase day predictions are made. Creating customer segments guides in taking the right approach to target the customers, take decisions based on their needs. Customers can be easily segmented based on the RFM metrics into low, mid and high value customers. These segments will give us more clarity in knowing whether we are targeting low, mid or high value customers.

  • Low – Here customers’ past activities are very low
  • Mid – This refers to the slow and steady or to consistent customers
  • High – Customers who have purchased more and brought more revenue

When it comes to segmentation, there can be more flavor, where customers can be segmented based on the channel that they prefer to purchase, and much more. This would help in sending promotional messages through the right medium. Another important aspect of segmentation is that you can easily see which segment they belong to and how to treat them.

For example, if your prediction says your customer is going to make a purchase in another 20-30 days, we can determine the appropriate customer segment, whether they fall into the high, mid, or low segment. If these customers belong to the low segment, wherein their past purchases have not been very active, more offers and discounts can be promoted to make customers purchase more. If they fall into the high segment bucket, decisions revolving around promotional messages can be questioned for the reason that their past activity has been very good.

Predicting next purchase day becomes a classification problem

Say that you have got your predictions for each customers in days, each customer is predicted to purchase in 10 or 50 or 100 days. Handling this type of prediction or taking decisions based on this type of prediction would be quite confusing and would require more work to be done. This then will be dealt as a classification problem, where a range of days are put as part of different classes.

For instance, let us consider the following:

  • Predicting customers who will purchase in 0-20 days: Class 1
  • Predicting customers who will purchase in 21-50 days: Class 2

Creating time period, as in the case dealt above, guides us to know if customers would buy during the period, where classification algorithms are put to work to help us find out if the customer would buy during that period – as in class 1 and class 2, time periods can be set based upon the requirement for predictions to be made. With customer segmentation in place, relevant actions in the form of promotions, offers and other types of engagement can be rolled out to prompt purchases.

Now, with the predicting engine built to know the customer next purchase day, and with customer segmentation to rely on, it is about taking the right action through targeted messages and promotions, increasing sales and winning more loyal customers.