I am very much intrigued by the Customer Loyalty programs I observe across eCommerce sites I encounter. Indeed they are very much common place and have become a tried and tested tool to drive loyalty. What makes a marketing campaign stand out is the definitely the ROI fetched on the campaign and this led me to think if we could apply some form of analysis to improve the returns. Before I pull the rabbit out of the hat, let me walk you through the approach I followed.
Cohort Analysis gives us great insight into consumer action. So I pulled up some Google Analytics eCommerce data and started experimenting with cohorts. I wanted to understand Repeat Purchase Behavior and hence focus on two cohorts in particular:
1. Customers who made only one purchase on the website
2. Customers who made multiple purchases on the website
Let us first examine the proportion of consumers belonging to both the above mentioned cohorts.
There is indeed a stark difference between the two cohorts. A huge majority (85%) of customers are one-time customers. They do not come back and purchase again. This is a challenge faced by many sites and is not limited to a singular instance. If we were to compare the revenue contribution of the two cohorts we see that while the one-time consumers contributed towards 60% of the total revenue, the other cohort contributed a ‘whopping’ 39.5%. I have a reason for using the word ‘whopping’ and I will state it now.
“15% of the consumers contributed towards 39.5% of the total site revenue.”
Sweet, let me tell that to my boss!
Yay! We have identified the problem that the consumers are not sticking. So lets target them with an aggressive retention campaign. Lets send out discount coupons to all the one-time consumers to encourage them for future purchases. But would it seem fair to target the entire one-time consumer base to drive sales? Some consumers would go for the 2nd purchase without the incentive. The point I’m trying to drive home is sending out discount coupons to all the one time consumers would also erode your profit margin to a large extent.
Moreover, assume that the discount coupon initiates a future purchase with a value of $50 in 10% of the cohort. If a voucher is sent to a consumer who would have re-purchased anyway, that results in a loss of $5 to the store owner. Note that these figures are ball park estimates but I encourage you to think about the implications.
Marketers have a limited budget and need to prove the efficacy of retention campaigns by measuring results. How would you do this? Predictive Analytics can be of help here. Where cohort analysis is descriptive in nature, predictive analysis helps you use your data to build models to predict consumer behavior. Think of it as a machine that converts data into actionable insights that give you better ROI.
Specifically in our case, predictive analytics can provide answer to this important question:
“Which customers should I target for discount coupons in order to maximize revenue?”
Does this question seem interesting? View a recorded version of our recent webinar where we show you how to build a Predictive Model for Discount Targeting using R
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