Looking for ways to boost the profitability of your e-commerce store? We have been working with a handful of online retailers of ranging sizes to tackle this problem using data science.
This article lays out what we did and some interesting things we found. For reasons of confidentiality, we will refer to the company as Store X. Store X is an online retailer of consumer products.
Overview of data science in e-commerce
Before jumping in, let’s talk about how data science is used in e-commerce. Here's a schematic of how a data science engine for e-commerce looks like:
Strategies fall under two categories:
- Customer acquisition: Knowing how much it costs to acquire a customer from a given channel and how much the lifetime value of that customer is.
- Customer retention: Figuring out how to make customers buy more and how to keep them as a happy customer for the longest period of time.
Data science insights (green boxes in diagram above)
Customer acquisition and retention strategy stem from several insights that we can draw using data science:
- Customer lifetime value predictions
- Persona analysis
- Churn detection
- Customer segmentation
- Cohort analysis
- Trend analysis
We'll be going through a bunch of them later with real world examples.
Marketing Actions (blue and red boxes in diagram above)
An online retailer can automate the implementation of an acquisition and retention strategy with an engine that integrates with its database, email marketing and ad platforms.
How much is a customer worth: predicting lifetime value?
Knowing how much a customer is worth helps you figure out how much you're willing to spend to acquire each customer. However, lifetime value is difficult to compute because we cannot know how much a customer will buy in future.
This is where data science comes in. It works by using statistics to model how a customer buys. We assume that each time a customer buys a product from our store, there is a chance that he will buy again. We also know how much the value of that next buy will be by looking at the historical average. With this information, we model and predict how much we can expect to earn from a customer over his/her lifetime. Some key inputs to the model are the size of the first purchase, size of repeated orders, the time between orders and a discount factor.
Applying this model to Store X, we see that the lifetime value of a customer in Store X is $4500. Most of Store X's customers are new customers that spend about $5000 on first purchase. Because Store X does not implement any customer retention techniques, few of these customers are repeat or active customers. Hence, the lifetime value of its customers is relatively small. We are also able to see some other interesting things about Store X.
The snapshot gives us a birds-eye view on how our customers behave on our platform. This allows us to make a business decision whether to focus on customer acquisition, retention or both efforts. For instance, we know that Store X needs to convert new customers to repeat customers. It should focus on implementing customer retention efforts such as email marketing campaigns and promotions for existing members to incentivise them to shop again.
How much should I acquire a customer for?
Principle: customer acquisition cost (CAC) < lifetime value (LTV)
To run a profitable store, you should acquire a customer for no more than the present value of his/her future profits to you.
To express this more succinctly, we introduce the concept of customer acquisition cost and lifetime value. You have a profitable store when the customer acquisition cost is less than the lifetime value of that customer. In reality, stores have to be a little more conservative than that. In the case where lifetime value is based off the present value of sales, stores typically target a customer acquisition cost to lifetime value ratio ranging from 0.2 - 0.33.
Customer acquisition cost (CAC) = Cost per click (CPC) / conversion rate from click to sale
Going a step further, e-commerce stores acquire customers from many channels - AdWords, Facebook, social media, bloggers, partnerships etc. You usually pay per click for these advertising channels. How cost per click translates to customer acquisition cost depends on the conversion rate:
Takeaway: Set appropriate cost-per-click targets
Set appropriate cost per click targets on your channels based on estimated lifetime value from each channel. Assuming a sales based lifetime value, the rule of thumb is CAC/LTV = 0.2-0.33. If your conversion rate of 1%, you'll need CPC = LTV/500 to LTV/300.
Who are my most valuable customer groups?
The purchase history of your customers can give you valuable insights as to who they are. For instance, John is a customer who shops for gifts for his female friends. Sophie is a shoe enthusiast who only buys shoes and related accessories from your store. There could be a large number of Johns and Sophies in your dataset. Customer segmentation helps you identify who they are.
Going a step further, segmentation helps you identify which of these segments are more or less valuable to you. The diagram below summarises how segmentation analysis can identify who your VIP, core, non-core and potential customers:
Let's look at data from Store X. In the diagram below, you can see that Store X's customers naturally fit into the 6 segments. Store X has a few key products that its core customers buy. Its VIP customer segment are purchasers some other products. There are also several customer segments where there is potential to increase customer lifetime value by focusing marketing efforts on them.
Takeaway: Focus your marketing efforts on potential customer segments
A natural next step for Store X is to focus on its VIP, core and potential customers. In particular, efforts should be focused on the potential customer segments, where there is a high chance that Store X could find ways to get those customers to spend more.
One of these ways is creating a customer engagement rules engine that sends tailored emails to customers based on a set of rules. For instance, you might want to send an email to any customer who has signed up but has not bought anything for a week. You might want to send promotions to customers who always buy on a discount.
The process of setting up these rules is often specific to your customer base and is determined on a trial-and-error basis. We can also use A/B testing to test out the effectiveness of certain email marketing campaigns or rules.
Which customers are at risk of leaving my platform?
Another key concept in customer retention strategy is figuring out which customers are at risk of leaving your platform. Knowing this information is valuable because you are able to take preventive steps to win them back them. The diagram below shows the lifetime value vs. churn rate. Churn rate is the probability (0 is impossible, 1 is certain) that a customer will leave your platform. Each dot on the chart represents a single customer.
Takeaway: Focus on retaining high-value at risk customers
From this chart, you can see that there is a group of customers in the upper right quadrant who are valuable to you but at risk of lost. These are the customers that you should pay most attention to. Customers in the top left quadrant are your loyal customers - they have high lifetime value and are likely to stick with you for a while. You should continue to keep them. Customers in the bottom quadrants are less worth your time unless you manage to find a way to enhance their lifetime value.
Want us to look at your data?
We are running a free pilot for a select number of online retailers. All we need is a connection to the database with your customer and invoice information. We integrate with all types of engines including Shopify, Magento, BigCommerce and bespoke systems.
If you are interested to have us take a peek at your data, visit our website and sign up for a pilot.
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