Data-driven E-commerce case study - cohorts and segmentation
How data can improve an E-commerce Business? — A slice of Practical Data Dictionary. Note: Unfortunately, the e-commerce sector is a really tough competitive market, so I could only write this case study by replacing the name of the company, product and numbers with something similar.
The Hiking Backpack E-Shop (if a so-called company does exist, apologies, I am not thinking of them, this is just a fictional example) began to analyze their data. They were curious about:
- Who is the best target group for them?
- What kind of product to offer to whom and when?
- Having answered these two questions, how can they reach the highest Revenue and higher Visitor-to-PremiumCustomer % in the long term?
The first thing they saw was that the sales performance fluctuates throughout the year.
This can be for a number of reasons of course, but knowing the circumstances we first thought that this is due to the nature of the product. To validate our suspicions, we looked at the 2013 vs. 2014 Revenue Chart on a monthly breakdown. The two years show a similar trend (we only see a small growth). We see the same for 2012 and 2011 as well.
As can be expected, we did a number of User interviews and Usability tests, and checked some obvious analysis’ based on different hypotheses. Most of these didn’t give us any exciting results — but one of the segmentations had an interesting outcome.
We segmented the Revenue on the below chart based on Payment types. We can see that there was a constant change in 2014 on whether the „simple” First Payments (so namely the first purchase) or the Repeat Purchasse (when a previous Customer purchased again) brought in more Revenue.
It jumps out that the Revenue generated by New Customers drops in autumn, but returning Customers cover this gap.
In light of this, we created a Cohort analysis for those who made their first Payment in the shop in 2014. We looked at exactly how much was spent and when as a Repeat Purchase. We found this:
So the Customers from 2014 brought the best Revenue from a Repeat Purchase at the end of the summer and beginning of autumn. In fact, we also know that Customers from February, March, April and May are really strong and spend a lot 4–5 months after they make their first purchase (so July, August, September and October).
From this, two obvious reports followed.
One is to take a look at the same metrics, but through many years. (This also showed that the February-May Customers spend a lot as a Repeat Purchase. It is clear that it was them who took this seriously and planned their „trips” ahead and with that their „trip equipment”. The rest shopped on an ad-hoc basis in the summer, or gave the backpack as a gift — typically around the Christmas period.)
The other is to define the exact product people purchase as a Repeat Purchase. This was a much simpler story. In short — they were able to find a well-targetable Customer Group and also what to sell them again and when.
The autumn campaign of 2015 was thus approached with a brand new strategy. Instead of aiming at new Customers, the current ones were targeted in these 3 months. This had its results.
Want to have the full 54 pages e-book right now? Download it here: http://data36.com/datadictionary/
Or read it chapter by chapter here:
Chapter_01: Activity-related events
Chapter_02: User-types from an activity perspective
Chapter_03: Payment-related events
Chapter_04: User-types from a payment perspective
Chapter_05: All your segments
Chapter_06: Analytics, metrics KPI-s — How to calculate retention or Life Time Value?
Chapter_07: Case studies — E-commerce
Chapter_07: Case studies — Startup