How do you build a long lasting relationship with your customers? Do you track if they come back after their first purchase of your products or services? How often do they come back and do a repeat purchase?
When you run an eCommerce website, tracking your customer retention is arguably one of the most significant force in keeping your business up and running. According to recent statistics you’ll spend five times less money on customer retention and selling to an existing customer is 40% more likely than converting a new lead to your site.
reference from: crazyegg
So what is customer retention rate analysis and how do I use it to grow my business?
For any business whether it is online or offline, customer retention rate is a way of measuring how often your clients or customers come back to do a specific action on your site e.g., engage a content on your site, complete a form or making a repeat purchase. By tracking customer retention rate, it allows you to understand the customer lifetime value.
How to track and start customer retention analysis?
While organizations have different ways of tracking customer retention, in this article I will share with you how to analyze your customer retention rate using Customer cohort in Tableau. We will segment customers based on time of acquisition and study if they have taken a similar actions during a specific period of time. Let’s get started.
In this demonstration I will use a sample dummy eCommerce data set and import to Tableau. The columns in this data set are date of transactions, customers user IDs, count of transactions, sales generated and transaction IDs.
Cohort Analysis in Tableau
After we load the data in Tableau, we need to group our customer acquisition cohorts on the month they made their first transaction or purchase. We can do it by clicking the Arrow Down icon on dimensions and selecting Create Calculated Field.
Below is the formula to group customers based on Month of Acquisition in Tableau.
I have also created another custom metric, the AOV (Average Order Value) to understand the average transaction/purchase size of each cohort. These two new custom calculations should immediately appear on your data frame.
Next is to put your Transaction Date on columns and your Custom Calculation (Month of Acquisition) on rows.
Put the User ID on Color option on the Marks card and use Square as type of visualization. Additionally, set User ID as metric to Count (Distinct). This is to get the unique count of customers each month.
Your Tableau sheet should start to form the Customer retention table by each cohort month.
Finally, you drag User ID on Label option on Marks card, convert as metric to Count (Distinct) and aggregate the output as a Percent of Total.
The final output should look like this.
How to interpret the report?
Note that the first cell will always be 100% as it is the total size of each cohort month. The next cells indicates the customers retained on each month as a percentage of the total size of the cohort by month.
The retention rate tableau table shows that February cohort shows the highest number of customers acquired for the whole year however it is very interesting to see that August and October cohort are the most loyal customers.
August and October cohort are the highest percentage with regards to repeat customers. It means that they came back and made a repeat transaction or sale.
You can also examine how each Cohort were acquired and sent back to the website continuously each month. What were the factors that keeps the customers to come back? Is it due to seasonality? Discounts or promotions you set towards the end of the year? Is due to a partnership with a vendor? Is it due to marketing spending on a specific period? Are the numbers statistically significant?
Also, the dataframe shows no cohort group for the month of April and November, how was the data collected and tracked?
Takeaways of Retention Rate analysis using Tableau
This method guided us on how to narrow down the reasons of high or low retention rates for each month. It helps us understand the habits of the customers including specific patterns that appear in their actions.
It also help us reflect how effective our offline and online marketing strategies including our analytics tracking. More importantly, this gave us clear visibility on what we can stop doing and start doing.
Furthermore, your analytics conclusion will guide your sales and digital marketing team to improve marketing performance for specific months with the lowest acquisition or retention rates.