Measuring Customer Retention
It's easier to sell again to an existing customer than it is to acquire a new customer.
Because of this fact of life and business, improving customer retention has immense leverage over revenues in many circumstances.
This article is not about the activities you can take to increase customer retention.
Instead, here I outline some approaches to measuring this objective.
By getting robust measurements in place, you'll be in a better position to assess which activities work best for you in driving repeat customer revenue.
Preface on Data Requirements
To measure customer retention, you need a way of identifying and tracking when the same customer comes back to your shop.
You can achieve this with many point-of-sale systems on the market.
Aside from these systems, loyalty programs are a classic approach used to incentivize customers to tell you who they are when they make a purchase.
The more transactions that have an associated customer, the more robust, granular, and actionable your measurement of customer retention will be.
A Single, Simple Metric?
Sorry, afraid not.
Unlike the question of "what was our revenue figure last month", the question of "what was our customer retention figure last month" has no single, obvious answer.
a few Options
Percentage of Revenue from Existing Customers
The problem with this metric is that it holds a competition between your customer retention and customer acquisition efforts.
A stellar month in new customer acquisition could make your retention look poor from this perspective, at least in the short term.
By all means, add this to your retention dashboard, but we also need some way of measuring customer retention that is independent of acquisition efforts.
Percentage of Customers Who Shop Again
The percentage of customers who come back and shop again is one way of adding this perspective.
There is an inherent lag on how we can measure this figure, and we must make some decisions about the parameters that define it.
In practice, we can calculate a rolling figure which shows the percentage of customers who shopped in a period that came back again in the following period.
A reasonable length for these periods will depend on the purchase frequency of your product lines.
As an example, it could show the percentage of shoppers who shopped between 31 and 60 days ago, who also shopped in the last 30 days.
While this is one useful metric to have in your retention report or dashboard, it doesn't distinguish between a customer who comes back once and one that shops every day.
We'll need to add something else to achieve visibility on that.
Average Transaction Count Per Customer
Of all customers who shopped between 30 and 60 days ago, what has been their average number of transactions in the last 60 days?
Now we have a metric which tells us more about the frequency of shopping, rather than just a yes or no on repeat custom.
Conclusion
These were just a few ways to get started in measuring the objective of driving customer retention.
By evaluating these metrics across customer, product and other dimensions, we can understand our strengths, opportunities, and weak spots when it comes to achieving this objective.
By incorporating the different actions we take to drive customer retention, we can get a view on what works, and what doesn't.
A quick example would be to overlay your social media activity and see if there is any corresponding uptake in customer retention when you are more active on specific platforms.*
Using such information to make decisions is at the heart of, and the key benefit of, data-driven decision making.
*Be mindful of other factors when drawing such judgments about cause and effect.