Customer Value Tip #2: Data-Led Customer Segmentation
How to increase customer value, engagement and retention - one idea at the time.
“Have you heard the story of the statistician that drowned when crossing a river that was only 3 feet deep?
(Spoiler alert: They didn’t trip over and bang their head etc. Keep thinking!)
I came across the concept in this story while at business school and it’s called the “Flaw of Averages”, developed by Sam L. Savage of Stanford University. It basically outlines the idea that the river is 3 feet deep on average, but this provides no insight into the data distribution of different depths throughout the whole river crossing. It could be anything from 1 foot deep to 10 feet deep, so understanding this is quite key to advance planning.
If we translate this to a business environment, it means that when we’re using data to describe different financial and customer states, there is a fine balancing act between keeping data summaries simple and high-level vs identifying variances within the data set and understanding if this requires a differentiated approach when it comes to execution of campaigns and business initiatives.
#1. Segment your customer data
A first important important step is to segment your top-level customer data. For example, you might have a $1.2m annual turnover in your business. This is $100k turnover per month, and let’s assume that you have 1,000 customers and they are spending $100 per month on average.
A common approach to data segmentation is to use something called a Decile Analysis. This involves breaking down the data into deciles (ten equal parts) and examine each individually.
To keep this really simple, we’re going to assume that our decile analysis reveals the following:
Deciles 1-4: 0$ (400 customers spend nothing)
Deciles 5-7: Spend $50 on average (300 customers)
Deciles 8-10: Spend $200 on average (300 customers)
By going deeper into the data and beyond the average values, we’ve now identified that there are 3 main customer segments in this business:
A) Inactives
B) Low-Spenders
C) High-Spenders.
#2. Take a second look at ‘one-size-fits-all’ campaigns
On paper, it looks like your customer persona is a mid-tier spender who is purchasing goods and services for $100 per month, but note that this customer persona / customer spend behaviour does not exist in practice. It is just an “average” of other behaviours and do not let your planning be misled by this.
Understanding these nuances in the data is really important and using a differentiated and targeted approach & messaging to each segment is therefore very important.
Customer campaigns and business initiatives can sometimes fall into the trap of having ONE message for all customers, ONE offer for all customers, ONE marketing distribution channel and so on. These “one-size-fits-all-campaigns” can be appealing to go after due to greater scale, simplicity, lower cost and speed of execution.
There are instances where generic messaging to all customers is the best approach, but more often than not, using a differentiated and targeted approach that aligns with customer needs and behaviours which may vary across customer segments is the better choice.
Whilst this approach will make things trickier in the short-term (more time, resources, complexity to execute), by focussing on the customer needs and not our needs is going to lead to greater effectiveness and ROI over time.
#3. Aligning campaign execution planning with customer segmentation
Examples:
Generic messaging with no segmentation:
Email all customers and say “spend $150-200 next month, and we’ll send you a free gift”
The drawback of this approach is that Segment A and B will likely not react to this campaign offer because their current spend levels are significantly below the spend hurdle. In contrast, this offer is music to the ears of Segment C is who already spending that amount. They can simply claim the free gift without spending more than they already are, which can backfire for the business.
Instead, you’ll want to ensure that your campaign execution approach aligns with the customer segmentation:
Campaign A:
Targets customers that are inactive with zero spend. A suitable offer might be: “Come back to us and spend $25 in the next month and claim your free gift”.
Campaign B:
Targets low-spending customers that spend 50$ per month. Similarly, you adapt the spend thresholds accordingly - perhaps: “Spend $75 in the next month and…'“
Campaign C:
Targets high-spending customers that spend 200$ per month. You apply the same logic above - perhaps: “Spend $250 per month and…”.
Recap
The above examples show the importance of drilling into customer data and going beyond the data averages. It further shows how create basic customer segmentation based on decile analysis and building out your sales & marketing efforts in a way that aligns to the actual data distribution and different customer segments and behaviours across your customers.
Hope this was useful and good luck!
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