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Let me start with the example of Thomas Cook. As you know, they were the industry leader in tourism. Unfortunately, they didn’t adapt to the online behavior of the modern customer. And when its impact compounded with some of their bad M&A and other business decisions, it led to the 178-year-old company to close down in September 2019.

They could have taken the hint from the continuously changing customer behaviour, as early as in 2009. The data was public, right under their nose. Google Trends was indicating the change in the expectations of the modern consumer, in terms of a personalized experience. Rest is history.

Why did I start with the above story?

Right now, Customer Lifetime Value (CLV) is going through a similar phase as the internet, and online businesses were in the 90s.

Sadly, only 20-30% of ecommerce players (the likes of Amazon and Costco) are making efforts to increase their customer lifetime value.

Others tend to focus too much on acquiring customers, and not in retaining them. And it leads them to failure, especially when the acquisitions budgets tend to be way higher than what the acquired customers are ever likely to spend in their stores.

Interestingly, between 2013 and 2018, the CPC on Facebook grew by 800%, whereas ecommerce sales worldwide increased by 158% only.

Remember, not all customers are created equal. They have different needs, expectations, emotions and yet, not every customer brings the same amount of value to your business.

If you can segment your customers according to their behavior, you’ll know whom to prioritize and encourage repeat-buying behavior.

While there are several ways to segment your customers, one of the simplest and most effective methods is RFM segmentation. It refers to analyzing and segmenting your customers based on three factors:

Recency (R)
Frequency (F)
Monetary Value (M)

The idea is to group your customers based on their purchase history (how recently, with what frequency and what was the monetary value of it) and use that data to predict their likelihood to buy again.

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