Sorry, It’s been a while since my last post but here in Sweden we’ve been busy preparing for winter!!
Referring back to my last entry, the first task is surely to segment a company’s customer base into two broad segments – those who are profitable today and those who are not. Using current techniques and tools, this process can be achieved in most industries today, and whilst profitability does not always mean revenue, a simple model of revenue minus cost may be sufficient to get started.
In order to identify the groups mentioned above we must now segment this same customer base according to what their profitability might be in the future. Now, the future probably needs to be at least five years hence, and in some cases it might be possible to derive real ‘lifetime value’ figures.
LTV is the total value that a customer provides to a company over the entire period that that person was a customer.
This task can be pretty complicated and lies in the realm of true Business Intelligence. Basically, data mining tools are used to build profitability models using past customer history, and then these models are used to score the current customer base to identify future value. In this art there are deep secrets and methodologies because getting this right can bring great rewards.
For many industries the distribution of customers into the three major behavioural segments lies in a common ratio. The high and low profitability customers will generally account for 20-25% of the total population, leaving around 75% or more that we might persuade to be just a little more profitable.
For the top tier – those that are profitable today and you predict always will be – you have to put together strategies to keep these people happy, as they are responsible for the major segment of your revenue. The good news is that it’s relatively easy to keep these people because they already like you, but the bad news is that if you lose just a few of them, your revenue will be dented big-time.
For the bottom segment, the segment Don Peppers refers to as ‘below zero’, we are not so worried. If these people stop being customers, margin actually improves a little, and as it’s likely that these people will then become customers of a competitor and burn their profits, we win two ways. If you can’t actively get rid of these customers, at least don’t spend any money on them!
If we look at the need to execute effective campaigns to grow the middle-tier customers (those that are today marginally profitable, but we believe can be made much more so) then we can see the dichotomy. To grow these discerning and high-risk customers we must behave in a much more personal manner. However, they are often vast in number so we have to put together offers that can be automatically tailored in a huge variety of ways. We need technology!