I am sometimes surprised at the amount of effort that goes into marketing electricity. I can’t help but feel that a lot of customer strategy is over engineered. So here I present a fairly straightforward approach that acknowledges that energy is a highly commoditised product. This post departs a little from the big themes of this blog but is still relevant because the data available from smart meters makes executing on an energy retail strategy a much more interesting proposition (although still a challenging data problem).
To start with let’s look at the distribution of energy consumers by consumption. This should be a familiar distribution shape to those in the know:
In effect what we have are two distributions overlayed: a normal distribution to the left overlaps with a Pareto distribution to the right. This first observation tells us that we have two discrete populations with the own rules governing the distribution of energy consumption. A normal distribution is a signature of human population characteristics and as such identifies what is commonly termed the electricity “mass market” essentially dominated by domestic households. The Pareto distribution to the right is typical of an interdependent network such as a stock market where a stock’s value, for example, is not independent of the value of other stocks. This is also similar to what we see when we look at the distribution of business sizes.
A quick look at the distribution of electricity consumption allows us to define two broad groups and because consumption is effectively a proxy for revenue we have a valuable measure in understanding customer value.
In our Pareto distribution we have a long tail of an ever decreasing number of customers with increasingly large consumption (and therefore contribution to revenue). To the left we have the largest number of customer but relatively low value (although mostly better that the customers at the top end of the normal distribution) and to the right a very few “mega-value” customers. We can therefore roughly define three “super-segments” as follows:
With VLC on the right revenue is king. Losing just a few of these customers will impact overall revenue so the strategy here is to retain at all costs. At the extreme right for example individual relationship management is a good idea as is bespoke product design and pricing. To the lower end of this segment a better option may be relationship managers with portfolios of customers. But the over-riding rule is 1:1 management where possible.
The middle segment is interesting in that both revenue and margin are important. Getting the balance right between these two measures is very important and the strategy depends on whether your organisation is in a growth or retain phase. If I was a new market entrant this is where I would be investing a lot of my energy. This is the segment of the market where some small wins could build a revenue base with good returns relatively quickly assuming that the VLC market will be fairly stable and avoids the risks inherent in the mass market. On the flip side, if I was a mature player then I would be keeping a careful eye on retention rates and making sure I had the mechanisms to fine tune the customer value proposition. An example might be offering “value-add” services which become possible with advanced metering infrastructure such as online tools which allow business owners to track productivity via portal access to real time energy data; or the ability to upload their own business data which can be merged and visualised with energy consumption data.
The mass market is really the focus of most retailers because often success metrics focus too heavily on customer numbers rather than revenue and margin, probably because this is easier to measure. The trap is that these customers have a high degree of variable profitability as described by the four drivers of customer lifetime value:
Understanding these drivers and developing an understanding of customer lifetime value is critical to developing tailored engagement strategies in this segment. Because these customers are the easiest to acquire, a strategy based around margin means that less profitable customers will be left for competitors to acquire. If those competitors are still focussed on customer counts as their measure for success then they will happily acquire unprofitable customers which in time will increase pressure to acquire even more because of falling margins. Thus the virtual circle above is replaced with a vicious cycle (thanks to David McCloskey for that epithet).
And so there we have the beginnings of a data driven customer strategy. There is of course much more to segmentation that this and there now very advanced methodologies for producing granular segmentation to help execute on customer strategy and provide competitive advantage. I’ll touch on these in future posts. But this is a good start.