From CRM to ARM: what utilities can learn from banks about maximising value

Last week in Brisbane a small metal clamp holding an overhead electric cable failed causing a meltdown on the Queensland Rail network and leading to the government compensating commuters with a free day of travel. I expect that there are tens or hundreds of thousands of these clamps across the network and in all likelihood they are all treated in more or less the same way and assigned the same value.

There are interesting parallels between the current transformation of utilities to smart grid and what happened in banks in regards to customer analytics at the turn of the  millennium. Can we gain insights from over a decade of experiences in the banking industry of customer relationship management (CRM) to move towards a principle of asset “relationship” management (ARM).

When I became involved in my first large CRM project over ten years ago, CRM was at that point only concerned about the “kit” – the software and hardware that was the operational aspects of CRM – and not on the ecology of customer data where the real value of CRM lay. To give just one example: we built a  system for delivering SMS reminders which was very popular with customers, but when we went to understand why it was so successful we realised that we had not recorded the contact in a way that was easy to retrieve and analyse. If we had designed CRM from the point of view of an ecology of customer data then we would have been able to leverage insight from the SMS reminder initiative faster and for lower cost.

Once we understood this design principle we were able to start delivering real return on investment in CRM including developing a data construct of customer which spanned the CRM touch points, point of sale, transactional data systems and data which  resided outside of the internal systems including public data and data supplied by third party providers. We also embarked on standardising processes for data capture and developing common logical data definitions across multiple systems and then the development of an analytical data environment. The real CRM came into being once we had developed this whole data ecology of customer that enabled a sophisticated understanding of customer lifetime value and  the capacity to to build a range of models which predict customer behaviour and provide platforms for executing on our customer strategy.

The term “relationship” has some anthropological connotations and it may seem crazy to apply this thinking to network assets.  From a customer strategy perspective, however, it has a purely logical application: how can we capture customer interactions to maximise customer lifetime value, increase retention and reduce the costs of acquiring new customers?

If we look at customer value drivers we seem some parallel with capital expenditure and asset management. Cost to acquire is roughly synonymous with asset purchase price. Lifetime value applies to both a customer and an asset. Cost to serve for a customer is a parallel with the cost maintain an asset. Costumer retention is equivalent to asset reliability. The difference with advanced analytical CRM is that these drivers are calculated not as averages across customer classes but for every single customer.

The development of smart devices and the associated data environments necessary to support smart grid now enables utilities to look at a similar approach. Why can we not develop an analytical environment in which we capture attributes for, say, 30 million assets across a network so that we can identify risks to network operation before they happen?

If we could assign an expected life and therefore predicted probability of failure to the metal clamp between Milton and Roma Street stations; a value-to-network based on downstream consequences of failure and balance this with a cost to maintain/replace then we would be applying the same lessons that banks have learnt from understanding CRM and customer lifetime value.