Some time ago Neil Raden and I did some research on analytics. It was clear
as we did this that there were two main threads of analytic use in companies –
risk analytics and opportunity analytics. I blogged before on the use of analytics to manage risk one
risk at a time so I thought I would write about opportunity analytics.
Risk analytics are about using historical data to make a prediction about the
risk of a particular customer, a particular transaction going or being bad in
some way. Risk analytics help you estimate and account for the downside risk of
a decision – if I get this wrong, what’s the worst that could happen?
Opportunity analytics, in contrast, are focused on estimating the upside – the
opportunity.
I regularly write about the importance of focusing analytics on operational decisions and their
role as a corporate asset. If we think about these kinds of operational
decisions then opportunity analytics come to bear on customer-centric decisions
like cross-sell and up-sell decisions, or decisions to retain a customer who has
called to cancel. Opportunity analytics are used to answer questions like how
profitable might this customer be in the future, how profitable might they be if
they accept this offer, which offer is most likely to attract them? Opportunity
analytics predict response, opportunity, potential. They predict the propensity
of customers to buy products, the likely profitability of a customer if they buy
a particular product, which offer is likely to be most appealing to a prospect.
Unlike risk decisions, there is often little difference between good and bad
opportunity-centric decisions. If a company gets such a decision right, they
might increase the profitability of a customer, or retain a customer into the
future. They have little exposure if they make a bad decision. While, in theory,
a bad cross-sell offer might so annoy a customer that they abandon their primary
purchase, this kind of negative impact is highly unlikely. With opportunity
analytics, companies are trying to maximize their upside not manage their
downside. A poorly made risk-centric decision can result in fraud, bad debts,
theft. A poorly made opportunity-centric decision simply wastes an opportunity
to increase profitability.
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This difference changes the cost justification of analytics. Risk decisions
have been the more common use of data mining and predictive analytics
historically because the time, hardware and skills involved could be easily
justified by avoiding the potentially huge downside. Opportunity analytics are
growing fast, however, as the tools get easier to use and the cost of hardware
and data management continue to drop precipitously. With new tools, and more
readily available experience, squeezing extra profit out of these decisions with
analytics is becoming more and more worthwhile. The embedding of analytics into
decision-making systems for marketing and CRM is increasingly common. Because
opportunity analytics are targeting small improvements, they must change rapidly
to take advantage of competitive and market circumstances. This drives an
ever-increasing use of adaptive analytic models, those that use automated
experimentation to constantly adapt and refine an analytic model.
Opportunity analytics may not have the pay off that risk analytics do but
companies should still be thinking about using their customer data to maximize
the value of every opportunity.
