Electricity customers buy on price and customer services, switching suppliers as a direct function of competiveness of price.

Despite the fact that the Retail Energy Supply Association claims that only 50 percent of residential consumers know they have a choice in where they buy their power, customer loyalty has been a relatively negligible factor.

Knowing that, customer attrition is an almost inevitable blotch in a power retailer’s profit/loss statement but planning for that attrition, with proper hedging and risk assessment, can determine the color of that blotch.  Not enough customers, combined with long contracts on power, turns that blotch dark red.  Setting the right rate and buying forward contracts for the right load quantity can keep the books in the black: the kind of outcome Ascend Analytics software users have come to expect.

Planning against customer attrition isn’t easy.  Guessing on the wrong assumptions with an unsophisticated deterministic model sets utility retailers on a high-risk trajectory.  With Ascend’s PowerSimm state-space modeling simulations, retailers gain a confident outlook of not only the expected impact of customer attrition on revenues, but also a clear idea of the uncertainty surrounding this revenue impact.  Using this information, a retailer can keep risks to a minimum.  “You know how much to contract for,” explains Ascend analyst Scott Nelson.  “You know how to hedge your loads properly.”

Ascend Analytics products operate at the state-of-the-art edge of statistical modeling.  With PowerSimm, retailers input their own predicted customer attrition curves.  These curves are then combined with the load and price simulations rigorously benchmarked to capture the key structural weather-load-price relationships and aligned to the current market outlook.  Within each simulation path, the incumbent rate is compared to the market price, and based on that differential, a percentage of the contracted load will likely leave.  The value of PowerSimm is in the precise quantification of the uncertainty surrounding this revenue impact.  Software that generates simulations using deterministic modeling can’t quantify the uncertainty of this revenue risk, since deterministic models fail to capture the uncertainty in volumetric risk, price risk and the correlations between the two.

Retailers often find themselves in a situation where a competitor can make a strategic decision to lower rates.  The shoppers might walk out of the store.  “All that revenue is basically at risk due to attrition,” Nelson notes.  Retailers cannot predict every competitor’s move but a robust simulation of market price can help anticipate the likelihood of such rate changes, allowing retail managers to plan accordingly.  “You might want to look into basically setting up some kind of hedge to mitigate that risk,” Nelson adds.  “You can’t actually model very effectively what rate a competitor is going to put together but you can say ‘this is where the market is and this is where it’s going.’”  If a retailer has an idea of where wholesale power prices are headed, that manager may be able to set a rate that slows or eliminates customer attrition.

So while customer attrition may be inevitable, the uncertainty around its impact can be calculated.  PowerSimm does that accurately, and automatically.  The customers might still walk out the door.  But when their departure is accounted for in Ascend’s PowerSimm, they won’t take the profits with them.