When we assess our clients’ capabilities in Pricing Analytics, we use a framework that points out strengths and weaknesses of the system, rather than focusing on a single component. We’ve seen too many demonstrations where the math becomes bleeding edge but the business gets no benefit. The equivalent problem in a factory would be to double the production machinery floor, but keep shipping capacity the same.
In short: Your pricing strategy is defined by the WORST performer in the chain, not the best.
So what do we look at to determine the total pricing effectiveness?
The old “Garbage In, Garbage Out” adage certainly applies, but in real-world situations it’s much more nuanced. When given the choice between marginal data or no data at all, we choose marginal data but only with the understanding of what it means for the precision of the final deliverable.
We’ve seen successful pricing teams deal with this in two ways.
a) No single strategy. Or 100 different ones.
This is the most common situation, as business leaders won’t sign up for a single unit, sales, or gross margin target. In this case the pricing team has to give the relationships and scenario-based results that then allow stakeholders to build their own recommendations.
b) Clear targets
When this happens, it’s the pricing team’s responsibility to pressure-test that strategy, and then drive like hell straight at it. One recent engagement knew they were missing their profit target in the critical Q4, so we had clear targets and highly successful delivery.
This is often the easiest problem to fix, because we frequently see two situations in the wild: those who have not done much at all in the way of analytics and just need to get started, and those whose analytic capabilities are far beyond the rest of their capabilities.
4) Engagement & Understanding
Many pricing experts fail to realize that a big part of their job is leading the organization to integrate pricing analytics into the business. In short: cheerleading. Some best-in-class retail clients of ours actually have two separate teams in pricing- analytics and consulting. They like to think of them as “front-of-house,” or those who spend time working directly with the business owners and “engine room” teams that are responsible for designing and executing the analytics.
One recent retail client had everything going for them- strong data and analytics, a clear target, and an engaged merchant community. They pushed prices down to the store and switched to in-flight monitoring mode… and saw only 25% of the expected impact in the first 3 weeks of their critical season. What happened? It turns out that the stores only posted updated prices when they packed out new inventory. The field organization scrambled and got the field to reprice immediately, but the delay was costly.