Apple, Beauty and Biscuit. No these are not the names of celebrity children. I am talking about hockey as a metaphor for KPIs. With the NHL playoffs right around the corner, my thoughts naturally center on KPIs and analytics. So lets get our Mitts and Twigs ready and dive into how these things are related.
For example, when down by 1 or more points, it is common for coaches to replace the goaltender, known as pulling the goalie, with an offensive player in the final 1-2 minutes of the game as a last-ditch effort to at least bring the game to a tie and force overtime. Of course, exposing the goal this way involves some risk-reward analysis, but could result in a Celly. If your sole objective is to win the game, exactly when is this best done?
Several recent studies employing decision science have revealed that the optimal time to pull the goalie when down by 1 point is actually with about 5 minutes left in the game, and with about 11 minutes left when down by 2 points. But, while the science at least appears to be influencing coaches to pull goalies slightly earlier now, it’s never by nearly that much. Why is that? Don’t they want to score a Gino and win?
Well, they do, but apparently winning isn’t the only objective. Another objective might be optimizing fan sentiment, which would be harmed by a higher point-margin loss resulting from pulling the goalie earlier, especially for fans that paid a small fortune to see a live Gongshow.
Recall in a previous post, New KPIs of Retail, the recommendation to tie KPIs to objectives and objectives to goals, for context in how to interpret them to inform critical decisions. If one of those two objectives above (winning and optimizing fan sentiment) weren’t considered, the decision on when to pull the goalie would be very different. Goals, objectives and KPIs are, wait for it…a trifecta!
Of course, Retail KPIs are no different. Without the context of a company’s goals and objectives, KPIs might not be properly applied to strategic decision-making. Gleaning insights from KPIs is the easy part – putting those insights into proper context to inform decisions is the more challenging part.
Oracle Retail Insights enables the mapping of KPIs to goals and objectives for a true balanced scorecard view, instead of a partial face wash view.
Inspired by the when-to-pull-the-goalie example, I thought I’d share my top 10 list of commonly-referenced KPIs that warrant heavy consideration of context, from such a balanced scorecard perspective or otherwise, before drawing any conclusions and calling in the Goons.
I selected these from among the 13,984+ packaged measures, metrics and KPIs of Oracle Retail Insights Cloud Service Suite v18. Note that this count includes various metric grains (WTD, MTD, YTD, etc.) as well as “As-is” and “As-was” views to account for item reclassification's and store relocation's.
Retailers don't need to worry about sitting in the Sin-bin, Oracle helps ensure that these and other metrics are properly cast to end-users in a manner that provides perspective, and context, beyond enabling balanced scorecard views. The packaged names and many of the derivations are configurable, and of course can be surfaced to Oracle Retail Home to:
There is no need to call in the Stripes. This is truly and fully a model-once-deploy-anywhere approach to retail analytics and decision science. It is not a schema-on-read Data Lake (where a user is entrusted to properly interpret raw data), but rather a highly-governed schema-on-write Data Warehouse, with the ability to augment with a Data Lake where specific use cases warrant.
And, similar to how NHL coaches are considering both when science tells them to pull their goalies along with their own intuition, Oracle Retail Insights Cloud Service Suite enables Adaptive Intelligence (yes, yet another of my favorite terms) which enables a balance between artificial intelligence and human judgment through a blend of extreme configurability, as well as a flexible array of federated tools and pervasive user experiences.