Analytics Without Upside Theories Fizzle
Don’t use analytics in distribution to grind existing information finer and faster. You will get interesting, but non-actionable data. Start instead with an improvement theory. Then, build an analytical model to validate an improvement theory for your distribution. You will find insights to exploit and can track subsequent change experiments with new metrics.
Distributors, for example, have a mix of very profitable and unprofitable items, picks, orders, and customers hiding within averaged-out, aggregate financial numbers. Instead, create a cost to serve (CTS) model to expose the big profit cross-subsidies and then pursue a new metric like: make 100% of customers profitable.
Laboring to find perfect models and metrics in a complex changing world is not worth it. Focus on good-enough metrics and keep tuning your CTS model. Super-winners and losers only fluctuate within their subgroups at the top and the bottom of the rankings. These profit cross-subsidies are market inefficiencies to which conventional industry wisdom is blind. It’s time for a new way.
Moneyball for Distributors
In the movie Moneyball, we learned that the 150-year-old sport of baseball was overvaluing factors easily seen, counted, and judged, like speed, hits, and physical appearance, while undervaluing less visible factors like on-base percentage and walks-to-strikeouts.
In any market, inefficiencies exist because all competitors follow conventional wisdom that is blinded by human cognitive biases. These, in turn, shape reporting systems that then reinforce experiential wisdom and expand blind spots.
Your financial accounting software, reports, and metrics make the auditors, loan officers, and tax authorities happy, but they don’t give you the “perfect service” metrics peculiar to your most net-profitable customer segment.
Courage to Act
In Moneyball, only Billy Beane, the general manager of the Oakland Athletics, saw and had the courage to pursue players based on smarter analytics. His scouts and the league at large thought he was a fool. But, his statistically improved decisions won ball games and his redemption. Today, all pro sports teams embrace analytics and Beane’s original, undervalued inefficiencies are fully priced, while new ones are being found.
Do you have the courage to act differently with your most profitable and unprofitable accounts? Not all teammates will easily change their conventional, data-free, beliefs. Embracing the truly new evokes four stages of reactions: inattentive confusion, mockery, fierce resistance, and then, after success, an “I was for it all the way” response.
Want to Find the Courage to Build an Analytical Model to Validate an Improvement Theory for Your Distribution?
Look for more posts that will continue this storyline and discuss how to build an analytical model to validate an improvement theory for your distribution. For a full immersion courage-fest, register for the APIC Conference on April 20-21 in Scottsdale Arizona!