Analytics have swept through Major League Baseball (MLB) over the past 17 years. The Oakland A’s got first analytical results. But, the Boston Red Sox were the first to go big in 2002. John Henry, the new owner, was a believer. He had gotten rich by trading commodities with his own invented analytics.
Continue reading 144. Baseball’s Adoption of Analytics v. DistributorsBig players (Amazon, Walmart, Home Depot, etc.) are combining many technologies to create the onrushing Cloud Omnichannels to which B2B brands must respond.
Amazon’s (AMZ) “doorstep-back-to-producers” value-channel is all in the cloud. It incorporates technologies like AI, machine learning, robots, and cobots. And, AMZ has already hugely pre-invested in exploiting 5G bandwidth, instant-startup-clone brands, info-videos with watch-me reward points, last-mile-uber-metro-delivery platforms, voice commerce, drones, fintech, blockchain, etc.
Continue reading 140. Channel-Model Innovations AheadPractice good-pricing hygiene. Don’t underprice SKUs or customers if they will continue to (happily) buy from you at higher prices. But, consider also the positive trade-off of lower prices in exchange for larger average order-size buying. What are your general buying and selling incentives for increasing order size?
For 2018, a $100MM contractor-supply distributor had roughly 4000 active accounts. More facts:
Continue reading 136. SELL WIN-WIN, LOWER PRICING (?)Amazon’s analytics can identity SKUs on which they “Can’t Realize a Profit” (CRaP). Even with their improving, world’s best fulfillment and last-mile delivery costs, they can’t cover costs on items lower than $15 per pick/order.
AMZ’s fixes to date?
Continue reading 135. Fix Your CRaP Items and Their Root CausesLollapalooza Effect: (A Charlie Munger term) When multiple cognitive biases reinforce one another within a group, irrational beliefs will take over.
When distributors create a Cost-to-ServE (CTS) model to estimate and rank customers by net-profitability, there are typically two, customer-group shocks:
Continue reading 134. The Small-Customer, Small-Order “Lollapalooza Effect”Charlie Munger, renowned investor, advises: “To become wise you’ve got to have a latticework of models in your head”.
Research proves our evolutionary brains are riddled with “cognitive biases”. Good for species survival, but bad for innovating service value.
We don’t know, what we don’t know. And, thanks to “confirmation bias”, we prefer to listen to people who share our data-free beliefs. Willful ignorance is common; we humans struggle to cope with too much math reality
Because models are simplifications of reality, they are flawed with blind spots. But, not as flawed as our thinking. And, multiple models can offset the others’ blind spots. Let’s look at some fill-rate models.
GMROI= Warehouse Gross Margin Dollars (divided by) average Inventory Investment.
TURN X EARN (GM%)
Having best fill-rates for all types of customers is tough. But, what if you have historically strong sales and fill-rates for a peculiar niche of customers? Or, do you want to target a specific niche? Then, model what the niche buys and beef up those SKUs.Best fill-rates will both retain and win more niche customers. Increased fill-rates also boosts average Gross-Margin-Dollars per order and employee which in turn cranks profit-dollars per employee.
Financial ratios for inventory don’t see any of my under-linings! Get a Cost-To-Serve model at the line/SKU model to win.