Category Archives: Profit Analytics

161. How Digital Disruptors (Will) Skim Distributor Sales

Digital-Disruptors are Multiplying!

Today: digital disruptors are attacking most industries. In ’95, a few startups invaded easiest B2C product categories with their own homemade digital tools. Today, raiders are targeting every “buying-journey experience” with off-the-shelf digital tools from cloud providers. Barriers to buying-journey innovation have fallen for both startups and your firm.  

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160. Baseball’s Player-Development Analytics v. Yours

New Book: The MVP Machine

Read the rave reviews for this just-out book at Amazon. Baseball fans will love it. Non-fans, who are trying to “upskill or re-skill” employees, can glean value by skimming it.

“MVP” details the third phase of the analytics revolution sweeping pro baseball. In Phase-One (detailed in “Moneyball”), teams used analytics to draft and trade under-valued players. Others got wise, copied and zeroed out that edge.    

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154. Forecast What You Can Control with Best Odds

Forecast the Global-Everything Bubble?

The global economy is slowing down. Do be more cautious with debt and big, long-term investments. Otherwise, focus on the net-profit gains that you can best forecast and control. Take your 5-10 biggest net-profit-upside-potential accounts to the “next-level”.  

Assumptions About Customers:

  • Top 20% customers may yield 80%-plus of your gross profit dollars (Pareto). But, one in five is typically unprofitable. They are big losers due to huge, small-dollar picks, orders, and/or returns.
  • For net-profit rankings: your top 2% will yield 70-90% of operating profit. (All net-profitable accounts – about 20-40% – will yield 120-150% of your financial profits. They pay for both the losing accounts and residual operating profit.)
  • A team effort will always find new, upside possibilities within best 2% accounts.  
  • Another 4% subset of customers are high-growth “gazelles”. They are run by ambitious, innovative leaders and have a tightly focused strategy at which they excel. Gazelle buying needs (and sales) grow 2-4X faster than their peers. As innovators, they are apt to be open to your ideas for replenishment-process improvements. Partner them, and they will grow you for years.
  • Your competitors are not thinking this way. Their reps do what they can. And, those reps won’t be able to (on their own) counter your total-team solutions.   

Action Steps:  

  1. Rank all customers by year-over-year increases-to-decreases in gross profit dollars. (Better by net-profit gains, if you have a cost-to-serve model). The most up and down accounts will shock you.   
  2. Focus first on most-up accounts. Why? Nobody wants to take credit for fumbled, down accounts. And, big-up accounts may be still expanding with unsolved replenishment process needs.
  3. Some accounts will be way up. Are they gazelles? Do more research to create a top-5, gazelle, target list.  
  4. Selling and installing next-level replenishment systems requires honcho-led team selling with three stages. Find new system needs to fill. Co-create and resource them. Then, do ongoing, proactive maintenance. For much more on – “Enterprise Account, Team Selling” – follow the link at the bottom to an appendix of past documents and how-to blogs.   

Will This Work for You?

Certainly! As you and colleagues skim through the appendix material, reasons for why you can’t change will arise. Work through them.  As Henry Ford said: “if you think you can or can’t, you are right.”

CLICK HERE to view the appendix

146. Analytics’ Credibility Within Your Firm?

Science Deniers Past and Present

The “scientific method” has enabled all the fruits of modern civilization.

The Scientific Method’s steps are (modified for distributor results):

  1. Observations.
  2. Questioning.
  3. Theories/Hypotheses.
  4. (then) Gather new data.
  5. Do iterative experiments ever smarter.
  6. Uncover Strategic Insights.
  7. Sometimes nail something new and profitable. 
  8. And, Scale it.

Until 1660, when the Royal Society of England was founded, scientists who published discoveries that conflicted with the beliefs of the church or dictators could be burned at the stake. Even today – politicians, churches, economists, CEOs, etc – will undermine any (scientific) facts that get in the way of their agendas (comp plans) or belief-identities. Want to get ahead? Tell these bosses what they want to hear! 

Science-denying leaders have good audiences for their data-free opinions. Besides obedient underlings, surveys reveal that citizens are OK on everyday-science facts.  But, they often struggle with the scientific method process and statistics.

Belief Types: Finance, Sales Relationships and Family-Company Values

Every company is dominated by the voice of finance. Be financially pragmatic. Pay timely taxes. Service debt and meet lender’s ratios. Meet the payroll. Please the auditors. But, what are the blind spots of financial operating assumptions like “buy-low, sell-high, and sell-more”?    

What “observations, questioning, theories, and new analytics” should challenge financial management? Do financial numbers measure the improving effectiveness of leaders, strategy, and the culture and systems that support the strategy? How measurably great and guaranteed your “service value” is for best, most net-profitable, target customers?  

Switching to the sanctity of “relationships that reps have with their customers”. Where are the metrics by which to manage and improve the quality and win-win economic benefits of these relationships?

Family businesses also have beliefs/values. But, to paraphrase Tolstoy, “Happy family businesses are all alike; every unhappy family business is unhappy in its own way.” Have any unhappy, family beliefs to question?

Concluding Questions:

In your company, if scientific method analytics clash with data-free beliefs, what happens? Is the C-Suite open to experiments? Or, do they want “new” data that supports the status quo beliefs and compensation schemes? Big profitable gains come (unfortunately) from big changes to old ways. What will happen to your company, in fast-changing times if scientific method analytics can’t challenge dysfunctional, profit-drain beliefs? For more on scientific, big-change analytics for distributors, be in touch:  bruce@merrifield.com

144. Baseball’s Adoption of Analytics v. Distributors

Red Sox ‘02 v The Orioles ‘19  

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.   

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