Category Archives: LIPA

220. Election Polls, Smart Risks and Dumb Mistakes

Lessons from Bad Polling Forecasts

Massive money was spent on polls and flipping Republican senate seats. Alas, no Blue Wave happened. Key lessons:

  1. Models – based on past results for a complex, ever-changing system – don’t accurately predict the future.
  2. Money wasted on “dumb bets” (like the $300MM democrats spent trying to flip 5 Republican senate seats unsuccessfully) can’t also be invested into smarter risks.
  3. If you want to get rich, you must have vital information – about stable systems – that no one else has and upon which you can act.

Continue reading 220. Election Polls, Smart Risks and Dumb Mistakes

175. Melting-Unicorn Wisdom for Distributors

Unicorn Melt-Downs?

Valuations for WeWork, Uber, Blue Apron, etc. have been tanking. They all prove that a company’s business service-cost model can’t spend more costs on a unit of activity than the unit’s margin-dollar content, and then make it up on volume.    

The same economic reality hit many retail dotcoms back in 2000. Remember eToys? In ’99, they were averaging $20 in margin per order while spending an all-in cost of $300 per order for fulfillment.

Continue reading 175. Melting-Unicorn Wisdom for Distributors

174. Doctors Prescribe: “Evidenced-Based Management”

“Data-Driven Decision-Making” vs. HiPPO Wisdom

When decisions are made at Amazon, “the best data and innovation metrics” win. No hearsay, golden-gut, follow the herd, or political self-interest beliefs. The HiPPO (Highest Paid Person Opinion) in the room doesn’t win with suck-ups bobbling in agreement. Continue reading 174. Doctors Prescribe: “Evidenced-Based Management”

170. Boost Your Corporate Analytics IQ

Poor ROI For Analytics Investments?

Corporate America (distributors included) is overwhelmingly NOT getting results from new analytical insights. Test your company’s Analytics IQ against:

  1. The number of analytical-dimension tools that you have
  2. The buy-In rate for new insights
  3. Your success rate at turning insights into results

The 4 Lenses: Financial + Three Service-Profit-Chain

Everybody uses financial analytics (lens 1). Distributors should also get/invent analytics for the three dimensions within the “Service Profit Chain”(SPC) (Google the term.) The SPC simplified: best quality people (lens 2) deliver service (lens 3), that yields(customer retention) profits (lens 4).  

The SPC outlines how Costco can pay (v. Sam’s/Walmart) 141% more per employee to get 157% more sales and margin dollars. And, how Costco also has legendary customer loyalty, and better sales growth – while selling at a measly margin of 13%.

These four analytical lenses each have blind spots. For example: “Inventory management” from a financial perspective stresses “Turn-and-earn” with minimal “dead and excess stock investments”. But, service-value and people-productivity lenses spotlight “fill-rates”.

Best fill-rates (tuned to a target-customer niche) from one location reduce outages which:

  1. Erode service-value
  2. Cause small-dollar, back-orders and interbranch, split-shipments
  3. These, in turn, boost transactional costs while lowering productivity and morale.

Seek the best total-economic balance!                           

And, Sell More!   

Financial thinking stresses pumping sales (and margin dollars) for economies of buying and operational fixed costs. Plus, get those fatter rebates from best-bribing vendors.   

But, service analytics asks:

  1. Grow sales from which target customers with what unique, service-value proposition? Selling commodities to all customers with standard service creates no service-value equity.  
  2. And, do all employees know the most net-profitable (potential) target-customers? And, how/why they should allocate extra service-hustle to them?       

Customer Profitability Analytics (CPA) Informs All 4 Lenses, But…

Most distributors are 110% focused on financial-belief activities. CPA reveals that some big, and many small customers, are unprofitable. Then, those who are incented on any sales/margin volume, resist. Why low buy-in for insight-plays that logically will deliver greater wins for all?  

Four Nobel Prize winners (over the past 40 years) have proven that our brains are riddled with cognitive biases. Stubborn, short-cut, data-free beliefs win over longer-term realities.    

For More on 4-Lens, Big, All-Win Gains:  

Book an initial (free) C-suite, virtual session with me (bruce@merrifield.com). And, for Waypoint Analytics clients, join me at the workshop in Phoenix on November 7th (link below).    

WayPoint Institute 2019

167. Human Biases Beat Analytical Insights 70-30

SURVEY STATS: THE BRAVE AND THE FADING

I scan a lot of articles. One of my research themes being ROI for Analytics, for which there are frustrating results. One survey found that 70% of companies are not benefitting from their analytics investments. And, worse:  the percentage of firms thinking themselves as “data-driven” has declined. Over the past three annual surveys:  37% were data-driven in ’17 to only 31% in 2019.  (Source? Google the – “2019 Big Data and AI Executive Survey” – by New Vantage Partners for more stats.)

WHY THE ANALYTICAL-INSIGHT EXECUTION GAP?

Be honest. Most individuals and businesses already know what they could do to be better. Who needs even more analytical insights for more improvement opportunities? What’s needed, instead, are more effective change-management strategies, tactics and tools. Most folks need help to be what they want to be.

How can a distributor immediately engage all employees’ minds, hearts, wallets, and team spirit – to move down a new path of innovation?  

TRY THIS LOGIC TRAIN

Discuss with all employees:

  1. “Who wants more total compensation along with job security, growth, and pride?” (100%!)
  2. Then we must, at least, grow Gross Profit Dollars per Full-Time Equivalent Employee (GP$s/FTEE), because only increased GP$s can pay for our wishes.
  3. “What controllable input activities can we do immediately to start to moving GP$s/FTEE higher by working smarter, not harder?”
  4. Order size assumptions: no one can do two customer-related activities at the same time, like: sales calls, order-taking, quotes, picks, deliveries, invoice-paper-matching, etc.  
  5. If for each activity the average GP$s involved was magically higher, then GP$/FTEE would rise.   
  6. “How, then, do we win more, large-GP$ orders while consolidating small-GP$ orders?”
  7. Let’s invent some new analytics. Why not rank all customers by their average GP$s/invoice (along with their total invoices)? And, on the side, let’s divide total orders for the year into operating expenses to find out what our average cost per invoice is.
  8. What do we discover? What next level of questions and new, invented analytics will arise? At some point, what might be our first, easiest experiment to try?
  9. Scary? Is fine-tuning the status quo, instead, a viable option?  
  10. Help? Have other distributors ever gone down this path that we could learn from?

ANSWER TO #10: Yes! Please feel free to request a copy of my, Core Renewal Roadmap by emailing me. bruce@merrifield.com 

CALLING ALL WAYPOINT USERS: It’s time to earn your analytics black belt. Join us November 6th & 7th in Old Town Scottsdale for a 2 day, dual-track training event to refine your data analysis skills, and maximize the competitive advantage that WayPoint gives you. CLICK HERE to take advantage of this great opportunity. Not yet a WayPoint customer? request a free demo HERE

165. Cool Digital Tools Aren’t A Profit-Growth Strategy (Part B)

Two Digital-Selling-Tool Paths

  • Invest in better digital-selling tools for all customers to (hopefully) use. 
  • Visit your most net-profitable customers to identify pain-points that can be reduced by applying off-the-shelf, digital tools. These may be one-off, custom solutions. Or, the solution may work with modifications for other same niche-need big customers. 

Consider these two contrasting case studies. Which path is a focused, profitable-growth strategy?

Case One: An APP for All

Consumers are besieged with APP offers. So, a contractor-supply distributor creates an APP that does two things: placing orders via your mobile phone, and getting fast delivery (options) from an Uber-type delivery partner. Results were weak. Why? The CEO wasn’t sure (yet).

My Questions to the CEO:

#1. For upfront customer research. What criteria were used to identify and visit 3-5 accounts most apt to use the APP? Then, did you brainstorm with them about:

  • How valuable this APP might be and why specifically?
  • For what percent of job scenarios would the delivery option be most beneficial?
  • What extra fee would the contractor pay for different delivery response times? 
  • If the prospects were excited, how could the APP be further improved? 

*Remember to be open to serendipitous insights*

#2. Post-mortem questions about APP 1.0 to a broader group of visited accounts:

  • How did you hear, (if you did), about the APP and its intended benefits?
  • Why did you not use it?
  • Is there any value within this ideaspace that can be re-developed? 
  • If an APP 2.0 has more promise, what additional marketing/education will be needed for wider adoption?

The team had done negligible pre and post-APP launch market research. (Will APP 2.0 happen? Stay tuned to future blogs.)  

CASE TWO: An App For One, Mongo Customer

The CEO of another contractor-supply distributor called on a huge account to explore buying-process “friction” possibilities. The customer had a niche doing a few standard jobs in big-contract quantities. Each job needed a fixed kit of SKUs with occasional tweaks. The two honchos oversaw the co-creation of a custom APP with order buttons for the standard kits. The contractor CEO insisted that everyone use the APP. Sales to the customer doubled and both parties realized other economic benefits.  

Bottom Line: Find and co-create digital solutions with best customers first.  

*Second of several case study comparisons.