Strategic Insights 18


The NFL regular season is over. The team that will win the Super Bowl will do it (odds are) on “positive turnovers” and better, intra-game adjustments. In the history of the Super Bowl, the team that has won the positive-turnover metric also won the game 32 out of 35 times.

How does your firm measure and win on positive turnovers and better real-time adjustments within key accounts to beat competitors? If you aren’t already measuring and managing these two factors, then why not start now to get lucky in 2012?


Your 2011 financial profits averaged out some big swings in net-profit gains and losses amongst top 10% biggest volume accounts.

1.     When looking at the annual net-profit totals for customers (margin dollars *less* cost-to-serve = net profit):

a.     What 10-30 accounts were up and down the most?

b.     What was the ratio of the bottom 10’s losses divided by the top 10’s gains?

c.     How can you get that ratio of losses to gains from 100% to 5% (as in case below)?

d.     Using deep-dive analytics why not determine the swings’ causes and adjust?

e.     How much of the net-profit change –at each account– was due to marketplace luck variables and/or team talent?

2.     Imagine you have reports that compute (and rank from most positive to most negative) the annual difference in net-profits for 2010 v. 2011 for: items and suppliers[1]; customers and sales territories; and for customers within each territory (aka “Delta PBIT reports”[2]):

a.     What does ranking reps by annual growth rate in net-profit for the territory tell you?

b.     Who grew their territory’s net-profit total the most? In what accounts? How much was luck? How much was skill in creating and seizing opportunities while anticipating and minimizing bad luck?

c.     Do all sales reps have the same “return on luck” (ROL) capability? If management visits key accounts to audit all elements of the existing replenishment relationship and uncovers improvement opportunities, how does the rep follow through?[3] The range will be from zero to awesome.

d.     How do you get your very best Ace Reps assigned to biggest upside accounts being sat on by your least effective reps?[4]

e.     NFL teams and quarterbacks have power-rating rankings. Baseball has “slugging average” + “on-base percentage” = “OPS” (On-base Plus Slugging) for overall batter effectiveness. Could annual growth rate in net-profits for sales reps could be as helpful as “OPS” is in the majors?   

Answer these questions and act to get lucky on positive turnovers in 2012.


A distributor of industrial OEM inputs (call them Very Lucky Now: VLN) started using a Line-Item Profit Analytics(LIPA) web service from Waypoint Analytics 24 months ago when they had the “customer profitability whale curve” from hell. (Google Image that phrase; spend 30 minutes researching the slides that pop up!)

In ’09, VLN lost $2.8MM. On their net-profit ranking report: they made $2.5MM on the top 15% of their net-profit customers. Thirty-five percent of the customers were breakeven. The bottom 50% lost ($5.3MM) with the bottom 5% losing ($3.8MM). In July ’10, the company had profit improvement kick-off day. All employees were put on net-profit teams and with a net-profit improvement bonus incentive. The overall goals were to:

1.     Find a way to make every customer profitable starting with transforming the biggest losers.

2.     Not lose any of the most profitable customers by improving the total service value proposition.

3.     Prioritize and pursue all other net-profit improvement opportunities that LIPA reports revealed.


For ’11, with SWAT team audit work, the #1-most-profitable customer increased net profits from $500K in ’10 to $1.125M! The biggest loser went from a net-profit loss of ($316K) to a $20K profit with much more profitable volume still to come.

The net-profit increases for the top 10 accounts at VLN for ’11 over ’10 was $1.618M. The profit decreases for the bottom 10 was ($80K) or 5% of the top 10 total. How lucky is that?!


To get a quick vision of what a comprehensive LIPA solution looks like, I would at least kick the Waypoint Analytics tires with a web demo to know the gap between what you have and what’s out there. Then, inquire about attending the LIPA conference in Phoenix on March 29-30th and/or visiting a power user like VLN.

[1] See Insight #17 for distributor case using supplier Delta PBIT report to find promotions had NO value.

[2] Delta is a scientific symbol/term for “change”; PBIT is short for “profit before interest and taxes”

[3] See Insight #15 for importance of mgt. involvement in “tuning buy-sell, inter-business process relationships”

[4] For how-to ideas see articles 4.11 – 4.13 at