Building Market Share With Market Research

Do you really know what your customers are thinking?  Do you know what they value?  Do you know which ones are complaining?  About what?

David Line, President of InfoSearch International discusses how market research combined with intelligent statistical analysis can be used to build market share.

David Line, President of InfoSearch International

Metrics – Lots & Lots of Metrics

Readers of this blog have seen a lot about the indispensable value of metrics.  First, ya’ gotta’ have a defined process.  Then ya’ gotta’ measure it.  That’s the only way to know if improvement has occurred or not, and at what rate.  It’s the only way you can prove your dedication to continuous improvement.  Show me the data!  If you don’t have data, you’re just blowing smoke.

OK, hard to debate, but…  It’s also crucial to have a lot of different metrics.  Without a variety of statistical perspectives, it’s easy to misinterpret what a given metric really means.  You might put some action plans in place that are incomplete, a bit off target or downright wrong-headed.

Here’s an example from the social/political world to illustrate the danger of using only one metric.  Consider income/wealth  inequality.  The chart on the left shows that the richest 1% of Americans have 1/3 of the money, and the poorest 50% have only 1/2 of it.  In other words, for every $1 someone in the poor group has, someone in the rich group has $676.  Or you could say $100 vs. $67,600; or $10,000 vs. $6,760,000.  One might conclude that such a big difference is just wrong, and that something should be done about it.  Like maybe taxing the rich group & subsidizing the poor one???

Let’s take a look at a different metric regarding this income disparity thing.  The poorest 5% of Americans are richer than 68% of the world’s inhabitants.  Compare the US numbers to India’s.  America’s poorest are, as a group, about as rich as India’s richest!  Is that also just plain wrong?  Should somebody do something about that too?  Like maybe taxing all Americans & shipping gigantic barrels of cash to everyone in India???

My point is not to debate what should or should not be done by whom regarding income and wealth disparity.  My point is that the way the question is framed and supported by data can have a really, REALLY big impact on the resulting action plan.  Different sets of numbers about the same thing can tell a radically different story, and lead to radically different decisions.  We need to think, discuss, debate, discover and learn just exactly what the numbers are telling us.

How do you objectively judge – using data, of course – the performance of a sales rep?  Total sales?  Sell cycle time?  Profitability?  Sales of old products to new accounts?  Sales of new products to old accounts?  Average sales size?  Number of sales to new accounts?  Number of sales to existing accounts?  (I could add the other 700 or so possible sales metrics I’ve collected over the years, but I’ll spare you!)

Is it one or two or three or fifty key indicators?  For every rep in every territory?  Regardless of rep experience or if it’s a newly penetrated geography or segment?  Is it some way cool index the MBA-toting, outside expert consultant cooked up?  Is it whatever the company president chewed out the sales VP for yesterday?

This may be a shock…  I don’t think the answer to that last set of questions matters all that much from a “continuous improvment of my sales process” standpoint.  The truly powerful value of lots of metrics is the discussion about them.

It’s the series of deep, intelligent, challenging, painful, heated, gloriously rewarding coaching conversations that matters.

Collect the data.  Analyze the data. Debate the daylights out of what the data really means.  Then go sell more.  Go sell it faster!

Give Me More Discipline & Accountability!

A comment on one of my recent posts about forecasting really got me thinking.  Here’s the comment, “Managers, grow a backbone.  Hold your people accountable and stop accepting excuses.”  My knee-jerk reaction was violent agreement.

Then I started thinking…  How effective is Atilla The Hun style management?  How did I and would I react to a “no-excuses, do what I say” attitude on the part of my boss?  How can accountability become an integral part of the sales culture without being heavy-handedly imposed from above?

The answer lies in the competitive nature of the sales beast.  It’s also embodied in W. Edwards Deming’s famous line, “What gets measured gets done.”  And it’s ridiculously simple to implement.

Choose an important metric and publish a top to bottom ranking every month.

Let’s say it’s forecast accuracy.  Somebody will be best, somebody will be worst.  Somebody #1 will feel good and strive for a repeat performance.  Somebody #2 will immediately conclude that bottom-of-the-pile notoriety is no fun and strive to move up at least into the middle of the pack.

Here’s what’s really cool.  Nobody wants to be last, but somebody will be last each month.  That means a whole lot of self-imposed, proactive action to improve performance will be going on.  Self-imposed and proactive; not pressure applied from above.  The absolute level of last place performance will slowly, relentless get better and better and better.

Sounds like a culture of continuous improvement doesn’t it?  Sounds like a culture where reps actually do demand more accountability.  Sounds like a culture where the reps will be intensely focused on identifying the “right” metrics, the really critical ones that produce sales growth.  Hmmm…  Maybe 3 or 4 or 5 important metrics should be published each month.

(A final note:  It isn’t necessary to publically post actual names next to the scores.  It IS necessary, however, to publically post the scores and let each individual know where he or she ranks.)

How many metrics does a sales manager need?

As a group, sales managers are not big on “managing by the numbers.” Only a very few use more than a half-dozen or so measurements to monitor the quality and effectiveness of sales performance. Most rely on two, revenue and profit. They are the ultimate indicators of success, right? Why would anyone need to know any more?

Consider this… Assume that you just “volunteered” to manage a little league baseball team. One of the first things you need to do is come up with a batting order. Lacking any information, your only choice is to list the names in random sequence. In other words, the success of your first management decision will be based purely on luck.

Now assume that you find a list of each kid’s batting average from last year. You now have a metric and can make a better batting order decision. For example, put the kid with the highest average first, second highest second, etc.

Next assume you also find each kid’s on-base percentage from last year. (This is different than batting average. In addition to actually getting a hit, a batter can get on base by drawing a walk, getting hit by a pitch, or due to error made by a fielder on the other team.) You can now make an even better batting order decision.

For example, put the kids with the three highest on-base percentages up to the plate first, second and third. Put the kid with the highest batting average up fourth. Doing so increases the odds that your best hitter will go to bat with three runners on base, thus increasing your odds of scoring more runs. One metric yields a better decision than no metrics. Two metrics yield a better decision than one.

The scenario can continue to change. What if you also knew each player’s stolen base percentage, runs-batted-in, extra-base-hit percentage, etc., etc., etc… Each additional metric enhances the manager’s ability to make a better decision.

Shift gears and look at the big leagues.  Since the Oakland Athletics pioneered the use of metrics and statistical analysis back in 1999, their use has skyrocketed.  Over a 7 year period from ’99 through’05, Oakland won 658 games.  That’s 22 fewer than New York Yankees over the same period – essentially equivalent results.  In the same time frame, Oakland paid out a total of $295 million in player salaries.  The Yanks?  $965 million!!! Each win cost Oakland $448K.  Each win cost New York $1.4 million.  For the math challenged, a win for the Athletics cost 1/3 of what win cost the Yankees.  No wonder every Major League Baseball team now has a statistician on board.

Maybe there really is something to this managing by the numbers stuff… Maybe it even applies to sales… Maybe my competitors will continue to mange like little leaguers…  Sadly, maybe you will continue to manage like a little leaguer.

Think about it…