A while ago one of our con­sul­tants shared a copy of “Mon­ey­ball: The Art of Win­ning an Unfair Game” by the best-selling author, Michael Lewis. While not con­sid­er­ing myself to be a hard­core base­ball fan, this con­sul­tant still encour­aged me to read the book. He felt it high­lighted com­mon prob­lems that both com­pa­nies and indus­tries strug­gle with when it comes to becom­ing more data-driven.

If you’re not famil­iar with Mon­ey­ball, Michael Lewis chron­i­cles the sur­pris­ing suc­cess of the small-market base­ball team, the Oak­land Ath­let­ics, which com­petes against large-market teams with much deeper pock­ets such as the New York Yan­kees or Boston Red Sox. In order to max­i­mize his player bud­get (a fifth of the size of larger teams’ bud­gets), Oak­land A’s Gen­eral Man­ager, Billy Beane, broke with tra­di­tion and applied an ana­lyt­i­cal approach to baseball’s flawed and sub­jec­tive scout­ing sys­tem. His staff drafted young, inex­pen­sive play­ers and obtained unwanted, afford­able vet­er­ans with high on-base per­cent­ages as well as unortho­dox pitch­ers who gen­er­ated a lot of ground outs. Using sta­tis­ti­cal analy­sis known as saber­met­rics, the Oak­land A’s were able to level the play­ing field and pro­ceed to out­smart and out­per­form much richer teams. All of the MLB teams had access to the same data; how­ever, the Oak­land A’s iden­ti­fied inef­fi­cien­cies in how the data was being used and cap­i­tal­ized on them.

Par­al­lels with Marketingball

As I read the book, I made five obser­va­tions about baseball’s chal­lenges that par­al­leled mar­keters’ strug­gles to become more data-driven. Like base­ball, mar­ket­ing has a his­tory of being sur­rounded by data but fail­ing to lever­age it very effec­tively. For­mer Pep­siCo and Apple CEO, John Scul­ley is attrib­uted with the state­ment: “No great mar­ket­ing deci­sions have ever been made on quan­ti­ta­tive data”. No doubt many mar­keters would agree with Mr. Scul­ley — and that’s okay because this tra­di­tional mar­ket­ing view­point gives data-driven mar­ket­ing orga­ni­za­tions an oppor­tu­nity to fly under the radar like the Oak­land A’s and gain mar­ket share from less savvy competitors.

1. Intu­ition instead of analysis

Tra­di­tional base­ball scouts relied on sev­eral sight-based scout­ing prej­u­dices. “The scout­ing dis­trust of right-handed pitch­ers, for instance, or the scout­ing dis­trust of skinny lit­tle guys who get on base. Or the scout­ing dis­taste for fat catch­ers.” Billy Beane’s staff went against base­ball scouting’s con­ven­tions by eval­u­at­ing young col­lege play­ers (who had more data avail­able than high school play­ers) not by what they looked like, or what they might become, but by what they had done. Mar­keters have their own prej­u­dices and gut-driven prac­tices. Too many clever mar­ket­ing cam­paigns have been launched with­out any con­sid­er­a­tion of what other sim­i­larly clever cam­paigns accom­plished. Too many web­sites have been entirely redesigned (at sig­nif­i­cant cost) with no more than a quick glance of reports show­ing their past performance.

2. Data to jus­tify deci­sions (not to inform decisions)

Michael Lewis shared an inter­est­ing story of how the Hous­ton Astros asked saber­met­rics con­sul­tants to ana­lyze the effect of mov­ing the Astrodomes’ fences in closer. They believed more home runs would sell more tick­ets. After per­form­ing the analy­sis, the con­sul­tants found that the Astros would actu­ally lose more games as their oppo­nents were more likely to hit long pop flies. Sud­denly, the Hous­ton Astros wanted the con­sul­tants to cover up the infor­ma­tion. “They didn’t want the infor­ma­tion to inform the deci­sion. They’d already made the deci­sion.” The same prac­tice of jus­ti­fy­ing or defend­ing a deci­sion with data hap­pens in mar­ket­ing. For best results, analy­sis should pre­cede mar­ket­ing deci­sions and inform them — not the reverse.

3. Cul­ture eats strat­egy for breakfast

In 1999, John Henry, a suc­cess­ful data-driven bil­lion­aire who used sta­tis­tics to take advan­tage of inef­fi­cien­cies in the finan­cial mar­kets, acquired the Florida Mar­lins. Henry once wrote that “peo­ple in both [base­ball and the stock mar­ket] oper­ate with beliefs and biases. To the extent you can elim­i­nate both and replace them with data, you gain a clear advan­tage.” Despite Henry’s avid fol­low­ing of saber­met­rics and best inten­tions, he faced an uphill bat­tle and suc­cumbed to the pre­vail­ing prac­tices in base­ball. “For a man who had never played pro­fes­sional base­ball to impose upon even a pathetic major league fran­chise an entirely new way of doing things meant alien­at­ing the base­ball insid­ers he employed: the man­ager, the scouts, the play­ers. In the end, he would have been ostra­cized by his own orga­ni­za­tion. And what was the point of being in base­ball if you weren’t in base­ball?” It can be dif­fi­cult for mar­ket­ing orga­ni­za­tions to become more data-driven when the cul­ture is fight­ing the trans­for­ma­tion process every step of the way. You need a con­certed effort — rather than just good inten­tions — to over­come orga­ni­za­tional iner­tia.

4. Wrong metrics

When base­ball met­rics were first invented in the late nine­teenth cen­tury, they were flawed. British-born jour­nal­ist Henry Chad­wick, who intro­duced many of baseball’s met­rics, decided that walks were caused by pitcher mis­takes and had noth­ing to do with the hitter’s exper­tise. As a result, the main met­ric for eval­u­at­ing hit­ting per­for­mance — bat­ting aver­age - excluded walks, and it also failed to place any value on extra base hits. Billy Beane’s scout­ing staff placed higher value on dif­fer­ent met­rics — on-base and slug­ging per­cent­age — which enabled his team to find dis­counted play­ers who actu­ally achieved what the Oak­land A’s needed — more runs per game (regard­less of how they got on base). How many mar­keters are just doing what every­one else is doing? Rather than chas­ing the KPI-du-jour or what­ever their com­peti­tors are doing, online mar­keters need to deter­mine what KPIs are right for mea­sur­ing their business.

5. Data numbness

Influ­en­tial saber­me­tri­cian Bill James was dis­ap­pointed that his sta­tis­ti­cal craft was being equated more often with recit­ing arcane base­ball stats than its intended pur­pose — gain­ing a bet­ter under­stand­ing of base­ball. James stated, “I won­der if we haven’t become so numbed by all these num­bers that we are no longer capa­ble of truly assim­i­lat­ing any knowl­edge which might result from them.” Some­times mar­ket­ing orga­ni­za­tions appear to over­com­pen­sate and start lever­ag­ing all kinds of met­rics and KPIs, becom­ing numb in the process. It’s impor­tant for mar­keters to focus on a few KPIs to deepen their under­stand­ing and bet­ter seize opti­miza­tion opportunities.

The data-driven rev­o­lu­tion that Billy Beane brought to bear on the estab­lished world of base­ball is a com­pelling story.  The same changes are hap­pen­ing within mar­ket­ing today. Just like there were inef­fi­cien­cies in base­ball, there are also inef­fi­cien­cies in mar­ket­ing. The rewards will go to the base­ball man­agers and mar­ket­ing man­agers who can take advan­tage of those inef­fi­cien­cies. I’d highly rec­om­mend pick­ing up this thought-provoking book even if you’re not a base­ball fan. If you’re a fan of Brad Pitt and don’t have time to read another book right now, you can catch the upcom­ing Mon­ey­ball movie in Sep­tem­ber (check out the trailer). If you’ve already read Michael Lewis’ Mon­ey­ball book, I’d be curi­ous to hear what insights you gleaned from it.