Making Data Work Harder for You
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Data AnalyticsStrategy Marketing Jul 6, 2015

Mak­ing Data Work Hard­er for You

A strate­gic approach to data ana­lyt­ics starts with ask­ing the right questions.

Data science 101 helps a leader understand data analytics.

Yevgenia Nayberg

Based on the research of

Eric T. Anderson

Florian Zettelmeyer

Listening: Insight in Person Data Analytics

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This month, Insight In Per­son looks at how busi­ness lead­ers can incor­po­rate data ana­lyt­ics into their com­pa­nies — from what the manager’s role should be to how big data can be used in prod­uct development.

Flo­ri­an Zettelmey­er, a pro­fes­sor of mar­ket­ing at the Kel­logg School and fac­ul­ty direc­tor of the pro­gram on data ana­lyt­ics at Kel­logg, explains why every leader needs a work­ing knowl­edge of data sci­ence,” and why it is impor­tant to encour­age data lit­er­a­cy across organizations.

Eric Ander­son, a pro­fes­sor and chair of the mar­ket­ing depart­ment at the Kel­logg School, dis­cuss­es how com­pa­nies can use ana­lyt­ics to see who is buy­ing their prod­ucts — and whether they’re the kind of cus­tomer that might be a har­bin­ger of failure.”

[Music prelude]

[Fred SCHMALZ: Hel­lo, and wel­come to Insight in Per­son, Kel­logg Insight’s month­ly pod­cast, pro­duced by the Kel­logg School of Man­age­ment at North­west­ern Uni­ver­si­ty. I’m your host, Fred Schmalz.

This month we take a look at how busi­ness lead­ers can incor­po­rate data ana­lyt­ics into their com­pa­nies — from why man­agers need to be involved in the data ana­lyt­ics process to how big data can be used in prod­uct devel­op­ment. In the first half of the pod­cast, we hear about how a work­ing knowl­edge of data sci­ence can help busi­ness lead­ers get ahead and man­age with confidence.

In the sec­ond half of the pod­cast, we look at how data ana­lyt­ics can tell us who’s buy­ing our prod­ucts — and whether they’re the kind of cus­tomer that might be a har­bin­ger of fail­ure for those products.

So stay with us.

[Music inter­lude]

ACT 1: Flo­ri­an Zettelmey­er on why a work­ing knowl­edge of data ana­lyt­ics is nec­es­sary for busi­ness leaders

Flo­ri­an ZETTELMEY­ER: One of the real­ly fas­ci­nat­ing devel­op­ments in our world is that it has become eas­i­er to mea­sure things.

[Fred SCHMALZ: That’s Flo­ri­an Zettelmey­er, a pro­fes­sor of mar­ket­ing at the Kel­logg School and fac­ul­ty direc­tor of the pro­gram on data ana­lyt­ics at Kellogg.

ZETTELMEY­ER: And because it’s eas­i­er to mea­sure things and it’s eas­i­er to visu­al­ize things, it is also eas­i­er to present such infor­ma­tion direct­ly to man­agers and direct­ly to deci­sion makers.

SCHMALZ: Zettelmey­er says that in today’s data-dri­ven world, man­agers need to think of data ana­lyt­ics as part of their reper­toire, rather than some­thing that falls out­side of their domain.

After all, lead­ers are the ones who will have to make strate­gic deci­sions based on the data, so they should be the gate­keep­ers when it comes to decid­ing how data can add val­ue to their business.

ZETTELMEY­ER: They now need to have a lev­el of aware­ness of what you can and what you can­not learn on the basis of the data that you observe — which, frankly, was not a skill that has tra­di­tion­al­ly been trained for a man­age­r­i­al class but was a skill that was the domain of the data sci­en­tist or the ana­lyt­ics people.

SCHMALZ: For the data ana­lyt­ics process to be effec­tive, Zettelmey­er says, man­agers need to be involved every step of the way. In order to do this, they need to have what he calls a work­ing knowl­edge” of data science.

So what exact­ly is a work­ing knowl­edge of data sci­ence? Zettelmey­er says the nec­es­sary skills are not learned in an advanced econo­met­rics class. In fact, they’re not real­ly tech­ni­cal skills at all — they’re think­ing skills.

ZETTELMEY­ER: You don’t real­ly need to be great at math or at stats or com­put­er sci­ence to obtain a work­ing knowl­edge. I’m not say­ing that it is super easy to get it, but it is real­ly a way of dis­ci­plined think­ing that needs some prac­tice but that fun­da­men­tal­ly does not need a lot of tech­ni­cal back­ground. There­fore, it is fun­da­men­tal­ly acquirable by peo­ple who have suc­ceed­ed in the busi­ness world, pre­cise­ly for the fact that they are good ana­lyt­i­cal thinkers.

SCHMALZ: These think­ing skills — and this dis­ci­pline — can save man­agers from blind­ly trust­ing data sci­en­tists by assum­ing that just because they have the num­bers, the analy­sis was per­formed in a rea­son­able way.

To do ana­lyt­ics right, you need more than data — you also need to draw upon an in-depth knowl­edge of your busi­ness, which is exact­ly the kind of domain exper­tise good lead­ers bring to the table.

ZETTELMEY­ER: This is some­thing that often data sci­en­tists are not so good at find­ing. But it’s some­thing that man­agers are excel­lent at find­ing. It real­ly speaks to this idea that you get both very good data sci­en­tists as well as very good busi­ness peo­ple involved in ana­lyt­ics because that’s how you make progress and avoid mistakes.

SCHMALZ: Cre­at­ing a cul­ture where ana­lyt­ics add val­ue to a busi­ness goes well beyond just putting togeth­er the right ana­lyt­ics team. Man­ag­ing ana­lyt­ics across the orga­ni­za­tion is key.

ZETTELMEY­ER: Many of the core bar­ri­ers to mak­ing ana­lyt­ics work have actu­al­ly noth­ing to do with ana­lyt­i­cal prob­lems per se. They are not about pick­ing the right algo­rithm; they’re often not even about pick­ing the right data.

What they are about is that, when you do ana­lyt­ics, you run into orga­ni­za­tion­al and incen­tive bar­ri­ers that pre­vent you from actu­al­ly doing the ana­lyt­ics or that pre­vent you from act­ing on the ana­lyt­ics. For exam­ple, imag­ine that you want to get a 360 degree view on the cus­tomer, which is a big mantra in ana­lyt­ics. Well, the only way you are going to do that is if you can get all the busi­ness units to actu­al­ly coop­er­ate to pro­vide data so that you do get a 360 degree view of the customer.

SCHMALZ: In oth­er words, if a company’s busi­ness units aren’t in the habit of work­ing col­lab­o­ra­tive­ly, using data sci­ence can quick­ly become a source of orga­ni­za­tion­al ten­sion. Per­form­ing ana­lyt­ics, like many aspects of man­ag­ing an orga­ni­za­tion, is fun­da­men­tal­ly political.

ZETTELMEY­ER: There are win­ners and there are losers from imple­ment­ing ana­lyt­ics. There are peo­ple who have dif­fer­ent agen­das. Before long, if you are a manger, you will find your­self in a sit­u­a­tion where you have team A report­ing back to you with result A backed up by data sci­ence team A.

You have team B report­ing back to you with result B backed up by data sci­ence team B. Guess what? Result A and result B are not going to be the same. The prob­lem is that you can’t real­ly go out there and say, Let me get my data sci­en­tist in order to some­how fig­ure out which of these two teams is right.” Because there are already ten data sci­en­tists in the room and they all dis­agree with each other.

SCHMALZ: Data ana­lyt­ics can­not be treat­ed as a sep­a­rate part of one’s busi­ness — it has to be done with orga­ni­za­tion­al chal­lenges in mind, and it has to be incor­po­rat­ed into the busi­ness plan itself. Too often, Zettelmey­er says, man­agers decide to col­lect data with­out know­ing exact­ly how they will use it, and this leads to prob­lems down the road.

ZETTELMEY­ER: You have to think about the gen­er­a­tion of data as a strate­gic imper­a­tive. You can’t just go out in a com­pa­ny and some­how hope that the data that is avail­able to you and the data that gets inci­den­tal­ly cre­at­ed in the course if busi­ness is the kind of data that’s going to lead to break­throughs or the kind of data from which you can learn some­thing about your business.

Ana­lyt­ics has to fun­da­men­tal­ly start with busi­ness prob­lems. I can’t tell you how often I’ve heard the sen­tence by an exec­u­tive who comes and wants to talk to me and says, You know, we have this enor­mous amount of data. We are over­whelmed by it. We don’t know what to do with it. But we are sure there’s some­thing incred­i­bly valu­able in there.”

SCHMALZ: Whether such data can add val­ue depends on what kinds of ques­tions are asked, how the data are gen­er­at­ed, and how the lead­er­ship makes deci­sions based on its analy­sis. When ana­lyt­ics goes bad, it usu­al­ly does so because the insights are non­sci­en­tif­ic, how­ev­er much they appear to offer.

For Zettelmey­er, the way to do ana­lyt­ics well is to start with the prob­lem and incor­po­rate the ana­lyt­ics into the busi­ness plan itself. It also means empow­er­ing every­one in the orga­ni­za­tion to use their ana­lyt­ic skills to inter­pret and ques­tion the data

ZETTELMEY­ER: If we want big data and ana­lyt­ics to suc­ceed, every­body needs to feel that they have this right to ques­tion. There has to be a cul­ture where you can’t get away with think­ing as opposed to knowing.

SCHMALZ: For Man­agers, hav­ing a work­ing knowl­edge of data sci­ence has three main ben­e­fits. First, it helps you judge what good data look like. Sec­ond, it helps you iden­ti­fy pre­cise­ly where ana­lyt­ics can help the busi­ness. And third, it helps you lead with con­fi­dence. Increas­ing­ly, hav­ing this knowl­edge will become the norm for all good busi­ness leaders.

ZETTELMEY­ER: Can you imag­ine a CFO going to the CEO and say­ing, You know what? I don’t real­ly know how to read a bal­anced sheet, but I have some­body on my team who is real­ly good at it.” Okay, we would laugh that per­son out of the room today. Now, I hate to tell you but I know a whole bunch of peo­ple in mar­ket­ing and oth­er areas at the C-suite who with­out blink­ing an eye would go to the CEO and say, You know what? This ana­lyt­ics stuff is com­pli­cat­ed. I don’t have a full grasp on it, but I have assem­bled a crack­er­jack ana­lyt­ics team that is going to push us to the next level.”

I think this is an answer that is no longer acceptable.

[Music inter­lude]

ACT 2: Eric Ander­son on Har­bin­gers of Failure

[Fred SCHMALZ: No mat­ter how often you go to the super­mar­ket, you are like­ly to see new prod­ucts — from break­fast cere­als to tasty yogurt fla­vors to house­hold cleansers — com­pet­ing for shelf space with estab­lished brands.

Yet new prod­ucts tend to fail at very high rates.

Which begs the ques­tion, how can so many prod­ucts make it through the devel­op­ment process and then flop once they land in stores?

In man­ag­ing the intro­duc­tion of new prod­ucts, it turns out that pre­dict­ing their suc­cess or fail­ure doesn’t just depend upon whether lead users buy those prod­ucts, but which lead users buy them.

Pro­fes­sor Eric Ander­son, chair of the mar­ket­ing depart­ment at the Kel­logg School, want­ed to know more about why prod­ucts failed and how to iden­ti­fy those prod­ucts ear­ly in the process.

In a recent study, Ander­son and his coau­thors found that the pres­ence of cer­tain buy­ers of a prod­uct may indi­cate that the prod­uct is like­ly to fail. And they found that these har­bin­gers of fail­ure” tend to buy a lot of these failed products.

Here’s Eric Anderson.

Eric ANDER­SON: What spawned out of this was a fair­ly cre­ative idea about how to think about suc­cess and fail­ure of new prod­ucts. We start­ed think­ing about con­sumers who might sys­tem­at­i­cal­ly buy either win­ners or losers. Prod­ucts that are win­ners are ones that sur­vive on the shelf for a long time, things that you see all the time, like Hellman’s may­on­naise. Losers are prod­ucts that you don’t know that well because they dis­ap­pear very quick­ly — prod­ucts like water­mel­on Ore­os that are on the shelf for a short peri­od of time and then quick­ly disappear.

SCHMALZ: When research­ing win­ning and los­ing prod­ucts, Ander­son found that there are indeed cus­tomers who have a sys­tem­at­ic pref­er­ence for prod­ucts that tend to fail.

ANDER­SON: It turns out that the more you buy of these failed prod­ucts, the more like­ly you are to buy anoth­er failed prod­uct. That real­ly rein­forced the notion that these cus­tomers were behav­ing systematically.

We can talk about cus­tomers buy­ing a soda prod­uct and sys­tem­at­i­cal­ly buy­ing failed soda. Then we can show that this helps us pre­dict in oth­er cat­e­gories, like house­hold cleaners.

SCHMALZ: So who are these cus­tomers? Are they peo­ple far out on the taste spec­trum, whose gro­cery carts are loaded down with rasp­ber­ry beer and kumquat cus­tard? Are they young peo­ple with adven­tur­ous, if curi­ous, dietary habits? Mil­len­ni­als with lit­tle of the brand loy­al­ty of their pre­de­ces­sors? Are they cus­tomers grav­i­tat­ing toward the cheap­est prod­ucts on the shelf?

The answer to this ques­tion sur­prised Ander­son and his coauthors.

ANDER­SON: What we’ve dis­cov­ered thus far is in the pack­aged-goods world, these cus­tomers tend to be fair­ly edu­cat­ed, fair­ly wealthy, and have large fam­i­ly sizes.

SCHMALZ: What’s curi­ous is that one would assume afflu­ent peo­ple with larg­er fam­i­lies would tend to spend more at the super­mar­ket, so intu­itive­ly, if cus­tomers buy more of some­thing, that’s a good thing, right? Not necessarily.

ANDER­SON: The twist is that the more you sell to cus­tomers who are these har­bin­gers of fail­ure, the more like­ly you are to fail. That’s what is the sur­prise. The more you sell, the more like­ly you are to fail when those sales are going to cus­tomers who buy Diet Crys­tal Pepsi.

That’s what peo­ple find inter­est­ing and shock­ing, that you can actu­al­ly pre­dict new prod­uct fail­ure. That insight is what opens up all sorts of new pos­si­bil­i­ties for future research.

SCHMALZ: From a data-ana­lyt­ics stand­point, the con­sumer goods indus­try is an excel­lent mar­ket to test for har­bin­gers, because, through super­mar­kets’ cus­tomer-loy­al­ty pro­grams, gran­u­lar infor­ma­tion is col­lect­ed on who buys what, which helps deter­mine suc­cess or fail­ure on a micro lev­el. But as we heard ear­li­er, hav­ing more infor­ma­tion is not auto­mat­i­cal­ly going to lead to insights.

ANDER­SON: A mis­con­cep­tion is that what you need to cre­ate val­ue is lots of data or per­haps tremen­dous horse­pow­er with respect to ana­lyt­ics and com­ing up with the next best algo­rithm for pro­cess­ing the data. Here nei­ther of those actu­al­ly leads to value.

What dri­ves val­ue is what I’ll think of as cre­ative insight. It’s the spark of an idea that thinks about the world in a com­plete­ly dif­fer­ent way and doesn’t take the sta­tus quo as giv­en. It paus­es for a sec­ond and says, hey, can we think about things dif­fer­ent­ly. Here, the ques­tion we asked, where we spun every­thing upside down, is, look, the new prod­uct-devel­op­ment process always takes pos­i­tive feed­back as a mea­sure of success.

Each one of these mea­sures tells me, yes, keep going, keep going. What we point out is, here’s a mea­sure of suc­cess. You sell more to these har­bin­gers of fail­ure, and it’s com­plete­ly neg­a­tive­ly cor­re­lat­ed with actu­al mar­ket per­for­mance. That’s what twists things and throws it upside down.

Much of the val­ue is locked into what kinds of ques­tions you ask. It’s very dif­fi­cult to get new insights with­out new questions.

SCHMALZ: Infor­ma­tion about the pres­ence and pur­chas­ing habits of har­bin­gers of fail­ure can help com­pa­nies improve the new prod­uct devel­op­ment process to give those prod­ucts that do appear a greater chance of suc­cess. The key is to devel­op met­rics that allow firms to clas­si­fy prod­uct-test­ing respon­dents into har­bin­gers and non-har­bin­gers as ear­ly in the process as possible.

ANDER­SON: By under­stand­ing that dif­fer­ence ear­ly on, you can start to make the appro­pri­ate invest­ments. You can align your invest­ment in the prod­uct and how you’re posi­tion­ing it with your retail part­ners to say, this is serv­ing a dif­fer­ent pur­pose. This prod­uct might be a niche prod­uct and is serv­ing a dif­fer­ent goal rather than being a mass-mar­ket product.

SCHMALZ: The rea­son these prod­ucts fail is not because they are inher­ent­ly bad ideas. It’s per­haps that they were incor­rect­ly posi­tioned as mass-mar­ket prod­ucts when they may have been niche prod­ucts. Data ana­lyt­ics can help deter­mine that prod­uct posi­tion­ing, both in the super­mar­ket and wher­ev­er peo­ple make purchases.

ANDER­SON: If we have that kind of data, I think we can start to explore, does this extend into home elec­tron­ics? Does it extend into appli­ances? We think it does. I think my con­jec­ture is that it will extend into oth­er indus­tries, but hope­ful­ly it will lead to new insights and new dis­cov­er­ies along the way as we start to explore oth­er categories.

[Music inter­lude]

SCHMALZ: This pro­gram was pro­duced by Drew Calvert, Fred Schmalz, Kate Pro­to, and Michael Spikes. Spe­cial thanks to Kel­logg School of Man­age­ment fac­ul­ty Flo­ri­an Zettelmey­er and Eric Anderson.

You can stream or down­load our month­ly pod­cast from iTunes, or from our web­site, where you can read more about Flo­ri­an Zettelmeyer’s and Eric Anderson’s research and get tips on data ana­lyt­ics for man­agers. Vis­it us at insight​.kel​logg​.north​west​ern​.edu. We will be back next month with anoth­er Insight In Per­son podcast.

Featured Faculty

Eric T. Anderson

Hartmarx Professor of Marketing, Professor of Marketing, Director of the Center for Global Marketing Practice

Florian Zettelmeyer

Nancy L. Ertle Professor of Marketing

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