Disrupting 2016: Data Science & The Marketing Lab

Col­lect­ing data, map­ping to your cus­tomer jour­ney, and deriv­ing insights can dra­mat­i­cal­ly increase cus­tomer sat­is­fac­tion and your mar­ket­ing ROI.

Lisa Ferry By Lisa Ferry from HackerAgency Consulting Services. Join the discussion » 0 comments

What is the buzz around data sci­ence? The data sci­en­tist role is in demand across cor­po­ra­tions, con­sul­tan­cies, and mar­ket­ing ser­vice providers. Accord­ing to research con­duct­ed by Accen­ture, 80 per­cent of data sci­en­tist posi­tions cre­at­ed aren’t filled. There’s a hint of des­per­a­tion — “If I hire a data sci­en­tist, it will solve every­thing.” But you need more. You need a lab.

With­out a data sci­en­tist, a mar­keter can’t know whether suc­cess in a sin­gle cam­paign leads to increased com­pa­ny return on invest­ment. Data sci­en­tists dis­cov­er pat­terns in the cus­tomer jour­ney that lead to impact­ful and effi­cient mar­ket­ing.

But how?

Through data explo­ration using knowl­edge dis­cov­ery tech­niques such as data min­ing, sta­tis­tics, and pre­dic­tive ana­lyt­ics, new con­sumer behav­ior pat­terns and data rela­tion­ships are test­ed in mar­ket­ing “exper­i­ments,” also known as cam­paigns.

Discoveries In The Marketing Lab — Why This Approach?

Data sci­en­tists need the free­dom afford­ed in the “lab exper­i­ment” sce­nario to devel­op mar­ket­ing hypothe­ses, test, refine and val­i­date, using results from a pre­vi­ous cam­paign. Only after val­i­da­tion can the data sci­en­tist con­fi­dent­ly say that the method­ol­o­gy is ready to “go to mar­ket.”

Why does this mat­ter?

Because in the fast-paced dig­i­tal-on-demand world, it’s easy for a com­pa­ny to skip the dis­ci­plined lab set­ting and allow untest­ed hypothe­ses to dri­ve data deci­sions.

The 2016 Customer Journey? It’s Complicated

What you’d real­ly like to know are the details: Did your cam­paign meet your goal for leads gen­er­at­ed? If so, did you mea­sure how many of those leads remained loy­al cus­tomers two years lat­er?

Sim­i­lar­ly, one would expect sci­en­tif­ic rig­or for an exper­i­men­tal can­cer drug: Did it yield a pos­i­tive out­come? Or, did it show promise and require more research? How did patients fare two years after treat­ment?

There is a struc­ture and dis­ci­pline required to build trust in data. That’s where the lab­o­ra­to­ry is essen­tial.

In the mar­ket­ing lab we can make sense of data in areas like mar­ket­ing automa­tion, machine learn­ing, and pro­gram­mat­ic adver­tis­ing, to name a few. In a lab envi­ron­ment, data sci­en­tists have the free­dom to pull nuggets from all of that unstruc­tured data that’s so chal­leng­ing — and fol­low the trail of the cus­tomer jour­ney from end to end.

Customer Data Ecosystem

Accord­ing to For­rester, the future inno­va­tion lab will be an ecosys­tem built around the needs of a spe­cif­ic con­sumer seg­ment. We must encour­age cross-fer­til­iza­tion between dif­fer­ent types of spe­cial­ists assem­bled for their exper­tise.

The rock stars of the new lab are the rare birds who “get” con­sumers, data, dig­i­tal, con­tent, dis­tri­b­u­tion, and new prod­uct mod­els and who can cajole diverse geeks to speak the same lan­guage and work togeth­er con­struc­tive­ly.

So, can the data sci­en­tist be the rock star? The answer is yes, if she has knowl­edge of your cus­tomer jour­ney, sta­tis­ti­cal knowl­edge, prac­ti­cal indus­try exper­tise and the abil­i­ty to dis­cern cau­sa­tion and cor­re­la­tion. This is a lot to ask of one per­son, so it will like­ly take a team of experts to make a great lab.

Speed Is Essential!

Ven­ture­Beat points out that mar­ket­ing agen­cies are well-posi­tioned to fit enter­pris­es’ loom­ing out­source needs for data-dri­ven mar­ket­ing, if they can build the omnichan­nel con­tent mar­ket­ing machines pow­ered by robust data ana­lyt­ics that busi­ness will require.

Data sci­en­tists oper­at­ing in a lab envi­ron­ment will find insights.

For exam­ple, in the finan­cial ser­vice cus­tomer jour­ney, we know that indi­vid­u­als who respond to mar­ket­ing efforts for cred­it do not nec­es­sar­i­ly become cus­tomers, or if they do, they may not be prof­itable. Why?

Data sci­ence shows that find­ing respon­sive indi­vid­u­als who also can pass rig­or­ous lend­ing under­writ­ing rules can be chal­leng­ing. Con­sumers with the high­est like­li­hood to respond typ­i­cal­ly need cred­it, but may have prob­lems mak­ing their month­ly pay­ments.

So, we build a bal­anced bridge. That is, we con­nect response, under­writ­ing and con­ver­sion in a fine-tuned algo­rithm that fits finan­cial prod­ucts to cus­tomer needs while meet­ing cred­it risk rules.

This is just the begin­ning of data sci­ence and the lab approach. Once insights are derived and the pre­dic­tive ana­lyt­ics are in place, the machine will do the work — scor­ing your cus­tomers and prospects, rank­ing them, and fit­ting a prod­uct solu­tion to the indi­vid­ual using the tools and infra­struc­ture you’ve built.

What’s Next?

Get start­ed by build­ing an inven­to­ry of skills and tools need­ed to launch your lab. Recruit with­in your orga­ni­za­tion. Who has knowl­edge of the indus­try, the cus­tomer jour­ney, and busi­ness chal­lenges?

Who are your data sci­ence and BI experts?

Once you’ve searched inter­nal­ly, fill gaps by going out­side your orga­ni­za­tion. Inter­view con­sul­tants, agen­cies, and sub­ject-mat­ter experts that will design, deliv­er and hand off. Set time aside for your new lab team to devote qual­i­ty time — ide­al­ly as their main focus.

Lisa Ferry

Written by Lisa Ferry

Director of Customer Insights & Analytics, HackerAgency Consulting Services

Lisa Ferry is Director of Customer Insights and Analytics at HackerAgency Consulting Services. She has strategic expertise in solution adoption, new product launch, and predictive analytics, serving as a senior consultant for Fortune 100 companies, including software, insurance, financial services, retail, and automotive industries.

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