Hello world! This is my first blog post, so an intro­duc­tion is cus­tom­ary. I have worked in the dig­i­tal mar­ket­ing, social media, brand­ing and ana­lyt­ics con­sult­ing space for prod­uct as well as ser­vices busi­nesses. As a result, I have had the rare oppor­tu­nity as a dig­i­tal mar­keter to also learn from and advise other busi­nesses on their mar­ket­ing strat­egy and analytics.

But as they say, more expe­ri­ence can lead to either more enlight­en­ment or more exas­per­a­tion. (Don’t ask me who said that, you know who just made it up.)

Truth be told, most of us mar­keters do not really value our data as much as we claim we do. We might have “Ana­lyt­ics” and “ROI” on our strat­egy slides and also a painfully con­structed data ware­house and reports. But is that all? Is mar­ket­ing really data-driven or is data still treated as impor­tant but used mostly post-mortem? Tra­di­tion­ally data has been used to answer ques­tions like:

  • Are we doing enough?
  • Are we doing things right?
  • How can we improve?

We ran a lit­tle pro­mo­tion in Ken­tucky retail stores – did it help us go past com­pe­ti­tion? Should we have gone for bet­ter place­ment as opposed to the price dis­count? Note the past tense in all these ques­tions. This method of using data and analy­sis to reach to use­ful busi­ness con­clu­sions is hardly enough in the dig­i­tal era. Here are some ques­tions I’d love to answer based on data. But how?

  • If I invest $5000 on this Face­book app, will it get me 10,000 active and engaged Likes from my tar­get audience?
  • If I replace the text con­tent on this par­tic­u­lar web­page with a video, how will it affect the aver­age time peo­ple spend on the web­site and in turn will it improve aware­ness about our products?
  • If I move a part of my email mar­ket­ing bud­get to ban­ner ads, will it result in rev­enue improve­ment? How much should I move?

Sounds much like a Kipling poem, doesn’t it? But the abil­ity to have answers to such ques­tions, in my opin­ion, would be the epit­ome of data-driven mar­ket­ing — the abil­ity to infuse more and more math­e­mat­ics and logic into a prac­tice largely based on hunches and cre­ative skills. At its most mature form, dig­i­tal mar­ket­ing should be a sci­ence where one can pre­dict tar­get appro­pri­a­tion, aware­ness gen­er­a­tion and have a con­stant tab on chan­nel returns. We’d then have very lit­tle scope to go wrong then, wouldn’t we?

As a result, my matu­rity model for data dri­ven mar­ket­ing has only three levels:

Level 1: Data What?

Here’s the dig­i­tal mar­ket­ing prac­tice that rep­re­sents quin­tes­sen­tially every­thing wrong in terms of using data – not know­ing where the right data is, unable to inte­grate it, invest­ing time, money and effort with not much method, proof or rea­son. They haven’t fig­ured out most of the P’s of data dri­ven mar­ket­ing as listed here.

Level 2: Dig­ging for Gold

The mar­ket­ing team that sits on a neat and healthy stock­pile of (non-explosive) data. They have what they need, know where to go, but are not answer­ing the right ques­tions yet. Cam­paign analy­sis, chan­nel per­for­mance, web ana­lyt­ics are all set up and handy but not used to answer mar­ket­ing ques­tions and impact the future course of busi­ness. They are yet to real­ize which is more impor­tant for them – more data or more analy­sis?

Level 3: Excel before Outlook

The “epit­ome”, as I’ve already called such an envi­ron­ment, leads the way and rides every wave be it Big Data, pre­dic­tive mar­ket­ing or Data Acti­va­tion (I hope you’ve read David Karnstedt’s blog Data Acti­va­tion & the Future of Dig­i­tal Mar­ket­ing). In these teams, the dig­i­tal mar­keter is also a data sci­en­tist who opens up his Excel work­sheet before send­ing emails in the morn­ing. This team can answer any of the fol­low­ing ques­tions at any time:

  • What does or tar­get audi­ence think of us or say about us?
  • What kind of infor­ma­tion are my users look­ing for?
  • How many leads am I able to gen­er­ate per $ spent on a channel?
  • What is the qual­ity of lead per chan­nel? Qual­ity being assessed by some­thing like (rev­enue + pipeline)$ per lead

I like to think the future of dig­i­tal mar­ket­ing lies in the intel­li­gent use of data and the abil­ity to inspire desired dig­i­tal behav­iour based on an unpar­al­leled under­stand­ing of con­tent, chan­nels and the tar­get audi­ence. This matu­rity model might not be a jump-out-of-the-bath kind of dis­cov­ery, but it ought to help us assess where we are and what we need to do to be the best.


What am I going to do this week? Ana­lyze dif­fer­ences in user behav­ior on the web­site across North Amer­ica and Europe. Hold on till my next blog!


Parth Mukher­jee is a global prod­uct mar­ket­ing man­ager for Adobe’s tech­ni­cal com­mu­ni­ca­tion port­fo­lio of prod­ucts. Parth loves tech­nol­ogy and also the use of tech­nol­ogy in dig­i­tal mar­ket­ing. On this blog, he shares his tri­als, tribu­la­tions, suc­cesses, and what he reads, hears or expe­ri­ences in the world of dig­i­tal mar­ket­ing. (Spolier alert: Parth uses the Adobe Dig­i­tal Mar­ket­ing Suite and loves it.)