Big Data in Marketing: 3 Prep Steps

In preparing case studies for my talk titled “Be a Big Data Voodoo Daddy” at Boston’s October 2012 FutureM conference, I noticed that almost half of our 40+ client projects over the recent years had to first devolve from “Implementation” projects to “Readiness” projects – equally valuable, and absolutely necessary.  How’s yours going?

Is your marketing automation, CRM, analytics, email marketing or other automation project going to deliver your desired payback?  Here are my top 3 warning signs that it may take longer to pay off than you think.

Stated differently, here are 3 must-do’s to ensure near-term ROI.

1.  The Right Stuff (Value based Goals).

Let’s first assume that you’ve already connected with the concept of Marketing as Moneyball.   Still, you may find that you are not gathering useful, relevant data to help you accomplish your stated strategic goal and implement the right CRM or analytics solution.  This may stem from having broad, imprecise goals.  For example:

  • “Grow revenue” is a great goal, but the paths are varied and nuanced.
  • “Increase Partner Channel Revenue” is, well, getting warm.
  • “Double Partner Channel Service Contract Revenue” is more like it.  Now you have a specific channel, identified players, and a specific product/service element attached to a numeric goal.  Specific, measurable goals and then measuring the right things are both essential elements if you are to to yield any meaningful analysis to motivate and support change.  No matter how efficiently you automate the wrong data, you risk stretching out the time horizon for any meaningful payback or, worse, running in multiple or wrong directions and wasting effort.  Strategy comes first.

2.) The Stuff, Right (Data Analysis and Process Maps).

Typically, your data is not homogeneous and some necessary processes don’t exist yet.  Data often exists in a variety of formats ranging from locked spreadhseets and various departmental databases to unstructured documents, such as paragraph text and visuals.  Processes that don’t yet exist can’t be mapped to a system; you can’t automate a vacuum.

Significant effort is involved in standardizing and preparing data for upload into your new automated solution, as well as  selecting the right tools to enable you to access and mine insights from unstructured  information.  At Fan Foundry, we are familiar with an array of powerful tools, and can develop custom, reusable upload frameworks to help clients address current and future needs for unstructured data.

This is where the scope of a project almost always expands, as additional valuable information repositories become included, because we often discover additional insights using all available data that just would not be possible otherwise.  You never know where the breakthrough “aha” discoveries may lie.  If you don’t have the luxury to expand your analysis, though, then rigorously insist on only analyzing the most salient data.

3) The Players (People).

The talent shortage is legendary.  If you are inadequately staffed or trained to assume the role of data manager, analyst and strategist, or transformational leader, let alone carry on administratively after implementation, you shouldn’t start the project.  The time to assign roles is up front.  Get any necessary talent aligned first so they can be involved in the project.  Some of your team can adapt; sometimes you need to extend your team to include a capable partner.  The single most effective way to stretch out the payoff time horizon is to not involve its eventual owners and primary users, or not have the stomach to lead a transformation effort.  Be prepared to change, or else don’t start.

The full list of must-do’s is extensive, but if you tend to these three first, most of the rest will fall in line, and you’ll enjoy a successful implementation.

Toward a “Measurement Culture”

You’ll know you are succeeding when you have established a “culture of measurement” in which the right things get measured, the data supports meaningful analysis, all meaningful data is reflected in a single, integrated, centrally accessible “record of truth”, and you are using the insights you have gained to achieve transformations like improve margins, speed to market, pricing accuracy, supply chain efficiency, sales growth, and other incremental and transformational improvements.

Finally, it must be stressed that human judgment is not taking a back seat to data.   Interpreting analytics in light of pragmatic experience and using that knowledge to take calculated risks is a hallmark of success.

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