Eve Ensler’s Advice on Being A Better Lover – And Data-Based Marketer

Playwright Eve Ensler's Vagina Monologues includes a bit of advice for being a better lover that could easily carry over to those responsible for collecting data for marketing purposes.

SLOW DOWN.

How this makes one a better lover is pretty obvious. So let's focus on the data part.

A report from the Economist Intelligence Unit quotes Wharton professor Eric Bradlow as saying "There is a disconnect between the ability to collect data and the ability to base decisions on them. People need to take a deep breath. They need to be more thoughtful about it. Because the data will not answer questions by themselves."

Bradlow is right. Not every data point which is collectable is worth analyzing. Facebook likes, for instance, can indicate affinity if they are organic… but if they have been used as a trigger mechanism to receive either a trial or a discount or some such, their validity lessens.

Tools to collect enormous amounts of data have proliferated. Marketers who have access to these tools are faced with two equal and opposite impulses.

This first is paralysis due to being overwhelmed by the opportunities all the data represent. This is especially true among larger brands that have traditionally relied on mass media: Real segmenting is scary because segmenting and measurement equates to accountability, and accountability is scary.

Second, when they do decide to tackle the data available to them, is the need to produce results quickly, which does not allow careful analysis and consideration of data.

That's if a marketer can process it at all. The Economist did a survey asking about the disconnect between collecting data and using them to generate strategy. Nearly one third of companies say there is no formal process around data management. And more than half say reconciling disparate data sources is a problem.

Asked about their organization's biggest challenges in extracting value from data, 45% said they have too much data and too few resources to manage them, and 30% mentioned not having the right skills to manage the data effectively.

Even before one tries to do analysis, one of the biggest complaints with trying to capture and integrate all this data is that companies often rush to participate in new channels such as social mediums and don't have a clear view with how these activities will fit into their overall strategy.

Marketers who attend enough conference sessions in which large brands talk about their social activities will hear the presenters rave about the likes or pass-alongs they get. But when someone asks about either follow-up efforts or return on their investment, these marketers often say they weren’t really considering those, and that the efforts were simply to gage proof of concept.

It's not easy to have high hopes for such pilot programs. At some point someone at the C level is going to want to see some sales figures.

Corporate goals should dictate which activities generate which types of data, and with every rush to embrace a new channel, this is forgotten. This is doubly true today, when the sheer amount of potential data has exploded. What is most important? Is it the location of someone using his or her mobile device to access your offerings? Is it the time that prospect does so? Is it non-transaction interactions, such as opening email or clicking on links? Is it the number of likes a marketer can rent from consumers in return for a promised benefit? Or is it as mundane and the recency, frequency and monetary value of purchases?

The hazard of big data is that information is being collected and stored, and maybe even analyzed, yet not incorporated with the overall marketing or corporate strategy.

It is possible to collect data with an eye toward seeing what needs may rise up in the future, even if there are no current applications. But even that requires a vision, and that is starting with looking at a specific client base and trying to determine its unmet needs. This goal-driven strategy is very different than grabbing every last scrap of data one can assemble and then trying to figure out what insight can come out of it.