Good Data – An Essential Part of the Marketing Recipe

July 20, 2016

Good Data – An Essential Part of the Marketing Recipe

Good Data – An Essential Part of the Marketing Recipe

Trillium recently hosted a webinar during which John Coe, a co-founder at, discussed the changing role that customer data has in today’s marketing environment. The key point was that data is no longer just the output of marketing activity; it increasingly is the fuel that drives that activity. No longer are companies content to do monthly reports – perhaps with pretty charts and graphs – that look back retrospectively on what happened. Now they want to use data that 1) they generate, 2) acquire from other parties or 3) can monitor in the ether of social media to better inform their analysis. The goal is continuous calibration and refinement of marketing activities. It is an iterative, never-ending feedback and action loop.

There’s been a lot of discussion in the marketing industry about the tools that can help in this cause. If you are familiar with Scott Brinker’s MarTech infographic, you’ll know that there are more than 3,800 companies that are currently operating in the marketing technology space. That’s a whole lot of technology from which to choose. But regardless of the diversity of choices, there’s one immutable fact – nothing can be done without data and the more sophisticated the tools, the more dependent they are on quality of that data. In fact, I’d argue that the sophistication of the tools can easily mask data inadequacies that imperil the results, and thus lead companies astray.

And that’s a real problem because marketing is focused on people. And in terms of business, people are “volatile”. They change jobs, companies, locations pretty frequently. How frequently? John Coe estimates that within the B2B market there is a 5% turnover on a monthly basis, representing material changes in data points about the person that may not be reflected in their data record. When you annualize that number you get a mind-boggling 60% change in the B2B space. That number seems so incredibly large that it might not seem credible. But if you pick an individual month and look at the US Labor statistics you’ll see some supporting numbers. For April of 2016, the hiring rate for new jobs was 3.5% and the separation (voluntary and involuntary departures) rate was 3.5%. Add those two together and reduce it with some consideration that some departures are the new hires somewhere else and you are in John’s 5% neighborhood. And that data does not capture job changes within a company. The B2B space is in state of perpetual change in terms of customer data.

With the advent of digital marketing over the last ten years, the pundits have taken to argue that the discipline of marketing has become more a science than it is art. Though there is much happening to support that argument, it carries with it some significant implications. The notion of science implies a certain discipline regarding scientific methods and the corresponding analysis. You can’t have a “control” or an “experiment”, the foundational elements of the scientific method without real control of the underlying elements. Want to A/B test your messaging? If the underlying contact data of those to whom you are messaging is not accurate then forget about it. Any determinations about the A/B test are spurious, because you can have no confidence that other variables were involved. Maybe the differences in response have nothing to do with the messaging and everything to do with the accuracy of the respective target populations. Want to analyze your pipeline to see what nurturing tactics are working? With a 5% per month churn in your contact population, you’ll be struggling to accurately understand what nurture tactics work. They might indeed work if you were reaching your targets – but how do you know? No matter how eloquent your marketing message, it won’t be persuasive unless it reaches its audience.

Focusing on the underlying accuracy of whatever data with which you are working may not spark the same kind of enthusiasm as working with some fancy predictive analytics tool, but ignore the quality of your data and you’ll be sorry.

Every good chef knows that all the cooking artistry in the world cannot overcome bad ingredients. The same goes for marketing.