Marketing has gone digital, and we can now measure our efforts like never before. As a result, marketers have fallen in love with data. Head over heels in love—to the point where we want data to drive our marketing, instead of people, like you and me. I think this has gone too far.
I'm a big proponent of data-driven marketing, in this article Ezra Fishman uses semantics to say this is bad, but what he is trying to get is there is a need to go beyond just the data. As I wrote in Data + Insight = Action, data all by itself cannot create actionable outcomes.
Data-informed marketing
Instead of focusing on data alone, data-informed marketing considers data as just one factor in making decisions. We then combine relevant data, past experiences, intuition, and qualitative input to make the best decisions we can.
Instead of poring over data hoping to find answers, we develop a theory and a hypothesis first, then test it out. We force ourselves to make more gut calls, but we validate those choices with data wherever possible so that our gut gets smarter with time.
This is what I was trying to articulate in my article. To be an excellent data-driven marketing organizations takes a little bit of "science" and a little bit of "art" to determine the best course of action. When a data scientist is driving your organization, there are years of experience being unused to help him understand even further what the data is saying.
Most times when a data scientist is off on their own, it takes an inordinate amount of time to come up with a conclusion, mostly because they lack the context of how the business is generating the data. How the strategy manipulates the data. How a customer being underserved may be an intentional outcome.
The ease of measurement trap
When we let data drive our marketing, we all too often optimize for things that are easy to measure, not necessarily what matters most.
Some results are very easy to measure. Others are significantly harder. Click-through rate on an email? Easy. Brand feelings evoked by a well-designed landing page? Hard. Conversion rate of visitors who touch your pricing page? Easy. Word-of-mouth generated from a delightful video campaign? Hard.
Right on! Of course the organizations that take the easy way out are ones that I would not consider to be data-driven. KPI's are a great item, but they can be deadly. There are usually so many moving parts that make up the business and the data being generated. This can cause business KPI's to look fine, yet drilling down into the performance from a customer perspective may show some very scary trends that would cause alarm. However, a non data-driven company will continue with their strategy because of the KPI's (hello RIM/Blackberry).
The local optimization trap
The local optimization trap typically rears its head when we try to optimize a specific part of the marketing funnel. We face this challenge routinely at Wistia when we try increase the conversion rate of new visitors. In isolation, improving the signup rate is a relatively straightforward optimization problem that can be "solved" with basic testing.
The problem is, we don't just want visitors to sign up for our Free Plan. We want them to sign up for our Free Plan, then use their account, then tell others how great Wistia is, then eventually purchase one of our paid plans (and along the way generate more and more positive feelings toward our brand).
This can be combined with the previous bullet. When analytics is only seen from a high level, simple statements like "we need to increase the number of signups, which will flow down at the same rate as we currently have, will increase conversion." Nothing could be further from the truth. To increase anything there needs to be an additional action. This action may include advertising to a different group of individuals or giving an incentive that will increase signups. The issue with this thinking is these aren't the same individuals that are converting in your current funnel. The proper strategy is to figure out the converters and try and target customers like them, which may actually decrease the size of the funnel if done right.
The data quality trap
We are rarely as critical of our data as we ought to be. Consider, for example, A/B tests, which have become the gold standard for marketing experimentation. In theory, these tests should produce repeatable and accurate results, since website visitors are assigned randomly to each page variant.
In practice, however, there are lots of ways even the simplest A/B tests can produce misleading results. If your website traffic is anything like ours, visitors come from a variety of sources: organic, direct, referral, paid search, and beyond. If one of those sources converts at a much higher rate than others, it's easy to get skewed results by treating your traffic as a single, uniform audience.
One should rarely just take the conversion or redemption results from the A/B test without digging into the data. Making sure all segments are driving the results is key. Don't take for granted the customers that were randomly selected for each group ended up being totally random. Ensure there was proper representation from each segment of the business and identify any other changes that could be tested based on different behaviors within the segments.
Data vigilance
As marketers, we should continue to explore new and better ways to harness the power of data, but we also must remain vigilant about becoming overly reliant on data.
Data can be a tremendous source of insight. Harness that. But don't pretend it's something more. And definitely don't put it in charge of your marketing team.
This reminds me when I was a product manager and we would receive these RFP's to determine if we were the right company to supply them with our product. Sometimes the requirements were such that we wondered if the company wanted humans to continue to work for them. I would comically refer to some of these as automated manager. It seemed companies wanted to press a button and have a system do everything for them. This is the trap Fishman is referring. Humans have great insight. Humans are the "art" in the equation to actionable outcomes. This is equally important as the "science".