What are the ground rules for data-driven marketing?by
The quickest way to make a CMO roll their eyes is to tell them they should be making more data-driven decisions. Of course we should, everyone knows that. Unfortunately there are a lot of 'buts' attached to making this a reality.
“But the data I need is fragmented throughout the organisation.”
“But I have no way of linking customer data across channels.”
“But we don’t have the analytical skills internally to analyse the data.”
“But I can’t trust available data.”
The list goes on. There are, however, marketing leaders among us who actually do a very good job at leveraging data to inform marketing decisions. The problem is, when we talk about Big Data and data-driven decisions for marketers, it tends to veer toward boil-the-ocean concepts that are too big and audacious for even the largest marketing teams and budgets to take on. So what’s the practical reality from CMOs who are successful at making consistent data-driven decisions? What does 'data-driven' mean in 2015?
Last year, Gleanster Research surveyed over 9,700 senior marketing professionals in companies of all sizes in Europe and the US. And while there are lots of things your organisation should be doing with customer data, not all of them are realistic. You need to devote limited resources, finite time, and tight budgets to leverage data-driven marketing decisions.
There are a handful of somewhat obvious rules and guidelines that successful data-driven marketers consistently follow. The one caveat here is that these things are somewhat difficult to quantify from a research perspective. They are derived from conversations, analysis, and hands-on experience. Consider these the soft skills you’ll need to stay out of the rat holes and maintain credibly as a marketing leader who values the numbers.
Data is everybody’s friend.
Every organisation is applying analytics to marketing decisions – meaning your colleagues and peers also want to be data-driven marketers. At times even the same data produces different perspectives from internal stakeholders. Everyone’s got data to support their decisions. It’s important to realise that there are no one-size-fits-all answers in analysing marketing data – but there are directional and discernible trends. Always test assumptions. There are no magic insights you can derive from any form of analysis, even if you pay statisticians boatloads of money. Test, validate, and test again. You don’t have all the answers, but rather a process for uncovering the most informed decision. What you bring to the equation that is unique is your interpretation of the data and the actions you recommend for marketing optimisation.
Everyone is risk averse.
Risk comes from not knowing what we are doing. For marketers there’s a risk in sticking your neck out there and analysing customer data. What if the data is inaccurate? What if you don’t have the full picture? Tenacity and perseverance in using data to inform marketing decisions pays off. You may not garner the credibility you want initially, but if you always return to the data, risk-averse leaders will gravitate toward your insights rather than to a peer who relies on anecdotal assumptions. When you analyse data, always try to remove the risk from the findings, dig a little deeper, test alternatives.
It’s not a 'Big Data' challenge.
Sure, there’s a ton of data on customers at every organisation. Your job in marketing is not to analyse all of it. It’s to prioritise decisions and figure out where the path of least resistance lies to improve conversion, save costs, save time, and increase revenue. Pick one or two duties in your job and fix something. Anything, big or little. The practical reality is that you don’t have to analyse big data in marketing. You have to pick small samples and populations of data that can inform one or more decisions. World hunger is solved one slice of bread at a time – and every slice makes a difference in the aggregate. Someday machine learning will help us uncover the gems in big data, but today you have to start somewhere, with currently available data, in areas where you can effect change.
Simplicity is the ultimate sophistication.
That’s actually a quote from Leonardo Da Vinci. Marketers are overworked and underpaid. You have regular duties and responsibilities in your job, and normally they don’t account for committing time to analysing data. So you need to look for leverage in how you commit time and resources. If you’re a senior leader, don’t waste your team’s time chasing questions that can’t be answered. Prioritise a few decisions that will make a difference for the organisation and consider that a huge win when they prove valuable.
Context begets analytics.
The challenge with data is analysis paralysis. You have to know when to stop digging and take action. Marketers’ unique skillset for the organization is their creativity and emotionally charged perspective on how to drive a visceral reaction from a target audience. Those skills come from having context about what drives your buyer – what makes them purchase, engage, share, and react. For marketers the insights in customer data usually aren’t black and white. It’s the interpretation of the data as much as the analytical process. Sometimes marketers sell themselves short because they aren’t statisticians or metric oriented. The truth is, the most skilled statistician probably can’t provide the context you can when looking at the same data. They can isolate correlations, but those just tell you where to dig further. Marketers are incredibly valuable because they can layer context over analysis, so be confident in the value you offer.
Frame the opportunity, not the problem.
Every organisation has challenges – especially with respect to analysis. According to Gleanster Research, 8 out of 10 CMOs at large enterprise organizations believe they could be doing a better job leveraging available data to inform marketing decisions. But your job when analysing data is ALWAYS to uncover the opportunity to make a better decision, improve process, or boost key performance indicators. Don’t waste time identifying the problems with the availably of data or the internal use of data. Stay focused on effecting changes with insights informed by data, and eventually you start to stand out as someone who knows how to dig into the data and act accordingly.
Act like a two-year-old from time to time.
No, don’t throw a tantrum. Ask why. Ask why a lot. Why helps delve into the heart of your analysis even after you think you have come to a conclusion. “Why don’t we look at…x?” “Why is this the ideal conclusion?” Why also gives marketers credibility because it’s an analytical question. All too often senior leaders default to “well I think...” and they may or may not have the right answer. But why is going to drive the entire organisation down a discovery path. “I think” closes it out and dictates a decision.
Ian Michiels is principal & CEO at Gleanster Research.