An old friend in business called me to share her “crisis of confidence” in using her pricey new database/CRM/analytics system. She had led the organization through a major investment in “cleaning, compiling and organizing” the data to make it more “usable.” It was a herculean task, and she was proud of her accomplishment – but she was struggling to produce a material outcome beyond project completion.
The Business Problem
After wrestling with their data and building reports for another six months, there was a sinking feeling, one you may have even experienced yourself … for all the effort – where was this going? How is it driving the business? Are we making better decisions for it – or are our decisions just different? How will we justify the investment and produce returns? The catch all “infrastructure spending” was tossed around briefly.
After looking at a handful of reports and documents, I had many questions for her. Not surprising for an exceptionally successful executive like her, she gave me some fair and honest answers. One response she used more than once was, “We don’t know.” That’s not trivial. In many organizations, it’s risky to think it, much less say it. Sometimes the best answer is “I don’t know” – but surrounded by data and smart people, it’s not entirely unreasonable for some folks to feel uncomfortable with a candid “I don’t know.”
Get Comfortable with ‘Not Knowing’
Just saying “I don’t know” can be the first step in solving the problem – so long as you’re wed to the fact that you “just know” you can’t even take the next step, which is problem definition, because you don’t have a problem if you “just know” something is good, important or even working – data and evidence aside. Take the challenge – I guarantee you this small act will spark more ideas, action and solutions for the de minimis time it takes than anything else you can do.
Take Away No. 1:
If you don’t know something, say so. Say it out loud, even. It will help in emotionally and logically moving on to defining the problem.
By now, I’m probably close to losing a few folks who are reading this. “I don’t know” is not something they’re comfortable with. If you are one of them remember, “I don’t know” is not where the process ends – it’s very often where the solution we’re hungry for begins to reveal itself.
Problem Definition: It’s 90 Percent of the Problem
In discussing what the problem really was, we found another common issue. The problem wasn’t well-defined in the first place. The problem was essentially to “clean up the data” and to “have organization.” While that was a good thing to do, it didn’t solve any business problem. The manifestation was they now had a big (expensive) bucket of data that was judged to be better, or more valuable, than it was beforehand.
How was it better? The answer was it was more organized and more clean. Did it answer any specific questions? After looking closer, we saw it did. But did it actually begin to solve any specific problem? This was less obvious.
Here is where problem definition is so important. The problems that were defined as “organization” and “cleaning” weren’t business problems. They were symptoms of a data capture process that didn’t work, and that process came from a lack of a clear strategy.
Boiling the Ocean: Solutions That Are All Things
The specific problems being experienced were many and diverse. Focus was low. This was, in large part, a solution that was intended to do all things for all people.
I’m a direct marketer. I started my career in software development. I have a great appreciation for large systems and for what is commonly known as the “data warehouse” – a large database system that often starts with financial system data. Warehouse solutions often contain every cost in the enterprise, every operations metric, inventory, logistics, marketing, human resources and more.
But surely these are not “function-specific” solutions. In the vast majority of cases they are starting points, and they are not solutions to the problem the marketer has in selling one more widget. Those solutions need to be borne of a very specific set of marketing problems, and utilize a specific set of data – and in a specific format and data model – to actually solve them. That marketing-specific solution would likely need substantial transformation if taken from that “warehouse” solution. And when complete, it would virtually be a whole new dataset, altogether.
Take Away No. 2:
Ask yourself, are we taking a “Boil The Ocean” approach?
After some discussion, we aligned that she had surely accomplished a lot, and that we could now access and view data about many things in the organization, including in marketing. But there were no specific capabilities that would speed the time-to-value present, and it was hard to make progress. Also, the data my friend’s organization quite reasonably thought was its most important failed to highlight the huge differences between the value of customers over the longer term. That created a strategic problem. The organization was trying to fix the long-term and its strategic business problems by looking at the wrong data and taking the wrong actions. These are very bright people with a compelling rationale for their course of action.
In the end, “Boil The Ocean” approaches are short on strategy, or are built on a strategy so grandiose, they become difficult or impossible to execute.
The Root Causes of These Strategic Challenges
So ultimately, how do such quality organizations go down an inefficient path like this? It ultimately comes down to a skills gap. What must change? It’s the skill set in marketing.
In the digital age, there are two major skill sets that we must buy, hire or develop in our organizations. Neither is trivial in marketing, and neither is possible without patience and focus.
Skill Set No. 1: Technology, Logic, Data
Marketers have traditionally come from a promotional and creative background. The big idea was always the highest-valued commodity. Today, things are changing faster – and permanently.
Marketers today are consistently spending more of their time with technologists, developers and data designers. The logical problem-solving skills by these folks are very different from those proposed by professionals with a creative or project management background. They need to solve problems that are not even being discussed on the way to solving the problems that are.
Because most organizations have some expertise with technology, and work with technology providers, the key takeaway here is that marketing data-specific applications require a different set of tech experience. Working with marketing data for marketing outcomes is unlike working with other types of data – the experience your IT department has working with finance or logistics data isn’t as useful as marketing “purpose-specific” data and technology experience.
Skill Set No. 2: Math
Barring some advanced direct marketers, marketers don’t always come from a math background. Only now are VPs beginning to have development, math and statistics experience. In an age of analytics, and now with the advent of tools and technologies to leverage large data sets, a solid understanding of math and basic statistics is becoming increasingly important.
Here’s an example to help make the point about the comfort level of using math and basic statistics to think about data:
You’re looking at the incomes and affluence of a customer base. With 1,000 members in the group, we have an average income of $100,000. That’s pretty telling, you might say.
We get more data, and the 1,001st customer is added to the sample – it’s Warren Buffett (net worth $67 billion). How useful is that average now?
There are many expressions for this common scenario – where outliers in your data can skew your numbers. From this come the expressions “The average lies” or “the tyranny of the averages.” Surely, the average isn’t the best number – though it’s a shortcut and a starting point. But it’s best to compare it to the median before taking too much faith in it – and a distribution histogram might tell an even better story about the composition of your customers’ incomes.
The takeaway here is the concepts required to evaluate and think about data require experienced and trained analysts. And those trained and experienced in evaluating marketing data are also required.
In the end in the scenario I described with my friend and client at the beginning of this column, her organization built a very large system, finished it on time and about at budget. But what the company invested in and created had some fundamental shortcomings. It was not a “purpose-specific” marketing solution – and it was conceived by a competent IT organization that was tactically adept – and strategically adrift.
Takeaway No. 3: Marketing Must Drive Marketing Outcomes
Marketing must drive marketing outcomes. Due to the discomfort that marketing often has with technology, math and statistics, key strategic decisions are quietly left to IT, a vendor or to chance.
This, of course, jeopardizes the results early. Marketing leadership can always ask good business and marketing questions and hold IT and technical resources accountable to prescribe only solutions that have a clear and simple strategy for achieving those goals.
The Bottom Line
Begin your database marketing endeavor with the “end in mind” by describing what success would look like in business terms. The decisions you’ll need to make will be easier.
If the discussions turn technical early, and they often do, ask business- and outcome-oriented questions to steer the conversation back on track. Maintain a focused strategy from the start and don’t let inexperience with math, programming or database science derail you. Get the help you need – and that means individuals and teams who know not only their discipline, but can answer the questions that matter most to you.
When you follow these strategic guideposts, your next big data-driven initiative will pay dividends now and into the future.