Today’s case studies about data-driven marketing present stories of leaders and their successes. After all, history is written by the winners (or their vendors). However, many CMOs may be feeling like they’ve been sucked into the jet engine of this Big Data thing, and are struggling to create some order from a chaos they’re chasing.
One form this pursuit takes is a rush to hire world-class data science/marketing analytics talent. This is hard because the envelope’s edge of this still-forming discipline encompasses so many complementary skills: domain expertise, infrastructure management, data acquisition and transformation, statistical modeling, visualization, and perhaps most importantly synthesis and communication. Yet this complex expression of the recruiting challenge makes us blind to three more fundamental talents, which if missing or out of balance you should get after immediately.
For short, we can call these capabilities “Experiencers,” “Optimizers” and “Builders:”
1. Think of Experiencers as artists.
They think in terms of customer persona portraits and journey map/purchase funnel landscapes to describe their work.
- When hiring for the Experiencer talent, look for a broad, dynamic sensibility for understanding and evaluating the customer experience. A good retail mystery shopper is an example of this orientation and level of skill. Also, really good analysts ground their cleanup and transformation work in clear, vivid, well-founded customer stories generating the data they work with.
- The most effective Experiencers don’t just tell stories based on anecdotes, but also think about numbers like volumes and conversion rates at different points along those stories, and prioritize suggestions for interventions. Like the best painters, they are masters of proportion.
- Look out for “experience fanatics” — they’ll fuss about font kerning like hawks hunt mice, but won’t be sensitive to a bigger problem that needs fixing more urgently; mostly they’ll hunker down in the domains they know (for example, in shopping cart abandonment).
2. Your team should also include Optimizers.
The quant traders of the marketing world, they think in terms of “selling” expensive media, and “buying” cheaper sources of demand.
- How can you recognize a strong Optimizer in an interview? Strong candidates will start by talking about trends in cost-per-lead (for example), and how they’re working to understand that better (through attribution analysis, for example). Weaker Optimizers start by telling you about their analysis.
- The best Optimizers keep their eyes on the big picture. They’re data “MacGyvers,” comfortable with messiness and incompleteness, focused on continuously better answers rather than tilting at windmills of analytic perfection.
- When working with Optimizers, beware of their tendency, like the drunk man who looks for his keys under the lamppost “because that’s where the light is,” to confine their efforts to data-rich channels. With these blinkers they often invest past the point of diminishing returns and miss other needs and opportunities.
3. Lastly, you need Builders who think first of the capabilities you need, technically and process-wise, for a successful analytic operation.
- If an interviewee dwells on a “target architecture” map or organization chart of all the goodies you might need to “fully harness the power of Big Data Analytics” (or some equally god-awful term), you haven’t found your Builder.
- Instead, look for someone who can also show you a practical roadmap for realizing a vision that’s sensitive both to where the greatest initial value and near-term feasibility in your business would be.
- The best ones should demonstrate a healthy “code-to-slide” ratio, and/or a track record of building strong teams, in their past and current work — that is, they have a clear idea where they’re headed, and a practical roadmap for getting there, but also momentum — a steady record of delivering progress against and value from such plans. To adapt a software business saying, Real Builders Ship.
The very best data scientists have all of these talents, developed well past threshold levels. But even if your data scientists can beat up your competition’s, they alone will be unproductive unless the rest of your marketing team’s mid-to-senior leaders (as well as key partners) don’t also have at least a spike on one of these dimensions and an appreciation for the others. With this balance, you’re more likely to be aligned on opportunities to pursue, fueled with the right data, and flexible enough operationally to realize the ROI your analytic investment promises.
