Build the Right Team for Data-Driven Marketing

Gartner’s Data-Driven Marketing Survey, 2015, revealed that marketers expect most of their decisions to be quantitatively driven by 2017. As a result, more than 50% of companies plan to grow their analytics teams. This will require hiring managers to identify and cultivate new sources of talent in an already competitive market, noted Christi Eubanks, research director, Gartner for Marketing Leaders.

The impact of this new reality of data-driven decision making doesn’t just affect hiring practices. Teams need to first assess their readiness to align organizationally and culturally around marketing analytics and evaluate to what degree they should centralize analytics activities. But to truly be successful in this shift, hiring managers and recruiters will have to think differently about the talent landscape. Tailor job descriptions to specific skills gaps. Not every company needs or has the right data to make use of a team of data scientists.
 

Pick roles based on needs

Respondents to the 2015 Gartner Data-Driven Marketing Survey reported that an average of 22 people in their marketing organization spend all or most of their time on analytics, and most leaders plan to hire even more in the next two years. Team size is highly dependent on industry and company size, but Ms. Eubanks noted that there is a rough benchmark of seven or eight analysts per billion dollars in revenue.

Here is a menu (as opposed to a checklist) of roles and responsibilities to consider based on needs and gaps:

Leadership

• Executive sponsor

Management

• Director of analytics

Specialists

• Web
• Social media
• Social listening
• Mobile app/games
• Segmentation
• Digital media
• Data visualization
• Statistician/modelling
• Tagging
• Data architect
   

Background may vary

Consider qualified candidates with diverse backgrounds. Many IT professionals have successfully transitioned into marketing analytics where JavaScript and SQL skills are a plus. Candidates with sociology and psychology backgrounds can bring perspectives around consumer behavior to Web and social analytics and knowledge of sampling and statistics that translates perfectly to optimization experiments.

Next Steps: “When you can’t find everything you’re looking for in a single candidate, cultivate talent from within,” said Ms. Eubanks. Enroll your analytics team in an R or Python course, take advantage of learning opportunities from vendors, or make mastering new analysis techniques part of their personal development plans.

Review your staff’s competencies to identify gaps in leadership, operational, analytical and technical proficiencies. Then hire talent based on those needs and prioritize new hires based on your analytics roadmap and data-driven marketing maturity.