A Data-Driven Culture Requires Analytics-Trained Team

Recently, Kellogg’s Admissions team sent me an invitation to schedule time with them to see how I might be a fit for their MS program. Their outreach was based on my academic background. So while Haas School of Business similarly contacted me last year to teach their executive MBA students (which I did), Kellogg’s marketing algorithm identified me as prospect for enrollment.

On LinkedIn, marketers can select their target audience for a campaign such as Kellogg’s by applying pertinent filters, including current or past job titles (“CEO” and “Advisor” for instance) and years of experience. Had that happened with this campaign, I would have been excluded automatically.

Needless to say, Kellogg wasted some marketing dollars in contacting me, and judging by the scale of the school, perhaps, wasted hundreds of thousands of dollars in their marketing effort overall. But Kellogg is far from alone. Every day, in every business sector, marketing dollars are being squandered on outreach to the wrong audiences, despite tools that can pinpoint campaigns with laser-like accuracy.
Same is happening in sales, product and operational functions as well.



So, how can such day-to-day decisions such as “how best to reach the target audience” be optimized?

By developing a data-driven culture, of course. Yes, it’s an over-hyped term. But using data to make critical business decisions is imperative for a company’s success.  

So, what does it take to become data-driven?

Over the years, I have become part of many companies’ data-driven culture. Here are my observations of a successful journey.

  1. Data-driven culture starts with data-driven leaders.
    In the example of my LinkedIn message, the CMO of Kellogg School of Management has to want/demand an optimized ROI for their marketing spend. Often, that demand is due to the CEO, and perhaps the board, holding executives accountable for the company KPI’s—revenue, profits, growth etc.
  2. The data-driven journey requires retraining the job force in analytics skills as well as mindset.
    In this example, I suspect Kellogg School’s marketing and admission offices would benefit tremendously from learning fundamental data-driven decision-making processes. Most decision makers (i.e. professionals who make decisions in their day-to-day workflow that impact a company’s top or bottom line directly or indirectly) can learn business analytics and optimize the solution to the problem at hand, using an everyday tool like Excel.

With a retrained workforce and motivated executives, other pieces of the data-driven journey, such as data maturity and the data-driven decision-making process, start falling into place.

Developing a data-driven culture is a journey, and analytics is as much a mindset as technical skills. But the good news is it can be learned. My clients have found success with a combination of training followed by real project mentoring. First we teach people the analytics process, and then they apply it to an internal project, whether it is optimizing marketing spend by better targeting or another issue. But most importantly, we remain with them as their thought partner and mentor. We hold hands in the journey and correct course as needed until they can proceed on their own.

And it doesn’t have to be expensive. For example, we offer low training costs by using blended learning:  a book club format combined with online training. Other training companies may follow a similar approach. Ours is to divide a company’s team into cohorts. Individuals complete weekly online modules on their own then attend weekly brown bag group meetings we facilitate to complete their online course. Finally, the team pairs off in groups of two to work on an internal project and solidify each person’s comfort in analytics. We do this for a cost of $1300 or less per person for a 12-week program.

If you are interested in retraining your workforce, please tell me more here or book time on my calendar here.

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