I challenged myself and my team at a $1BN retailer to find these answers in the midst of the harrowing 2007-09 recession and we came out not just alive, but profitable, stronger and with superior vendor relationships than ever before. Below I have provided some high level observations from this period and what that journey looked like.
AI, Machine Learning (ML), Predictive Models, Algorithmns and so on, all these analytics that are designed to drive change, to identify new trends, new patterns and behaviours have one, vital prerequistite. Data. Data is to analytics what oil and gasoline is to the internal combustion engine. No data, no insight.
While Data mining has been available for decades, in my company, it was still not used as a mainstream tool for analysis by either specialist teams or management. While we were fortunate to have a deep and granular history of data, we weren’t using it to drive growth. As part of our modernization strategy, we determined that this data needed to be collated, mined and analyzed. Our crucial edge was that we determined the objective to be answers to the following questions
“Why do people come to our stores? What do we need to do to keep them coming and how do we know we’re right?”
Leadership needs to be curious or have people who are.
There are many books on leadership, I know, I sold them all, but perhaps the book “Moneyball” can be used to best exemplify the point. Since Billy Beane’s adaptation of recruiting players based on statistics for Oakland, every major baseball team has adapted this same method. And yet consistently, despite this new practice quickly become standard practice, not all the teams have the same performance.
A critical task to achieve is to create a plan around the findings of the data so that senior leadership down to the store staff are aligned, informed and most importantly, measured on the new findings. Billy Beane didn’t ask for anyone to trust his hunches, he proved the analysis with data that everyone could measure for themselves and matched the data to the outcomes. The proof was there, what was left, was getting others to get behind the strategy.
At my firm, we mined the data, we hired the people or skills we needed to do this and we came out with our insights, through trial and error, through intuition and ideas, we discovered what we thought was the reason why our customers were delighted with us and how we could manage and adapt to this as the economy nose dived and everyone else’s sales died. Now we needed to present not just findings, but a strategy to management to get behind our recommendations.
Check out Part 3 of this series “Be Brave, Trust your Customers, Fail Fast and Often”