Ginny Johnson spent seven and a half years at Google as an analytical lead covering direct-to-consumer retailers, including some of the biggest names in retail through COVID. She now runs her own advisory business, and she keeps meeting the same problem: companies have the largest menu of information they have ever had and no idea how to make sense of any of it.
Her diagnosis is specific rather than general. The failure is not a shortage of tools, it is the order the work is done in: define the problem first and use what is around you to solve it, instead of buying a solution and reverse-engineering a problem statement that justifies it. That is why so many AI strategies never touch the business question underneath them.
Also in here: what measurement is actually for, the point at which more data stops helping, why the growth wall arrives sooner than anyone plans for, and the case that retail was the original data industry. Retailers have held complete customer journeys and thirty-year datasets since long before anyone called it AI; what changed is that the processing power caught up.
One for anyone doing brand work. The aspirational customer a team believes buys the brand and the person who actually buys it are usually not the same person, and teams in London and New York tend to find that out late.
