Stephen Yu’s recent and extremely thought-provoking piece on AI started me wondering once again about the dangers of data overload and whether we’ll ever really, really understand the purchasing decisions people make, how they make them and be able to track them accurately.
Because today’s machines gobble data and — like my dog eats anything he can get jaws around — we marketers seem to search for more and more bytes in the hope that sifting through this mega data will hold the keys to the holy grail of maximum profitability. Perhaps it will. But as a disciple of Lester Wunderman, I can’t let go of his oft-expressed prescient warning that “Data is an expense. Knowledge is a bargain.”
Admittedly, when this was first expressed, data was one hell of a lot more expensive to keep and handle than it is today and shaking knowledge out of it was very difficult. But that’s hardly the point. Our trade press is now overflowing with titles like “Planning and Measuring Social Media Campaigns” (Sysomos), the “Email Marketing Metric You May Not Know” and unnumbered guides to the customer journey. But I’m still waiting for the definitive article that leaves all of the peripheral data by the side of the road and presents a usable and believable knowledge-based metric model to measure the cost of each step in the journey from awareness through to final purchase. In today’s multi-media environment, that’s the metric model we are all waiting for. Will we ever get it? Will AI provide it? I’m not so sure.
There is historically a different focus between top management whose attention is quite sensibly on macro numbers and operational marketers who know that it is the micro numbers that spotlight big opportunities. The ROMI, the return on the total marketing investment, is the bottom line for both: How much did we earn for how much marketing money invested? Simple.
But at what milestones in the customer journey did the momentum toward purchase increase and at what others did the potential customer take a turn away from purchase and why? That’s the type of data we need if we are to optimize our practice and it will surely impact the ROMI. Sadly in many cases, we will never know.
Recently, some of my Brazilian colleagues created a very strong email campaign as the first stage in persuading well-segmented prospects to clickthrough to a website to register interest and gain a price advantage in making a major purchase. The client reported that while the website was receiving a lot of activity, only a tiny fraction came as the expected clickthrough from the emails. The client was understandably angry and it didn’t make any sense.
Every adult Brazilian has a unique CPF number, which is regularly requested and used to identify the individual in financial transactions. It’s rather like an American Social Security number. Because my colleagues were fortunate enough to have the CPFs of the prospects to whom the emails had been sent and as registration on the website also required a CPF, it was a relatively easy task to compare the two groups to determine how many of the registrants had been sent the emails, even if they hadn’t availed themselves of the clickthrough option. It turned out to be a happily large percentage.
While research has been undertaken to determine why, any measurement of the relation of emails to registrations and their cost would have been both misguided and meaningless. If the marketers had decided to stop using the emails because, as they said, ”emails didn’t generate any response,” they would have been making a critical error.
Perhaps that’s a long way around the issue of just why, with all of the enormous data and sophisticated tools at our disposal, we just can’t develop a meaningful metric model that reliably tracks the prospect along the path to becoming a customer. And it argues that while AI will certainly add valuable knowledge, getting inside the head of a prospect and truly understanding his/her actions is a long way off.