Schooling ‘Curmudgeon’ Denny Hatch on ‘New-School’ MarketingFebruary 28, 2014 By Mark Klein
Even old-school curmudgeons want to make their marketing communications relevant. They just don't know the best way to do it.
Target Marketing's Denny Hatch argued eloquently for the old-school approach in his recent column. Hatch is a smart and experienced direct marketer who is still relying on demographics and behavioral data, which he describes as "Hobbies and interests such as pets in the house, history of travel, etc." Under the heading "Direct Marketing 101" he says to make a relevant offer, an organization needs demographic and psychographic information. Do this, says Hatch, and "You'll generate some revenue." He's right, but it won't be nearly as much revenue as you'll receive if you use transaction data and individualized marketing.
The Holy Grail for marketers has always been individualized marketing, where each person receives a unique message/offer tailored to his or her own needs and wants. This means if your company sends 5 million emails, each email is different and includes offers appropriate to the recipient. Most companies still don't realize this is now feasible, practical and relatively easy, so instead they may send just one, or if they are sophisticated, perhaps 10 different messages to their 5 million customers.
New-School vs. Old-School Marketing
Good old-school marketing is based on demographics, psychographics and segmentation. Logistic regression is often used to determine the segments. The approach is to look at a set of variables related to buyers of a specific product (where they live, what are their interests, etc.) to find a group of other customers in the population who might buy that product. It's a product-centric approach, and the associated metrics are those that typically measure what happened in the past. Relevance hopefully comes from considering those demographics and hobbies.
In contrast, "new-school" marketing uses transaction data (who bought what, and when). Analysis is done at the individual customer level, recognizing that you can't predict what a particular customer will do unless you analyze at the customer level. The key metrics are predictive and forward-looking: "Risk Score," (measuring who will stay loyal and not defect); "Likely Buyer Score," (who is likely to purchase in the next 30 days); and purchase propensities that predict the probabilities of each customer buying each product or product category. Relevance comes from the offers made based on the purchase propensities.