How to Use Variation Over Time in Data Driven Marketing - dummies

How to Use Variation Over Time in Data Driven Marketing

By David Semmelroth

It’s important to understand variation over time in data driven marketing. For example, you encounter averages on a daily basis. You can watch the Dow Jones average bounce around on the ticker to your heart’s content.

Athletes are judged on their batting averages, average points per game, or first-serve percentage. Endless studies report that Americans eat an average of so many pounds of beef, potato chips, or broccoli per year. But what do these averages actually tell us?

By themselves, they don’t really tell us a lot. As a data driven marketer, you’re much more concerned with understanding how certain traits vary from customer to customer. Statisticians call these traits variables. (This is one of those rare occasions when a technical term actually reflects what it means.) Customer age, household income, number of children, and date of last purchase are examples of variables you frequently encounter.

Variables can vary in several different ways. Some scenarios you’re likely to see in your attempts to understand your customer data. In comparing these scenarios, you’ll come to appreciate how little an average, taken by itself, is really worth.

Suppose you’ve saved up $1,000 and you’re considering an investment in the stock market. Do you really care whether the Dow Jones average is at 15,000 or 1,500? The simple answer is no. What you really care about is whether it’s going up or down.

Trends are sometimes more important than the actual value of an average. A trend represents the general direction that something is moving. Trends discount small fluctuations along the way. For example, I-95 extends from south Florida all the way up through Maine. You can find places along the way where the highway travels east, west, north, and everything in between. But the general trend is northeast.

Part of your job as a data driven marketer is to spot trends in your customer data. Detecting a potentially negative trend early on gives you the opportunity to intervene. Recognizing a positive trend allows you to encourage it and “ride the wave.”

As an example, when examining several years’ worth of a bank’s customer data, one observation that popped out was that the average age of their customers was increasing steadily. This became deeply concerning when they realized that the customer base was aging quite a bit faster than was the nation as a whole.

There were a number of alarming factors here. Obviously, mortality being what it is, it meant a shrinking customer base. But this trend also explained why deposits and loans weren’t growing. As the customer base aged, more customers were on fixed incomes. And older customers also tend not to take out loans or carry balances on their credit cards. All bad news for the bank’s bottom line.

This discovery led the bank to begin actively pursuing younger customers. They targeted college students and young professionals with marketing programs. This is a case where the marketing database yielded insights that changed the entire corporate marketing strategy.

By continually tracking key customer traits over time, you can respond to trends as they are happening. Develop tracking reports and run them on a regular basis. Even just taking a monthly or quarterly look at the state of your customer base can help you spot potential problems or opportunities in time to act.