Let’s assume you conduct a search on your favorite retail Web site for an “umbrella.” Now assume that nine months later this retailer sends you an e-mail campaign featuring a series of umbrellas.
Maybe this marketing message is relevant to you. Often, the message isn't relevant. You had a need for an umbrella nine months ago, and you filled that need.
In this case, the “half-life” of your umbrella query is small, maybe just a few hours. Within a few hours, half of the people who conducted a similar search for umbrellas purchased an umbrella.
Recency was an important concept in an analog marketing world. “Half-life” is an important concept in a digital marketing world.
You measure half-life by identifying an activity and then calculating the time that passes before half of the individuals will act upon the activity.
Twenty years ago, the half-life of a subsequent purchase was long, maybe six months or a year. This meant that businesses could store names and addresses in a database and market to those customers for a long period of time.
Today, the half-life of a subsequent purchase varies by channel and activity:
- the consumer shopping over the telephone might have a six-month half-life;
- the consumer shopping online might have a three-month half-life;
- the consumer shopping in a store might have a two-month half-life;
- the consumer who receives an e-mail campaign might have a response-based half-life of just 12 hours; and
- the consumer who searches for an “umbrella” might have a response-based half-life of just 12 minutes.
Database marketers capture information to mine it at a later point in time. Increasingly, they have an opportunity to transform the information into something more meaningful. In the case of half-life, you can create a series of 1/0 indicators in a database that tell you if the customer is within the half-life window for a various activity.
- Companies:
- MineThatData
- People:
- Kevin Hillstrom