Our future includes a shift from pure campaign-centric work to grid-centric triggers. Oh, I know, countless pundits have been telling us this for decades ... they tell you how their cloud-based machine learning application will uncover patterns you'd never anticipate.
But the reality is that we must combine business experience with math ... unfortunately, we tend to omit the business experience portion of the equation.
Take a look at this grid ... the grid illustrates how much a customer with various recency-oriented direct-channel and retail-channel attributes will spend online in the next year:
Grids help us understand customer value. Grids help us understand whether we should change our behaviors or not.
Look down the "Direct Recency = 25 to 36 Months" column. This column shows us how much a 25 to 36 month direct channel buyer will spend online, in the next year, by in-store retail recency segments.
Let's pretend that we have an online buyer with no retail history ... that customer is expected to spend $14.51 online in the next twelve months. Now, look at what happens when the customer buys from a retail store ... the customer moves up the column, and moves into the "0 to 1 Month" recency segment. Online value increases ... from $14.51 to $37.58.
Conversely, read across the bottom row ... let's pretend that the customer buys online instead of buying in-store. Now, the customer moves from $14.51 of online value to $141.77 of online value.
The retail purchase has very little influence on online value. The retail purchase would likely trigger a different email cadence - one that encourages the customer to learn more about the store.
The online purchase has significant influence on online value. The online purchase would trigger a different email cadence - one that encourages the customer to continue to shop online.
Grids quickly become complicated ... it's hard to move beyond two dimensions. A good idea, then, is to create models that combine numerous attributes into one dimension. You predict, on an A/B/C/D/F basis, the value of the customer online, and you predict, on an A/B/C/D/F basis, the value of the customer to a retail store. Then you have a 5x5 grid ... and better yet, you have multiple 5x5 grids that show retail value, online value, and company value. Based on customer value, you trigger the customer into different marketing programs.
In the coming months, we're going to talk more about grids, about models that combine attributes, and the triggers we use to capitalize on customer behavior. The goal is to expand business knowledge ... in terms of your career, business knowledge > machine learning applications.
But the reality is that we must combine business experience with math ... unfortunately, we tend to omit the business experience portion of the equation.
Take a look at this grid ... the grid illustrates how much a customer with various recency-oriented direct-channel and retail-channel attributes will spend online in the next year:
Grids help us understand customer value. Grids help us understand whether we should change our behaviors or not.
Look down the "Direct Recency = 25 to 36 Months" column. This column shows us how much a 25 to 36 month direct channel buyer will spend online, in the next year, by in-store retail recency segments.
Let's pretend that we have an online buyer with no retail history ... that customer is expected to spend $14.51 online in the next twelve months. Now, look at what happens when the customer buys from a retail store ... the customer moves up the column, and moves into the "0 to 1 Month" recency segment. Online value increases ... from $14.51 to $37.58.
Conversely, read across the bottom row ... let's pretend that the customer buys online instead of buying in-store. Now, the customer moves from $14.51 of online value to $141.77 of online value.
The retail purchase has very little influence on online value. The retail purchase would likely trigger a different email cadence - one that encourages the customer to learn more about the store.
The online purchase has significant influence on online value. The online purchase would trigger a different email cadence - one that encourages the customer to continue to shop online.
Grids quickly become complicated ... it's hard to move beyond two dimensions. A good idea, then, is to create models that combine numerous attributes into one dimension. You predict, on an A/B/C/D/F basis, the value of the customer online, and you predict, on an A/B/C/D/F basis, the value of the customer to a retail store. Then you have a 5x5 grid ... and better yet, you have multiple 5x5 grids that show retail value, online value, and company value. Based on customer value, you trigger the customer into different marketing programs.
In the coming months, we're going to talk more about grids, about models that combine attributes, and the triggers we use to capitalize on customer behavior. The goal is to expand business knowledge ... in terms of your career, business knowledge > machine learning applications.
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