What is the Process of Segmentation?
To operate systematically and effectively, growing companies shift their focus to a particular set of audiences that closely mirrors their current audiences, rather than a wide range of potential audiences.
The question I attempt to answer in this post: Is there a scientific way of processing segmentation based on several dimensions?
You all know that you can plot the shape of a curve on a two-dimensional graph or draw the shape of an object on a three-dimensional graph.
However, once we have more than three perspectives, the mind starts to wonder how to visualize the object's shape.
Trying to model the outcome of a process that has multiple dimensions is more complex than can be represented in an Euclidean space.
Even more difficult is to find the optimum of that shape. Let us say you wanted to see the lowest cost of marketing to a community of a million customers. Further, you know that they interact with you via multiple channels, such as web browsers, email, chat rooms, call centers, mobile phones, and tablets. You also know that the process of communication is either initiated by the sender ( marketer)or the receiver (prospect or customer). The stage of the receiver’s buying cycle also influences the outcome of this interaction.
Brand awareness, price sensitivity, the receiver's affluence, and the promotional offers on the table are just a few of the factors influencing the decision. The array of factors affecting the outcome of this marketing game is too numerous to list. So, how do you model this complex world of b2c or b2b marketing?
Early in our lives, we have been taught to use cryptic language to represent ideas—the language of mathematics. So very early on, you learned that you can define a straight line by an equation.
You also learned to define the line by its slope, the height of the Y-axis where it intersects it, and a pair of points on a two-dimensional graph by a set of points like (X, Y). You can use the same approach to represent the marketing scenario mentioned above by a multidimensional shape.
This process of segmentation is called modeling. You attempt to fit the abstract representation of the real world into an equation that defines its shape. In marketing, there are two questions we try to answer.
What is the probability of a favorable outcome (someone buying something)?
What is the amount of revenue that can be generated if the outcome is favorable? In other words, if one were to buy, how much would they buy?
The first question has a YES/NO binary answer, and the second question has a discrete answer ($58.25). Both are estimates, but their nature is different. So the technique to answer them is also different. The first is called modeling response, and the second is called modeling revenue.
The first question concerns the classification of the outcome as yes or no. The technique used for this is called logistic regression.
The second question estimated the return amount. The technique used for this is called linear regression.
Humans are creatures of habit, so we assume they will behave the same way they have under normal circumstances. In effect, they will tend to fall back (regress) to their habit. This hypothesis led to the method of observing the receiver's behavior and reaching a conclusion about the two answers we are seeking. Marketers and statisticians have been using this method to observe last year’s buyers' behavior and create an equation to predict the likelihood of purchase and, better still, the amount of money likely to be spent by the customer.
By using these segmentation processes, you can create a score (very much akin to the FICO score lenders use to measure us). You can then use this score to rank customers from highest to lowest by their probability of buying. You can also rank the customers by the amount of money they are likely to spend. These two scores themselves reveal a lot.
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We could decide who to send marketing collateral to and who not to send it to. Thus, by holding off sending it to the lost causes or the sleeping dogs, we can improve the return on investment of our marketing efforts. After all, marketing costs real dollars.
We could also multiply the probability of purchase by the amount of money the customer is likely to spend to create a final score for each customer.
By ranking customers top to bottom by this final score, we can identify the best-of-the-best customers to market to. Now you know why some of us are such magnets for junk mail!!!
