The question I attempt to answer in this post:
Is there a scientific way of segmenting customers based on a number of dimensions?
We all know that we 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 crossed the number of perspectives to be more than three dimensions the mind starts to wonder how one can visualize the shape of the object. Trying to model the outcome of a process that has multiple dimensions is more complex than can be represented in a euclidean space.
Even more difficult is to find the optimum of that shape. So let us say that we wanted to find the lowest cost of marketing to the community of a million customers. Further we know that they interact with us via multiple channels, such as web browsers, email, chat rooms, call centers, mobile phones, tablets. We 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 has an influence on the outcome of this interaction. The awareness of the brand, the price sensitivity, the affluence of the receiver, the promotional offers on the table are just a few of the influencing factors. The array of factors that influence the outcome of this marketing game are too many to articulate. So how do we model this complex world of b2c or b2b marketing?
Early in our life we have been taught to use a cryptic language to represent ideas. The language of mathematics. So very early on we learned that we can represent a straight line by an equation. We 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). We can use the same approach to represent the marketing scenario mentioned above by a multidimensional shape.
This process is called modeling. We attempt to fit the abstract representation of the real world, to an equation that defines the shape of this world. 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 will they buy?
The first question has a YES/NO kind of binary answer and the second question has a discrete number ($58.25) kind of answer. 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 is of classification of the outcome as a 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.
Now humans are creatures of habit, so we assume that they will behave the same like they have been behaving under normal circumstances. In effect, they will tend to fall back (regress) to their habit. This hypothesis gave rise to the method of observing the behavior of the receiver and coming to the conclusion about the two answers we are seeking. Marketers and the statisticians have been using this method to observe the behavior of the last year’s buyers, and create a equation to predict the likelihood of purchase and better still, the amount of money likely to be spent by the customer.
By the use of these techniques we can create a score (very much akin to the FICO score we all are measured by lenders). We can then use this score to rank the customers from the highest to the lowest by the probability of buying. We can also rank the customers by the amount of likely money to be spent. These two scores themselves reveal a lot.
We could decide whom to send a marketing collateral and whom not to send it. 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 it costs real dollars for marketing.
We could also multiply the probability of buying with the amount of money likely to be spent by the customer, to create a final score for each customer. By ranking the customers top to bottom by this final score we can get the best of the best customers to market to. Now you know why some of us are such magnets for junk mail!!!