What Kind of People Buy from Me? Using the Baseline Comparison Method to Find the Answer

If you have ever spent some time thinking about your customers, one of the natural questions that has probably come to mind is, “Who are my customers? What type of people buy from me?”

Indeed, if you are already asking yourself these types of questions, you are already ahead of the game: most business owners spend far too little time thinking about who their best customers are or how to reach them.

To answer this type of question, of course, you will need to collect some data about your customers. The types of customer data that yield the most insights are:

a. demographic: age, income, marital status, owns vs. rents, etc.
b. psychographic: attitudes and opinions about entertainment, the economy, politics, advertising, etc.
c. media consumption: website usage, TV watching habits, radio listening behaviors, etc.
d. behavioral: how often they buy from you, what they buy, how much money they spend with you, etc.
e. geographic: where they live and work

Once you have collected any of these types of data about your customers, you will next need to find a way of making sense of that data. There are essentially three approaches to putting your customer data into perspective: Descriptive Statistics, Grouping & Prioritizing, and Baseline Comparison. As you will see below, each method can be useful in terms of yielding insights into what kind of people buy from you. But, each method is exponentially more precise than the one before it in terms of yielding insights about whom you should be targeting in your marketing outreach and advertising efforts. Here is a brief overview of each approach:

1. Descriptive Statistics: This approach involves counting or averaging the data you have collected about your customers. For example, if you have collected income data on your customers, you might determine that their average income is $43,343. This is useful data, but it has a crucial limitation: it fails to recognize the likelihood that – taken individually – your customers represent a spectrum of income levels. In other words: the median tells the story of the group as a whole, but it does not yield meaningful information about specific customers.

2. Grouping & Prioritizing: This method entails grouping your customers by sub-category (for each specific category of data, such as income) and then looking for patterns in terms of the relative representation in each sub-category. Often, this approach also involves grouping each sub-category of data according to the relative percentage of the total that each one represents. For example, suppose that further analysis yielded the following income-related sub-categories of data about your customers:

35% have incomes of $20,000 to $30,000 per year
55% have incomes of $30,000 to $50,000 per year
10% have incomes of $50,000 or more per year

Knowing this, you are now in the position to adjust and focus your markeing efforts in order to reach out to those in the $30,000 to $50,000 per year sub-category, since they represent well over half of your customers.

3. Baseline Comparison: However, to find out in a much more precise fashion which customer groupings are the most signficant, you should introduce the concept of a “baseline” to your calculations. To do so, start by turning the data in each sub-category into percentages (as was done in the example in #2 above). Then, do the same for the baseline group (this could be a trade area, for example). Finally, perform a simple division calculation, whereby the numerator is the data from your customer list and the denominator is the data from the baseline population of your trade area as a whole. This simple calculation allows you to perform a very powerful feat: to create an index for each sub-category that determines which are the most well-represented (and therefore, significant), relative to your trade area.

To illustrate this point, and building upon the example in #2 above, if just 3% of your trade area (i.e., the baseline) has incomes in the $50,000 or more per year sub-category, then that income sub-category indexes at (10% / 3% x 100 =) 333. This means that prospects who are members of this income sub-category are 3.33 times more likely to become your customer, relative to the average prospect. Meanwhile, the $30,000 to $50,000 per year sub-category might only index at 75, meaning they are just 0.75 times as likely to become your customer.

Thus, by adding a baseline to your calculations, you have gained additional insights that may change your entire marketing focus – insights that you could not have garnered from merely performing the “Grouping & Prioritizing” work described above. After all, if you had stopped at #2 (Grouping & Prioritizing), you would have made a very different decision about which prospects to go after. Now (and only now) do you have actionable information that you can immediately put to use by focusing your marketing outreach and advertising efforts on this group.

This method becomes even more powerful when you apply it to not only demographic data (as in the above example) but to psychographic, media consumption, and other customer data as well.