A Tale of Two Customers – Using Analytics to See Your Customers Differently

As a business owner, you have in your head all kinds of details about who your best customers are and what they look like. For example, you probably know which products or services they tend to buy from you, their average age, and even how they dress or what sports teams they like. In fact, most business owners will tell you that they know their best customers better than anybody else. And in a sense, that is absolutely true.

The trouble is: this type of customer information does not paint a complete picture of who your best customers are because it is primarily observation-based and anecdotal. While this type of information provides a sometimes vivid, first-person view into who your best customers are, it is limited in its effectiveness in guiding your marketing decisions for the following reasons:

1. It has a low level of statistical significance, since the data is ultimately based upon a small sample size of your customer base

2. It is situational, meaning that it is dependent upon the idiosyncrasies of the “who, how, where, what and when” of the business owner interacting with his customers

3. It is self-reinforcing, meaning that over time the business owner tends to only recognize those customers who fit his pre-conceived notions, ignoring new data that might cause him to update his personal “best customer model”

4. It does not afford the opportunity to compare customers to non-customers who live and work in the trade area, meaning that there is no basis for making assessments of just how significant any observed customer trait really is relative to the population-at-large

5. It does not provide insights into media consumption behaviors, psychological views & attitudes, and buying behaviors that current customers hold dear – insights that could inform specific, tactical media buying decisions

  • The Value of Psycho-Behavioral Segmentation-Based Profiles

    To fill in the gaps inherent in observation-based, anecdotal best customer models, we can turn to research-based customer segmentation techniques that leverage psycho-behavioral data about your customers and others like them. Psycho-behavioral segmentation starts in the same place as does observation-based modeling: with your current customer base. However, it goes much further by leveraging massive databases that contain data on your existing customers – and then groups (or segments) your best customers with others like them. Once they have been grouped in this way, we can mine a wealth of useful information about your best customers and then apply that information to prospects who have not yet spent a dime with you.

    The result is your ability to know exactly which prospects in your trade area are 3-7 times more likely to buy from you than average.

    The customer profile that emerges from this type of quantitative customer analysis, combined with what you already know about your best customers, can serve as a very robust model that will help inform your marketing decisions. These two customer models (i.e., observation-based and research-based) will effectively let you see your customers from two very different, complementary angles. You are effectively weaving a tale of two customers – or rather, two perspectives of the same set of customers.

    5 Benefits of Research-Based Customer Segmentation Profiles

    Research-based customer segmentation efforts can bolster your best customer profile in the following ways that observation-based models alone cannot:

    1. Provides a data-driven, rather than merely anecdotal, business case for making marketing and advertising decisions:
    Analysis paralysis. We all know the feeling. It is when you are faced with myriad individual, disconnected data points about your marketing plan such that you are unable to make heads or tails about what it all means. What every marketing manager really wants to know is, “What should I do next?” By taking a data-driven approach to your marketing decisions, you can silence all of those inner doubts because you are able to look past anecdotes and focus in on what the data is telling you to do next.

    2. Allows you to view your best customers in the context of all of the prospects in your trade area, rather than merely as isolated individuals
    If you have been in business for a while, you likely already know more than a little about your customers. But, it is something altogether different to know how your best customers stack up against all of the households/people/businesses in your trade area. It is only when viewing them in the context of everybody else in your market that you can really get a sense of who your best customers are.

    3. Gives you the opportunity to look beyond typical customers to find additional lucrative segments that may not fit your standard profile
    You may have a sense of who your typical, or average, best customer is. But, there are likely other segments of prospects who would be equally attractive as prospects for you but who do not fit this profile. Sometimes, by focusing too much on the averages, we can miss out on the meaningful “outliers” that represent real business opportunities.

    4. Allows you to see patterns that lead to meaningful customer groupings, rather than looking at customers in a piecemeal fashion
    A data-driven, segmentation-oriented approach to understanding your best customers can help you spot patterns that you could not spot by trying to process data in a fragmented, piecemeal fashion. At this level of analysis, the human brain can only process a few pieces of information at time. By relying upon data aggregation and summation, you can spot patterns across large amounts of data and then act upon those patterns.

    5. Can help your marketing plan break out of a self-referential feedback loop that continues to target and capture the same customers because “targeting folks like them in the past just always seemed to make sense”
    The longer that companies stay in business, the easier it is for them to start developing self-fulfilling prophesies about who their best customers are. Essentially, they keep targeting the same people in the same ways and therefore keep getting the same mediocre results. However, taking a psycho-behavioral segmentation approach can reveal opportunities for targeting prospects who fall outside of your traditional target range. The result: new opportunities for business growth open up right before your eyes.

    If you have been in business for several months or years and have held onto your customer data, you can leverage that data into insights that will help guide your marketing plans in the most efficient ways possible. Even though you know who your best customers are, you may just find out that your customers can be seen in a completely different light. These two views of your customers can be combined to create a robust and lucrative marketing program for your business.

    Examples of Each Type of Customer Profile

    Providing exhaustive examples of each type of profile would require more space than is available here, but in terms of a high-level view, here are some brief examples.

    An example of an observation-based, anecdotal customer profile written by a restaurant owner might be:

    “My customers tend to be middle-aged, middle-class customers who live within about 5 miles of my location. My best customers come in 2-3 days per week and tend to order desserts with their meals. My customers lean a bit toward the conservative side in terms of political views, but they run the political spectrum overall. They are highly-responsive to discounts and other promotions, so I try to stay creative with promotions. More than a few of my customers have commented on how much they like our restaurant’s recent remodeling, so I think they value that. I sometimes advertise on local radio, switching between a country station and AM sports radio. I’m not sure which one pulls in the most traffic, however. I also run occasional direct mail campaigns, choosing recipient households by income and age.”

    By contrast, an example of a research-based customer profile for the same restaurant owner might look as follows:

    “My best customers are chiefly divided into two groups: those under the age of 45 with children and mature segments over 45. They live in both suburban and town and country (i.e., rural) areas. The average customer travels 3.8 miles to my location. More facts about them:

    1. They are primarily college-educated
    2. 65% of them own homes, while the rest lease
    3. They are primarily white, with some Hispanic and black segments
    4. Average median household income is $78,834
    5. The majority of their personal and professional time is spent within city limits
    6. They are Internet savvy, live active lifestyles and enjoy being first in their peer circle to engage in new trends. They place emphasis on value and will buy on credit rather than wait.

    My best customers, as a group, are 3.8 times more likely than average to respond to my marketing outreach, and they spend 1.8 times more money with me when they do become a customer. I can find my best customers in the highest concentrations the following zip codes: 78703, 78701, 78756, 78757, and 78722.

    Notable psycho-behavioral traits of my best customers include:

    1. Own Game Boy Advance
    2. Obtain Childcare or Parenting Information
    3. Own Nintendo DS
    4. My Kids Have an Impact on the Brands I Choose- Agree
    5. I Would Use Internet on My Cell More Often if it were Easier to Read- Agree
    6. Country Radio- Net Audience
    7. Sirius Radio- Net Audience
    8. Classic Rock Radio- Net Audience
    9. Own Horse
    10. Buy Treadmill
    11. Go Hunting with Gun”

    As is evident from the two brief examples above, the research-driven report provides a much deeper, more detailed account of who their best customer really is and how to find more of them.

    Contact MindEcology today to get started.