Customer prospecting allows you to locate regions with ideal demographic characteristics for targeting new customers.
Customer data is typically required to adequately locate geographies matching the demographics that represent your customers. You can manually enter these variables and ranges if you know what demographics represent your customers.
To use the Customer Prospecting wizard, you must know what type of customers to look for. You may be unsure of the profile of your best customers. You can use customer profiling to find the demographic profile of a set of customers. Customer profiling works this way: every area—ZIP Codes, block groups, and tracts—has demographic data associated with it. The example below shows each block group with an average household income.
In this example, four customers living in different block groups are being profiled. Each customer is tagged with the value of the block group they fall within. The values for each customer are totaled and divided by the number of customers.
You can locate these ideal geographies in two ways:
- Manually set the demographic profile to locate similar geographies: This is recommended if you understand the statistical breakdown of your demographic attributes. In the image below, any or all geographies that match your criteria are returned.
- Find the similar geographies based on the Principal Components Analysis (PCA) method: This is recommended if you know the demographic attributes but want Business Analyst to help return and automatically rank similar geographies. In the image below, geographies that match your criteria are automatically returned and ranked. The areas in red represent areas most like your demographic profile and areas in yellow are least like your demographic profile.
Now that you know the profile of your customers, you can use these values in customer prospecting to look for other areas with the same type of customers.
Some examples of customer profiling include the following:
- A national retail chain uses customer profiling to provide benchmark customer characteristics for each regional division. The data is analyzed to uncover regional differences in the customer database. These characteristics are used to fine-tune the merchandise mix in different regions of the country.
- Another retail chain uses customer profiling to determine that it serves three distinct markets: inner city, suburban, and freestanding small city. Each market responds to different types of advertising.
- A large insurance company finds that the number and policy types vary considerably by customer profile. This information is used to provide better agent leads.
- Using lifestyle segmentation data, in the Household data option, provides a profile of customers based on the dominant lifestyle segmentation data code. You can identify how many customers you have in each segment. The lifestyle segmentation data descriptions provide detailed profiles of each segment. They can be used to find other segments nationwide at the census tract level to locate more areas similar to your existing customers. Lifestyle segmentation data is only available in the Esri demographic dataset.
Learn more about Customer Prospecting.