As an industry veteran committed to helping organizations leverage data to make better decisions, I am often reminded of the great Yogi Berra’s comment “It’s déjà vu all over again” when I hear marketers debate the benefits of individual versus geodemographic data.
This argument is perhaps even more relevant today in the era of Big Data, when the volume, velocity and types of data collected by organizations have increased exponentially. The question as to which data type is best may seem obvious. Why wouldn’t businesses prefer using their own customer point-of-sale transaction data rather than geodemographic data from an outside source that’s been aggregated or modeled to a geographic level? But before jumping to conclusions, marketers should first ask themselves the following questions:
What problem are we trying to solve?
What insights are we trying to acquire?
What data have my organization already collected?
What resources (money and people) are available to acquire and analyze data?
The answers to these questions will vary depending on industry sector. For instance, individual-level data are excellent when the challenge is targeting customers for upsell and cross-sell campaigns in industries with high-frequency transactions, like banking, telco and grocery retailers. But individual-level data are not as useful with high-value, low-frequency purchases in the automotive, home furnishings and appliances industries.
The new reality is that marketing organizations should use a combination of individual and geodemographic data to optimize customer insights, improve targeting and communications, and realize the most value from their investment in collecting and managing data
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First, let’s be clear on what we mean when we talk about using geodemographic data. While there are many definitions, I prefer the following: Using the attributes of small areas as surrogates for individual demographics, behaviours, attitudes, beliefs or preferences—usually for the purpose of linking disparate data together to provide an integrated view.
In a perfect world, individual-level data on your customers or prospects would almost always produce superior analytical or targeting solutions. After all, your transactional data would show what they bought, when they bought it and how much they spent. But as we know, the world is not perfect and the data that we collect about our customers are often incomplete, leaving marketers without the critical information they need.
For example, while individual-level data may provide very specific information about customer purchase behaviour, they don’t provide contextual information on customer motivations, aspirations and social influences. But conducting primary research to collect this psychographic data is generally cost prohibitive.
By contrast, geodemographic data can provide this kind of information much more cost effectively. In addition, geodemographic data can give marketers insights into other key areas, including the following:
Share of wallet
Companies with rich transactional data can easily determine how much a customer spends with them over a specific period. But this information gives no insight into the percentage of that customer’s spend in a specific category, which would help determine the customer’s loyalty and untapped potential. For example, loyalty cards allow grocers to know exactly how much a customer spends with them per week, but they don’t know that customer’s total grocery spend (or wallet size) per week. And a non-for-profit may know how much a donor has contributed in the past year but has no clue about the donor’s total charitable contributions. Consumer data available from geodemographic segmentation systems like PRIZM can provide data at the six-digit postal code level as a proxy, allowing marketers to factor in this key information and determine share of wallet.
Even the best customer data often have little to offer when it comes to prospects. But organizations can leverage individual-level data on existing customers with geodemographic data to create profiles of potential customers and locate “lookalikes” on the ground. For example, one hospital foundation, hoping to acquire more donors to buy new lifesaving equipment, typically targeted its direct mail appeals to households the facility. But when a PRIZM analysis of its donor file identified prospects with similar demographics, lifestyles and values in postal codes many miles away, the foundation added those neighbourhoods to its campaign. The result: thousands of new donors and a 250% increase in donations.
By leveraging both individual and geodemographic data, organizations have the best opportunity to achieve success. And while Yogi asserted “it’s tough to make predictions, especially about the future,” the right combination of data can help marketers score a home run.