Many believed the era of customer relationship management and one-to-one marketing would spell the demise of geodemography for marketing. But the opposite has occurred. Here are the reasons why.
Geodemography is a branch of market research that assigns the attributes of small areas – usually neighbourhoods – to the consumers who live within them and, based on this assignment, divides the consumer marketplace into meaningful segments that are locatable and reachable. The discipline leverages spatial and mathematical patterns in how people live and shop to help marketers make inferences about consumer behaviour. These techniques have been widely used since the early ‘70’s to answer the marketing questions: Who are my customers? Where do they live? and How can I best reach them?
During this same 40-year period, technology has put powerful computers on everyone’s desktop. Software systems that deliver modeling algorithms to the non-statistician are affordable and usable.
Geographic Information Systems (GIS) have gone way beyond simple mapping to bring location intelligence to business databases. And consumer marketers build large databases about their customers and know how to mine the information contained within them.
And yet the number of users and the range of applications for geodemographic cluster systems have grown – not decreased – during this period. Why, with all these new tools and lots of actual consumer information available, do marketers still use geodemography?Here are 10 good reasons.
Everyone does not have unit record customer data. While large retailers, financial institutions and charities keep track of their customers and donors, there are still large numbers of businesses that do not collect much personal demographic information. Postal code collection programs in Canada are a popular way to understand how far customers travel. Combined with geodemographic segmentation systems, postal codes are a reliable way to profile customers in terms of demographics and behaviour.
Even among those businesses that have customer data, the data about individuals are incomplete. Except for businesses that offer financial services, customer income is usually not tracked nor is lifestage, household size or ethnicity. Yet all these variables are well represented in a good multi-dimensional cluster system. Privacy is also a primary concern for businesses and consumers. Geodemographic overlays are a privacy-compliant way to enrich transactional databases. Many analysts use the clusters as well as individual variables in custom models.
Geodemography leverages the rich survey data that exists in Canada. Government and non-government organizations conduct reliable national sample surveys on spending, media preference, technology adoption, leisure activities, tourism and many other aspects of day to day living. For most surveys, the sample size is sufficient to release data for Canada and the provinces and the larger markets. But these survey-based variables only become usable for trade area analysis and local marketing when they are combined with geodemographic segments – using typological inference. Because Canada has more than 1,500 good-quality census variables at the neighbourhood level, analysts can develop a robust segmentation system built on a broad range of reliable and comprehensive data. This allows an analyst to combine the survey-based measure by segment for a behaviour like “go the movies”, for example, with information on which segments live around a location to determine the viability of a new cinema. Without geodemography the amount of local marketing data would be greatly reduced.
While mining your in-house database helps grow your business based on cross-sell, up-sell and retention strategies, geodemography is the easiest way to define your best potential customers based on your existing file and find more people with a similar profile.
Media preferences of a consistent target group defined by geodemography can be determined across multiple channels. Since the popular segmentation systems in Canada are linked to media measurement surveys like PMB, BBM Canada RTS and NADbank, the segmentation system becomes a data integration tool amongst disparate sample surveys. Marketers can use these surveys to determine, for example, that a target group “reads the sports section”, “listens to traffic reports” and “uses the Internet” but “ranks low on TV”.
The results are executable. While much market research is descriptive and can help with product conceptualization, brand awareness and advertising, only geodemography can effectively link the customer, product and brand profiles to site selection, local marketing (including direct mail or flyer drops), merchandising, category management and media planning.
Clusters are easy for marketers and executives to understand. Cluster systems feature icons and clever nicknames for a reason. Claritas’ Shotguns & Pickups, MapInfo’s Kindergarten Boom and Environics Analytics’ Lunch at Tim’s conjure up images of groups of consumers much more readily than cumbersome descriptors like “upscale empty nesters in condos”.
Geodemographic segments are uniquely positioned for “mass” targeting. There are some products for which every consumer is a “target”. By developing cluster profiles of a product’s potential by market, companies can spend against who’s in each market – varying the spend on a market by market basis – but still marketing to all.
Results are measurable. Because marketers can tie segments back to the ground (stores, postal codes, markets, etc.), to channels and to their own transactional data, campaigns can be measured and modified on an on-going basis.
Adding the spatial dimension to the customer database means that it can be used for more than direct mail. This includes applications such as crafting advertising messages, defining product mix, selecting store formats or sites and planning media. As a result, large (and, in many organizations, still not justified) expenditures on developing customer relationship management (CRM) can be leveraged. And the fact that geodemography is inexpensive (compared to the database build and maintenance) makes it a good way to increase the return on the whole customer database investment. Some predicted in the late ‘90’s that the move to CRM systems, one-to-one marketing and household level models would mean the end of geo-based cluster analysis. This has turned out not to be true. In fact, what has happened is that the savvy marketers who embraced the “new technologies” continue to incorporate geodemography into their analysis and have developed more sophisticated ways to use it.