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Why Geodemographics continues to be relevant and useful – even in the Big Data era

Geodemographics – the analysis of people by where they live – is an approach that has been around for the best part of 40 years. Is it still be relevant today, when marketers have access to vastly more timely and specific data about consumers?

Geodemographics is often defined as ‘the analysis of people by where they live’ – it’s a science which brings together geography and demographics into a single discipline. The power of geodemographics rests on two key principles. The first is that birds of a feather flock together, i.e. two people living in the same neighbourhood are more likely to be similar to one another than two people chosen at random. And the second is that areas can be segmented on the profiles of their residents – neighbourhoods in the same segment will contain similar kinds of people, even though they may be far apart. Based on these principles, geodemographic segmentation helps to infer the characteristics and likely behaviour patterns of the residents in each area, with the additional benefit that the geographical locations of neighbourhoods and segments are known and reachable.

In 1979 there was just one neighbourhood classification in the UK. Nowadays, nearly 40 years on, the geodemographics market is highly sophisticated and evolved – in total, around 40 information products are now on offer. Segmentation systems are available at different levels of geography, some going down to household and individual levels. As well as general-purpose systems, there are products designed for various industries, such as the financial services, health and leisure sectors. And the market goes beyond classifications into raw and derived data products, such as small area estimates of income and retail spending potential. My book discusses all of these approaches and their applications.

Why is geodemographics still relevant in the era of big data?

We all know that, over recent years, a huge amount of information has become available about each individual – covering their demographics, purchases they have made and their online browsing behaviour. This information is timely, detailed and specific to that individual; yet it becomes easier to lose sight of the contextual data that can help to explain social influences to which consumers are subjected, as well as their motivations and aspirations. Geodemographics can provide some of this contextual capability that is missing from big data.

Furthermore, in many markets – such as grocery purchasing, credit cards, savings and investments – consumers are not necessarily 100% loyal to a single brand; they will typically spread their transactions across multiple companies. A customer database may capture the purchasing behaviour on that brand, but cannot know what the customer has done elsewhere. Geodemographics can provide a complete or holistic view of each customer and predict their total market expenditure across all brands.

As predictive analytics extends into applications which go beyond targeting sales, such as different types of risk assessment and fraud detection, it becomes important to have information that tells you more about each individual. Geodemographic variables are straightforward to append onto each customer, and are always worth examining along with database attributes. In my experience, the results have been informative and useful on many occasions.

Therefore, my advice would always be to examine geodemographics alongside the other data sources available to you, and do not necessarily rely on big data alone for targeting your advertising and campaigns – big data can identify the specific timely opportunity, while geodemographics can help you to understand the context and plan the message to deliver.

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