Customer Profile Characteristics For The Betting Industry

Customer Profile Characteristics For The Betting Industry

The biggest factor affecting betting shop location is the number of customers: in order to offer attractive odds the profit margin on gambling is inherently low.

It makes it therefore vital to find a site with a large number of likely customers, rather than to rely on a few high-spending regulars. With what is known about buyer behaviour and what defines the likely customer, location analytics can help identify the perfect location and the ideal customer pool.

Consumer Demographics

Consumer demographics is one of the key distinguishing features of the betting shop customer base, but it’s not a one-size-fits-all factor. Gamblers on sports, particularly horse racing, skew older, with 45 to 54 the peak demographic. People beyond this demographic may also have a keen interest in such gambling, but they often have less disposable income to wager.

Gambling machines on the other hand are considerably more likely used by younger people. Depending on a betting shop operator’s preferred product mix, running location analytics to factor in the age distribution in an area is crucial.

Consumer Psychographics

Psychographics is another key element that has a great impact on the success and failure of betting operators. While this social science measures highly personal and individual traits, it can reflect wider social behaviours. For example, the International Gambling Studies journal notes that materialism is often strong in gamblers, so it may be worth using location analytics to find areas where retail shops specialising in non-essential or branded goods are flourishing.

Most Common Characteristic

One big change to watch for when running location data analysis is employment levels in the local area, particularly among the demographics that are typically more favourable to the specific forms of gambling you plan to concentrate on.

Reuters reported that in the early 1990s slump, bookmakers appeared to be recession-proof, possibly because customers viewed sports and gambled in order to escape from the day-to-day hardships. However, in the 2008 recession the number of gamblers appeared to drop off as jobs became less secure and unemployment rose.

That appears to be linked to the rise in gambling machines. While sports betting seems to be more habitual, gambling machine use is often more opportunistic and in turn more sensitive to the public’s level of financial confidence.

Conclusion

With the above in mind, it is important for betting operators to be early adopters for this type of solution. Besides being able to locate ideal areas for betting shops, betting operators can use their consumer research data and overlay it with other public or third party data in order to expand their scope.

By using location analytics, companies in the betting industry will have a better overview of the business landscape in which they operate.

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