Sales & Marketing

A method to predict a competitor's consumer spending may have been uncovered

3 min read

28 September 2015

A recent study from The University of Texas has used a new methodology to allegedly predict consumer spending at competitors.

According to a study published in the International Journal of Research in Marketing, you can predict a company’s consumer spend by examining share-of-wallet – a measure of how much of a customer’s spending is captured by a retailer. 

“For example, if a customer spends $100 per month on fiction books – $80 at Amazon.com and $20 at Barnes and Noble – then Amazon’s share-of-wallet would be 80 per cent,” the report read.

Share-of-wallet is related to untapped customer potential, effectiveness of marketing activities and competitive benchmarking, said Ashutosh Prasad, professor of marketing in the Naveen Jindal School of Management.

“It’s a useful metric to track because you can determine the spending at the level of the individual,” he said. “Then you can ask questions like, ‘Which customers should we target with our marketing activities?'”

However, measuring share-of-wallet is problematic because expenditures at competing stores are not easily available, Prasad said. But marketers can use past information, obtained from surveys or information aggregators, and sales at their own store, to predict it.

Read more about consumer spending habits:

He proposed a better methodology for prediction. For example, if Amazon has data of expenditures on not only fiction books at Amazon and Barnes and Noble, but also non-fiction books, then the interrelationships between spending on fiction and on non-fiction can be studied to make a better prediction. With this model, managers can better target customers who spend a large amount of money on fiction books, but do not spend much of that money at their store.

“These are the customers who are potentially very profitable to target,” Prasad said. “You hope to increase the amount they spend with you because they already spend a lot of money.”

The firm can also determine the order of cross-selling. For example, Prasad said, if consumers who buy fiction books also buy more non-fiction books, but less so the reverse, then a store should promote the fiction books.

Application of the model to credit card use uncovered a large segment of consumers whose expenditures are based primarily on habit. A smaller segment of consumers tended to be sensitive to how much income they had, and affected how much they spent on one category.

“The allocating segment, the ones influenced by income, spends on average twice as much as the habitual segment,” Prasad said. “They’re also a little bit older, and they have a higher proportion of men, and a higher proportion is self-employed. On average, they also have higher income. The data reveals different segments, and that’s really useful.”