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The next couple of years will be tough for the retail sector. Monetizing customer behavioral data can help retailers remain profitable while providing banks and credit lenders with an alternative data source for credit scoring first-time credit applicants. 

The retail sector is facing its worst year since 2008 with major retailers issuing profit warnings and consumer confidence dropping to the lowest levels in decades. Inflation rates are reaching record levels of 8.6% in the US and 9.1% in the UK (the highest inflation rate in 40 years for both markets) and 6.5% in South Africa.

The combination of a post pandemic world and the Russia-Ukraine war has pushed up shipping costs, retail wages, and the cost of food, fuel and material costs, leaving little room for retail profit.

To survive the “economic hurricane” that JP Morgan Chase CEO Jamie Dimon warned investors to brace for retailers need to get creative and either find ways to reduce costs even further or preferably find new, untapped revenue streams.

How the monetization of customer data creates new revenue streams

Innovative retailers like Walmart, Kroger, CVS and Walgreens have been sharing customer data with brands that sell products in their stores, or allowing targeted advertising and communication to their base as a way of diversifying their revenue portfolio.

However, few have yet to realize the value of their customers’ behavioral data to financial institutions searching for alternative data sources for credit scoring.

Using alternative data to grow financial inclusion

Banks and other credit lenders rely on consumer credit scores to determine a credit applicant’s probability to pay back a loan. If a person has no credit history – and this includes not only the poor but also recent immigrants and young people – they will struggle to get a loan or credit, which puts them at a major financial disadvantage and limits economic growth.

According to the World Bank, 1.7 billion people worldwide are unbanked, or have no access to a bank account primarily due to a lack of credit history. For financial institutions, this is an untapped market – estimated by Accenture to be worth $ 380 billion globally – which they’ve been unable to access without high-risk.

As a result, financial institutions have been eagerly searching for alternative data, or behavioral data for credit applicants from sources other than the traditional credit bureaus, that could help them determine a credit risk profile.

This enables credit lenders to not only lower the risk of lending to credit invisibles, but also to also offer more favorable lending terms to those who do have a credit history by supplementing the data sourced from credit bureaus with additional insights. According to credit bureau Experian, 65% of lenders in 2019 used some information beyond the traditional credit scores to make lending decisions. And 71% of Americans would be willing to share more personal data with a lender if it resulted in a fairer credit decision.

Some examples of alternative data are a credit applicant’s payment history on rental and utilities, their social media profile, network and activities, their mobile phone usage data, and more recently shopping behavior data.

Shopper data as an alternative data source for credit scoring

Correlating shopping behavior and good credit payment behavior can improve predictability of credit repayment.

A recent study showed that specifically grocery shopping data picks up some behaviors of lower-income consumers that correlated with creditworthiness, but cannot be captured by traditional data such as income. The study overlapped data from a credit card issuer and grocery retailer to match shared customers’ shopping data with their credit card payment history.

The study showed that shopping behavior such as shopping the same day of the week, buying the same things regularly, buying healthy foods, and taking advantage of promotions or discounts were indicators of responsible payment behavior.

Although all shopping behavior data collected by retailers can provide insight into a credit applicant’s risk profile, using grocery shopping data is ideal for growing financial inclusion – and therefore particularly valuable to financial services institutions – because every adult shops for groceries no matter what income group they belong to.

The significance of loyalty programs to financial inclusion

Retailers’ loyalty and rewards programs are crucial in using shopper data for financial inclusion, because they include purchase behavior from credit invisibles who make cash purchases and would otherwise remain invisible.

Many retailers have long resisted offering loyalty programs due to their cost and impact on their margins, specifically those retailers using Everyday Low Price pricing strategies that serve lower-income shoppers. However, those same retailers are now beginning to embrace loyalty programs for their ability to track and analyze their customers’ behaviors, commercialize this data, and generate new revenue.

Diversify revenue streams to grow and remain profitable

Even the world’s largest retailer, Walmart, has acknowledged that “retail may not be enough to power its future” and is diversifying its revenue streams by offering new services, including commercializing its customer data via Walmart Luminate to offer other businesses customer insights for better decision-making.

With growing inflation and plunging consumer confidence, the near future of retail is looking rocky. It’s time to get creative and identify new revenue opportunities that lie untapped within your customer database.

Are you ready to generate new revenue from your customer data?

If you’re a retailer interested in discovering how to generate revenue from your customer data in a privacy-compliant way, contact Omnisient about our “Data Monetization in a Box” offering. We’ve worked with leading retailers and financial services institutions to bring them together to create new sources of revenue and grow financial inclusion while respecting consumer privacy.

If you’ve got the data, we’ll help you find the value and the buyers and set you up in a secure and compliant data collaboration and monetization environment quickly and easily.

About the Author

Julian Diaz is Omnisient’s CMO. Julian is a B2B tech marketer for start-ups with over 20 years of experience marketing in USA, UK, Europe and South Africa.