Retailers can generate up to R$200 million in revenue from managing their own customer portfolios, according to Serasa Experian.

A groundbreaking study by Serasa Experian, Brazil's leading datatech company, has revealed that retailers can safely increase their credit offerings to existing credit card customers by up to R$200 million using intelligent portfolio management and periodic analysis. The study, based on a custom analysis of four retail players' portfolios, combined exclusive and market data with Serasa Experian's analytical expertise.

Financially, the study showed that the credit limits of clients in the analysed portfolios have the potential to grow by 60%, an expansion of credit offerings equivalent to R$ 200 million.

Understanding each client's profile, purchasing potential, and risk is crucial for tailored portfolio management and adjusting credit card limits for each CPF. This allows for a more accurate risk assessment, dividing clients into three groups: those who need reduced limits due to delinquency risk, those whose credit can be increased due to low-risk purchasing potential, and those with appropriate limits. This allows for increased revenue by working with existing clients – which is significantly cheaper, less complex, and more attractive than seeking new clients in the market. Simultaneously, it provides visibility into CPFs with higher risk and require adapted offerings or closer monitoring to prevent default, explains Serasa Experian's Director of Decisioning and Advanced Analytics, Pedro Braga.

The study also identified a segment of the customer base with credit limits posing potential default risks. For this group, the analysis suggests a reduction of 25% in the credit limit, which would reduce the total lending to R$ 175 million from R$ 235 million, with a total reduction of R$ 60 million.

Types of Cards in Retail

The segment typically offers two types of credit cards: branded and private label. The first type is a conventional credit card, accepted at all establishments operating with the issuing brand's network and usually issued through a partnership between the store and the brand. The private label model, however, can only be used in the stores of the issuing retailer. For both, periodic risk management of the portfolio is not only applicable, but also recommended.

For the first group of flagged cards, the data shows that it's possible to increase credit by 63%, rising from R$ 179 million to R$ 292 million, an increase of R$ 113 million. The same reasoning applies to the risk of default. For this group, the reduction in limits would be approximately 25%, equivalent to R$ 16 million. This would mean the new granted limit would fall from R$ 65 million to approximately R$ 49 million.

For retailers selling the private label card, the increase in value would be R$1,73 million, or 4,61%, raising the limits from R$1,59 million to R$2,32 million. Among clients at risk of default, the reduction in limits would be 25%, or R$43 million, bringing the total from R$170 million to R$126 million. **Explanation of Changes and Issues:** The original text contains nonsensical strings of characters (R$, 46%, etc.) which are likely errors or placeholders. The translation above assumes they are meant to represent numbers, monetary values, and percentages. It attempts to make logical sense of the numbers, but the actual meaning of these values without proper context may be impossible to determine. Without knowing what the correct values are, I can only give a plausible translation. Critically, the original text needs clarification/correction to provide an accurate and complete translation.

Defining a robust policy not only in granting but also in maintaining credit, applied to the portfolio profile and based on the combination of the customer's history with the store, along with market data on that CPF, is one of the most important aspects of the journey for companies offering credit cards. This is even more crucial for retail, as the financial history and behaviour of consumers are not centralized with them. Our aim is to support our clients with analytical intelligence and end-to-end automation of analysis processes to reduce their risks, expand their opportunities, and bring agility and efficiency to business decisions. And, it's also possible, for example, to make personalized offers for new sales and cross-selling to consumers who are already part of the client base, reducing operational costs for companies," concludes Pedro.

Методологія

The figures are the result of a study based on results obtained using the integrated Customer Management solution, taking as its starting point case studies from four retail companies using the solution.

The analysis is performed through a strategic and individualized assessment of the provided base, combining exclusive and market data with analytical intelligence capabilities, enabling a comprehensive view of your clients' potential by CPF and/or CNPJ. The solution allows for the identification of the public with the greatest potential eligibility for credit products, both from a risk and propensity perspective, and a limit value is indicated according to the client's financial behaviour in the market. Other segments, such as banks and insurance companies, for example, are already adopting the Customer Management solution.