The Data Analytics Blog

Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

All Posts

Cash Vs Credit For Retailers: A Principa Case Study

February 17, 2020 at 9:00 AM

cash vs creditPrincipa’s Cash Vs Credit offering uses advanced analytics to help retailers answer a variety of questions. We have undergone such projects for many of South Africa’s leading retailers, and this case study describes the methodology Principa utilises to help retailers in the fashion industry.

The two big questions answered by a Cash Vs Credit solution are:

  1. How much more profit can be expected by converting cash customers to credit customers?
    Retailers are interested in exploring whether converting traditionally cash-only customers to store cards cannibalise their cash base with little return on the bottom line.
  2. What proportion of sales margin can the Financial Services department include in their profit calculation?
    A Financial Services Department includes interest, fees, insurance, etc. as their revenue streams, and excludes any margin from retail sales. Although this is in line with current industry practice, there has always been a general belief that having a credit offering available (which gives the customer additional convenience) does potentially encourage spend. Therefore, a retailer’s Financial Services department could include a percentage of the sales margin when calculating profit.

How do we do it?

A primary objective of a Cash Vs Credit project is to determine how much more a customer would spend if given a facility to enable spend, compared to having no facility. This helps to ascertain the financial benefit (from the business’s point of view) of having a credit vehicle available.

Since we cannot observe the same customer both having and not having a credit facility at the same time, Principa makes use of matched sampling and regression techniques. The matched sampling method enables us to find customers who are very similar across various profiles so that we can remove the effects of other “unwanted” variables and more accurately determine the impact of having a credit facility.

“…Principa makes use of matched sampling and regression techniques.”

Where there are no cash customers to match to credit customers for a particular profile, Principa makes use of a regression technique. This allows us to extrapolate to a cash customer’s spend for these profiles, based on what we know about cash customers close to these profiles. Using cash customers nearby ensures a more accurate spend prediction.

To conduct the matched sampling and regression, Principa follows four key steps, as outlined below:

  1. Principa takes demographic and bureau data, as at the observation date, relating to the retailer’s in-store card base and cash base. This is combined with spend data relating to the 12 months before the observation date to better understand the transactional behaviour of the customer. The population is then segmented into credit and cash segments on a customer-level.
  2. Using this data, we are able to determine the most important variables that distinguish credit customers from their cash counterparts. These variables are then used as the profile variables to find the matched samples.
  3. We want to ensure that the effect of having a credit facility takes the entire credit base into account. If the matched samples in the previous step do not capture the entire credit base, we use regressions to be able to predict what the cash spend would have been, had there been a cash customer with the same profile as the un-matched credit customer.
  4. After finding the extrapolated cash customer, we are able to match all credit customers to a cash customer and determine the additional spend of a customer when given a credit facility.

What have we learned?

“…the spend of a customer with an in-store card is likely to be between 1.5 and 3.8 times higher than a customer without the in-store card…”

Previous analysis has shown that once the differences between the profiles of cash and credit customers have been removed, and the only remaining difference is the credit facility itself, the spend of a customer who happens to have an in-store card is likely to be between 1.5 and 3.8 times higher than a customer without the in-store card in the 12 months following the observation date.

Knowing how much more a credit customer spends because of their access to the in-store card can help answer the questions initially posed. Unless the costs and risks associated with servicing an in-store card exceed the additional spend achieved from credit customers, bringing on more credit customers should have a positive impact on the bottom line. In addition to this, growing the credit customer base will also allow for a larger base for future product diversification, value-added products, etc.

Determining how much more a credit customer spends because they have the facility to do so also allows us to calculate the percentage of sales margin the Financial Services department could include in their profit equation. This gives a better reflection of financial results and allows retailers to better self-fund any further initiatives.

Megan Wilks
Megan Wilks
Megan Wilks is a consultant within the Decision Analytics team at Principa. Prior to joining in 2016, she worked in the consulting and data analytics field, covering a range of industries, including retail, credit and non-profit in Southern and East Africa.

Latest Posts

The 7 types of credit risk in SME lending

  It is common knowledge in the industry that the credit risk assessment of a consumer applying for credit is far less complex than that of a business that is applying for credit. Why is this the case? Simply put, consumers are usually very similar in their requirements and risks (homogenous) whilst businesses have far more varying risk elements (heterogenous). In this blog we will look at all the different risk elements within a business (here SME) credit application. These are: Risk of proprietors Risk of business Reason for loan Financial ratios Size of loan Risk industry Risk of region Before we delve into this list, it is worth noting that all of these factors need to be deployable as assessment tools within your originations system so it is key that you ensure your system can manage them. If you are on the look out for a loans origination system, then look no further than Principa’s AppSmart. If you are looking for a decision engine to manage your scorecards, policy rules and terms of business then take a look at our DecisionSmart business rules engine. AppSmart and DecisionSmart are part of Principa’s FinSmart Universe allowing for effective credit management across the customer life-cycle.   The different risk elements within a business credit application 1) Risk of proprietors For smaller organisations the risk of the business is inextricably linked to the financial well-being of the proprietors. How small is small? The rule of thumb is companies with up to two to three proprietors should have their proprietors assessed for risk too. This fits in with the SME segment. What data should be looked at? Generally in countries with mature credit bureaux, credit data is looked at including the score (there is normally a score cut-off) and then negative information such as the existence of judgements or defaults; these are typically used within policy rules. Those businesses with proprietors with excessive numbers of “negatives” may be disqualified from the loan application. Some credit bureaux offer a score of an individual based on the performance of all the businesses with which they are associated. This can also be useful in the credit risk assessment process. Another innovation being adopted internationally is the use of psychometrics in credit evaluation of the proprietors. To find out more about adopting credit scoring, read our blog on how to adopt credit scoring.   2) Risk of business The risk of the business should be managed through both scores and policy rules. Lenders will look at information such as the age of company, the experience of directors and the size of company etc. within a score. Alternatively, many lenders utilise the business score offered by credit bureaux. These scores are typically not as strong as consumer scores as the underlying data is limited and sometimes problematic. For example, large successful organisations may have judgements registered against their name which, unlike for consumers, is not necessarily a direct indication of the inability to service debt.   3) Reason for loan The reason for a loan is used more widely in business lending as opposed to unsecured consumer lending. Venture capital, working capital, invoice discounting and bridging finance are just some of many types of loan/facilities available and lenders need to equip themselves with the ability to manage each of these customer types whether it is within originations or collections. Prudent lenders venturing into the SME space for the first time often focus on one or two of these loan types and then expand later – as the operational implication for each type of loan is complex.   4) Financial ratios Financial ratios are core to commercial credit risk assessment. The main challenge here is to ensure that reliable financials are available from the customer. Small businesses may not be audited and thus the financials may be less trustworthy. Financial ratios can be divided into four categories: Profitability Leverage Coverage Liquidity Profitability can be further divided into margin ratios and return ratios. Lenders are frequently interested in gross profit margins; this is normally explicit on the income statement. The EBIDTA margin and operating profit margins are also used as well as return ratios such as return on assets, return on equity and risk-adjusted-returns. Leverage ratios are useful to lenders as they reflect the portion of the business that is financed by debt. Lower leverage ratios indicate stability. Leverage ratios assessed often incorporate debt-to-asset, debt-to-equity and asset-to-equity. Coverage ratios indicate the coverage that income or assets provide for the servicing of debt or interest expenses. The higher the coverage ratio the better it is for the lender. Coverage ratios are worked out considering the loan/facility that is being applied for. Finally, liquidity ratios indicate the ability for a company to convert its assets into cash. There are a variety of ratios used here. The current ratio is simply the ratio of assets to liabilities. The quick ratio is the ability for the business to pay its current debts off with readily available assets. The higher the liquidity ratios the better. Ratios are used both within credit scorecards as well as within policy rules. You can read more about these ratios here.   5) Size of loan When assessing credit risk for a consumer, the risk of the consumer does not normally change with the change of loan amount or facility (subject to the consumer passing affordability criteria). With business loans, loan amounts can range quite dramatically, and the risk of the applicant is normally tied to the loan amount requested. The loan/facility amount will of course change the ratios (mentioned in the last section) which could affect a positive/negative outcome. The outcome of the loan application is usually directly linked to a loan amount and any marked change to this loan amount would change the risk profile of the application.   6) Risk of industry The risk of an industry in which the SME operates can have a strong deterministic relationship with the entity being able to service the debt. Some lenders use this and those who do not normally identify this as a missing element in their risk assessment process. The identification of industry is always important. If you are in manufacturing, but your clients are the mines, then you are perhaps better identified as operating in mining as opposed to manufacturing. Most lenders who assess industry, will periodically rule out certain industries and perhaps also incorporate industry within their scorecard. Others take a more scientific approach. In the graph below the performance of an industry is tracked for two years and then projected over the next 6 months; this is then compared to the country’s GDP. As the industry appears to track above the projected GDP, a positive outlook is given to this applicant and this may affect them favourably in the credit application.                   7) Risk of Region   The last area of assessment is risk of region. Of the seven, this one is used the least. Here businesses,  either on book or on the bureau, are assessed against their geo-code. Each geo-code is clustered, and the projected outlook is given as positive, static or negative. As with industry this can be used within the assessment process as a policy rule or within a scorecard.   Bringing the seven risk categories together in a risk assessment These seven risk assessment categories are all important in the risk assessment process. How you bring it all together is critical. If you would like to discuss your SME evaluation challenges or find out more about what we offer in credit management software (like AppSmart and DecisionSmart), get in touch with us here.

Collections Resilience post COVID-19 - part 2

Principa Decisions (Pty) L

Collections Resilience post COVID-19

Principa Decisions (Pty) L