The Data Analytics Blog

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

All Posts

How Scorecards Can Be Used To Determine Customer Loyalty

May 28, 2018 at 2:55 PM

The right customers are important for every business. Marketing to and serving customers who are not profitable removes your focus from your best customers and ensuring they remain loyal to your business. (Click to Tweet!) Scorecards can help you identify and focus on your ideal customers by ranking your customers by the common criteria historically shown to be shared by your best customers. 

Learning from the Financial services industry

Scorecards have a long tradition in the financial industry. Scorecards help loan officers decide whom to lend money to and how much to lend, based on a points system developed by analysing customer data to determine the ideal customer. Different industries have different criteria for the optimal customer. For the financial industry, the ideal criteria include a high probability to pay back a loan within a designated period. Credit risk managers use such criteria as demographics, credit scores, home ownership, employment status, and income - all determined after analysis of data collected from credit bureaux on good borrowers.

The underlying principles here can be applied to other industries and the companies' relationships with their customers. Many businesses have taken the "good borrower" idea and applied it to create a scorecard that determine their most loyal customers and those they most want to remain loyal to their business. (Click to Tweet!)

How can you know which customers are good customers?

In today's digital and social networking world, customer loyalty not only means how often a customer frequents and buys from your business, but also how often they engage with your brand, whether they are willing to leave comments, write online reviews, attend brand launches, or make recommendations to their friends. 

Traditionally marketers used scoring systems such as the Net Promoter Score (NPS), Customer Satisfaction Score (CSAT) and the Customer Effort Score (CES), which measures how likely a customer is willing to recommend a brand to others. However, this method of measuring customer loyalty has been criticised for not providing an accurate gauge of loyalty.  Just because your customer is satisfied and is willing to tell a friend how satisfied they are with your service, doesn’t mean they are loyal. (Click to Tweet!)

Read our blog on why NPS is not enough to measure customer loyalty

Making use of a scorecard as a segmentation and measuring tool could prove more successful as it incorporates far more Key Performance Indicators (KPI) than the traditional methods above, and if done correctly could provide a deeper understanding of your customer, their needs and behaviours.

What are Key Performance Indicators?

Key Performance Indicators (KPI) are the characteristics that guide the in-depth analysis of a customer's loyalty to your brand. Best practice KPIs for measuring Customer loyalty are: 

  • acquisition rate, 
  • attrition rate, 
  • retention rate, 
  • wallet share, 
  • customer participation, 
  • customer price elasticity, 
  • sales increase due to customers, and 
  • funnel drop-off.

For example, analysis over time of the first four characteristics above will give you a good idea of whether you attract enough new customers and whether you are losing too many customers. As the adage goes, it is cheaper to retain customers than to acquire new customers.

The last four indicators mentioned above tell you how you're faring with respect to increasing the loyalty of individual customers. Perhaps these indicators are more difficult to measure than the first four, but there are definitely ways of measuring. For instance, you can measure the number of positive reviews on your website or the percentage of loyal customers who leave positive reviews. If you ask the right questions on your website, you can tell which new customers came to you by recommendation from another loyal customer. You can also use surveys to gather customer information.

Customer price elasticity is a strong indicator that your customers value your product and your services to the point that they don't mind if prices increase along the way.

Measuring Customer Loyalty

Veritude is one company that put customer loyalty scorecards into play. Veritude provides outsource staffing and hiring services. The company developed an on-line customer survey that asked customers how likely they would be to recommend Veritude's services to others. Survey respondents ranked on a scale of 1-10 for loyalty based on that question. Other questions asked what Veritude does to earn customer recommendations and what Veritude needs to do to earn the customer's recommendation. Veritude scored customers who rated 9 or 10 as "promoters". Veritude labelled customers scoring 7 or 8 as "passive" and customers scoring less than 7 as "detractors". Tracking gave individual customer scores as well as an overall score for the company. Veritude obtained the overall score by subtracting the number of "detractors" from the number of "promoters."

Veritude paired this survey with other customer email surveys and follow-up interviews. Each customer making a negative comment received a follow-up phone call. After the follow-up interviews, the company analysed the feedback to determine causes for dissatisfaction and to develop responses. As a result of the scorecard-built system for measuring customer loyalty, Veritude was able to provide its staff with tools to turn detractors into passives and finally into promoters. By understanding the frustrations and needs of their customers, they were able better to communicate and service their customers, proving the usefulness of a loyalty scorecard.

Measuring attrition rates in South Africa

Locally, in South Africa, one large retailer has used scoring to predict attrition, or the likelihood of a store card account holder closing their account, and loyalty. They identified rate and size of payment of debt as key criteria to identify account holders who were likely to remain good account holders in the long-term and those who were likely to close their account. The higher the payment size above the minimum balance due and the more frequent these higher than necessary payments were made, the more likely that the account holder had used credit for a once off large purchase to pay off quickly. These were identified as less loyal to the brand. Those who were making frequent purchases and frequent small payments to cover their minimal payment were identified as loyal customers who enjoyed spending and relied on credit to purchase. By using scoring to identify these two segments of their customer base, this retailer was able to focus efforts on maintaining loyalty from the one and building loyalty with the other through targeted campaigns.

More and more companies are looking for new ways of building and maintaining loyalty to increase share of wallet in a period when economic conditions are decreasing overall spend by many consumers. Scorecards is one innovative way adopted from the financial services industry that retailers can use data science to help them increase customer loyalty and profitability. (Click to Tweet!)

Read more on 4 Types of Data Analytics to learn more about descriptive, diagnostic, predictive and prescriptive analytics as other innovative ways to segment customers to improve acquisition and retention.

To learn more and to discuss the development of scorecards that support your Customer Loyalty and Retention strategies, contact us for a chat over a cup of coffee.

Mark Roberts
Mark Roberts
Mark has over 15 years’ experience within the credit life cycle with 10 years’ specialised in Collections and Recoveries having been gained through exposure in both B2B and B2C markets across Europe, UK, and South Africa. With both extensive operational and strategic experience, Mark has successfully delivered and lead a number of initiatives within collection strategy, operating processes, platforms, and payment solutions. He holds a B.Comm degree in Actuarial Sciences from University of Pretoria.

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