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The Snakes And Ladders Of Customer Loyalty

October 20, 2016 at 8:11 AM

Your customers go through numerous milestones in their journey through your business: the initial interest, the first purchase and opening of an account, (hopefully) paying their accounts on time, maybe signing up for your loyalty programme (and being comfortable to tell you more about themselves). 

In each of these instances, there is a chance to improve or worsen the relationship with your customer, resulting in quite opposite results. Much like a game of snakes and ladders. 

Think of the winning square as being a very loyal customer who has no interest in forming a relationship with one of your competitors; the losing square is where they are about to take their business elsewhere. Make the right decision at the right time and the customer moves closer to the end square; make the wrong one and they slide closer to your competitor. How do you ensure your decisions along the customer journey lead towards loyalty ladders and away from snakes that sever ties to your brand? 

Predict your customers’ next moves with data insights

In the world of advanced customer management systems, data is (or should be) very rich. Many insights can be drawn from this data that will help you to better understand what your customers want at any point in their journey with you. But as with most things in the world of data, things can get tricky quite quickly. 

Assuming that the data feeds are stable and you are already drawing good descriptive insights, the next step is to use this data to predict what the customer is likely to want or do next and treat the customer accordingly. This data is constantly changing and setting up a system that tracks recent changes makes a lot of business sense. 

However, you wouldn’t want ten “hens’ teeth” data scientists to be manually creating static predictive models from manually extracted data, would you? Surely you would want a system that is automated, reliable and is not onerous to run and can integrate with your operational systems. 

Automating decisions that lead to loyalty ladders 

There has been a lot of talk in the market around the benefits of Machine Learning. Through Machine Learning’s ability to regularly self-train from more data, better data driven decisions will be made possible, positively affecting more of the steps in your customer’s journey – i.e. more ladders and fewer snakes. 

Many of the operational easy wins have been realised and now is the age of automated decisions that are good for you and your customers. So, if you’re in the Customer Loyalty game to win, consider Machine Learning as your path to glory. I invite you to read the following blog posts to get you started: 

When you’re ready, give us a call to learn how we can build that winning path to Customer Loyalty for your business using data analytics and Machine Learning, or learn more about our Machine Learning as a Service offering, Genius. 

using data analytics for customer engagement

Image credit: Snakes and Ladders by Jacqui Brown is licensed under CC BY 2.0.

Robin Davies
Robin Davies
Robin Davies was the Head of Product Development at Principa for many years during which Robin’s team packaged complex concepts into easy-to-use products that help our clients to lift their business in often unexpected ways. Robin is currently the Head of Machine Learning at a prestigious firm in the UK.

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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. 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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.

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