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Closing The Loop With Customer Lifetime Value Insights

November 6, 2015 at 10:18 AM

With marketing budgets increasingly stretched to cover the myriad of channels and touch points out there today, CMOs might feel a little uncertain about whether they’re focusing on the right areas for their acquisition strategies. But even if your marketing spend does deliver positive on-boarding results, it’s still only the first step in a much longer journey with your customer. With the cost of acquiring new business usually five times more expensive than retaining existing customers, and with over 60% of revenue coming from existing customer bases, it only makes sense to build watertight retention strategies that not only retain customers, but preserve the best among them.

Customer Lifetime Value (CLV) is the predicted gross economic value of an individual customer over the entire duration of their relationship with your products and/or services. It is a very useful metric to have for segmenting, managing and propositioning your customer base more effectively, and gives your business insights into its most profitable customers. Also, it allows your business to align marketing, operational, and product strategies more accurately with the needs of the different segments within a given market.

CLV helps reduce churn rates and increase wallet-share

When applied over the entire “lifetime” of a customer’s relationship with your brand, data analytics are instrumental in maximising both the time and profitability of individual customers or market segments. Analysing customer behavioural triggers garners insights into how to reduce churn rates, increase wallet-share or maximise the lifetime value of your customers.  In a report on how data is driving lifecycle management, industry analysts Forrester Research, define customer lifetime value as “[The] Customers’ relationship with a brand as they continue to discover new options, explore their needs, make purchases, and engage with the product experience and their peers. Key to this definition is that customers’ relationships with brands should include new discoveries of value-adds and “reminders” of why they chose your brand in the first place. But this means knowing what your customers perceive as value - and this is where your data comes in. By focusing on real-time customer-based metrics, you get an understanding of factors that influence brand sentiment, profitability, fluctuating needs and customer expectations, and cater to these changing factors rapidly.

Knowing the value of your customers makes managing them easier

Historically, businesses would view Customer Lifetime Value through the lens of campaign executions. This alignment too often overlooked the behavioural patterns and other influences on buying decisions. It also denied marketing and sales departments the right “angle” from which to engage those customers who are already buying their products, with the result being ineffective – and often intrusive - brand messaging that had no bearing on a specific individual. This shot in the dark approach may have been the only strategy available to us in the past, but with the volumes of personal, statistical, transactional and historic data we have access to today, strategies can be built around insights garnered from real-time data that allows for closer alignment and personalisation down to individual customers. Modern analytics also allows us to identify our potentially most valuable customers and focus on those segments which will yield greater returns in the course of their relationship with your brand.

Read our blog on Customer Retention strategies and why its time to get personal

Applying the Pareto Principle to your lifecycle management strategy

The Pareto Principle states that 80% of the effects come from 20% of the causes. In business, this can mean that 80% of your revenue typically comes from 20% of your customers - and it is almost always from the ones who have been with your brand for a considerable length of time. This makes it so important for us marketers to focus more attention on the customers who are spending their money on our brands and listen to what our data is telling us about. For subscription-based business models whose revenue growth is only realised over longer periods of time, as opposed to those based on once-off sales, this is even more pertinent. In the age of the consumer, an iterative approach to customer lifetime value that invests the time and effort to speak to – and not at – your customers will pay dividends and close the loop on our marketing and sales efforts.

Principa has been at the helm of making data work wonders for years now and we’re only started thanks to opportunities availing themselves daily through the medium of technology. We apply data analytics disciplines and methodologies across a wide spectrum of industries and continue our adventures into wonderful world of data. To learn more about how we can help you build better business strategies with your information assets, visit our solution pages.

using data analytics for customer engagement

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Luke Turnbull
Luke Turnbull
Luke Turnbull was the Head of Customer and Lead Analytics at Principa, until the end of 2017, after which he returned to his home country of New Zealand. He worked in the financial services industry since 1995, during which time he worked in process, strategy and operational design across a range of organisations in New Zealand, the United Kingdom and South Africa. Luke had been with Principa for 9 years and led consulting engagements with Principa’s local retail clients across the customer lifecycle, with a particular focus on customer engagement and lead generation.

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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. 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.

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