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How One Bank Uses Speech Analytics To Improve Customer Loyalty

May 9, 2016 at 3:14 PM

In order to improve customer loyalty, we need to listen to our customers more. Increasing our share of wallet and maximising customer lifetime value (CLV) will only happen when customers are prepared to choose our brands and products over our competitors. Fortunately, businesses are uncovering clues to improving customer loyalty in new places thanks to data and evolving analytics platforms. This led one bank in Canada to turn to the thousands of conversations between its representatives and its customers for new insights into elevating the customer experience.

Learn how Machine Learning is being used in Call Centres to improve the Customer Experience

Listening to your Customer is Loving your Customer

The Bank of Montreal has used speech analytics as a means to boost customer loyalty with great success.  By analysing the phone interactions between its representatives and customers, the bank was able to elevate their contact centre credibility, reduce call handling times and ultimately increase the bank’s Net Promoter Score for customer satisfaction. According to its CIO, Marc Demers," ..anytime you’re making your customers happy and driving their experience, the ROI is immeasurable.” The data showed that the +-2 000 call centre agents, who handle up to 50 000 customer calls each day, needed better product training to expand their knowledge on its suite of available financial products. This newly gained insight helped them refocus their training strategies to turn customer loyalty and satisfaction levels around and gain in-roads to new business.   

Mining Captured Conversations for Golden Nuggets

Bruce Boyle, Operations Manager at Bank of Montreal explains,“The voice of the customer [could] tell us about our products and about the processes and positions we were putting our agents in . Considering that more than 70% of customers still prefer speaking to a company representative on the phone as opposed to using other contact methods, it’s only logical for contact centres to turn to speech analytics as a source of invaluable business insights. Capturing what gets said during these conversations and analysing the questions, feedback and dialogue can help companies detect trends and reveal the insights needed to make big, radical and lucrative changes in their businesses.

Verbal interchanges with customers can also be converted into different formats and indexed for more effective data mining. For example, companies can search entire indexes of text versions of conversations to find keywords that act as indicators of customer sentiment around a myriad of factors. This information, in turn, can be passed on to relevant business units for improved product development, supply chain management, marketing, etc.

Learn how else Speech Analytics can be used to improve Customer Loyalty

Listening is a sign of Respect

As with any human relationship, rapport and trust are built by the sense that your voice matters and that you are being listened to. It’s a simple sign of respect. By using speech analytics to enable them to listen to and understand their customers, the Bank of Montreal is gaining valuable insights about their customers’ pain points and working on minimising them before their customers decide to leave them for a competing bank. Imagine what you could learn from your customers if you truly were able to listen?

Learn how else Banks are using Analytics to improve Customer Loyalty

If you’d like to learn about using analytics to listen to your customers and grow their loyalty to your brand, contact us!

using data analytics for customer engagement

Image credit: https://www.linkedin.com/pulse/speech-analytics-know-your-customers-business-saiteja-kancherla

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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