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How Machine Learning Is Helping Call Centres Improve Their CX

October 29, 2019 at 3:07 PM

The call centre world, unsurprisingly, ranks as one of the highest adopters of data analytics platforms year on year. This is largely due to the invaluable insights we gain through the analysis of thousands of calls received each day by the typical call centre. With speed being of the essence in making the right decision at the right time for each caller many call centres are turning to machine learning to automate their data analysis and make crucial customer experience decisions within seconds.

Watch our video to meet Agent X, the call centre virtual assistant to solve your call centre agents' problems. 

Reducing call duration and increasing first call resolutions

Whether you’re running an inbound or outbound contact centre, the interactions between your company representatives and your customers is a crucial area for customer success. Thanks to machine learning algorithms, businesses are able to manage those customer-facing moments more efficiently. According to, “Emotion analysis through text and speech analytics can paint a more complete picture when combined with the overall first call resolution (FCR) metric, indicating the level of confidence customers feel about whether the answer they received has resolved the issue at hand.”

New to Machine Learning? Read our blog post What is Machine Learning?

Machine learning algorithms are also helping customers reach the right representative in a shorter space of time (and alleviating much of their frustration in the process) by smartly routing calls based on their nature to the right person with the appropriate knowledge and skill-level. This, in turn, reduces call duration periods, repeat calls or call abandonment rates by unhappy callers. On-screen prompts based on the machine learning analyses of callers’ moods, or other indicators, are also allowing contact centre agents to more effectively deal with customer queries or problems and reduces the need for customers to make often frustrating repeat calls to the business.

Machine Learning to determine the right time and number to call can be used in call centres for both sales to reach prospects and debt collection to reach debtors. 

Download our guide to using machine learning in business, where we explore how you can use machine learning to better tap into your business data and gain valuable, informing insights to improve your business revenue. 

Reducing call volumes and increasing customer satisfaction

Machine learning can be used to reduce call volumes by eliminating the need for customers to call when there is, for example, a network fault for a telecommunications company / ISP or reception issues with a satellite company. By analysing voice / speech patterns, emotions and words from incoming calls, machine learning can identify 1) that there is a an issue (anger or irritation based on tone of voice), 2) what the issue might be (“slow line speed” or “no reception”) and 3) where it might be (based on the caller’s location). By analysing an influx of calls coming in and identifying these types of patterns, machine learning could kick off a notification to technical support to notify them of the issue and enable the contact centre to send out a pre-emptive SMS / email / WhatsApp to all subscribers in the affected area or post a message on Twitter / Facebook to let them know that they are aware of the issue and are working on having it resolved.

Increasing revenue by seizing sales opportunities

To serve the customer better – and boost profits in the process – businesses need to make smart decisions on the fly. Direct interchanges between consumers and call centres are golden opportunities to make this happen. According to John Magliocca, chief consultant for contact centre services outsourcing company, ISG, “There have been efforts underway to put contact data to work to best understand the current mood of the customer and other information that can immediately mould client strategy and direction [for some time].”  With volumes of customer and transaction data available, machine learning platforms can inform contact centre staff on ideal product suggestions based on past purchases or upgrade a subscription service to premium if a customer’s financial situation has changed. Data-driven solutions will continue to inform customer insights while simultaneously helping business raise bottom lines.

Machine learning will require user buy-in

Although machine learning can enrich the customer experience considerably, it would have little impact on productivity, profitability or customer satisfaction levels without buy-in from the people intended to use it. Forrester advises business leaders who want to adopt new technology platforms as a means to improve the customer experience, “Senior executives set the tone for the need for a customer-centric culture and new processes and tools that support customers more effectively. Leaders must plan for changes in the work practices needed to meet customer management goals” . This means involving users in new technology implementations that will ultimately add value to the workforce and importantly, the customer.

Keen to improve your the customer experience in your call centre environment? Find out more about our call centre virtual assistant which uses artificial intelligence/machine learning to deliver relevant data-driven insights to agents about a customer, account holder or prospect during a call.

Using machine learning in business - download guide

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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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