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Why Intelligence Trumps Analytics In Your Customer Retention Strategies

February 16, 2016 at 12:32 PM

Data has redefined how businesses understand their customer base and make decisions. For instance, it’s transformed marketing from a relatively intangible expense into a clear-cut investment with a measurable ROI and targetable initiatives. However, strategically applied data has more uses than strengthening your marketing efforts alone, especially when it comes to understanding your existing customers and better attending to their needs.

Data analytics capabilities, in particular, have blown up in recent years, giving businesses access to a far wider range of data-related insights than ever before. But it’s important to differentiate between vanity metrics – and genuinely actionable information that can further your business goals.

In this article, we’ll outline why data intelligence is far more valuable than basic analytics – especially when it comes to your customer retention strategies.

The difference between data analytics and data intelligence.

First and foremost, it’s important to differentiate between data analytics and data intelligence. According to Techopedia, data intelligence is

the analysis of various forms of data in such a way that it can be used by companies to expand their services or investments.

This is distinct from their definition of data analytics, which, according to TechTarget, is the

science of examining raw data with the purpose of drawing conclusions about that information” 

While these terms are inherently related, data intelligence involves a more specific and forward-looking use of data to reap deeper insights that can be used to grow the business and to get the most from investments. Where analytics identifies trends in your customer, data intelligence applies that information to craft an effective strategy that can be put into action in a realistic timeframe.

Your customer retention strategies need to be forward-thinking.

Data intelligence means using analytics as a vehicle to customer (and, in fact, business-wide) insights that facilitate action and positive change. This is vital in the context of customer retention strategies, as each strategic decision must be made with the future in mind – be it gaining customers that are more likely to stick around, pleasing existing customers or knowing which customers need extra attention to avoid losing them. Sure it’s interesting to know the satisfaction levels of certain customers at your company’s various contact points, but unless that data is being used to inform an action that can positively impact future customer satisfaction, it’s of little tangible use – your metrics need to work for you. This point is highlighted in a great article by Forrester,  where the research firm predicts that companies who make customer satisfaction – and thereby customer retention – a focal point will shine in 2016. And to do this, it’s crucial to close the gap between the insight of data analytics and the action needed to make positive change.

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

Changing how your company uses data analytics.

So you understand the value of using data in you customer retention strategies, but aren’t sure how to go about evolving your standard analytics into next-level intelligence? It’s a fair question. Beyond recognising the need for data intelligence, perhaps the most important step is to invest in a data analytics solution that combines machine learning and predictive analytics.

There are a number of key processes that can help you predict customer behaviour and act accordingly. For example, by combining data from previous customers that have left with that of current customers, machine learning can pick up on tell-tale signs of a customer that’s about to leave the brand and let the company know. This makes you better equipped at anticipating defection from your service or programme and sending an offer that gets a customer to stay. Doing so increases overall customer security and lowers the rate of attrition, as well as providing key insights that can supplement your on-boarding process to help build stronger customer relationships right from the start.

Contact us to learn more about how data intelligence and data analytics can boost your customer retention strategies and how, in partnership with Principa, you can make your data work wonders across various areas of your business.

using data analytics for customer engagement

 Image credit: https://securityintelligence.com/

 

Julian Diaz
Julian Diaz
Julian Diaz was Head of Marketing for Principa until 2017, after which he became Head of Marketing for Honeybee CRM. American born and raised, Julian has worked in the IT industry for over 20 years. Having begun his career at a major software company in Germany, Julian made the move to South Africa in 1998 when he joined Dimension Data and later MWEB (leading South African ISP). Since then, Julian has helped launch various South African technology brands into international markets, including Principa.

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  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. 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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. 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