The digital era is upon us, and as a result, we now create as much data in two days as we did from the dawn of man through to 2003, according to Eric Schmidt from Google. The International Data Corporation (IDC) reports that the digital universe is doubling every two years, and will reach 40,000 exabytes (40 trillion gigabytes) by 2020 – a single exabyte of storage can contain 50,000 years’ worth of DVD-quality video.
With 25 percent of all business data being generated by the financial services sector, this means that the banking industry is data rich. Banks of all sizes capture masses of data, capable of providing much insight into the operational and financial aspects of a business. If made accessible, analysed and used effectively, this data can provide significant competitive differentiation and transform the ways banks understand and engage with their clientele. It is well known that the high level of consumer adoption of mobile devices is unprecedented and has contributed to changes in consumer behaviours and expectations – the customer is now in control. For banks to succeed in this new digital era they have to become more customer-centric and gain a much deeper insight into their behaviours, traits and needs. Banks must leverage the power of the data that they have to do this.
Yet, for this to happen, data has to be analysed and acted upon, and many banks are struggling to do this. A recent Gartner study has revealed that only seven percent of the data collected by businesses is analysed. This has, in large part, been due to the lack of resources and tools available to access the data, let alone to be able to perform any effective analysis and then act upon it. For banks to unleash the power of their data, they need to invest in business intelligence solutions with analytical capabilities. This will enable banks to expose, access and analyse the data that is potentially the lifeblood of their business and could be the deciding factor of their future success.
For banks to unleash the power of their data, they need to invest in business intelligence solutions with analytical capabilities
Data-driven profit
Data-driven organisations are more profitable and productive than their peers. These companies optimise the data they hold by leveraging four types of analytics (see Fig. 1) in their decision-making processes. These are descriptive (what happened); diagnostic (why did it happen); predictive (what will happen), and prescriptive (what should I do about it). Together, these capabilities provide financial institutions with an enhanced understanding of customers and assist in building customer relationships, devising strategies and rolling out successful marketing campaigns. A data-driven organisation approaches maturity when it can successfully leverage all of these. Data is money – a 10 percent increase in data accessibility translates into an additional $465.7m in net income for a typical Fortune 100 company, according to data from Baseline magazine.
Traditionally, captured data has been limited to revealing demographic and transactional information. However, by utilising digital channels and technologies such as mobile location analytics, web analytics and social media, it’s now possible to analyse customer behaviours, intentions and attitudes.This results in an in-depth insight into customers’ day-to-day lives, enabling financial institutions to provide contextual, personalised offers driving customer value, revenue opportunities and innovation. By analysing this type of customer data, banks can identify specific customer segments. Rather than conducting blanket marketing campaigns, or campaigns based on demographic data, banks can now deliver targeted campaigns to specific customer profiles and sub-segments grouped by behaviour and buying traits, resulting in a much higher response rate.
Typically, 10 percent of a bank’s customers contribute 50 percent of its revenue. Banks can use analytics to make decisions such as whether to harvest the already profitable 10 percent, or to try to better understand the behaviours and traits of the remaining 90 percent, and look at smarter ways to engage with them to increase their individual customer value.
Build or buy
When looking at solutions for sophisticated data analysis, banks essentially have two options – build or buy. Traditionally, many banks have taken a build approach, by first choosing one of the major business intelligence (BI) platforms, and then having consultants or in-house staff build bespoke applications. However, this approach can prove to be very expensive to build and maintain, and, due to the rapid growth of technologies, in-house builds can become obsolete very quickly. These projects are also very prone to failure, with Gartner quoting a failure rate of up to 80 percent.
One of the key challenges for banks is to be able to expose and access the data that is traditionally hidden and residing in back office-banking systems. This data needs to become visible in the first instance, to the right users within the bank, who can then analyse it and use it effectively for decision making and for driving the user experience at the various customer touch-points. A leading banking software vendor with a packaged BI solution will be able to achieve this far easier, with much reduced risk than an in-house build enabling the bank to leverage the power of the data more quickly.
A packaged solution, such as Temenos Insight, offers a much faster time-to-value, through pre-packaged descriptive, diagnostic, predictive, and prescriptive analytics. This approach provides banks with an accelerated BI starting point that they can refine for their specific needs and vision. Banks can then quickly harness the wealth of customer information available and look to transform data into valuable insights to guide decisions and interactions.
Temenos is the market’s leading provider of banking software systems, with over 1,600 customer deployments in more than 150 countries across the world. Temenos’ BI offering, Insight, is the only BI platform developed specifically for the banking sector that provides the full stack of BI applications, from reporting, dashboards, visualisations, data discovery, analytics and mobile. Insight is the choice of more than 120 banks and financial institutions worldwide, including 50 percent of the top 20 largest credit unions in Canada.
Predictive analytics is a powerful component of such a packaged solution, enabling banks to use data and increase the value of existing customers. This involves advanced analytical algorithms processing historical data to ‘learn’ what has happened in the past, and create models that can be applied to make judgments about current or future cases. Applying predictive analytics in any one of these areas can generate significant value.
Contextual engagement
Predictive analytics use real-time data, delivering targeted products or value-added services to reach the customer at the right time. For instance, a customer may have a large sum of money credited to their account as a result of a company bonus. This large deposit into a current account could be an event that triggers an offer to the customer of a high-interest savings account. The offer could be delivered via the channels that the customer is known to use, i.e. through a mobile device or online banking service.
A typical BI solution comes with an analytics framework enabling banks to build predictive models for specific scenarios. In some cases, solutions also provide a library of pre-built predictive models. For example, Temenos Insight Analytics can be packaged with two standard models – ‘Next Best Product’ and ‘Customer Attrition’.
By analysing purchasing patterns of customer segments and looking at bundles of products typically purchased together, for example, mortgage and home insurance, ‘Next Best Product’ can predict the percentage probability of a customer purchasing a specific product. This in turn can drive a tailored marketing campaign to all customers above a certain percentage probability, increasing product uptake, customer value and return on investment on marketing activity. A McKinsey analysis of more than 250 engagements over five years indicates that companies that put data at the centre of the marketing and sales decisions improve their marketing return on investment by 15 to 20 percent.
Combatting attrition
Customer attrition is a key challenge for banks. In a mature market, acquiring a new customer costs more than retaining an existing one – in fact, typically five to 12 times as much. In addition, a 10 percent increase in customer retention has been shown to result in a 30 percent increase in the value of the company, according to research by Bain and Co. At the same time, offering incentives to customers to retain their loyalty can
be expensive.
Using predictive models such as Temenos’ Customer Attrition allows financial institutions to identify which customers should receive an incentive to deter them from switching, which customers will stay without the incentive, and which customers should be allowed to walk away. By gaining an understanding of which customers are likely to leave and why, a retention plan can be developed that addresses the right issues and targets the right customers with the most appropriate course of action.
Banks need to be capable of predicting what the customers are likely to do next before they realise it themselves. By harnessing real-time and predictive analytics and combining them with a superior user experience, banks have the opportunity to be one step ahead. Rather than just being the traditional custodians of data, the banks that recognise the power of the data they hold, and put solutions in place to exploit it and use it effectively for competitive advantage are the ones that will succeed.
For further information email twinship@temenos.com
