Using data to improve the customer experience in retail banking

I was recently asked to write a researched board-level two page paper as part of a job interview. It seemed a shame to let it languish in an email inbox, so here it is for the world to enjoy.... Happy to hear your thoughts!

Introduction.

In recent years, banks have been dealing with recovery and heightened capital requirements due to the financial crisis, a low-interest rate environment and implementing defences to financial crime. Many have struggled for growth, and underinvested in areas such as digital, whilst operations have become more complex. This has eroded the emotional connection between customers and the frontline, whilst ‘bad profits’ have led to regulatory pressure, fines and public mistrust, as well as reducing employee engagement.

The relationship between sustainable growth, profitability, customer loyalty and employee engagement is well established[1], however, even before the financial crisis[2] retail banking has consistently ranked among the least admired and trusted sectors[3]. In a survey of the customers of 362 companies, only 8% of customers described their experience as ‘superior’, yet 80% of the companies surveyed believe that the experience they have been providing is indeed superior [4]. Leaders and the frontline have lost touch with customers. Operational and credit skills used to confer long-term advantage, but today those capabilities are table stakes. Today’s competitive advantage is customer centricity: deeply understanding your customers’ needs and fulfilling them better than anybody else[5]. Industry leaders in customer loyalty have double the growth[6], and on average, a bank’s relative customer loyalty score explains roughly half of the variation in its relative win rate of new accounts[7].

Threat.

Adding to the industry’s woes, fintech and tech groups are threatening technological disruption, particularly in retail banking and wealth management (RBWM). Beyond customer disillusionment, regulators also want to see change. The EU’s incoming PSD2 legislation aims to give rise to open banking[8] by forcing banks to allow third parties access to customer data if authorised. As a global bank's CEO warned in a recent staff webcast[9], new platforms (or digital financial ‘ecosystems’) risk doing to RBWM, what Skype and others did to the profits of the telecommunications industry[10]. Between 2012 and 2018 it is estimated the industry will lose $386bn[11], with value captured by customers and not companies (Skype sold for ‘just’ $8.5bn in 2011[12]). This is the perfect example of what disruptive technology can do to profits[13], and the dilemma posed to incumbents[14]. This ecosystem approach is particularly prevalent in China[15], and shows signs of high customer loyalty[16].

A bank these days can be thought of as just a big database[17]. The risk is that they become just ‘dumb pipes’ if their propriety data is not used as a source of competitive advantage. This paper summarises how to do this in three steps, using data to improve customer experience

1. Establish a customer experience framework to drive continuous improvement and learning.

The first step is to set up a system based on the Net Promoter Score (NPS) to bring the customer voice into the organisation, and engage the frontline and wider company to work together on improving customer experience. Customers are surveyed after interactions with the bank, with a follow-up to ‘fix, learn and listen’. The feedback loop is closed by sharing learnings with frontline management and midlevel / senior executives. This first step is about making client experience metrics pervasive, to create the organisational habits of continuous improvement and learning. It is worth noting that if the business requires the heroism of employees to keep customers happy, then you have bad service by design. Employee self-sacrifice is rarely a sustainable resource. [i]he first s[i]he first step is to set up a Traditionally focus has been on ‘moments of truth’ – interactions when customers invest a high amount of emotional energy in the outcome. After a positive experience, more than 85% of customers increase their value to the bank, but more than 70% reduce their commitment when things turn sour. system based on the Net Promoter Score (NPS) to bring the .customer voice into the organisation, and engage the frontline and wider company to work together on improving customer experience. Customers are surveyed after interactions with the bank, with a follow-up to ‘fix, learn and listen’. The feedback loop is closed by sharing learnings with frontline management and midlevel / senior executives. This first step is about making client experience metrics pervasive, to create the organisational habits of continuous improvement and learning. It is worth noting that if the business requires the heroism of employees to keep customers happy, then you have bad service by design. Employee self-sacrifice is rarely a sustainable resource.

Traditionally focus has been on ‘moments of truth’ – interactions when customers invest a high amount of emotional energy in the outcome. After a positive experience, more than 85% of customers increase their value to the bank, but more than 70% reduce their commitment when things turn sour.[19] Recent research shows that overall routine interactions contribute more to the NPS than sales or service transactions, because of the cumulative effect of their much higher frequency.[20] In fact, frequent mobile users can be 40% less likely to switch banks versus regular branch users who are three times more likely to switch banks.

2. Use data proactively to drive profits.

This second step is about using data to drive improvements in customer experience, cost reductions and revenue growth. For example, by getting customers to switch to low-cost channels such as mobile. Customers don’t use mobile for several reasons, notably habit, but also lack of knowledge and access issues[21]. It’s not just older customers, but also younger, mobile-savvy ones learning the basics of banking[22]. It is therefore important to use data (demographic, contribution, behaviour and attitudes) created automatically in systems or by staff to drive action (e.g. CRM campaigns). In fact, today’s advances in Artificial Intelligence (AI) and machine-learning can automate much of this and deliver appropriate content and guidance to customers (this can be an ‘on demand’ service requiring minimum development, e.g. from IBM’s Watson). We can also use segmentation and robo-advisors to improve customer experience by nudging customers to better investment behaviours, and help to prevent conduct risk issues[23].

Data mining can identify ‘hidden defection’ i.e. when customers purchase a new product from a competitor. Challenger banks, fintechs and others are taking a growing share of new business on the strength of their simple product lines and streamlined user experience[24]. They cherry-pick the customers and products with the most attractive profits[25]. One way to combat this, is to aggregate customer revenue data to price products based on the total relationship, rather than in silos. Convenience and making customers aware of the price reductions thus drives sales.

NPS and other data points are meaningless in isolation. It’s important to benchmark in relation to the competition, over time and across geographies. In some countries, the gaps between the best and worst can be as high as 86 percentage points or as low as 12[26]. Whilst a global bank can take best practice / technology from one market and deploy globally, it needs to use local customer experience data to determine if the local proposition needs to be altered. Knowing which investments to pursue and where to focus management time and effort is not always straightforward. Strategic choices are about trade-offs – data is vital to understand how to distribute these limited resources, and thus improve the customer experience to drive growth. 

3. Use data strategically, transforming into an agile organisation.

Ecosystems are developing around customer needs, rather than sticking to traditional industry lines. Leaders need strong data-analytics capabilities to develop insights from the torrent of customer information[27]. They need to be asking different questions, moving beyond the correlation-focused mindset, as this does not necessarily equal causality. What’s important is to home in on the progress that customers are trying to make in each circumstance – what they hope to accomplish. This is called ‘jobs to be done’ theory[28]. This transforms our understanding of customer choice, driving proposition design. Jobs are never simply about function – they have powerful social and emotional dimensions, and include ‘negative jobs’ (i.e. the tasks that people want to avoid), and non-consumption (i.e. the customers not ‘hiring’ your products). Tesla for example wants to offer customers a single price that includes financing, insurance and maintenance[29]. Chinese tech insurgents such as Alipay and WeChat have leapfrogged the West and offer a compelling example of what Silicon Valley has been threatening to do at scale[30]. This is the most complex step, and many will fail to reach it.

Conclusion.

The threat of technological disruption wiping out profits is real. New competitors are ‘data first’ companies, that know how to leverage customer-centric design, AI and automation to create new low-cost platforms, ecosystems and business models. Despite this, barriers to entry remain such as regulation, access to cheap funding, large capital and customer bases, and propriety customer data. Furthermore, there is enormous human capital within banks. Whilst algorithms can suggest remedies, only humans can fix or improve the underlying processes or policies that make up the customer experience, be they physical or digital[31]. Banks must therefore become fast, lean, automated, omnichannel and get things right the first time – this is only possible if they learn to use data to improve the customer experience, driving cost reductions and revenue growth.

About the author.

Sébastien is an expert at improving organisational performance, using customer experience and technology to refocus company strategy, business / operating models and processes with due consideration for risk management and regulatory dimensions. He has delivered results in local, regional and global projects across the financial services and hospitality sectors, living and working in 8 countries across 5 continents.

Currently studying part-time on the Executive MBA programme at the University of Cambridge Judge Business School and a volunteer mentor with Virgin StartUp and IncuBus Ventures.

Sources.

[1] Heskett, J. et al (1994). Putting the Service-Profit Chain to Work. Harvard Business Review.

[2] Ensor, B. (2006). Segmenting Financial Consumers. Forrester Research.

[3] Murgia, A. and Arnold, M. (2017). Bank of Tech poses growing threat to traditional institutions. Financial Times. February 14th, 2017.

[4] Meyer, C. and Schwager, A. (2007). Understanding Customer Experience. Harvard Business Review.

[5] Van den Driest, F. et al (2016). Building an Insights Engine: How Unilever got to know its customers. Harvard Business Review.

[6] Reichheld, F. (2012). The Ultimate Question: How Net Promoter Companies Thrive in a Customer Driven World. HBR Press.

[7] Du Toit, G. et al (2014). Loyalty in Retail Banking 2013. Bain & Company.

[8] Arnold, M. and Brunsden, J. (2017). Fintech sector fears dilution of EU ‘open banking’ legislation. Financial Times. February 1st, 2017.

[9] Confidential (2017). Employee webcast and Q&A. February 23rd, 2017.

[10] The Economist (2005). How the internet killed the phone business. The Economist. September 15th, 2005.

[11] Heinrich, E. (2014). Telecom companies count $386 billion in lost revenue to Skype, WhatsApp, others. Fortune. June 23rd, 2014.

[12] Taylor, P. (2011). Skype’s changing traffic growth. Financial Times. May 10th, 2011.

[13] Thrasyvoulou, X. (2015). Understanding the Innovator’s Dilemma. Wired.

[14] Christensen, C. (1997). The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail. HBR Press.

[15] Hendrichs, M. (2015). Why Alipay is more than just the Chinese equivalent of PayPayl. Tech in Asia.

[16] Dietz, M. et al (2016). Bracing for seven critical changes as fintech matures. McKinsey & Company.

[17] Bertoni, S. (2014). Why Alibaba’s Alipay And PayPal Will, And Should, Destroy Physical Banks. Fortune. May 8th, 2014.

[18] Frei, F. (2008). The Four Things a Service Business Must Get Right. Harvard Business Review.

[19] Beaujean, M. et al (2006). The ‘moment of truth’ in customer service. McKinsey Quarterly.

[20] Schofield, M. et al (2016). Just Make It Easy: A Guide to Loyalty in Banking. Bain & Company.

[21] Du Toit, G. and Burns, M. (2016). Customer Loyalty in Retail Banking: Global Edition 2016. Bain & Company.

[22] Burns, M. et al (2016). Bank Branch And Call Centre Traffic Jam: Why do customers keep visiting tellers and calling the contact center? Bain & Company.

[23] Edwards, E. and Rodrigues, A. (2016). Are You Ready for Robo Advising? Bain & Company.

[24] Du Toit, G. and Burns, M. (2016). Customer Loyalty in Retail Banking: Global Edition 2016. Bain & Company.

[25] Fielding, J. et al (2016). Retail Banks: Manage for Glory or Cash? Bain & Company.

[26] Du Toit, G. and Burns, M. (2016). Customer Loyalty in Retail Banking: Global Edition 2016. Bain & Company.

[27] Dietz, M. et al (2016). Bracing for seven critical changes as fintech matures. McKinsey & Company.

[28] Christensen, C. (2016). Know Your Customers’ “Jobs to Be Done”. Harvard Business Review.

[29] Ehterington, D. (2017). Tesla wants to offer vehicles with one price, including insurance and maintenance. TechCrunch. February 22nd, 2017.

[30] Du Toit, G. and Burns, M. (2015). Customer Behavior and Loyalty in Retail Banking: Mobilizing for loyalty. Bain & Company.

[31] Brahm, C. (2016). What Big Data Means for Customer Loyalty. Bain & Company.