Customer experience (CX) has become a critical and competitive arena for financial services companies. According to Gartner, 89% of companies across all sectors now compete primarily on customer experience, compared with just 36% in 2010. This shift is driven by significant changes in consumer behavior and expectations, which have fueled the mass migration of services into digital and mobile channels. The adoption of digital channels by consumers has, in turn, accelerated cost reductions among established financial services players as they seek to compete with digital-only fintech entrants.
The emergence of customer experience as a critical issue for financial services companies is a direct result of the success seen by digital companies outside the financial sector that have raised the bar on customer experience. Amazon founder Jeff Bezos observed: “If there’s one reason we have done better than our peers… it is because we have focused like a laser on customer experience.”
The impact of companies such as Amazon is significant, with ripples extending well beyond the markets in which these digital leaders operate. The experiences customers have with these “experience leaders” shape their expectations of brands in every other sector. This matters because it extends the peer group against which consumers judge any business in customer experience terms; the competitive set for financial services companies is now much wider than their direct commercial rivals.
It is therefore hardly surprising that 75% of companies responding to a survey by Forbes said that their top objective was to improve the customer experience. But how, in practical terms, should they approach this challenge? And where specifically should they direct investment in their customer experience to unlock growth?
Investment in improving CX is a major priority for many financial services companies. According to a 2021 survey by Microsoft, 86% of insurers, banks, and other financial services firms assign at least a quarter of their overall budget to CX. Almost half the respondents (45%) devoted half their budget or more to CX investment.
The most common metric among financial services companies to assess performance in CX terms is the Net Promoter Score (NPS). While many companies value NPS because it allows simple comparisons between peers, its use presents banks with challenges. In particular, they struggle to identify which investments will lead to an improvement in their score.
The financial industry needs a new standard for measuring and improving CX. This is borne out by what leaders in charge of CX within UK retail banks are saying:
“NPS isn’t a nuanced enough metric to properly measure experience.”
“We’re shifting away from NPS to tracking metrics that are proven to be leading indicators of great experiences.”
— Both CX leaders within leading UK financial services brands
The Customer Experience Growth Index (CXGX), developed by Publicis Sapient, helps companies measure customer experience rigorously and identify which CX investments are most likely to lead to improvements in business performance and growth rate. The CXGX—which is currently in beta—surveys customers using a framework based on the “Three Es”: experience, expectation, and emotion. In the examples that follow, we have concentrated on UK retail banks and their customers.
For each interaction between the customer and their bank, the CXGX methodology looks at the customer’s response through the lens of the Three Es:
Customer responses are linked to one or more of 11 possible touchpoints between the customer and a financial services brand: call center, desktop website, email, expert reviews, user reviews, live chat, mobile app, mobile web, social media, text, and branch visit. This gives a granular, channel-specific view of each customer interaction and allows comparisons between the customer experiences that different channels deliver.
It’s important to reiterate that CXGX is still in beta. We have a single set of survey data right now and will continue to collect and analyze pertinent data as time goes by. As this dataset grows, we will be able to draw stronger conclusions about what’s driving great CX.
In interpreting the responses gathered from customers, the CXGX framework draws on the insights of Daniel Kahneman, the psychologist and Nobel laureate, into what he called “the remembered self.” “We do not choose between experiences; we choose between memories of experiences,” Kahneman observed, highlighting a distinction that is critical in understanding the role that CX plays in consumers’ judgments about brands.
The vast majority of customer experiences are forgotten. These interactions fall into the grey area between especially good and especially bad—they broadly meet our expectations and deliver the outcome we were seeking. We call this the “Valley of Meh.” The experiences that matter most lie at either end of the spectrum—the ones the customer remembers because they were especially good or bad. These are the experiences that determine their view of a brand, whether or not they took place in an earlier encounter with that same brand or with an “experience leader” that influences their view of multiple other brands. And these are the experiences that form the consumer’s “remembered self,” which sets their expectations of their future engagement with the brand.
Not all customer experiences are created equal. The CXGX framework highlights those that lie at either end of the spectrum—and that therefore contain the most actionable insights for brands. The data collected from CXGX surveys are converted into a CX score linked to the touchpoint that was involved, with scores ranging from +100 for strongly positive experiences down to -100 for negative ones. Satisfactory, non-memorable interactions fall in the middle ground, around zero.
This scoring system allows us to understand which of each brand’s consumer touchpoints tend to produce more positive or negative experiences among their customers, and which emotions customers associate most strongly with each touchpoint.
We can combine the CX scores for each channel to give a single, overall customer experience score, as we have done in this example using the major UK retail banks.
When we plotted these overall CX scores for the main UK retail banks against respondents’ intentions to use these brands more or less in the future, we found a strong link between the banks that achieved higher customer experience scores and those that consumers said they were likely to use more in the future.
This indicates that high customer experience scores today are linked to greater willingness among consumers to interact with a bank in the future. For banks looking to sustain or accelerate growth, this is an important insight.
But our findings provide a stronger link between CX scores and future business performance than self-declared future intentions alone. We also plotted each bank’s overall CX score against its performance as measured by net gains or losses in personal current accounts, using the industry-wide current account switching data published by Pay.UK.
A clear pattern emerged: there is a correlation between those banks that achieved the largest net gains from consumers’ decisions to switch their current account to a different bank, and the banks that achieved the highest overall CX scores using our CXGX methodology.
Our findings demonstrate a clear relationship between the CX scores derived using our framework and both claimed future intended usage and net gains in current account numbers. CX scores therefore represent an index of growth potential. This provides the foundation for a new method of transforming CX data into a tool to guide future investment decisions in a critical area linked to business growth.
A scatter plot of Net Change in User Growth (0% to 70%) versus CXGX Score (0 to 100) for UK retail banks shows a positive correlation (R² = 0.648), with neobanks generally outperforming established banks.
For each brand (in this case, retail banks), we can create a value chain for its 11 touchpoints by combining the CX scores awarded to each touchpoint with data showing how many of the bank’s customers use each touchpoint. This lets us see the relationship between the most heavily used channels in terms of users, and those that achieve the highest CX scores.
| % of Use | Touchpoint | CXGX Score |
|---|---|---|
| 48.0 | mobile app | 13.4 |
| 21.2 | mobile website | 11.9 |
| 30.8 | desktop website | 0.8 |
| 39.4 | branch visit | 8.7 |
| 15.2 | call center | 7.3 |
| 12.0 | social media | 27.5 |
| 13.1 | live chat | 15.4 |
| 7.6 | text | 5.3 |
| 16.7 | -2.7 |
This exercise highlights the two major “axes of value” available to banks that want to improve their overall CX score. They can either persuade more customers to use their highest-performing channels in CX terms, or they can improve the experience that their most heavily used channels offer to their customers. Success on either of these axes should lead to an improvement in the bank’s overall CX score.
A bubble chart plotting Average CXGX Score (Y-axis) vs. % of Population (X-axis) shows:
Actionable insight: Reviews are seldom used but generate the highest scores. Banks should consider how to drive better adoption of reviews. Mobile apps and desktop website are the most popular touchpoints but generate only modest scores. How can banks improve their CXGX of these two touchpoints?
Returning to the example of the Top-Tier Bank A CX value chain, we can see how the CX value chain can be used to suggest where investments in customer experience have the best chance of generating better outcomes. Top-Tier Bank A’s mobile app is performing well with the public. It’s the bank’s highest-performing touchpoint. But there is a huge gap between the score the bank registers for its desktop website service and its mobile app, which highlights the potential to direct investment toward touchpoints that perform better in CX terms. If this bank were to migrate 5% of its desktop website users to the app, that would increase its overall CXGX score from 8.6 to 8.9.
Live chat provides a good example of how CX scores can be used to highlight potential areas as priorities for investment. Across the main retail banks, live chat achieves the second-highest CX score on average among our 11 touchpoints and performs significantly better than the channel it is most likely to replace: call centers. This suggests that live chat represents a key element of a bank’s overall customer experience—and an attractive area in which to invest since it combines lower operating costs than call centers with higher CX performance.
However, there are challenges in ensuring a good customer experience via live chat, as illustrated by the main banks’ widely divergent CX scores for this channel.
In the case of a bank such as Tier 2 Bank E, viewing performance through the CX lens offers useful insights. Tier 2 Bank E’s live chat function scores among the best of the main banks, with 18.3. But only 12% of its customers use this channel, compared with 18% that use its call centers. If T2B were able to encourage more of its customers to use its high-performing live chat service, its overall CX score—all else equal—would improve. At the other extreme, Tier 2 Bank D’s live chat has a CX score of -13.3 yet some 7% of its customers use this channel, compared with the 19% that use Tier 2 Bank E’s call centers. Successful investment in improving the customer experience of its live chat users would have a material effect on the bank’s overall CX score.
To return to the Top-Tier Bank A example, the bank performs well above average for live chat when compared to competitors. But this CX score for its live chat is also much higher than the bank’s score for its call centers. And yet, these two touchpoints are used by similar percentages of its customer base (15.2% for call centers versus 13.1% for live chat). In this case, if it were to migrate 5% of its call center users to live chat, this—all else equal—would increase its overall CX score from 8.6 to 8.8. Our research indicates that banks which successfully invest in delivering a strong customer experience via live chat will be well-positioned to improve their overall CX score, and they will benefit from the lower operating costs of live chat versus call centers.
Plotting CX scores against the usage rate for each touchpoint makes clear where the biggest opportunities lie for companies that want to invest in improving their customer experience. This demonstrates how the framework we propose starts to turn CX data into a tool that can guide brands’ investment decisions.
Touchpoints can produce very different CX scores, and one of the key attributes of the CXGX is that it allows brands to identify which of their touchpoints create the most positive (or negative) experiences for their customers. For example, our research with customers of Top-Tier Bank A analyzed two of its most important customer touchpoints in terms of experience ("Did you get what you wanted?") and expectation ("Was the experience better or worse than you expected?").
The bank’s desktop website channel achieved a score of close to zero—right in the middle of our two extremes—because 79% of respondents reported that they got what they wanted, and the experience was in line with their expectations. This indicated that customers were satisfied with the experience but neither so impressed that it made them want to engage more intensively with the brand, nor so poor that it might encourage them to leave.
However, the bank’s mobile app achieved a positive score comfortably above zero, because 19% of people found it easy or enjoyable to achieve their goal and 37% said the experience was better than they expected. This helped to create a more memorable experience for these consumers that left them with a positive view of this interaction.
In terms of the emotion ("how did the experience make you feel?"), the data again shows that different touchpoints tend to produce different emotional responses in customers. Our analysis of consumer responses for all UK banks across all 11 touchpoints, using 18 emotional responses, allows us to compare emotional responses between channels; in this example, call centers and live chat.
| Emotion | Call Center | Desktop Website | Expert Review | Live Chat | Mobile App | Mobile Website | Social Media | Text | User Review | Branch Visit | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Alienated | 1.97 | 0.53 | 0.47 | 2.14 | 1.46 | 0.51 | 0.78 | 1.31 | 1.08 | 0.98 | 0.70 |
| Angry | 1.39 | 0.31 | 0.67 | 1.06 | 1.17 | 0.31 | 0.72 | 1.20 | 1.01 | 1.22 | 0.77 |
| Appreciated | 1.08 | 0.68 | 0.98 | 1.15 | 1.21 | 1.12 | 0.96 | 0.29 | 0.81 | 0.96 | 1.13 |
| Bored | 1.07 | 0.85 | 0.92 | 1.14 | 1.11 | 0.62 | 0.56 | 0.29 | 0.81 | 0.98 | 1.10 |
| Cared for | 1.03 | 0.92 | 0.98 | 1.63 | 1.62 | 1.01 | 1.01 | 1.01 | 1.08 | 1.14 | 0.84 |
| Confused | 1.53 | 0.70 | 0.61 | 1.45 | 1.22 | 0.49 | 0.74 | 1.14 | 1.03 | 1.57 | 0.66 |
| Disappointed | 1.53 | 0.68 | 1.06 | 1.21 | 1.17 | 0.87 | 0.84 | 1.24 | 1.02 | 1.56 | 0.64 |
| Entertained | 0.62 | 0.6 | 0.6 | 1.79 | 1.12 | 0.53 | 0.74 | 1.00 | 1.00 | 1.58 | 0.81 |
| Excited | 1.07 | 0.7 | 0.7 | 1.41 | 1.08 | 0.84 | 0.83 | 0.74 | 1.00 | 1.21 | 0.66 |
| Frustrated | 1.18 | 0.72 | 0.7 | 0.84 | 1.06 | 0.67 | 0.97 | 1.21 | 0.97 | 1.22 | 1.06 |
| Fulfilled | 1.15 | 0.97 | 0.91 | 1.47 | 1.07 | 1.06 | 0.99 | 0.97 | 0.95 | 1.20 | 1.04 |
| Impressed | 1.06 | 0.85 | 0.93 | 1.41 | 1.00 | 0.91 | 0.94 | 0.95 | 1.00 | 1.05 | 1.01 |
| Looked after | 1.25 | 0.91 | 0.79 | 1.00 | 1.01 | 0.92 | 0.91 | 0.86 | 0.94 | 1.02 | 1.33 |
| Motivated | 0.94 | 0.85 | 0.93 | 1.17 | 1.01 | 0.84 | 0.92 | 1.36 | 0.94 | 1.02 | 0.83 |
| Proud | 1.05 | 0.76 | 0.79 | 1.01 | 1.01 | 0.81 | 0.94 | 1.39 | 0.82 | 1.31 | 0.66 |
| Relieved | 1.08 | 0.76 | 0.93 | 1.01 | 1.21 | 1.07 | 0.98 | 0.99 | 0.95 | 1.01 | 1.01 |
| Satisfied | 1.09 | 0.91 | 0.97 | 1.03 | 1.09 | 1.10 | 0.97 | 0.96 | 1.10 | 1.01 | 1.01 |
| Uplifted | 0.78 | 0.64 | 0.73 | 1.44 | 1.02 | 0.77 | 0.96 | 0.98 | 1.09 | 1.37 | 0.74 |
The data for call centers and live chat show some important differences. Call centers produce a wider range of emotional scores than live chat, ranging from 0.6 (entertained) to 1.53 (disappointed). By contrast, the scores for live chat are more closely bunched around the average of 1, indicating fewer strong emotional responses either positive or negative. There are also some sharp divergences between these channels: for example, “entertained,” which scored 0.6 – well below average – for call centers and 1.12 – slightly above – for live chat.
Users are more likely to feel alienated (+46%) and frustrated (+43%) by live chat, suggesting that elements of the experience are falling short, while call center users are more likely to report feeling angry (+39%) and disappointed (+53%) after engaging with a bank’s call center. However, the scores both channels receive for making consumers feel “cared for” are not only well above average but almost identical, indicating no risk of decreasing customers’ positive impressions if they move from telephone-based contacts to live chat.
Our research so far has identified a highly suggestive and potentially valuable link between CX scores and subsequent user intentions and business performance. Companies have understood the importance of CX for many years, but until now have lacked a robust methodology for working out which investments in CX are likely to be most effective in shifting higher-level business metrics and financial performance. Similarly, they have had no way of measuring the return on those investments.
The CXGX methodology delivers a set of metrics that can unlock the predictive power of CX data, which highlights its potential to provide a compelling complement to existing metrics such as NPS.
The results we have generated so far are only preliminary since they are based on a single set of survey data. The CXGX is in beta and will now be piloted with selected financial services brands to generate a much larger, longitudinal dataset that will allow us to demonstrate the relationship between CXGX and business growth over time, and to show how the methodology can be used to prioritize investments in CX and measure the returns on them.
Ultimately, we see CXGX operating at three levels within financial services companies:
There is space and appetite for a methodology that can turn CX data into a tool to guide investment decisions in a critical area for financial services businesses. Until now, companies have been flying largely blind in CX terms, with no way to link the data they collect on CX to changes in business performance. They have had no reliable way to benchmark their own CX performance internally over time, nor to compare themselves to competitors or to “experience leaders.” This has led to findings such as Bain’s—that 80% of companies believe they deliver “superior experiences” but just 8% of customers agree.
We believe that CXGX offers a major opportunity to close that perception gap and give financial services businesses a toolkit to improve business performance using CX data.
The CXGX is based on an overall quantitative survey of 7,130 participants, of which 2,433 responded on retail banking experiences. 882 participants completed 2,647 brand evaluations across 14 UK retail banking brands (10 established and 4 neobanks). The established banks were selected based on total retail deposits, and neobanks were chosen by sector experts identifying the most interesting and relevant disruptor brands in the market.
Simon James
Head of Data & AI
simon.james@publicissapient.com
Magnus Fitchett
Head of CX & Innovation Consulting
magnus.fitchett@publicissapient.com
Emma Kenworthy
Consultant, Customer Experience & Innovation
emma.kenworthy@publicissapient.com
Find out more about how we’re building the next generation of financial services by visiting:
publicissapient.com/fs
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