CSP Happenings





Tagged: insights

3 Lessons Banks Can Learn from Wells Fargo’s Mistakes

October 17, 2016

wells fargo made mistakes that should give other banks pause

One of the biggest banks in the U.S., Wells Fargo, made one of the biggest mistakes in recent banking industry history. By pressuring their sales staff to grow the number of customer accounts by nearly any means necessary, they wound up crossing some major ethical and legal lines and created a scandal that has hurt the bank in more ways than one.

In September 2016, after the scandal broke, Wells Fargo’s stock (WFC) fell to the lowest levels seen since early 2014, and the bank saw profits drop 2.6% for that quarter. Regulators issued $185M in fines, and lawsuits are lining up from consumers, employees, and shareholders. CEO John Stumpf was publicly grilled by Sen. Elizabeth Warren, the video of which quickly went viral, and he retired shortly thereafter.

This could happen anywhere.

There is nothing particularly special about Wells Fargo that made it the breeding ground for shady practices. In the competition for customers, all banks face continuing pressure to prove their success to shareholders and grow the business. Wells Fargo may have had the audacity to push the envelope into scandalous territory, but in theory, this could have happened anywhere. So what can banks learn from their mistakes?

1. Don’t sacrifice Quality at the expense of Quantity.

Wells Fargo was driven to these practices by a hunger for more – more customers, more accounts, more sources of revenue from fees associated with said accounts, more impressive numbers to show shareholders. Obsessing over the numbers is not the only way to grow a business. Ideally, customers choose you and stay with you because of the quality you provide. When a bank constantly strives to improve the quality of its customer experience, everyone wins.  

2. Don’t assume customers will tolerate anything.

Wells Fargo is one of the oldest and most recognized names in banking. Once a business is that established and secure, it’s easy to fall into the trap of assuming that customers will tolerate misbehavior like aggressive sales tactics or public scandals. Switching banks isn’t easy, especially once a customer has multiple accounts and assets tied up with one institution. Maybe Wells Fargo assumed that the potential risk of angering or losing customers was too low to worry about. That’s a dangerous assumption to make; it’s safer to assume that customers are always watching and waiting for you to give them an excuse to switch to a competitor. Customers have already been letting Wells Fargo know how they feel: branch visits fell 10%, checking accounts 25%, and credit card applications 20%, compared to the previous year.

3. Don’t gamble with regulatory compliance.

There is simply too much at stake to risk weaseling your way through the maze of financial regulations or playing in the gray area. Fines, lawsuits, and brand reputation scandals are nothing to trifle with. Wells Fargo will likely survive this crisis, but they have a long and uphill road ahead to recover from the damage to their brand. Banks need to hold themselves accountable for compliance before regulators or customers force them to do so. (More insights into proactively protecting yourself from non-compliance risks: keep reading.)

Wells Fargo’s mistakes will likely go down in banking industry history as examples of What Not to Do. Don’t let the same thing happen to your bank, whether you’re a national household name or a regional staple. If you’re going to earn press headlines, make sure they’re good ones. Listen to the Voice of the Customer, build an internal culture to support customer experience quality, and stay on the regulatory straight-and-narrow.


Our readers in the banking industry may also be interested in:

Sign up for our monthly email newsletter (see form on bottom of home page) or follow us on LinkedIn to stay updated.

Insights at a Glance: The Power (and Pitfalls) of Data Visualization

April 8, 2016

The process of gathering and analyzing customer experience data involves several translations.

  • Desired outcomes are translated into measurable attributes.
  • Attributes are translated into feedback tools (such as survey questions).
  • Customers translate their sentiments into quantifiable scores – data points.
  • Data points get translated into ratios, averages, and frequencies.
  • The collected data can then be translated into knowledge.

Customer experience researchers and data analysts are charged with the task of following all these translations step-by-step, but in the end, most non-analysts are only interested in that ultimate goal – the knowledge.

That’s where data visualization comes in. As humans, our understanding of data relies heavily on how that data is presented to us. Visualizations are among the best tools for making that final translation from information to insight.

Visualizations make data memorable.

Have you ever struggled to remember the name of a particular actor even though you can see his or her face clearly in your mind? For most people, it’s easier to remember something they have seen than something they have heard or read. Once translated into an image or graphic, data becomes less abstract and makes a distinct impression – one with staying power.

Visualizations make clear connections between parts of the whole.

Insight comes from connecting A to B to C and so on. A number by itself doesn’t say much until it’s put into a context of other numbers. Sometimes, even a simple table can help, but the more complex your data, the more difficult it is to glean insights just from reviewing the figures. Visualizations are handy shortcuts that make the relationships between data points immediately clear, getting you straight to that “light bulb moment.”

Visualizations influence how data gets interpreted.

Data is objective, but visualizations are subjective. There are a number of factors that influence the message a person receives from looking at a graphic: size, scale, color, even font choice. What’s more, the most basic types of visualizations – pie charts, bar charts, line graphs, and scatter plots – are each best suited to different purposes. Using the wrong kind of graphic for the type of data can be misleading or obscure the possible conclusions.

This pie chart can show what percentage of respondents in 2015 chose each answer. It cannot show a comparison to other years, nor can it show any other metrics.

This pie chart can show what percentage of respondents in 2015 chose each answer. It cannot show a comparison to other years, nor can it show any other metrics.

This bar chart can show all three years’ worth of data at once. It’s decent for showing the change in each measurement over time, but a line chart would be a better fit.

This bar chart can show all three years’ worth of data at once. It’s decent for showing the change in each measurement over time, but a line chart would be a better fit.

This line chart shows how each measurement changed over time. Note that instead of representing the total for each year between 2013 and 2015 (which would produce very short lines with little variation), this chart shows month-over-month trends for each response. Line charts work best when comparing a more thorough set of dates.

This line chart shows how each measurement changed over time. Note that instead of representing the total for each year between 2013 and 2015 (which would produce very short lines with little variation), this chart shows month-over-month trends for each response. Line charts work best when comparing a more thorough set of dates.

A scatter plot, not shown here, would be a good choice for displaying each response to a particular survey question. The above charts each show the total number of responses for each category, out of 1,285 responses to the same question, and each of those totals represents one point on the chart (or one slice of the pie). In a scatter plot, each of those 1,285 responses would generate its own dot, and the way those dots group together would reveal the trend.

Beware: Things aren’t always what they seem.

Visualizations are useful for drawing conclusions at a glance, but sometimes looks can be deceiving. Like statistics, visualizations can be manipulated to produce a particular effect – for better or for worse. For example, bar and line graphs depend on the scale of their vertical and horizontal axes. By increasing or decreasing the scale of either axis, bars can be made to look smaller or larger, or trends to look more or less dramatic.

Because the maximum value on this chart is 560, the top of the range (vertical axis) is 600. The red bars for “mostly satisfied” are far longer than any of the light blue bars for “completely dissatisfied,” making it look like hardly any respondents chose the latter. Because the maximum value on this bar chart is 560, the top of the range (vertical axis) is 600. The red bars for “mostly satisfied” are far longer than any of the light blue bars for “completely dissatisfied,” making it look like hardly any respondents chose the latter.

This chart uses the same data as the previous one, but omitting some of the categories. Without the higher-scoring “mostly satisfied” values, now the maximum value is 338, making the top of the range 400 instead of 600. Even though none of the actual values changed, now the blue “completely satisfied” bars look much more significant. Likewise, some of the light blue “completely dissatisfied” bars that barely appeared on the first graph are now visible here. The second chart uses the same data as the previous one, but omitting some of the categories. Without the higher-scoring “mostly satisfied” values, now the maximum value is 338, making the top of the range 400 instead of 600. Even though none of the actual values changed, now the blue “completely satisfied” bars look much more significant. Likewise, some of the light blue “completely dissatisfied” bars that barely appeared on the first graph are now visible here.

All it takes is a closer look to see whether the graph’s scale is skewing the effect, but ideally, you should be able to get an accurate sense of the information at just a glance. Otherwise, the visualization isn’t doing its job effectively.

These aren’t the only kinds of visualizations, of course, especially in this age of customized metrics and creative infographics. They do, however, represent the basis of data visualization, and knowing how to read them prevents those valuable insights from getting lost in translation.


More articles on using data to your advantage:

What is Customer Experience Research?

March 6, 2015

Traditionally, Customer Experience Research falls into two main categories.

In the first category, there are market research firms that take an academic or scientific approach to collecting data and presenting the findings. These providers emphasize the purity of their data and the rigor of their methods and processes for collecting that information.

In the second category, there are data collection firms that specialize in gathering, storing, and organizing vast amounts of data from a variety of sources. Through their proprietary systems and tools, they make their findings accessible and digestible to the end user.

What does customer experience research capture?

The two metrics most important to customer experience management are customer satisfaction and customer engagement, which exist on a continuum and influence each other in both directions.

Customer satisfaction is an immediate measurement of an experience, from something as small as an interaction with a customer service representative to the overall feeling a customer has that his or her expectations and needs are being met. This is arguably the starting point for all customer research.

Customer engagement is what keeps customers coming back. It captures the long-term equity that is built on satisfying experiences by measuring things like loyalty and how likely a customer is to refer others to their preferred brands and businesses. In this way, it’s a more useful measurement than simple satisfaction: customers who are strongly engaged over time are more willing to overlook or tolerate the occasional less-than-satisfying experience.

A great example of this comes from the consumer technology industry. Brands like Apple and Google each have dedicated, loyal audiences that will continue to buy their products and tout their benefits to friends and family, even when the products themselves fall short of 100% satisfaction (think: buggy software releases or smartphones so thin they bend in your back pocket). This is the kind of engagement every brand dreams of.

The Journey From Data to Information to Knowledge

data information and knowledge

Both the academic and data-collection approaches to customer experience research have value. Market research can reveal trends, insights, and patterns across large populations and broader spans of time. Data collection, meanwhile, has grown so sophisticated as to merit its own industry, aimed at helping the everyday business manager access intelligence about their customer – because it’s unlikely they have the expertise or time to sift through it all themselves.

Both methods also have their limits. Statistical research may be useful in an ideal world where all customers have the same expectations and needs, and all businesses face the same challenges in meeting those expectations. But in a real-world setting, the insights garnered from this research often ends up “watered down” and are unlikely to apply to each unique business or brand the same way.

It’s not unlike the idea of the self-help book, which can be a useful way to talk about people in general, but won’t always apply on an individual level. You can do everything “by the book” and still fall short of your goals if the book you’re going by doesn’t account for the nuances of your business or your customers.

In turn, data collection is exactly what it sounds like: collecting data and presenting it as information. But turning that into knowledge that you can act on? That part is up to you. These firms often step out of the picture at that point, leaving you to figure out how that information factors into your strategies and tactics, what merits your attention and what doesn’t, and what steps come next.

Bridging the Gap Between Research and Reality

The shortfalls of traditional customer experience research are how businesses end up thinking they know their customers, without actually knowing them. There’s a break in the process that prevents them from getting to that next level of knowledge and using that knowledge to improve their customer experience.

In our 20+ years of customer experience research, CSP’s guiding principle has been to not only gather and present the information, but to then guide our clients in creating the roadmap to a better customer experience based on a thorough understanding of their unique customers.

Why should anyone have to figure this out from scratch? CSP has seen it all before, and we know what works and what doesn’t. Our experts are flexible enough to adapt to any given brand or business with a methodology that’s personalized every step of the way. Your specific questions about your customers, your market, and your competition are built right into the program, along with ongoing support, tools, and coaching to help you define and achieve your goals.

This level of customization and personal attention is hard to come by with traditional research models, but we believe it’s the key ingredient to successful customer experience management. We’re not passionate about data – we’re passionate about improving the customer experience, full stop.

For more information about CSP’s customer experience research methodologies and the programs we build to support them, contact us today by phone at (402) 399-8790 ext:101, via our website, or on Twitter @csprofiles

How Voice of the Customer Insights Can Improve Employee Training

December 22, 2014

The frontlines of customer service and sales are where the most direct and personal customer experiences happen. It’s also an area where customers tend to be more vocal about their satisfaction, or lack thereof. A good experience can make someone’s day, and a bad one can ruin it – and humans are just predisposed to complain more than compliment.

Positive and negative customer experiences also influence retention and attrition; a good experience can keep a customer coming back for years, but one bad interaction and they might write you off forever.

And in the days of social media, one customer’s bad experience can easily spread to others and affect public perception of your brand.

Training ClassroomWith all of this at stake, no business can afford to deprioritize employee training and coaching. Training builds bridges between customers and customer-facing employees.

Creating, maintaining, and delivering an effective training program is no simple feat, but your customers will thank you for the effort.

One size never fits all.

As convenient as it would be, a one-size-fits-all approach to training is likely to miss the mark in more ways than one.

Every customer base is different, as is every workforce. Employee training initiatives must take into account not only the unique customers’ expectations, but the internal culture of the company. The better aligned these two conditions are, the better experience customers are likely to get, and the more productive employees can be.

Education is always evolving.

Customer expectations change with time, influenced not only by their relationship with your business, but trends and innovations in the marketplace as a whole. What was satisfactory last year may be insufficient today.

Static, standardized training is not sustainable. Regular evaluations of your materials, curriculum, and methods will keep your program responsive and current.

Mind the gaps.

So how do you optimize your employee training? Listen to the voice of the customer. VOC research and insights highlight gaps in employee performance and customer sentiment. This creates the opportunity to customize your training initiatives to focus on the attributes picked up within the research.

Knowledge is power – as long as you act on it. Measurements alone don’t do anything for anyone. At the end of the day, a customized, optimized, VOC-informed training program creates the opportunities for conversations that lead to loyalty and sales.

 

Customized training solutions based on VOC insights are part of the package of services CSP provides our clients. To find out more, visit our Coaching & Training page, or contact us directly with your questions.