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Tagged: research

A Message from Our President: What Will Customer Experience Mean in 2017?

January 4, 2017

from Steve Kutilek, President & CEO of CSP

The more things change, the more they stay the same.

This often-quoted bit of irony seems appropriate as I reflect on the customer experience lessons of 2016 and look ahead to 2017. The details of customer experience management continue to change – technological innovations, new channels of communication, and evolving customer expectations, just to name a few. But the big picture of customer experience is what it always has been: relationships.

That theme is at the core of what CSP does for our clients. Sure, we’re a customer experience insights firm, but so much of the value of what we do is really about relationships. In 2016, we’ve debuted new solutions that not only deepen our relationships with our clients, but are designed around understanding the influence of inter-office relationships on the customer experience.

So let’s look at the connections that are and will continue to be important in the coming year:
  • The relationship between data and the customer experience, which is at the foundation of everything we do. Like most relationships, it’s a two-way street: you need data to inform your strategies and actions, while the data alone doesn’t mean much unless it’s followed by action. CSP prides ourselves on not just doing the research and issuing reports, but guiding our clients on concrete ways to use that information to their benefit.
  • The relationships between banking customers and their branches: This has been an ongoing conversation in the financial services industry for many years. Online banking, mobile banking, and virtual customer service have contributed significant changes to the way banks do business. But it’s clear that customers still value brick-and-mortar branches and the people who staff them. Their needs are evolving, but they still have needs. This evolution is largely fueled by the need for their bank to be the resident expert on any and all financial advice and direction.
  • The relationships between people and their devices. Smartphones, tablets, and even plain old computers are at the center of many consumers’ professional and personal lives. The rapid pace of innovation in this sphere has created immense pressure within other industries to keep up with their customers’ demands and expectations. But as this relates to Point 2 above, there are still plenty of folks who don’t use (or don’t have access to) the latest and greatest pieces of technology, or simply prefer the traditional ways of doing things. The continuing challenge to banks in 2017 will involve figuring out how to straddle both worlds.
  • The relationships between managers and employees. No matter your suite of products, solutions, and strategies for keeping customers happy, a healthy customer experience relies on healthy relationships behind the scenes. More companies are realizing and prioritizing the importance of manager development, coaching and training, and providing a well-balanced employee experience. All of these elements together create the infrastructure to support exceptional customer service and satisfaction. The more that managers understand what is truly driving the customer experience, the better they can coach and train their staffs to execute on those drivers.

Customer experience has always been about relationships, but no relationship exists in a vacuum. As the context and influencers continue to change, so too will our strategies – without ever losing sight of that core truth.

We at CSP are ready and eager to support you in the New Year and beyond. So as the calendar flips over this month, I invite you to take a moment and reflect on these relationships. How did they affect things for your business in 2016? Where do you see them going in 2017? And how can we help?

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:

Report: Techy Competitors Turning Bank Customers’ Heads

April 29, 2015

Capgemini has released the 2015 World Retail Banking Report and their Customer Experience Index, calculated from the results of a comprehensive Voice of the Customer survey of more than 16,000 respondents in 32 countries.

The CEI has dropped only slightly from 72.9 in 2014 to 72.7 in 2015, indicating that customer satisfaction is stagnating as banks try to keep up with modern consumer demands and innovative competitors in the digital space.

More highlights from the report:

  • smartphoneGen Y customers registered lower customer experience levels than other age groups.
  • North America continued to have the highest level of overall positive experience compared to other countries, but still saw a dip in positive experiences compared to last year.
  • Customers around the world reported increased likelihood to leave their bank within the next six months. Gen Y in particular has a tendency to move banks, and are more open to internet-based providers or simple financial products offered by retailers.
  • Banks and customers don’t agree on the role of the branch. Banks would prefer that customers purchase simple products online, and visit a branch for help with more complex solutions. Customers continue to use banks for simple transactions and don’t trust that the online options will be as helpful to them as a live person.
  • The rise of FinTech firms means customers can complete their entire banking lifecycle without ever approaching a bank.

You can read the full report here.

Customers are clearly not thrilled with the status quo. They want their banks to keep in step with the other digitally savvy experience they’re having elsewhere in the consumer marketplace, from retail to healthcare to entertainment. The newest young adults have grown up with the convenience of instant, constant connectivity, and highly customizable products and solutions.

“Status quo” is what you get when you assume you already know your customers. The global numbers won’t tell you what intelligence you’ll gain from your own Voice of the Customer research. Every bank serves different customers and it’s their needs and expectations you need to be listening to, measuring, evaluating, and integrating into your customer experience.

If you’re concerned about your status quo or want to know what you can do to change it, contact Customer Service Profiles today by phone at (402) 399-8790 ext:101, via our website, or on Twitter @csprofiles

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

Release the Customer Intelligence Trapped Within Data Silos

February 25, 2015

Big Data is a big topic. In all the conversation about the potential for data to reveal key customer insights, broad statements and big promises are far more common than specific examples of the real-world answers a business might hope to gain.

There’s a reason that the word “data” is plural. An individual datum reveals very little on its own; it’s the connections, intersections, and overlaps of data that form the actionable patterns and trends.

Yet customer data is often still “siloed” into separate sources: direct customer feedback, employee performance measurements, web and mobile analytics, customer service interactions, standardized reports and evaluations, and social media impressions, to name a few.

Together, all of these data points can shed an entirely new light on the customer experience, but businesses are still learning how to effectively and efficiently connect the dots. Customer data is hardly self-explanatory, and legacy systems weren’t designed with this kind of interconnectivity in mind, leaving those insights trapped in silos.

transaction dataFor an example, consider financial service institutions. An article in BAI’s online edition rightly points out how these businesses are flush with transaction data generated by customers using credit cards, debit cards, checking accounts, ATMs, and mobile payments on a day-to-day basis.

This resource alone can tell the institution the number, types, locations, and frequency of transactions; the devices being used to complete these transactions; the breakdown of service and card types; and withdrawal/deposit amounts on a real-time basis.

Not only does this summarize the customer experience, there’s a direct connection to daily operations like cash inventory and technical support for digital tools (think: error code reports from a malfunctioning ATM).

Without systems in place to gather all of this customer data and deliver it in a digestible and useful format, to the correct teams, and in real time, valuable customer intelligence stays trapped in the Transaction Data silo.

Data is a living organism.

It consumes, it grows, it morphs and takes on new shapes, dimensions, and patterns. Each source of data depends on the others; by keeping customer data in silos dictated by their sources, a business risks starving itself of the vital information it seeks to gain by gathering data in the first place.

At CSP, we believe in the indispensable value of customized data delivery solutions designed around each business’s unique goals and customer experience. Don’t miss out on trapped data – talk to our customer intelligence experts today.

CSP can be reached by phone at (402) 399-8790 ext:101, via our website, or on Twitter @csprofiles.

Mid-Year Check-in: Technology Driving Customer Experience Trends

August 6, 2014

With 2014 just a little over halfway behind us, it’s an ideal moment to step back and take a big-picture view of customer experience management as a discipline, to see what forces are coming together to influence customer expectations and best business practices for driving loyalty.

Without a doubt, technology continues to provide both the incentive and the tools to improve customer service across all channels.

Consumers are usually faster to try, adopt and master new technologies than businesses are. Few organizations were prepared for the mobile explosion, and even now, several years into the “smart device” age, many are still catching up to what consumers have come to expect.

It’s not just the mobile platforms themselves that merit attention. Because of them, consumers have grown accustomed to new habits and behaviors – swiping and tapping instead of pointing and clicking, cameras that do much more than snap a photo, and thumbprint-based identification, to name a few.

Suddenly, a typical ATM interface feels about as sleek, sophisticated and modern as an Atari.

This shift in customer expectations and behaviors outside the walls of your business is one of this year’s major motivators to be proactive in improving the customer experience.

On the other side of the technology coin, though, is data. All of these interactions across the different channels produce an abundance of information that enterprises can use to identify, measure, and track the key drivers of customer satisfaction and loyalty.

Leadership and shareholders alike are beginning to see voice of the customer research as a must-have, enabling them to turn all this data into action steps like customized employee education programs and initiatives to align the organization’s sales approach with the overall culture.

Basically, they are realizing what we at CSP have touted for decades: The better the understanding of the customer at the enterprise level, the better equipped the enterprise is to deliver the optimal experience at every touchpoint.

It seems simple, but it takes the right combination of tools, resources and expertise to create the bridge from research to results. While the marketplace at large is showing more proactive interest in the voice of the customer, there’s still a lot of room for improvement over the rest of this year and beyond.