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How to Create a Successful Customer Experience Strategy

September 8, 2017

 

 

CSP is happy to have guest-blogger, Andrew Huber of Harland Clarke return this month and share his insights on creating a customer experience strategy that is successful.

 

“How are we doing?”

This question is at the foundation of any organization’s quest for continuous improvement. For banks and credit unions, the answer encompasses more than an institution’s financial statements.

In customer-centric organizations, the role of customer feedback is critical to sustaining and deepening account holder relationships, and contributing to long-term profitability.

But, are we there yet?

While many financial institutions say they want to improve the customer experience, are they taking the necessary steps to get there?  A true voice of the customer strategy is a multi-faceted process whose focus is to understand the customer experience via actionable data and analysis on multiple levels.

Below are three important things to keep in mind if your financial institution desires a truly comprehensive customer survey experience.

3 Considerations for Creating a Useful Voice of the Customer Strategy

#1 – Consider All Customer Experience Touchpoints

First comes the design and deployment of surveys using a variety of methodologies. The focus is on gathering, measuring and interpreting customer experience feedback at every touchpoint, from new account openings in the branch to the call center and online channels. Every customer experience touchpoint must be considered, in order for your business to plan for it.

#2 – Ensure You’re Gathering the Right Data

Surveys are just the start.

One of the keys to a successful customer experience program lies in the data accumulated from everything that’s happened to this point. The data gathered needs to be both actionable and all-inclusive. In other words, it needs to include real-time knowledge across significant customer satisfaction metrics that can be applied directly to specific operational and frontline areas that impact the account holder experience. Measuring net promoter score may only scratch the surface of what your financial institution would like to learn.

Learn important satisfaction metrics to measure outside of net promoter score in the white paper, “Customer Experience: Beyond Net Promoter Score.”

Download Your Copy Here.

#3 – Figure Out (in Advance) How You’ll Analyze the Data

While the core value that such a program can provide shouldn’t be underestimated, there can also be a thin line between a comprehensive service that yields insightful customer understanding and one with reams of survey data but little customer insight that can be used to directly affect bottom line performance.

This is why it’s important to answer these questions in advance of implementing your survey strategy: once you’ve gathered the data, then what? Who will mine the data for actionable insights?

If you don’t have a data scientist on staff, consider outsourcing to a third-party.

In today’s customer-focused world, dissecting and analyzing the customer experience can provide key insight that banks and credit unions can use to ensure they are truly putting the customer first. This mindset paves the way for multiple benefits including:

  • Improved customer satisfaction
  • Greater loyalty and retention
  • Better performance

What Does Customer Experience Mean For Financial Institutions?

August 2, 2017

 

 

CSP is happy to have guest-blogger, Andrew Huber, Program Manager at Harland Clarke, share his insights about customer experience (CX) and the need for financial institutions to deliver outstanding service at every touchpoint.

 

 

You likely know that the key to any strong, long-lasting business is delivering an exceptional customer experience (CX).

Unfortunately, when it comes to financial institutions, there can be a big disconnect between the experience they think they’re providing vs. the experience account holders are receiving. For instance, 41 percent of banks and credit unions consider themselves “relationship focused,” while just 13 percent of consumers say the same.

So how can financial institutions stay competitive and deliver an outstanding CX? Especially when, in the age of mobile devices and social media, everyone wants something tailored just for them?

The answer is surprisingly simple (and yet incredibly difficult) – financial institutions must deliver outstanding service at every touchpoint in the customer experience, from in-branch to call center and from online to mobile device.

This white paper reveals that account holders remain loyal to their financial institutions for five main reasons:

  • They were treated well
  • They experienced good communication
  • They received high quality advice
  • Their problems were resolved quickly
  • They had a personal relationship with at least one financial institution employee

Financial institutions have a strong incentive to keep account holders happy: increasing customer retention just 5 percent can show a 25-95 percent increase in profits. This is because acquiring a new customer is anywhere from 5-25 times more expensive than keeping an existing one, with customers having a positive experience spending 140 times more than ones who have a bad experience.

If you think about it, this makes perfect sense. Regardless of the context, people are loyal to who and what makes them happy, they’re more willing to recommend the source of their happiness, and they’re likely to want more from this source. Their financial institutions are no exception.

Creating a positive CX sounds easy enough, but these statistics only convey the benefits, not how crucial it is to get customer experience right.

In one study, 41 percent of account openers and 33 percent of account closers cited customer experience as the number one reason for making their decision, outranking competitive interest rates, low fees and location.

It can take years to build a positive customer experience, but a single negative experience, a single episode of poor customer service, or a single complaint that goes unaddressed can cost a financial institution an account holder — or more, thanks to the power of social media.

CX Best Practices
Want to ensure your financial institution is prepared to deliver an outstanding CX? Find eight best practices to implement in this white paper, “Customer Experience: Best Practices for Growing Revenue.”

> Download your copy here

 

Positive customer feedback matters

May 24, 2017

Passionate customers tell you what makes your brand exceptional

Often, customer experience research focuses too heavily on business shortcomings.  Managers want to know when customers are dissatisfied, what caused their dissatisfaction, and how to fix the problem.  As a result, decision makers overlook positive customer feedback.  Managers expect positive feedback, and when it’s received, they don’t celebrate the occasion. Instead, managers continue to search for shortcomings in their businesses – they don’t want to be complacent, even if their customers are happy.  However, this oversight misses an opportunity: a chance to understand what drives customer passion and excitement.

Word-of-mouth advocacy is a powerful driver of new business, and positive customer testimonials received during customer experience research help highlight the topics brand advocates are most likely to talk about with friends and family.  To maximize the value of this feedback, businesses should ask customers the following questions about their experiences:

  • How does our service/product/interaction make you feel?  When a customer describes a positive experience, asking them about their feelings helps businesses understand the type of value their services bring.  Are customers relieved? Excited?  Do they feel in-control?  Understanding the specific emotions they feel helps businesses understand why a service/product/interaction is important, and what emotions are driving the customer’s behavior.
  • How is our business different from others?  When it comes to positive customer experiences, unique positive experiences are true brand differentiators.  Identifying those unique positive experiences allows businesses to replicate that experiences across their customer base.  Once the experience is consistent, that unique positive experience is a brand differentiator, which can be used to solicit new customers.
  • How does our business make a difference in your life, even if it is small?  Asking customers to relate a business’s services to their lives helps communicate those services in the customers’ language.  For example, customers might not care about the UX testing, which guided development of a bank’s mobile app; but they DO care that the app is easy to use and saves them time.  Managers and directors are prone to talk about the services they provide in their own terms – from the behind-the-scenes perspective, talking about the nuanced details of the services they provide.  Conversely, customer feedback vocalizes positive experiences in ways mangers struggle to verbalize, and their feedback provides a template for how managers should talk about the services they provide.

Beyond the benefits of analyzing positive customer feedback, the process provides a venue to build morale among employees and recognize their hard work.  By addressing positive feedback, employees are incentivized to continue (and increase) positive behaviors, which lead to positive customer experiences, because they know their good deeds are noticed and valued.

In 2017 and beyond, managers continue to look at positive customer experiences to identify, replicate and reinforce aspects of their businesses leading to positive feedback.  Once reinforced, branding/marketing managers use these competitive advantages to drive new business, while customers drive business on their own through brand advocacy.

Responding to negative customer feedback is important, but most organizations already do a good job at identifying their own shortcomings.  Many managers overlook positive feedback at their own detriment, and those who utilize feedback to create a model for consistent positive experiences will come out on top.

The Dangers of Overlooking Low-Value Customers

March 23, 2017

One way to segment your customers is by their lifetime value. Compared to many other measurable customer attributes, lifetime value is the kind of big-picture description that can be difficult to observe or estimate at a glance. But it’s also one of the most valuable pieces of information you can have about your customers.

Customer lifetime value is a prediction of how profitable your relationship with a given customer will be over time. Lifetime value can be calculated a number of different ways, from simple formulas to complex equations. Some of the factors that go into this calculation include how long you expect the customer to stay a customer; how much money that customer tends to spend with you, and how often; what it costs to keep that customer loyal; and the average rate of churn throughout your customer base.

Essentially, a lifetime value measurement boils down your relationship with a customer to a dollar amount. But the benefit of is not just quantitative: it influences businesses to prioritize the long-term maintenance of customer loyalty, compared to more expensive efforts like customer acquisition.

Using Customer Lifetime Value for Segmentation

customer segmentation based on lifetime customer valueOnce you’ve predicted the lifetime value of each customer, you can then group them into tiers, from most to least valuable. The most valuable customers are those who shop with you frequently, generate the most profit, and are most likely to stay. The least valuable customers may be new or casual shoppers who split their attention and money between you and your competitors. In the middle are the rest: regular, if not devoted, customers who don’t have the most to offer you, but don’t cost you much to keep, either. These are customers who could possibly be influenced toward more value if tended correctly – and if not, may slip down to the bottom tier.

For the highest third, the ones you can’t ask much more of, the goal is to make sure they stay. For the middle third, you can try to grow their value by appealing to them with additional services or products, or offering loyalty rewards, like discounts.

But what about the lowest tier – the one that experiences the most churn? Do they have anything to offer? Would you be better off without them?

Don’t Write Off Your Low-Value Customers  

Once you’ve singled out your most valuable customers, it’s only natural to want to gravitate in their direction – to reward their loyalty with perks, to provide them the best service, and to otherwise do everything in your power to keep them around and keep them spending. All this effort is still less costly than investing the time, energy, and budget to convert lower-value customers up the ladder.

All of that would seem to justify prioritizing your top tier. But do so at your own peril. Every customer’s “lifetime” with your business has to start somewhere, and many of them start in that lowest tier. It’s rare to simply acquire a high-value customer out of the gate: they must be nurtured, and this tier is where that relationship-building has the most impact.

Converting an existing low-value customer into a higher-value one is still less expensive than acquiring a new one, with unknown value. Despite the investment they demand, it’s easier to see a customer move up the value ladder, while the ones at the top are not terribly at risk of slipping back down (unless you really mess up). Besides, if you don’t devote attention to the least committed customers, chances are that your competitors would be more than happy to take them off your hands.

The easiest way to make your low-value customers into VIPs is to treat them like VIPs.

No matter how many invisible dollar signs hang over their heads, every interaction with your business and brand should make them feel valued and respected. They can also be a valuable source of insights and intelligence. Don’t be afraid to ask them directly: What would make you shop here more? How can we best serve you? Voice of the Customer research is your friend, especially among this group. What applies to some can likely be used to woo others.

Of course, some customers will just never be converted and end up taking up more resources than they’ll ever be worth to you. Customer divestment was once practically an anomaly, but these days, some companies see it as a smarter move than keeping low-value customers on the books. (Read more: The Right Way to Manage Unprofitable Customers on HBR.org – though we at CSP don’t necessarily stand behind that headline.)

Bottom line: Give each customer the attentive and inviting customer experience they deserve, and watch the overall value of your customer relationships grow.


You may also want to read:

Customer Segmentation in the Big Data Age: Where Banks Find Value

February 8, 2017

Customer segmentation helps banks get to know their customers on a more granular level. Segmentation reveals specific intelligence that could otherwise be obscured by the sheer volume of data. These insights, in turn, inform messaging strategies for marketing and customer service strategies. Segmentation can also help banks better understand the customer lifecycle and predict customer behavior.

Examples of common customer segmentation criteria:
  • Customer value – How many products & services customers purchase and what kind of revenue that generates for the bank – past, current, and predicted for the future
  • Demographics – Age, geography, gender, generation (e.g. Millennials and Baby Boomers), income level, marital status, and other “vital statistics”
  • Life stage – Slightly different from age, focused instead on customers’ journeys through various milestones and markers; for example, graduating college or starting a family
  • Attitude – Customers’ subjective stances on things like the financial industry as a whole, online and mobile banking, the economy, and their satisfaction with their bank
  • Behavior – Interactions and transactions between customers and their bank, which channels they use and how often, and which products they adopt

Similar criteria can be applied to banks’ business customers – profitability, number of employees, “life” stage (start-up, established, legacy), and so forth.

These are the traditional ways that customers have been segmented for decades. However, relying just on these categories is not going to yield many actionable insights.

In the age of Big Data, you sometimes have to think small. The real power of segmentation is not the quantity of data you can collect – which, with today’s technology and methods, is virtually infinite. It’s in the ability to drill down to the information that actually teaches you something about your customers.

Often it’s not the segments themselves, but where they overlap, where you’ll find the most valuable intelligence.

customer segmentationSome examples: unmarried, home-owning, degree-holding women under 45; middle-income married parents of high-school-age children in a particular school district; and minority Millennials who are starting their own digitally driven businesses.  Any of these micro-segments may prove valuable customer niches for banks to prioritize. But first, you have to conceive of their existence. Second, ask the right questions. And third, conduct the relevant research to answer those questions.

To understand how this can come in handy for banks, just think about the sometimes bizarre categories that show up in your Netflix queue based on what you’ve been watching lately. Vintage sci-fi with a strong female lead? Critically acclaimed British nature documentaries? Criminal investigation murder mysteries based on books? The more they know your tastes, the more likely you are to keep using their service based on their recommendations.

The options for how segments can overlap are nearly limitless.

Nearly. There is a bell curve to the usefulness of segmentation. Too broad, and the results are less than insightful. Too narrow, and the value of the insights gained will have minimal bottom-line impact.

This is where it helps to have experienced data scientists on your side. The purpose and advantages of segmentation are easy to enough to grasp, but the farther you get into analytic methodology, the more highly technical it becomes, and the more you need to understand about mathematical models and formulas. If things like our guide to data visualization make your eyes glaze over, chances are that the nuances of segmentation will put you right to sleep, too.

But you’re in luck, because CSP’s customer experience & research experts are passionate about getting you the insights you need out of the wealth of data we can gather. So if you are interested in getting to know your customers down to the niche level that segmentation empowers, give John Berigan a call at (800) 841-7954 ext. 101 or contact us by email to start a conversation.


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

What is Customer Intelligence?

January 21, 2015

what is customer intelligence

 

Customer Intelligence (CI) is a discipline within Customer Relationship Management (CRM) that relies on the collection of customer information to gain insights into behavior.

Using Customer Intelligence methodologies, companies can assemble and examine data to uncover customers’ preferences, motivations, patterns, wants and needs, and ground their strategy in that information to deliver a better customer experience.

Measurement & Analytics

Customers reveal things about themselves in their daily actions and inactions. Customer experience research, Voice of the Customer programs, and market research create a detailed and specific picture of the customer journey.

Integration & Context

The value of Customer Intelligence is in the scalability of the knowledge it confers. Within the cloud of data, you can find valuable insights about macro trends across your customer base and micro variations from customer to customer.

Prediction & Personalization

Let your customers know you value the quality of their experience by using customer intelligence to optimize their journey, target your messaging and efforts, and adapt proactively.

Conversion & Retention

By continuously striving to improve the customer experience, expect to have an impact on customer satisfaction, referrals, and opportunities to cross-sell.

 

Move from thinking you know your customers to really knowing them. Find out what kind of customer intelligence you could be missing when you talk to an expert at CSP today.

Customer Intelligence Lies Buried in the Code Halo

December 10, 2014

If you’ve ever been on a diet that involved calorie-counting, maybe you can relate to looking at a piece of food and automatically estimating its caloric content, as if a little number were suspended in the air right above the plate.

sun haloImagine that kind of information-enhanced meta-vision projected onto consumers, and you have the visualization of Big Data. Every interaction we have with businesses across every channel produces a parcel of data, and together those parcels orbit around each person in a cloud – or, you might say, a halo.

Published in April 2014, the book Code Halos: How the Digital Lives of People, Things, and Organizations are Changing the Rules of Business has gotten attention and positive reviews for the authors’ examination of the complexity of data in the modern marketplace and what that means for businesses.

The idea is that these code halos are transforming interactions between individuals, and between customers and brands. Businesses are awash in readily-available information about customers, and it’s more common these days to hear someone describe their enterprise as “data-driven.”

Gone are the days of typifying customers into pre-determined demographics and limited personality profiles. Instead, the buzzwords of the day are customization, personalization, and seamlessness.

It’s the Age of the Customer, alright.

Still, you have to admit … things were a little simpler back in before the data revolution. Customer profiling didn’t arise out of nowhere; it was a useful tool in its day, as secret shoppers, customer surveys, and other tried-and-true methods have been.

Big Data has exploded faster than many enterprises have been able to wrap their heads around it. An entire new spectrum of possibilities and variables has been opened to us, and it’s difficult to determine where to start, what’s valuable, what’s actionable, and what merits attention.

Code Halo points to successful businesses like Amazon, Apple, Google, and Netflix as pioneers who owe their success to masterful management of customer information that propelled them above and beyond traditional competitors like Borders, Kodak, Yahoo! and Blockbuster.

It takes a sharp eye to look at a halo of ever-changing data parcels and find a coherent string of insight, and many customer experience managers are finding that they need to adjust their vision for this new paradigm.

The good news is: you don’t have to go it alone. Helping you make sense of customer intelligence is what experts like CSP are here for. So if Big Data is giving you a Big Headache, we likely have a solution for that.

Follow us on Twitter, Facebook, and LinkedIn for regular news and views on customer experience management, analytics, and voice of the customer research.

The Rise of Predictive Analytics in Customer Experience Management

September 17, 2014

The emergence of Big Data has been one of the most disruptive events of the new millennium, impacting practically every industry.

Technology has leapt forward again and again over the last decade, and brought us new tools for accessing, collecting and delivering data with mind-boggling volume and velocity. With the floodgates open, businesses large and small are still looking for the best way to turn this vast ocean of disparate information into valuable insights, action steps and outcomes.

One new tool enabled by Big Data fits comfortably into the customer experience manager’s tool belt: predictive analytics. By streamlining internal and external sources of customer information, this method of data-mining is applied to anticipate an individual customer’s needs and wants with greater speed and reliability than has ever been possible.

Finding Shapes in the Cloud

Predictive analytics, in a nutshell, means identifying patterns in an existing data set and extrapolating those patterns to deduce what is most likely to occur next. Businesses were already doing this before the Information Age, but by largely outsourcing the task to algorithms, we’re now able to crunch much larger data sets in much less time and come out with much more nuanced portraits of customers.

Another advantage of predictive analytics is the ability to quickly and easily drill down to the individual level. A single customer produces a wealth of data on a daily basis by simply going through the motions of his/her life. By applying resources to examine just that customer (rather than the general demographic or profile he/she fits), a business can design a tailor-made experience with the best likelihood of producing the desired outcome – be that sales, loyalty, or resolution of a complaint.

Export that ability across every individual in your customer base, and you can see how the lines between responsive and proactive are blurring. For example, Wells Fargo rolled out ATMs that deliver a unique display of buttons and options each time a customer signs in, reflecting how that particular customer has used ATMs in the past and will likely use it this time.

Big Data vs. Big Brother

In a way, predictive analytics has taken us back to the Main Street General Store model of doing business, where the proprietor not only knows your name but has your shopping cart all but ready to go when you set foot inside the door. This kind of personal attention is what customers want and what keeps them coming back, right? Yes – to an extent. But it’s deceptively easy to cross the line.

You may recall this headline from 2012: Target figured out one of its customers, a teen girl, was pregnant before her father did. The retailer relied on patterns in her customer data to reach this conclusion and ‘congratulated’ the young woman with personalized coupons for maternity and baby gear. Her father intercepted the mail, leading to a very irate confrontation with an oblivious store manager who had nothing to do with the decision to target (no pun intended) this customer with maternity messaging.

As it turned out, the data didn’t lie, but the damage was done and not limited to just that household. The story spread rapidly across the Internet and became part of the growing narrative of distrustful consumers and intrusive, creepy companies who know just a little too much. Brands want relationships with customers, and customers do respond well to the personal touch, but they sure don’t want to be stalked.

That’s why, even as the technology continues to leap forward, there’s still no real substitute for the kind of expertise that comes from years of hands-on customer experience management. With great power comes great responsibility, and as a discipline, predictive analytics is still maturing. Leaving all decision-making to the algorithms may be accurate, but wisdom doesn’t translate well to automated code.

By integrating CSP’s Voice of the Customer research with actual sales results, our Predictive Sales Analysis (PSA) brings together the best of both worlds. We produce a unique set of key drivers determined by employee behaviors that have the greatest correlation to sales metrics, and coach clients to focus on the areas that are most important to their business strategy. Learn more about PSA.