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Tagged: analysis

Customer Segmentation Pitfalls and Potholes

March 9, 2017

Customer segmentation can be an immensely useful tool in getting actionable insights from your customer research. From those insights, you can devise strategies to improve the customer experience, because you have a more specific understanding of what customers want. But segmentation is far from simple.

To get the most out of it, you need to understand a few things about the art and science of conducting customer research. That’s just what CSP has been doing for more than thirty years, so we thought we’d share some of our pointers.

What Can Go Wrong with Customer Segmentation

customer segmentation

Methodology Mishaps

What does your business have in common with the Large Hadron Collider – the massive facility in Switzerland that smashes atoms together to better understand physics? You both rely on the Scientific Method. Or at least, you should (and they certainly should).

Any good research, whether studying customers or plants or animals or atoms, is based on these standards, which have been the guiding principles of science since the mid-1700s. To get good results out of your research, your methods must be scientifically sound, unbiased, and verifiable.

Research is not just conducted, but designed. That means knowing how to create a sound and testable hypothesis, conducting the right kind of ‘experiment’ to test it, and verifying your results with the proper vigor. Get any of these parts wrong, and the rest unravels from there.

Contaminated Sample

Sometimes research starts from scratch, but often, it relies on parsing data you already have on hand. That might include one or more customer databases or Customer Relationship Management (CRM) tools. These databases must be meticulously maintained so as to avoid contaminating the results. Examples of database disruptors include duplicate entries, incomplete entries, “dead” entries (meaning, invalid or out-of-date information, such as dead email addresses), and false categorization.

A slip-up here or there may seem like not a big deal, but it can lead to disasters in customer communication. For example, in 2011, the New York Times erroneously sent out a special discount offer to a small list of 300 recent ex-subscribers to entice them back – except that it was delivered to 8 million contacts, including many current subscribers who suddenly became aware of a discount they were not being offered. Things like this can happen when database entries are not correctly or clearly identified and grouped.

You Know What They Say About Assumptions…

Everyone has conscious and unconscious biases and makes assumptions based on those biases – it’s only human, and it’s rarely malicious. But such assumptions, no matter how logical or benign, can still affect the viability of research results and the value you get out of them.

A good example of where you see this happening is in discussions of the different generations – Boomers, Gen X, Millennials, and so forth. Many sweeping generalizations have been made about each group, some supported by sound research, and others just created by socialization. Eye-grabbing headlines and op-eds easily filter through to form your beliefs about these potential customer segments.

When that happens, you are more prone to leaping to the wrong conclusion. Don’t assume that seniors don’t use mobile banking because they’re technologically illiterate, or that lower-income customers don’t have smartphones. Any conclusions derived from research must be supported by that research.

It Pays to Have an Expert on Your Side

Done well, customer segmentation can lead you to valuable insights and an improved customer experience. Done poorly, it can just as easily lead you astray, or not lead you anywhere at all. If it were easy, businesses like CSP wouldn’t need to exist – but luckily, we do. To learn more about how we guide businesses in using their data to provide stellar customer service, contact us


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SOURCES

New York Times email mishap
Unsupported assumptions

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.


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Employee Training: All at Once, or One at a Time? It Depends

July 13, 2016

Employee training is pulling away from the model of slideshows in a dark conference room with stale bagels. Because attention spans and time are both in short supply, training must cut to the core issues and deliver worthwhile solutions – or in other words, you need to know what you’re doing and do it well.

Companies, on average, do not allocate much of their budgets to employee training – a little more than $1,200 and about 30 hours per employee each year. Instead of seeing this as a cost, treat it as an investment.  So, do you diversify your investment by plugging into individuals? Or do you put all your eggs in one basket by focusing on full enterprise training?

graph-963016_640Data instantly pinpoints weak links.

If you’re not sure where to start, look at the stats. Using comprehensive data, like the extensive reports provided by CSP, you can develop or choose beneficial team training programs. The data highlights the areas of concern, be it employee performance or customer satisfaction, and zooms in on detailed aspects with matching metrics.

Now you know not to spend time on teaching key phrases and language, for example, but improving listening and critical thinking abilities. More importantly, you’ll know if you need to address the entire team or pull someone aside for one-on-one coaching.

Team training moves everyone forward, together.

When employees are overlooked or employee training isn’t properly implemented, companies can experience dizzying unrest: high turnover rates, lack of engagement, dissatisfaction with other co-workers, low confidence and company pride, among other roadblocks.

Team training can open a dialogue between departments as well as junior and senior employees, thus developing a relationship more personable in nature. Ideal scenarios for team learning can include the following:

  • employee training for all or for oneNew material or technology
  • Changes in leadership
  • Continued education
  • Need to challenge complacency
  • Knowledge transfer
  • Fuel for employee loyalty

Team training sets a tone for the company. All of the gears and levers are oiled in a cohesive tune-up. But what happens when one little wheel keeps sticking?

Invest in the individual to see both a return and a contribution to the greater good of the team.

Think of a group fitness class compared to a personal training session. Unless the class is made of cloned robots, no two participants are wired the same. If one person is constantly falling behind the group, that gap is likely to grow each class unless there’s an intervention.

In a one-on-one setting, a personal trainer can take the time to check positioning and mobility, reintroduce basics that perhaps a client missed, and ultimately launch a game plan for the future.

As essential as training is for this person, so is following up with them and establishing an accountability system. Regular check-ins and feedback from the client are crucial for effective future training efforts. It’s up to the employer to recognize changes, improving the weak links and maximizing talent. The return on your investment could propel the entire team forward.

 

It’s unrealistic to know what each employee is doing or not doing well, and the impact of that performance on the team, without some guidance from statistics. Use data to outline a strategy that effectively combines both team and solo training. Customization based on your company’s needs will keep costs down and training, simplified.  

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Get more from your employee training efforts.

CSP’s customizable Employee Training program provides expert guidance, supports accountability, and promotes transparent communication. Contact us online or call John Berigan to learn more – (402) 399-8790 ext:101.

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.


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5 Compelling Reasons to Measure Employee Engagement

March 2, 2016

The ongoing cycle of customer experience success is comprised of four main influencers: Employees, Customers, Management, and Data. In this series, CSP examines the Employee segment of that cycle and the benefits of focusing on internal culture to drive success.

Understanding Employee Engagement

As defined by the Corporate Leadership Council, “Engagement is the extent to which employees commit to something or someone in their organization and how hard they work and how long they stay as a result of that commitment.”

Engagement is all about intentionally creating a motivating workplace environment, while simultaneously aligning individual employee talents with business strategy. Employees engaged in their work are likely to be motivated, to work with passion, to remain committed to their employer, and to stay focused on achieving business goals and driving the organization’s future.

Why It’s Important to Measure Employee Engagement

1 – Employee engagement directly correlates with performance and business results

No business can expect to grow and achieve sustainable success without an engaged workforce. This is especially true as it applies to customer service and satisfaction. Customer experience is nurtured from the inside out, and relies on competent, well-trained, and highly motivated employees. Sure, you may deliver annual performance reviews and objectives, but that’s only scratching the surface of the overall success of your team.

2 – Being a ‘Great Place to Work’ attracts top talent

A company’s reputation as an employer is one factor determining the quality of applicants for open positions, be they external or internal applicants. Being a great place to work is not just about bragging rights and publicity, it demonstrates to potential candidates (not to mention customers and the general public) that you are doing the all the right things to keep your employees feeling fulfilled. Any worthy candidate will see this as extra incentive to join your team. It may even attract them away from competitors with similar openings available.

3 – Engaged employees are emotionally invested

Your contract with your employees goes beyond tangible benefits like compensation and benefits. How employees feel about their jobs – their managers, their workload and hours, the company’s mission and the quality of the product or service – makes the difference between a job that looks good on paper and one that is satisfying in practice. Employees who are emotionally invested in the company’s success are among your greatest assets.   

CostofReplacing4 – High engagement reduces employee churn

The highest-engaged employees are the least likely to look for or take other employment opportunities that come their way. Naturally, it follows that those who are the least engaged are also the least committed to staying on the team. Not only is it costly to regularly lose employees and have to replace them, voluntary departures affect the morale of the rest of the team.

5 – Engagement can’t be ‘felt,’ it must be measured

While a manager may have a sense of who the most and least engaged members of the team are, many employees will fall somewhere in the invisible middle. These employees are susceptible to being swayed in either direction. Improving individual and overall engagement is only achievable if you know the baseline from which you’re working. Employee engagement metrics take many of the intangible motivators of performance success and make them tangible, visible, and trackable. That’s an essential step for setting goals and implementing internal initiatives to improve engagement.

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Improving the Customer Experience Through Benchmarking

August 11, 2015

Benchmarking is the process companies use to identify and establish key performance standards, or benchmarks, and measure their performance against those standards over time. With a benchmark analysis, a company can compare its current scores in critical areas against its own past performance, as well as against its competitors.

NewBAR

Done in-house from the ground up, benchmarking can be a dauntingly complex process. Benchmarks must be agreed upon, measurement tools and strategies implemented, research assigned and completed (which, in some cases, means navigating security and permission concerns), and reports compiled. The information in the final analysis can be invaluable, if the right resources, attention, and talent are invested in it.

What’s more, benchmarking is not a one-time exercise, but a living process that depends on continuing collection and interpretation of current data. The shelf life of a single analysis report is fairly short, but properly maintained, a benchmarking strategy can be a gift that keeps giving.

Where does benchmarking fit into improving the customer experience?

Often used to determine how a company is faring against its peers financially, benchmark analysis also has a qualitative application. This includes measuring the critical metrics of customer service and experience that carry the most weight with overall customer satisfaction – what CSP calls key drivers.

Responses to Voice of the Customer initiatives like surveys can be translated into scores and percentages, which then get used to identify the top, bottom, and average range of responses to those metrics. Comparing the most current available scores against these ranges gives an indication of whether the customer experience is excelling, lagging, or falling behind.

Benchmarking is a way for managers to reality-check their perception of how their strategies and employees are performing against what the customers are actually saying.

Benchmarking provides a competitive advantage

The quality of a business’s customer service is often a make-or-break factor in customer satisfaction, loyalty, and likelihood to promote that company to others. In many ways, customer experience is the marketing that keeps happening even after you’ve initially earned the customer’s business.

Benchmarking not only demonstrates a company’s performance against itself, but against a defined peer group of its competitors, measured by uniform standards. While a direct Company A vs. Company B comparison may not reveal much of use, there is valuable insight in identifying one’s overall standing among the rest of the pack.

For instance, let’s say a manager has grown concerned about how long customers are kept waiting before they speak to a representative. Maybe she has noticed longer lines on the sales floor, or customers looking frustrated or impatient while in line.

Through benchmarking, she has been tracking “wait time” as a key driver for six months, and sees that this month, customers have indeed indicated a drop in satisfaction against this metric. She then reviews the wait time satisfaction scores of her peer competitors and determines that they have seen a slight increase in the same period of time, dropping her company back in the ratings from the “top” to “average” category. Now there is a risk she may start to lose customers to the better-performing competitors.

This intelligence informs the manager of an opportunity to improve the customer experience by implementing new strategies to affect the wait time at her location. Continued benchmarking will help her track progress against that goal, and identify any new opportunities for improvement that may come along.

It doesn’t end with the report

Benchmarking is one step in the process – a critical one, but nonetheless, just one. As with all Voice of the Customer data, its ultimate value depends on how the information is used to improve the customer experience with well-informed training, continued evaluation, and timely reporting.

That’s why CSP’s new Benchmark Analytics Reporting Dashboard pairs so nicely with our training and employee support, such as the STARS library available to our clients, to create a balanced ecosystem of process, performance, and progress. The dashboard takes much of the rigorous research and reporting aspects of benchmarking and delivers an easy-to-read analysis that can tell you, at a glance, where you fall among your peer group.

To learn more about benchmarking, the new dashboard, STARS, or any other component of customer experience management, contact us with your questions.

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.