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Tagged: analytics

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.


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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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Email Analytics: Dig Deeper to Uncover Customer Insights

December 7, 2016

email analytics reporting tells you more than just opens and clicks.The line between customer experience management (CEM or CXM) and traditional marketing responsibilities has been blurred, and email is a great example. Email campaigns need not be just about generating business or converting sales. They’re also a useful platform for building and continuing customer relationships. Email analytics tell you a lot about how customers are receiving and reacting to your messages.

Email analytics 101: The basic measures of the success of an email marketing campaign include Opens, Clicks, Bounces, and Unsubscribes. Email marketing software records these types of reader behavior within a customer relationship management (CRM) database. The level of detail of the data collected will vary from provider to provider – for example, what device or operating system your readers are using. From within that CRM tool, you can generate reports and track trends in each rate over time. That said…

Email analytics tell you more about your customers than their email reading habits.

You just have to know where to look.

What links are customers clicking on?

What topics, subjects, or messages are getting the most attention? Where are they positioned within your template design? How were they presented – as text, as images or icons (e.g. a button)? These small but significant factors can all have an impact on engagement.

With this information, you can: tailor the content and/or design of future campaigns to best match your customers’ interests and visual preferences.

Who are your most frequent openers and clickers?

Are they current customers, or prospects? How did they get on your list? Did they sign up voluntarily, or were they added automatically through another process? Pay attention to infrequent engagement, too – whose name is new since last time you sent a campaign? And who never opens or clicks – do they belong on this list, or is their information out of date?

With this information, you can: follow up with more personalized messages targeted at your most engaged subscribers, and make adjustments to your list-building strategy, including cleaning outdated or inactive subscribers.

When are your customers reading and engaging?

Typically, open and click engagement rates spike in the first few hours after a campaign is delivered. Some internet users still jump at every incoming notification or try to keep their inboxes clear of unread messages. But if you are varying your delivery times (as you should be), you may see that timing makes a difference. Review the timestamps on opens and clicks to see when your readers are most likely to open, and whether they click through immediately, or come back to the message later.

With this information, you can: optimize the timing of your regular campaigns for when users are most likely to engage. You may even be surprised by what you find; it may seem counterintuitive to send emails on a Sunday night, but if the analytics support it, go for it!

Was there a sudden spike in a given metric?

Outliers – campaigns that defy your typical averages or medians – are worth your attention. A spike in Opens could indicate that you hit the sweet spot with your subject line. Spikes in Clicks can reveal a hot topic or an effective graphic. A bump in Bounces is a red flag that your list needs some cleaning up, while high Unsubscribes warn that something you did got under your customers’ skin.

With this information, you can: optimize future subject lines and inside content in favor of the tactics that produced the spike – unless you’re talking Unsubscribes – and clean your list so that the next delivery only goes to valid subscribers.

PRO TIP: Some email marketing providers ask Unsubscribers to indicate the reason they’re opting out before their contact information is deactivated. Use this information!

Have you tried an A/B split test?

A split test is a great way to gauge the effectiveness of different email techniques. This involves splitting your list into two (or more) groups, each of which gets a different version of the same message.

With this information, you can: learn which variables – subject lines, template design, inside content, special offers – get your subscribers’ attention, and apply that learning to future campaigns.

PRO TIP: This works best with very large lists; if you have fewer than 500 contacts, it’s harder to get statistically significant results.

Where did customers go after clicking through?

Click-throughs might be the most valuable action a customer can take from an email, but that’s just the start. Ideally, the content they landed on will keep them engaged for a while. After a campaign is delivered, check your website analytics and follow the trail of breadcrumbs. (Again, your mileage will vary depending on the sophistication of your website analytic tools.)  

With this information, you can: make improvements to the landing spots linked to from your emails to pull customers further down the funnel or encourage them to take a desired action.

Bottom line: Email marketing is not a “set it and forget it” endeavor.

There’s a time and a place for automation in your customer communications. But if you are running email campaigns, why not use the email analytics they produce to learn more about your customers?

Data is at the core of CSP’s services, practices, and philosophy. We can’t emphasize this enough: analytics are only as powerful as what you do with them. In this age of Big Data, knowing how to use the infinite information at your fingertips makes all the difference.


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Why You Need More Than One Metric to Describe Customer Loyalty

August 31, 2016

As a business leader, you know the importance of keeping your finger on the pulse of customer loyalty. A critical part of customer relationship management, customer loyalty goes well beyond a customer making a purchase. Loyalty is steeped in the relationship between the company and purchaser.

A loyal customer believes your organization offers the best option. Loyal customers will purchase a product or service from the same brand, over a long period of time, while turning down competitors, and spreading satisfaction through word of mouth. Loyal customers will stay with you even in trying times. 

Customer loyalty can’t be summed up in a single number.

customer loyalty can't be summed up in a single numberWhile loyalty may appear as a single topic on your priority list, it would be a mistake to try to measure it with just one indicator.

As an example, many businesses looking to improve customer satisfaction use a Net Promoter ScoreSM (NPS®). This system measures the likelihood that customers will recommend a product, service, or company to others, and is often touted as “the only number you need to know.” Likelihood to recommend is certainly worth measuring; CSP uses the NPS® system ourselves. However, this score alone does not tell you enough.

Think of it this way: You wouldn’t use your blood pressure as the sole indicator of your total health, right? It’s important, sure, and it would be convenient if that was all you needed to pay attention to, but it’s not the only vital statistic your doctor needs to track to assess your overall well-being. The same logic applies to customer loyalty.

Instead, what you should aim for is a customer loyalty index that reflects multiple measurement methods and tracks them over time. This allows you to break down the customer relationship into feedback, perceptions, and issue resolutions. Ultimately, you’ll be able to see what you need to do to maintain and increase your loyal customers.

Aim for a full picture of your organization’s brand loyalty.

Measuring customer loyalty in a variety of ways gives you a more comprehensive, multi-dimensional view of your customer loyalty situation. In addition to at-a-glance scores like NPS®, a customer loyalty index can include attitudes and behaviors such as overall satisfaction with customer service, and likelihood of a customer to make a future purchase.

Capturing this data will yield many benefits, among them:

  • Producing a good view of your current standings with the customer,
  • Predicting future retention, and
  • Providing the foundation for building a loyalty profile for your customer.
Closely examine your metrics at the outset.

According to IRI, 44% of Millennials claim to be brand loyal. With their impressive purchasing power, figures like that should motivate you to keep the company-customer relationship at the forefront of your strategic planning.

What do you want your measurements to tell you? Start with the results you want to see to help you decide how to prioritize the data you collect. You will likely find you need more indicators than you thought, but taken together, all these measurements complement one another.

Studying the results of your customer research will produce opportunities to compare your organization against industry standards and your direct competitors, identify your strengths and weaknesses, and zero in on customer preferences. CSP’s Customer Experience Management solutions are designed to provide exactly these opportunities, with the added benefit of guidance from seasoned experts to help you identify what to focus on and what steps to take.


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Net Promoter, NPS, and the NPS-related emoticons are registered trademarks, and Net Promoter Score and Net Promoter System are service marks, of Bain & Company, Inc., Satmetrix Systems, Inc. and Fred Reichheld.

Left Brain, Right Brain: Aligning Internal Culture and Customer Analytics

August 5, 2016

A version of this post was featured by influential Customer Experience speaker and teacher Shep Hyken as a guest blog in August 2016. See it here.

Data inspires confidence because it serves as a rational, objective bottom line that provides order and structure to the customer experience. It appeals to the logical, pattern-oriented left brain, involved in making decisions that shape the customer experience. But customer analytics have more to tell you than scores alone. By reading between the lines, the shape of your company’s internal culture can emerge.

billiard balls in alignment, representing alignment of a company's internal cultureWhy is internal culture relevant?

Think of data as representing the ongoing feedback loop between a company’s internal culture and its customers. This loop runs smoothly when the culture is well aligned with the customers’ needs, wants, and expectations. A productive, motivated, well-informed staff produces satisfied customers, and vice versa.

If the culture is misaligned, though – if priorities are skewed, if there is distrust between leadership and employees, if there are significant obstacles to cooperation across departments, if employees don’t feel valued and morale is low – the impact on customer service is direct and immediate. Inefficient processes, gaps in information and communication, and employees who are just ‘going through the motions’ are all symptoms of a unhealthy internal culture that needs attention.

Customers tend not to tolerate these symptoms for long. Remember, a single negative interaction with your business can sour a customer’s opinion and undo a long history of positive interactions in a matter of minutes. Studies have shown that negative experiences have more staying power than positive ones; not only are people more likely to remember them, they are more likely to tell others about them, too. Social media has given customers a megaphone for complaints that they might otherwise just have grumbled about under their breath.

If data represents the left brain, culture represents the right brain.

Together, these elements form the foundation of customer experience management.

Customer analytics, used appropriately, can be the healing salve for a broken internal culture. By examining the trends, gaps, and other insights captured within the data, all employees, from upper management down to the individual customer service representatives, get a clear sense of the goals they are working toward as a team and what they can do to affect positive change.

This requires a degree of transparency between those who have access to the data and who make decisions, and those who carry out those decisions in their daily interactions with customers. A stern top-down directive given without context or reason is easily ignored or deprioritized, while one that is presented as a productive initiative backed by solid information is more motivating and harder to argue with.

Of course, transparency must go both ways if the staff is to work as a team. Employees at all levels of the company should feel empowered to ask questions, make suggestions, or otherwise participate in the shaping of the culture, and not just be beholden to policies. By valuing the voice of your employees, especially those who are in the position to directly interact with customers, you create an internal culture that nourishes the customer experience – and the data is bound to reflect that.

As a right-brain, intuitive element of the customer experience, cultural alignment can be felt as much as observed. Take this opportunity to do a “gut check” about the culture in your office and within the enterprise as a whole. Do you notice any symptoms? Have they emerged recently, or have they persisted, unattended, for some time? Do you feel empowered to do anything about them?

And remember, whether you need a complete diagnosis, a check-up, or an emergency treatment, CSP is always on call.

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.  

You may also enjoy these articles on employee coaching and training:

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.

4 Things a Net Promoter Score℠ (NPS®) Can Do for Your Business

May 31, 2016

Think about a brand you absolutely love. They’ve got a five-star product in your opinion, or you’ve fallen for their fantastic performance, or you’re super happy with how they manage their business. On a scale of 0-10, how likely would you be to recommend that brand’s products or services to a friend? If you answered 9 or 10, you can consider yourself a promoter.

This idea serves as the foundation of the Net Promoter Score℠ (NPS®).

A Net Promoter Score is a way to measure the loyalty between a company and its customers. The measurement comes from a score calculated based on the answer to this question:

On a scale of 0-10, how likely are you to recommend this product/service/company to a friend or colleague?

clipboard showing customer satisfaction scores similar to NPSBased on their answers, customers are categorized into one of the following three groups:

  • Score of 0-6: Detractors. Not likely to recommend. These customers are overall unhappy with your brand and can cause damage through negative word-of-mouth talk.
  • Score of 7-8: Passives. Somewhat likely to recommend. These people don’t hate your brand, but they’re not thrilled with it either. They might easily switch to a competitor; they lack brand loyalty.
  • Score of 9-10. Promoters. Extremely likely to recommend. These respondents love you. They are your repeat customers, and they’ll happily tell others how satisfied they are with you.

To calculate your NPS®, you take the percentage of customers who are promoters minus the percentage who are detractors. You end up with a score between -100 and 100. The higher the score, the more promoters you have, and the better you can infer your business is performing.

According to Bain & Company, which first introduced NPS®, “High scores on this question correlated strongly with repurchases, referrals and other actions that contribute to a company’s growth.” And their case studies show that the NPS® question is tops when it comes to predicting behavior. Therefore, the score is often seen as a good indicator of future growth.

When looking at your NPS®, here are four things your score can tell you:
  1. It can show what your company is doing well. Higher scores often reflect a healthy business. Results can reveal areas of strength that should be maintained or built up even further.
  2. It can uncover what needs to be fixed or improved. A lower score can indicate the need for probing into customer satisfaction or loyalty issues.
  3. It can initiate relationship building. The Net Promoter SystemSM encourages reaching out to customers to address their concerns, leading to one-on-one interactions that can be powerful.
  4. It can help motivate employees. Feedback related to your score can give your team members incentive for making improvements and providing a great customer experience.

Companies in all types of industries are using Net Promoter Score® – from financial to healthcare, tech to retail, and more. CSP is licensed to use the NPS®, as well as other metrics, to help businesses grow loyalty and customer satisfaction. To know more about how we incorporate these powerful analytics into a customer experience strategy, contact CSP with your questions.

 

Net Promoter, NPS, and the NPS-related emoticons are registered trademarks, and Net Promoter Score and Net Promoter System are service marks, of Bain & Company, Inc., Satmetrix Systems, Inc. and Fred Reichheld.

Sources:

http://www.medallia.com/
http://www.netpromotersystem.com/
https://www.netpromoter.com/

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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You Have Employee Engagement Analytics. Now What?

April 1, 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.

So you’ve been convinced of the value of employee engagement metrics. You want to see what can happen when you prioritize employee engagement. You’ve enlisted the help of an objective outside party, such as CSP, to collect information from your staff and learn what the key drivers of engagement are in your unique environment. Now what?

Data is the essential foundation of any strategy aimed at improving the employee experience. When you make decisions based on hard evidence, rather than personal opinions or anecdotal success stories you’ve read about from other managers, you’re already on the right track to effecting positive change.

Making the numbers “talk” is the next part of the journey. This is where evidence meets intuition – where data meets with the human touch. With an experienced analytical eye, the raw data begins to tell the story of your organization from the employee’s point of view.

Visualizing the Data 
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Our understanding of data is largely influenced by how that data is presented. A spreadsheet might contain all the necessary information, but often it takes a visual representation of that information for the insights within to become clear.

Bar charts, pie charts, scatter plots, and line graphs are among the most common, and most effective, ways of turning data into recognizable patterns. These days, it’s also not hard to find measurement tools that generate custom visualizations, such as CSP’s benchmarking dashboard gauges. 

BARLoyalty for websiteWhy does this matter? The exact same data can be conveyed in many different ways, and each will have an effect on how that data is interpreted. What you see is what you get; how you see it determines what you get out of it.

For example, pie charts convey percentages of a whole, while scatter plots convey the frequency of each possible response. You can neither get a bell curve out of a pie chart, nor deduce a percentage out of a scatter plot. And depending on what it is you’re measuring, a percentage may tell you more than the frequency, or vice versa. (We’ll be discussing the nuances of data visualization more in an upcoming post.)

Writing the End to the Story

Once the right match has been made between the data and the presentation, and patterns are revealed, the last thing you want to do is just sit on the intelligence you’ve gathered. Now is the time to start asking the questions that will bring this story to a satisfying conclusion:

  • What can be changed right now? While there is no “quick fix” to the overall employee experience, the data may point to one or two pain points where change can happen with the least investment of time and resources.
  • What needs more attention or discussion? Maybe the results of the survey were mixed enough that there is no obvious conclusion without a closer look, or the solution to resolve the pattern is more complex and involves input from other decision-makers.
  • Is there a larger scale cultural change that needs to happen? In some cases, the data may indicate that the internal culture of your workplace is in need of more than just a tune-up.
  • Is there anything that can’t be changed? Some things will inevitably be outside of your locus of control, or otherwise limited by the availability of resources to resolve them. What might need to change is how you address these sensitive issues with employees.

These questions can help you prioritize the drivers of engagement that need to be prioritized in your employee engagement strategy. With this information, you can begin to embrace change and reap the benefits.


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