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Category: Analysis

Trust is a Must: Five Strategies to Build Trust Using Your CX Program

How trusting are you of people? Now, what if I asked how trusting you are of businesses? I have a hunch your answer may have shifted. As customer experience professionals, we need to understand why trust matters to customers and what can be done to build it.

Enter Dan Ariely, professor of psychology and behavioral economics at Duke University. In The Trust Factory, he outlines five key mechanisms that boost trust and how businesses can apply them. Taking it a step further, I share my take on how we can use his findings to build trust through customer experience (CX) initiatives.

Here are the five elements of trust and how you can put them to work in your CX program:

  1. Established Relationships
    Trusted Partner

    Research shows that people trust more when they think their interactions will extend for a longer period of time. However, flashy promotions and the ability to easily shop around on the web can lead to less loyal customers and less trust in suppliers. Now more than ever, companies must foster long-term relationships to maintain trust.

    How to build relationships using your CX strategy:Identify loyal customers through ongoing voice-of-customer research.

    • Recognize loyal customers and reward them with benefits, acknowledgments (think loyalty badges), programs or products to show you appreciate their loyalty.
  2. Transparency
    It’s no secret that transparency helps build trust. Customers like to know what’s going on in a business, and they feel more comfortable knowing company behaviors are being monitored.

    How to improve transparency using your CX strategy:

    • Encourage all feedback – good and bad – and make it easy for customers to provide it.
    • Share your findings with customers – outline what you are doing or will be doing to address any shortcomings.
  3. Intentionality
    People judge based on the underlying reasons behind a decision. That’s why intentionality plays a critical role in building trust. Customers want assurance that business decisions are intentional and well thought out.

    How to demonstrate intentionality using your CX strategy:

    • Provide the explanation behind your decisions – if you base decisions on your CX research, let your customers know!
    • Use existing or future CX communications to demonstrate that your actions align with customer values.
  4. Revenge
    Somewhat surprisingly, revenge contributes to building trust. When the possibility for revenge – or the ability to provide feedback, reviews, etc. – exists, it reduces the sense that one party (the business) has more control than the other (the customer). Allowing for “revenge” can help increase customer trust.

    How to enable revenge using your CX strategy:

    • Devise a voice-of-customer survey, online tool or feedback forum that easily allows customers to share their thoughts.
    • Promise customers something upfront if you fail to meet obligations. This could be the promise of free product returns if expectations are not met or even a free product or service to make up for it.
  5. Aligned Incentives
    Playing the role of an adviser, rather than just a seller of a product or service, can be powerful in building trust. When companies recommend things clearly not in their best interest (i.e. recommending a less expensive offering), incentives become aligned with the customer. This creates trust in company intentions. 

    How to establish aligned incentives using your CX strategy:

    • Have salespeople, account managers and other customer-facing staff encourage customers to provide their feedback, especially any negative feedback they may have.
    • Offer customers simple ways to compare your products or services with those of competitors – take it a step further by allowing customers to provide feedback on competitors in your voice-of-customer surveys. 

Now that we know the five key mechanisms of trust, you might still be asking, “Why does this matter?” Well, research indicates that greater customer trust leads to greater customer loyalty. And we all know greater loyalty can have a big impact on your business. What can you be doing today to boost trust through your CX program?

Amanda Wray

Text analytics can be hard, but does it always have to be?

No quirky title from me today. Rather, I'm going to be straight with you: text analytics can be hard. There are too many different ways to handle unstructured/textual data. People don't use language in clear, concise, consistent ways. Sentiment analysis is often inaccurate. Even with automated tools, human intervention and guidance are still required.

Really, this list could go on and on.

Creating a robust, sustainable, and accurate text analytics program is a challenge – but do we always have to look for that all-encompassing, diamond-in-the-rough-finding program? Instead, could we maybe find a quick, simple, and straightforward method to use our unstructured data? This idea has been floating around at Walker recently, as I'm sure it's been debated in many other companies interested in what their customers have to say. What can we do to simplify our analysis, while still providing valuable customer experience insights?

One possibility is to look at the most common nouns that are appearing in your data set, and then mapping those nouns to a quantitative metric or rating score available. Let's say you measure NPS, for example. You ask customers how likely they are to recommend you, and then you have an open-ended question that asks why the customer provided that rating. Using text analytics software, we can identify what the common nouns and noun phrases are in the data, essentially identifying the main themes being discussed. We can then filter those nouns by the ratings that were provided; for example to look at what Detractors versus Promoters are saying, and if the themes vary.

No manual coding. No taxonomy or rule-set creation. No extensive processing times. Minimal confusion.

Simple? Indeed. Life-changing? Probably not. However, not every single text analytics venture has to change the direction of your company – a pitfall I often see customer experience professionals encountering. Use some of these small insights to inspire an overall understanding of where your customers are coming from. Then, maybe follow up with certain groups individually to get more "layers" to the story. Start small and build from there.



Troy Powell

Reducing Friction to Deliver a Personalized Experience

A few months ago I was asked to contribute my point-of-view on a keynote address, and eventual Viewpoint for Leaders document, by EMC's Kevin Roche titled "Unlocking Customer Intimacy Through Big Data." My post is live on EMC's InFocus site, and you should definitely check it out! 

I was excited to provide my thoughts because Kevin Roche's perspective and approach fit perfectly into my philosophy on using data and analytics to make a difference. I will summarize what he said, but you should also go look at it for yourself here.

Here is the heart of Kevin Roche's message:

"I believe one of the greatest opportunities we have today is the ability to better understand customer needs, personalize the experience, and unlock customer intimacy. Although customer intimacy may look different from industry to industry or company to company, I believe all companies have the opportunity to use big data to enhance the customer experience and outcomes. At EMC, we have had great success doing this with a three-pronged approach:

  1. Invest in enabling technology that provides real-time, predictive analytics, including a company-wide data lake.
  2. Shift responsibility of data from IT to the line of businesses and design agile processes & governance to fully leverage insights.
  3. Focus on cultural psychology to empower internal teams to trust and act on big data to drive improved customer outcomes.

As I have thought through my POV on this message since I wrote it, I keep coming back to one key idea: Most organizations fail to effectively use data and analytics to personalize the customer experience because there is too much friction between the technology/data/analysis and customer-facing people tasked with delivering the experience.

This is the role of the agile processes & governance discussed above (#2). It is the oil, the lubricant that allows the machinery for unlocking customer intimacy through data to work effectively and efficiently. So read through Kevin's thoughts and my view on it, and then go create a well-lubricated system to turn your data into an personalized experience for your customers!


Amanda Wray

Playing with Emotions

If you're keeping up with the latest trends in customer experience measurement, then I'm sure you've heard that identifying and tracking your customers' emotions is important – vital, even. Recent CX research shows that emotion can influence a customer's behavior more than "effectiveness or ease," according to "How To Measure Emotion In Customer Experience" by Maxie Schmidt-Subramanian with Forrester.

Unfortunately, as the report further explains, measuring emotion is very difficult, particularly when relying only on traditional measurement methods, such as quantitative survey questions. Even planning on which emotions to measure is a chore! There are some straightforward feelings, probably those we learned to express very early on in our childhoods – happy, sad, mad – but then there are those underlying and complicated emotions that might not always be as easily explained – fulfilled, uncertain, eager. If we struggle to identify and discuss emotions even as intelligent and perceptive humans, how do we expect any sort of automation or measurement to capture that same understanding?

Well, unfortunately again, I'm still trying to figure that out, along with a big portion of CX professionals I'd assume. Naturally, I'm very interested in learning more about how we can use text analytics to identify some of the biggest, most impactful emotional experiences customers have (for the record, Forrester also suggests using open-ended, unstructured data to help with this process). We've already started playing around with an emotions model that covers some of the main touch points a customer can experience.

Speaking of touch points … I also think emotions pair quite nicely with journey mapping. After all, that's what journey mapping is, pretty much – identifying how a customer moves throughout a business and how they feel about each step in the process. Perhaps aligning our emotions measurements to the journey would be a great way to narrow down what to focus on. For example, do they feel anxiety or excitement when searching for a vendor? Are they frustrated or comforted when speaking to support, or when installing a product? Do their interactions with the company leave them smiling or shouting?

As I dig into this a little further, I think I'll be more discerning about which emotions to measure, and how they align with the whole customer journey. What about you? How do you plan to tackle this exciting challenge? Are you anxious and uncertain, or optimistic and fearless?

Troy Powell

Increasing analytics adoption in CX and the NFL

At this point, most business people will have heard the concept that "Good is the enemy of great" uttered at least once. This, of course, comes from Jim Collins' incredible book Good to Great. The idea is that very few companies do the hard work necessary to become Great because they get satisfied with being Good. 

I frequently get asked the question: How do I get my organization to make analytics a core competency? Many companies give analytics lip service, but many companies are still not very mature in the commitment to and use of analytics. For instance, a 2014 report by the International Institute of Analytics found that only 32% of health care companies have advanced beyond the 3rd level of maturity. I think one thing holding companies back from greater adoption is that current, non-analytic ways are working pretty well. If a company continues to hit their sales growth targets with their current approach, they are unlikely to go through the pain and cost of creating a cutting edge, analytically-driven sales operations group that might yield higher growth.   

For years I have been watching the adoption of analytics across major sports leagues with a special focus on the NFL. The NFL has generally lagged behind many other sports in their use of advanced analytics, and I think one big reason is a lack on incentive. Even the worst team in the sport is worth $1.4B. If you look back at the beginning of analytics in baseball (the Moneyball story), it was driven by some level of desperation. The organization was not in a good place, so Billy Beane tried something crazy in an effort to make the organization viable and successful. Unfortunately, that is often what it takes for new ideas to get a chance. We don't act until we realize something is really broken.

I think we may be seeing this tipping point in the NFL. This off-season the Cleveland Browns, one of the worst performing franchises over the last decade, hired two analytically minded executives to run their football operations. The Browns have not been a good franchise for a while, even though they are worth $1.5B. They have tried to change that in many normal football ways – new coaches, new players, new schemes –  but I think they reached a point where trying something against the norms of the NFL was less scary then continuing to flounder. Now this experiment could fail and the NFL could remain an analytical laggard for years, but I think the very act of trying will be enough to encourage other franchises to follow suit. There are already thriving analytic groups in other franchises like the Denver Broncos, Chicago Bears, and New England Patriots, but this is the first time analysts have been put in charge of personnel decisions for an organization. It will be interesting to see where it goes.

So, how does this apply to CX analysts? If we want to increase the analytic maturity of our organizations, we need to proceed along a two-pronged path:

  1. Be idealistic and champion the stance that analytics can help the organization become great. I truly believe that no organization can become great or sustain greatness in the current market context without a deep commitment to analytics.
  2. Be pragmatic: Find areas where the customer experience is broken and show the organization how analytics can fix it. 

It's not easy to do both of these, especially if you are a lone wolf or a small team, but it is important. And the three suggestions in my previous blog on analytics in the NFL might also help. At the end of the day, we all need help to do great things. Find someone who shares your vision and ask for help. And if you don't know someone who will help, come to our CX Summit in May, and you will meet a whole bunch of people, including me!


Amanda Wray

Are We Thinking of Text Analytics the Right Way?

If you’ve listened to recent webcasts I’ve led or have read some of my previous blogs, you’ve probably heard me talk about the misconceptions associated with text analytics – particularly that it’s our silver bullet, or our knight in shining armor, sent to rescue CX programs and guide us through our CX journey.

Of course, we all know that one data source and one analytical method aren’t enough to fully understand a customer base. We know that we must look across a wide variety of information to make any conclusions. However, for some reason, we tend to think of text analytics differently – it’s not treated as simply the data source that it is. It’s treated as something separate and mystical.

So how are we CX professionals supposed to fight this misunderstanding, both within our own companies or with our clients?

One idea that we’re tossing around here at Walker is that text analytics isn’t always an appropriate solution. It’s not meant for every qualitative data set, regardless of what certain marketing/sales folks would have you believe. Sometimes, we’re trying to simply manage a data set, rather than analyze it.

That differentiation might seem like semantics, and I concede that the approaches are similar. They both deal with turning unruly qualitative data sets into understandable themes or topics. They both reduce the need to read an entire data set to understand what’s being said.

Thus, the main difference lies in what the ultimate outcome of the text processing is, and how those results are used. For example, if you’re simply managing text, then you might be pleased with seeing what main ideas pop out of the data – that Products is talked about in 60% of the comments, and 45% of that set is negative; or that 90% of Service comments mention Timeliness. You’re simply quantifying the results to understand main themes.

To analyze the text, you might want to take the results a few steps further. Who is mentioning the negative Products comments? Are they more likely to talk about Product Features or Product Performance when speaking negatively? How does their feedback impact other behaviors? Are these results in-line with other metrics we’ve collected? Tet analytics asks us to fit the qualitative results into a broader CX approach or understanding; this is where human intervention and expertise come into play.

Again, I admit that these approaches are very closely related to each other, and we're still hashing out some of the details and explanations. However, I think identifying that there are different methods of handling automated text processing will help us to alleviate some of the misunderstandings we’ve encountered in our CX programs.