Customer Loyalty Management

According to Walker’s Customers 2020 study, the role of the CX professional is changing. (This study is focused on how the customer experience industry will look in the year 2020.) Several emerging leadership roles on the customer experience management team are identified. New roles will evolve, all roles will change, and the expertise, requirements, and responsibilities will be a bit different from what they are today.

For example, the role of customer intelligence advocate will require even more in the future. These professionals will be responsible for supporting the use of customer intelligence within their part of the business which will be increasingly broad – internal operations, such as HR and Finance, Product groups, such as R&D and Product Development, and client-facing roles, such as Sales and Account Management.

What does this mean for customer intelligence advocates?

• They will be key communicators within the organization. They will need to understand, socialize and “sell” customer insights to help make change happen.
• They will work cross functionally, communicating both internally and externally.
• In addition to understanding customer feedback & insights, other critical skills will include: establishing credibility, exerting influence, and of course being great communicators.

Based on a conversation with a group of customer strategists, my take is that the customer intelligence advocate role of the future builds on what is happening today - yet kicks it up several notches. More focus and dedicated attention will be needed, along with additional skills and experience. To be successful, our VoC teams will require executive support & buy-in, along with a culture of being customer centric.

Do you have what it takes?

Kitty Radcliff
Vice President

Validation involves demonstrating how customer initiatives are impactful on other authoritative sources of information that drive companies’ business success. Characteristics of world-class validation include:

• Financial linkage is actively understood and used in account planning and decision-making, such as forecast refinements.
• Integrated metrics (customer/operational/financial/quality/employee) are actively managed as indicators of overall business health.
• Clear employee-related goals are linked with customer/partner-related goals, which are aligned with strategic goals.
• Feedback is customized by area and included in incentive compensation plans.

In complex customer relationships, a company can see a huge impact by implementing a follow-up system that triggers alerts that notify account managers of customer issues and opportunities. This is all done by setting up a system that includes the following:

1. Good lists - insights are gathered from the right customers
2. Good design - to incorporate triggers to identify issues, opportunities
3. Good training - account managers understand their role
4. Good buy-in - everybody sees the benefit for them and for the company
5. Good tools - an online documentation system ensures follow up
6. Good measurement - the ROI is measured to validate the payoff

Julie is a strategic account manager. She manages a number of complex business accounts and each one of them involves multiple relationships that all are important to maintain. She is focused on the performance of her accounts and her activity is aimed at driving growth within each of those accounts. It’s personal too – her income is directly connected to the performance of her customers. She is busy, aggressive, and determined.

YOU are in charge of customer experience initiatives. It's your job to provide Julie and other strategic account managers with customer intelligence to help them retain and grow their accounts. While Julie cares about her customers, she doesn't really care much about the reports and information you provide. Here are five reasons why Julie isn't very customer-focused and why it's your fault:

• It's a hassle. Instead of viewing your information as helpful, Julie sees it as more work. 
• It's too hard to access. It's a pain to log into another system, so customer intelligence is often overlooked.
• It's too complex. You provide great information, but it's too much. She doesn't have time to wade through it all.
• It's not relevant. Too much of the information doesn't really relate directly to her work with her accounts.
• It's not actionable. You haven't provided any training, so there is no clear instruction on how to put it to use.

Simple, fast, and relevant – those are the keys to driving results through strategic account managers like Julie. She already has more tools to access, more reports to file, and more company emails to read than she desires. Giving her one more thing to do, will not help. To get Julie on-board the customer intelligence you provide must help her retain and grow her accounts, which of course will help her be more successful.

Patrick Gibbons
Principal/SVP
Walker

The last business book I was reading took a familiar path when it came to the discussion of customer experience. They raved about Zappos, Southwest Airlines, and Apple. Yawn.

Don't get me wrong - they are great companies to hold up as role models. It's just that they seem to be cited all the time. Zappos' great service, Southwest Airlines' no-nonsense approach, and Apple's awesome products. I get it and I'm impressed. I'm just tired of hearing about it. 

So what do I find interesting? B-to-B examples!

B-to-B seems to go under the radar, but I love the way the way they are more down to earth. They are not focused on a slick brand or a rock star CEO. Instead, B-to-B examples are often more practical and bottom-line focused. My favorites include:

• An account manager that salvaged a customer account when he he read their feedback and jumped into action.
• A company that cut costs by diagnosing a process improvement from a stream of customer comments.
• A channel partner that boosted sales by following up with customers that shared their interest in a new solution.

Without a doubt, B-to-B CX is different and it's time we focus more on its unique characteristics. Toward that end my organization is even planning a customer experience summit specifically for B-to-B companies. 

So please - make my day! Share your stories of B-to-B customer experience success. 

Patrick Gibbons
Principal/SVP
Walker

Through our employee loyalty program here at Walker this year, a priority that was identified was an increased focus on mentoring.  As such, we have charged an internal team to re-energize our mentoring program.  There are three primary objectives for this:

1. Stay Competitive: increased competition drives the need for continuous learning, and talent development can yield competitive differentiation in a crowded market.
2. Stay Connected: increasing remote/virtual communications, social media, etc. are decreasing human connections and relationships, and mentoring offers proven ways to share our knowledge, experiences, and best practices (often in face-to-face, personal methods).
3. Stay Ahead: mentoring is shown to improve leadership elements such as retention rates, employee morale, individual self-confidence, trust, organizational commitment, job satisfaction, accelerated leadership development, reduced stress, stronger/more cohesive teams, and heightened learning.

A 2011 Sun Microsystems survey (a study conducted over 5 years) cited additional statistics to support the advantages of mentoring:

• Mentors are promoted 6x faster than those not in a program of this type
• Mentees are promoted 5x faster than those not in a program of this type
• Retention rates for both mentees (72%) and mentors (69%) are significantly higher than those who did not participate in mentoring (49%)

In addition, our own exit interviews and employee survey results show people want to stay when they are invested in relationships within the company.  With its impact on employee satisfaction and loyalty, mentoring contributes to the Service Profit Chain linking internal development investments to business success:

As the diagram depicts, mentoring can be a contributor to higher Employee Loyalty, which translates to better customer service and customer experiences, which influence stronger Customer Loyalty, which aligns with and impacts a company’s improved financial performance leading to success of the business.

Bottom line, not only is the decision to invest in mentoring reap internal benefits (such as retention, morale, trust, stronger teams, and deeper talent), but it also is strongly related to external advantages that give a company a competitive edge and a leadership advantage.

Please share your own experiences where being mentored or mentoring someone else led to a positive outcome for you in your career.

Brad Linville,

Walker Information

In my last post I made the case that the future of business will require more reliance on powerful, computer-based analytics that interact with (not supplant) skilled data scientists who bring both analytic and business context knowledge into the process. Modern, effective customer insight analysis requires this kind of efficient cooperation between the analyst and the analytic engine to meet the business objectives of our customer insight function.

While this cooperation between analyst and the analytic tools is critical to effectively producing useful customer insights, it is equally critical to getting those insights implemented within the business. This is especially true when attempting to apply predictive analytics to the way we manage and interact with our customers. We need a system that is designed around the people for whom the insight is produced - a system that allows the insights to support the decision-making of the people and processes that directly impact your customers.

I have been part of many predictive analytics projects over the last 5 years. Nearly all of them have successfully extracted a set of useful insights that have impacted the way companies think about or interact with their customers. However, very few of them have made an impact on the specific actions taken toward specific customers, which is necessary to experience the full power of predictive analytics. For instance, I once built a model that predicted whether or not a channel partner's revenue to the OEM was going to decrease in the following year. It was a very accurate model, and it highlighted a number of key reasons behind declining revenue. The drivers of decline were immediately seized upon by channel management, and task forces were put in place to better understand and fix them. However, no one was able to find a use for the ability of the model to identify specific partners who were likely to decline in the coming year.

There are many reasons that customer (or partner) focused predictive analytics fail to impact specific decisions made about specific customers. A majority of the reasons I've encountered can be attributed to an excessive gap between the point of insight production and the point where the insights result in actions toward specific customers. In the pioneering book on enterprise decision management - Smart (Enough) Systems - Neil Raden and James Taylor dub this phenomenon the 'insight-to-action gap.'    

In my experience, there are two critical reasons that this gap exists for customer-focused predictive analysis projects:

1. The analysis project was not focused on a specific, customer-focused decision-point. Many insight projects start with the goal to see what we can find that will predict an increase in revenue. This is not a bad thing. Sometimes we need to start this way to understand where to dig further. Just don't expect these projects to yield quick and specific action.

2. There was no forethought or buy-in to the process by which insights will be delivered to the front-line employee for action. This often happens when a customer strategy group gets access to previously unintegrated datasets about the customer and has been tasked with providing insights about a particular issue like low renewal rates. They, rightly, decide that predicting who will and will not renew is more actionable than just looking for drivers of renewal, and a predictive project is launched. I fully support this thinking except there is no definition around getting the 'actionable' insight into the hands of those who will act.

The reason both these situations yield a gap between the insight and action is illustrated in the figure above. After an insight is developed, the next defined step is developing a report and presenting it to group of managers or executives. We bring our amazing project and results, and lay it out for them to approve or disapprove of. The best case is that they approve and say, "We need to use this information to intervene and stop the predicted bad things from happening while capitalizing on the predicted good things!" Then what? Then a group of people, which may now include at least a manager of those who will take the action, is convened to create a strategy for operationalizing the insight. By the time the insight actually impacts a customer it could be months later.

Thankfully, there is a better way, and it's all about beginning with the end in mind. Start by defining the specific customer outcome you want to predict - not something broad like customer revenue, but a specific aspect like customer spend on new products. The next step involves identifying the decision or interaction point at which you will intervene. This requires involving the group who will be responsible for the intervention and designing the process/system to do it. Then we move on to identifying and integrating data, running analysis, putting the insights into action, via the pre-defined and agreed-upon process, and validating the impact. An upcoming post will discuss this process in more detail, but the most important step is knowing exactly what to predict and what you will do with that prediction when it comes. Without that, your well-crafted insights are likely to stagnate. We need to mind the gap.

I saw this article this week on how much time is wasted at work.  According to this article, almost 50% of employees waste between one and five hours a day on non-work websites and 64% visit non-work websites every day.   I have seen articles like this before, especially when college basketball tourney is going on and how much time is wasted, blah, blah, blah.   The article states, "Yet time is precious, and every hour spent on non-work related activities impacts the bottom line."  Really, is it really impacting the bottom line?  How do you know, are they not getting their job done, THAT is the question that needs to be asked. 

My question to all of this is, "Who cares?"  If organizations are giving their employees specific objectives to accomplish, and then ensuring those tasks are accomplished and if not, working to determine what changes need to happen to ensure the tasks get done, who cares how much time is spent on non-work related websites.  This is the entire problem I have written about before on caring too much about the hours worked and caring too little about what actually gets accomplished.  I have a buddy of mine who was just told to really stay at the organization, he needed to work 60 hours a week.  What about telling him the specific things they needed him to accomplish in the next 90 days and then let him figure out how long that will take. Yeah, he left and found a better job.

However, I do like the last page of the article where it mentions the problem being employees aren't challenged enough.  That I would agree with.  And I really like this "[t]here are those who are fully engaged in their work, who are given incentives to do better, who are satisfied with their career, are not bored — these are all reasons why people work hard, and the converse is true. If they’re bored or unsatisfied or feel like they’re working too many hours or are not motivated enough, that’s management’s role to try to fix that."  Although the employee has a role in this too, they can't just sit back and expect their boss to notice they are bored.  They need to be proactive and determine their career path and then take steps to move along that path. 

As January comes to a close, I thought it might be beneficial to pause for a moment and provide some thoughts on what trends we see emerging in the science of customer listening. Over the next several blogs, I will comment on a key trend and discuss the implications for customer strategists. Before delving into our first prediction, we need to discuss what we saw in 2012 from a customer loyalty perspective.

Loyalty in 2012: More of the Same

Source: Walker calculations based on data from http://finance.yahoo.com

It may go without saying that 2012 was a volatile year – the chart above shows the progression of a $100 investment on 1/3/2012 in each of the major stock indices – the Dow 30, S&P 500 and the NASDAQ Composite. The values are not as important as the shape of the curves – it demonstrates that 2012 had many ups and downs in the market. There are a number of reasons for this – the broader global economy, geopolitical unrest, the year-end drama related to the fiscal cliff, and so on. In this period of volatility, the impact on customer loyalty is not surprising – among Walker’s client base, the percentage of customers who are Truly Loyal was flat between 2011 and 2012; on a quarterly basis, we saw more variance in the loyalty scores with a drop in the last quarter of the year.  This is not isolated to Walker; many of the other industry benchmarks suggest that customer sentiment was flat at best in 2012 (and some have not yet published Q4 results).

Source: Walker Benchmark Database, B2B-Oriented Programs

Is Loyalty Dead (or Even Achievable)?

Despite some claims that customer loyalty is dead (or dying), I would offer that loyalty is still achievable – however, the bar is higher. The underlying reason for this relates to the divergence of expectations vs. reality – that is, as the economy has tightened, funds have become more scarce; this scarcity means that buyers are more selective in how they use their funds. This results in customers not only being more price-sensitive, but they also have heightened expectations on what they will receive. At the same time, providers have been forced to scale back on resources (as a result of constrained growth and/or uncertainty in the market), which ultimately means that they are ill-equipped to deliver on these expectations. This creates a “perfect storm” of dissatisfaction, which means that customer loyalty is harder to achieve.

So, this brings me to my first prediction/forecast for the coming year:

Quarterly loyalty scores will remain volatile as long as there is excessive uncertainty in the markets and/or the economy continues to be challenged.

What can we do about this? I would offer the following strategies for consideration:

1. Review your workflows to ensure an “outside-in” perspective – Conducting a review of the experience that your customers have with your organization can be quite revealing. Conducting a customer journey mapping exercise can help to highlight where gaps exist and can assist you in re-tooling processes to ensure an optimal customer experience. This is particularly critical among companies that have recently merged with or acquired another company.
    
2. Communicate – both externally and internally – During periods of economic turbulence, it is better to over-communicate. From a customer perspective, seek to gain an understanding of not only their expectations, but also what drives those expectations. Understand how their business works and how your products and services can help them to achieve their goals.
   
   From an internal perspective, reinforce the role that customer centricity plays in your strategy; on the front line, managers should spend time with their direct reports to identify how to integrate customer perspective into their day-to-day operations. Above all else, though, leaders must walk the talk – leading by example communicates better (and louder) than words.
    
3. Control what you can control – Much (if not most) of the volatility in the markets are outside our control; it is tempting, then, to throw our hands up and surrender – that is, just let the chips fall where they will.

Rather than take a sit-and-wait approach, we would recommend that you identify (from the steps noted above) what aspects you do control – for example, you control how your organization approaches the customer experience, you control how you choose to innovate, and you control how you respond to competitive threats. Focusing on what you control – and then acting on it – can be much more empowering that waiting for the winds of change to blow in your favor.

Even though customer loyalty is more difficult to achieve in these economic times, those firms who successfully crack the code will find that they have one of the few sources of long-term sustainable competitive advantage in their strategic arsenal. I would argue that the payoff is worth the effort necessary to realize customer loyalty.

Over the next few blogs, we will review the other factors that I am forecasting will be the trends and themes that customer strategists should be thinking about in the coming year. In the meantime, I would be eager to hear your thoughts – what are the themes and trends that you are tracking?

Mark A. Ratekin
Senior Vice President, Consulting Services

Five things a CEO should know from their customers:

• How much revenue is at risk? Aligning Voice of the Customer feedback with purchase behaviors pinpoints how much revenue is at risk and where there is potential to grow market share.
• How do we stack up?  Customers can provide insight into how the organization performs against the competition.
• What’s driving customer loyalty? With an understanding of what influences customer loyalty, executives can allocate the appropriate resources.
• What are the changing needs of customers?  Design with the customer in mind by understanding their current and future needs.
• What about key customers? Customers have unique needs and companies need a system for retaining and growing their most important customer relationships.

For the past few weeks, it seems as though all people are talking about in the US is the potential for going over the “Fiscal Cliff”.  However, I would like to suggest taking time to reflect on another topic.  This is when we traditionally reflect on the year past and look forward to the year ahead - so it is also a good time to reflect on your customer strategy. 

Take stock of what went well and what was accomplished in 2012. And, identify obstacles and root causes for those things that didn’t quite go as planned.  Ask yourself: What were our goals?  Which goals did we achieve and why?  Armed with that information, you will be better equipped to determine what to do differently this next year.

As a customer strategist, you might consider the input from others when answering those questions and in helping to guide what you will do differently (aka “new year’s resolutions”) for 2013.  A great example is a customer experience manager who is focused on bringing the voice of the customer into all areas of her organization. She is in the process of actively soliciting feedback from key stakeholders, as well as reviewing progress on action plans that have been implemented based on customer feedback.  Her goal is to ensure awareness and understanding of the information and insights, how it is being used, and to identify ways to have even more impact next year.

Spending time to reflect on 2012 will help you to refine your VoC strategy and set the direction to achieve 2013 goals.  

"When obstacles arise, you change your direction to reach your goal; you do not change your decision to get there.”  ― Zig Ziglar

Kitty Radcliff
Vice President, Consulting Services

A recent TED Talk by Shyam Sankar (also the subject of this recent blog post by Leslie Pagel) nicely argues that man-machine cooperation is the real story of technological development. In a 2010 article, chess grandmaster Garry Kasparov, who famously played IBM's Deep Blue in the late 1980s, relays a wonderful anecdote illustrating the power of this symbiosis between humans and computers. In 2005, a freestyle chess tournament was held where humans and computers could work together or play separately. At this point in history, a computer program running on a standard laptop could routinely beat many grandmasters. However, this freestyle tournament produced two interesting and relevant results:

1. a strong human player with a weak laptop soundly defeated even the best stand-alone chess computer, and
2. the overall winner of the tournament was not a grandmaster with a powerful computer, it was a pair of amateur chess players using three relatively weak laptops. 

According to Kasparov analysis, the team's winning edge was a superior interface between all humans and computers that effectively counteracted the superior chess knowledge and/or computational power of their opponents.

From this story both Kasparov and Sankar conclude that the decisive factor for determining the analytic capability of any human-computer combo is the friction between humans and computers. By designing a better interface that reduces the friction, you increase the analytic capabilities derived from the same human and computer at an ever-increasing, convex, rate.

While I completely agree that designing friction out of the interface is a decisive factor, I think there is one critical element behind the success of the human-computer team that both Kasparov and Sankar overlook - the rules of the game favored a cooperative strategy. The rules of freestyle chess require players to make moves much faster than regular chess. Therefore, the ability to have a computer crunch the data and provide the human player with a short list of potential moves is a critical advantage. Given more time between moves, the experience, knowledge and creativity of a chess grandmaster begins to override the speed, computational power, and efficiency of a computer chess program.

These two elements of success in chess are perfectly relevant to customer predictive analytics and customer-focused decision-making.

• First, useful predictive analytics require the smooth interaction of a skilled data scientist and a powerful, but usable, analytic workbench and process. It is not uncommon for an analyst to only spend 10%-40% of their project time actually running analysis and extracting usable insights. The rest of their time is spent transforming and merging data or getting outputs into usable/shareable formats. We need more work on reducing the friction between the analyst and the analytic tools if we are to realize the full benefit of predictive analytics. 
• Second, data scientists need to fully understand the context they are working in and the rules that apply to it so they can apply predictive analytics in the right place and using the right approach. For instance, conducting a predictive analytics project within a strategic accounts group with a small set of accounts and deeply embedded account managers is not going to produce as much return as a similar exercise in an outbound call center where fairly inexperienced sales reps are each responsible for hundreds of accounts.

The future of business will require a reliance on powerful, computer-based analytics that easily interact with (not supplant) skilled data scientists who bring both analytic and business context knowledge into the process.

Interest in capturing and quantifying the voice of employees is growing – and in this case, I’m not talking about "employee satisfaction" but rather "voice of the customer" THROUGH the employee’s view point.

This is something companies do all the time, but often in a more anecdotal way.  Some voices are inevitably much louder than others, and often times the anecdotes are not strong enough to persuade quick actions.  Capturing the employee perspective in a more thoughtful, organized, and balanced way can become far more compelling.

An example – recently we’ve developed a targeted study aimed at understanding the factors behind a handful of very large lost deals from the perspective of the sales team.  In this study, we’ll be able to organize a lot of different inputs and learnings about the competition, and make that info available to the sales force at large to help strengthen their positioning on future deals.  We’ll also be able to quantify the loss drivers – price, value, solution, etc.  What’s more, I think we will also gain some important goodwill and buy-in to their overall VOC architecture from this critical internal stakeholder group as a result.

Now, you’ll see the obvious push-back that some VOC managers will face --- Why do we need to debrief?  We’ve got to stay 100% focused on the next deal...   A colleague of mine put it very astutely when he countered … "Tell me about any NFL coach that doesn’t watch reels of film after a game loss."  There’s always something we can learn from our experiences if we take the time to analyze the data in an objective way.

Krista Roseberry

VP, Consulting Services

Walker

Below are five tips for building a great account-level contact list:

1. Get the account team engaged in the process. Without their involvement the results might not be considered valid or valuable.
2. Let the customer participate too. Having someone from the customer organization add, modify, and delete contacts helps secure their buy-in.
3. Identify a good starting point. A CRM system is a good starting point. Another great starting point is the contact list from a previous survey wave or another initiative.
4. Make the process as easy as possible. Use a simple Web form to collect customer information from account managers.
5. Provide clear and simple instructions. Create a quick start guide.

One of the first rules of acting on customer insights is to simply let them know that you're listening. Unfortunately, too often, customers feel like their feedback goes into a deep dark black hole.

Here is a real-world example: I attended a meeting recently with a small group of customer strategists from technology companies responsible for their voice-of-the-partner programs. Also invited to the meeting were a small group of value added resellers (VARs) - precisely the individuals that they commonly collect feedback from. At some point in the conversation one of the customer strategists innocently asked if their partner managers ever comment on the feedback provided by the VARs.

Silence. Nothing. All the VARs just shrugged and said, "Nope."

The customer strategists groaned as if to say, "Really? I put all this effort into collecting insights from our partners, analyzing what they have to say, and deliver it to the partner managers and they don't even mention it to the partners?!" Their frustration (and embarrassment) was obvious.

Several common obstacles could be the cause of this:

1. They aren't aware. Since they don't know about it or don't receive feedback from their customers, naturally they're not going to mention it.
2. They don't understand it. They may receive reports from the person running the customer insight program, but that doesn't mean it makes sense to them. If they don't understand it, they're not going to talk about it with customers.
3. They don't believe it. They may receive the information, but if it isn't relevant or they don't trust it, they are not going to bring it up with their customers.

Ultimately, this comes down to communication and training. Every customer strategist has the responsibility to make sure the voice of the customer gets put to use. But to do that you have to make sure everyone is aware of it, understands it, and believes in it.

Only then will they start a conversation with their customers with those all-important words, "I heard what you had to say!"

Patrick Gibbons
Principal, SVP
Walker

In a recent Harvard Business Review article, researchers set out to determine which types of hotels are more likely to post fake reviews on websites such as Yelp!, TripAdvisor and Expedia. They found that independent, small-owner, and small-company hotels appeared to be more likely to manipulate reviews on websites.

What is the key lesson for this finding for customer strategists? I would argure that it is less about these specific findings and more about a basic aspect of a customer listening program – that is, are the results accurate and reliable? In other words, do the results reflect the overall population (and, by extension, what population do the results represent)? I have written on numerous occasions about the importance of the quality of customer lists (see here, here and here for examples); what I would add today to the discussion is the point that diversification matters.

There are two ways to define diversification in this context – first, how much variety is included in the customer list within a given account? This clearly aligns better with a B2B customer listening program – for example, do we have a good mix of end users, decision makers, and executives for each account in our listening data? Having a variety of contacts from the same account will yield, in the words of James Surowiecki, the “wisdom of crowds.” This means that the group in total is generally smarter than any one participant in that group. Sampling theory tells us that as the base size of a study increases, the error around the mean estimate will be minimized. This is applied in the context of a total sample, but it can also apply to the findings from an account – think of each account as a sub-sample. By getting a variety of perspectives from customers within each account, we increase the likelihood that we will be getting a clearer picture of the health of those accounts. This can provide clues to astute strategic account managers – for example, if there is significant variance in the scores from within an account, it may speak to confusion that should be teased out and/or managed; this can often lead to incremental sales opportunities.

The second type of diversification relates to how we listen – when customer loyalty research was in its infancy (called customer satisfaction research at that time), we asked customers for feedback because there were no other mechanisms to gather those perspectives. CRM systems did not exist that allowed us to tie together disparate data to arrive at “the big picture.” This is why we had to ask about which products a user was familiar with, even though the customer would (rightfully) argue that we should already know that information – there was no way to tie all the data together.

The world is changing, though. Not only are CRM systems providing a more well-rounded view of customers, there are new and different ways of listening to – and analyzing – customer feedback. In the same HBR article, the authors talked to doing a “web scrape” to gather data on the hotel ratings. In addition, we can scan data in Twitter, look at Facebook pages for the number of “likes” a company has, and so on. Operationally, we can link customer behavioral metrics as well as internal quality indicators to our listening data. We can literally swim in the data.

All of this data prompts some pundits to proclaim that the need for customer listening via surveys is gone. I could not disagree more. It is certainly getting more difficult to gather customer feedback – response rates are more challenging to achieve, and the proliferation of DIY survey tools means that anyone can send a survey, regardless of how bad it is – but to say that surveys are dead is a bit extreme. I would argue that this diversity of data sources provides an opportunity to gather, analyze and understand customer sentiment from a variety of perspectives, which is valuable. My colleague Jen Batley recently wrote about the uses and challenges of this. The bottom line: we have many sources of customer data to rely on, and we should be wary of focusing on just one. Making the connections among these sources means we increase our odds of understanding what makes our customers tick.

Mark A. Ratekin
Sr. Vice President, Consulting Services

Last week I was at the TSIA’s 2012 Technology Services World event in Las Vegas, where I had the pleasure of speaking during John Ragsdale’s Power Hour session: Big Data – Three Inspiring Stories of Service Analytics.

Walker used our time to play a new version of Face Value (the customer card game) specifically developed to make a few key points about how, with the convergence of cloud, consumption economics, renewals-based revenue, and big data, it is more critical than ever that companies have an alerting system founded on customer analytics that will trigger attention towards customers at real risk of not renewing their business.

Participants agreed there were two critical takeaways from the session:

1. Having the right data matters.
   There are hundreds of potential pieces of information to consider about any one customer, but not all of them will have the same influence on their likelihood to continue to do business with you.  Customer analytics identifies what matters.
    
2. The more data you have, the harder it gets to manage.
   Even if you have all the ‘right’ data about customers, the more of it there is, the more challenging it becomes to consistently assess renewal risks in a timely way.  An automated customer analytics-based tool makes all the difference.

By harnessing the power of big customer data sets, companies can establish a ‘radar’ founded on proven customer predictive analytics that considers all the relevant information and flags customers in need of proactive intervention before it is too late.

Predictive analytics leverages a variety of techniques that use current and historical data to make predictions about the future. Predictive analytics has tremendous value for customer experience management by confidently detecting unknown or future issues to mitigate risks and take full advantage of growth potential.

Below are three examples of how predictive analytics can be used:

• Predicting customers who are not likely to renew enables the organization to take preemptive actions to increase renewal potential and drive top-line growth.
• Predicting customers who are likely to purchase new products creates efficient targeting and faster market penetration of emerging technologies.
• Intervening with customers who are likely to be dissatisfied with their support case mitigates the negative impact on overall loyalty to the company.

Although often overlooked, an essential element of well-run customer-focused initiatives is good communication. Too often, companies can get caught up in the details of gathering customer insights and analyzing data, but forget to really get the word out within the company. This leads to weak engagement, inaction, and a lack of measurable ROI.

Here are three quick ideas to avoid having your customer initiatives getting lost in the clutter of all the other things going on in your company.  

Brand it. Come up with a good name and even a logo to represent your customer initiatives. Too often companies resort to just calling it the customer satisfaction survey or the customer loyalty program. Not very inspiring is it? Here is a previous blog to help generate ideas.

Plan it. Too often, a good communication plan is an afterthought. Create a plan that identifies the audiences, the messages, and the vehicles you are going to to use to get the word out.

Deliver it. Communication is not one-size-fits-all. You need to develop customized reports for various stakeholders. Each individual needs to understand the customer insights they receive and know what action needs to be taken.

Companies today have so many important initiatives that are all competing for attention. For customer strategists to implement results-oriented initiatives, communication is an essential element.

Patrick Gibbons
Principal/SVP
Walker

In this week’s season premiere of “New Girl” (Tuesdays on FOX), one of the main characters, Schmidt, after a summer of problems, decided he needed to re-brand himself and thus threw a big party inviting everyone he knew to attend and be introduced to his new “dangerous” personality brand (complete with martial arts-style swinging fireballs).  I found myself thinking, no one would really go to this extent to re-brand themselves.  Correct, no individual person would – but companies are constantly concerned with their brand(s) and what they say about the company.

By its very nature, brand research is about differentiating one’s brand from others in the market, understanding a customer's choice leading to purchase, and ultimately being able to charge a premium price for your products, services, or solutions.  This set of expectations is different than other types of traditional customer market research.

I’ve recently been involved in two separate types of brand research, each with distinctly different objectives and designs.

Two Flavors of Brand Research

• Brand Discovery – objectives here are to understand various attributes; associations with each brand; position relative to others’ brands; who owns or doesn’t own each attribute; and how to use this by segmenting and profiling prospect/customers.  This leads to different strategies to address each segment and profile.

• Market Tracking – objectives here are to awareness, familiarity, consideration, and purchase choice; understanding awareness of advertising and resulting behavioral effects; as well as competitive positioning related to these objectives.

Have you seen or conducted other types of brand research?  What did you learn?  Please share and discuss.  Take a page out of Schmidt’s book and determine how you want to manage your brand(s).

Brad Linville

Walker Information