Category Archives: Service Value Optimization and Measurement

Vixxo – Monetizing Data & Analytics

WEBINAR WITH WARREN WELLER,

VIXXO CHIEF SALES AND MARKETING OFFICER

This webinar was hosted by the Center for Services Leadership Community of Practice on Monetizing Data and Analytics.


About Vixxo: Vixxo is a leading technology-enabled asset management and business insight company providing integrated facility management solutions and services that unite asset and facility management. Through deep expertise across 100+ trades, time-tested processes, and a comprehensive technology platform, Vixxo delivers the complete asset management that allows clients to focus their energy instead on their customers.

About Warren WellerWarren Weller is the Chief Sales and Marketing Officer at Vixxo, responsible for all aspects of sales, marketing and profitable revenue growth. Prior to joining Vixxo, Mr. Weller held various leadership positions at IBM, serving as the Vice President, Financial Services and also as Vice President, Mid-Market Services. During his 25+ year tenure, he drove operational excellence and innovation across the organization.

Vixxo Case Study

Over the years, Vixxo’s core business model has evolved from providing traditional facility management services, such as lighting, plumbing etc., to offering asset management and optimization services. Moving beyond improving efficiency of traditional assets (e.g. refrigerating, heating), Vixxo has built a highly successful business model around improving efficiency of revenue generating assets, e.g. coffee brewing machines at Starbucks or the baking ovens at supermarkets. The company’s services enable Vixxo’s clients to understand how these assets perform over time, and subsequently make better asset decisions from cost and investment perspectives.

Vixxo currently supports over a billion-dollar worth of spend across 65,000 assets in over 250,000 physical locations. Its primary client segment consists of businesses with widely-distributed retail-estate portfolio (supermarkets, restaurants, convenience stores etc.), where ensuring effective asset management across all locations is a major challenge. By leveraging its expansive supplier network of over 150,000 certified local suppliers, Vixxo is able to provide high quality, consistent services in a very cost-effective manner.

Vixxo’s value proposition to the customers is driven by the company’s 15-year experience in data collection and analytics. Over the years, the company has been able to collect clean and reliable data by leveraging emerging technologies such as mobile devices (tablets, smartphones), to integrate information from clients, suppliers and service centers. Vixxo applies its deep analytics and data mining capabilities to generate insights for clients to improve their CapEx management programs – understanding which assets to repair, replace, invest in etc. for greater customer experience, product reliability and profit maximization.

In the next phase of its evolution, Vixxo is working to monetize IoT and M2M capabilities, by placing sensors inside assets to get real time asset performance information. Sensors can detect and signal issues in assets, allowing Vixxo to dispatch technicians even while the asset is still operating. Vixxo is also focusing on developing the entire IoT eco-system. This includes collaborating with manufacturers to help build assets equipped with IoT capabilities, in exchange for data & insights on asset performance.

Vixxo’s revenue model is based on charging clients for various asset management services they use. The company takes a strong position to ensure clients are paying a fair and transparent price for received services, while the suppliers are guaranteed a prompt payment by Vixxo after each servicing call. Vixxo achieves this by automating its entire work-order management process through the “Continuously Analyzed Pricing System” (CAPS) application, where each supplier locks details of each service they provide to Vixxo’s client. CAPS contains pre-determined and agreed on rates for each element of the work order management process (such as for labor, duration, materials etc), guaranteeing that clients pay a fair market price and receive an itemized breakdown for delivered services. Moreover, Vixxo uses other features such as geo-fencing and supplier rating system to ensure that suppliers provide high quality and timely service. In return, suppliers receive fair and prompt payment for their services as well as training and development.

Backed by its extensive supplier network and over 15 years of data analytical capabilities, Vixxo is a clear leader in the asset management services industry. By implementing and harnessing the IoT and M2M capabilities, the company will be favorably positioned to take full advantage of analyzing granular, real-time data for deeper insights, and to help clients achieve higher profits & operational optimization.

Seven Effective Practices For Preventing Customer Rage

By Mary Murcott

“What we’ve got here is a failure to communicate”. That is a reference to the 1967 movie Cool Hand Luke, and my assessment after rereading the findings of our Consumer Study on Customer Rage associated with complaints and the complaint handling process.

The communication problem exists on many levels – between customers and company front line representatives for sure – but also between marketing and their customers, front line and back line operations, and due to inadequate communications and policy training within call center operations. Furthermore, consumers of all ages, especially older story-telling adults, have a hard time with clearly stating their problem and their expectation for a satisfactory resolution.

As a customer experience expert, I have had the opportunity to listen to many thousands of consumer complaint calls over the years and can offer some advice for both companies and customers on how to better communicate and stop the rage.

Companies have spent billions of dollars over the years adding staff, installing additional complaint handling channels and instituting extensive training, all in the effort to improve brand loyalty through improved complaint handling. But the recent Rage Study conducted by Dialog Direct, consulting firm Customer Care Measurement & Consulting and the Center for Services Leadership at W. P. Carey School of Business at ASU indicates these efforts are failing miserably. Only 14% of complaints are truly dealt with on the first call and most complaints require 4.2 contacts to resolve the issue. Further analysis may show that certain type of complaints, e.g. credit card disputes for example may have an even higher number of contacts to resolve. Dismal results, given that the contact center industry touts an average of 70% “First Contact Resolution” on all calls. Many companies are delusional – believing they are on track with customer satisfaction. But, as any experienced consultant can tell you – averages can lie. Companies need to analyze First Contact Resolution (FCR) at a call type level (e.g. complaints vs. order requests vs. info calls), in order to truly identify what issues need to be prioritized from a procedural and training perspective.

Many company executives with whom I speak, acknowledge that their complaint handling process falls into one of two categories:

  • either, the company formally tells its employees to “do whatever it takes to satisfy the customer”, often with some approval guidelines but without formal policies, procedures or communications training;
  • or the company has prescriptive detailed complaint handling guidelines, often with suggested verbal scripts. In the former case, companies leave the customer complaint journey completely in the hands of newly trained employees or those who have not been schooled in customer experience theory and practice.

Results depend on who the customer gets – which is never optimal. In the latter case, the complaint handling procedures or guidelines are often based on what the company believes the customer wants, and is with the approval of their legal department. These procedures are often based on little if any first person research as to what the customer really wants and that would significantly improve brand loyalty. That is the revelation of the Rage Study. It provides a data-driven prescriptive framework for a Complaint Handling and Service Recovery Program that actually improves individual brand loyalty and improves word of mouth advertising!

What can companies do to prevent customer rage? 

Our advice for companies wishing to improve their complaint handling process and Stop The Rage, is to consider these seven tactics:

  1. Get real and stop measuring averages– step one, understand your true FCR at the complaint level by measuring the number of contacts for complaint resolution. Step two, is to measure product type complaint resolution. You may find root cause to the problems is at the marketing or operational level, and may have an opportunity to stop the complaint altogether – which is infinitely more critical and beneficial than improving the resolution process.
  2. Sincerely and personally apologize – 50% of executives confide to me that they do not allow their employees to apologize, as they are often prohibited to do so by their legal department. However, our Rage Study indicates that 75% of customers want an apology, but only 28% of customers say they receive an apology. Why is legal being so difficult? Because they believe by apologizing, the company is accepting liability. This is shortsighted thinking. In fact, studies show the opposite. Fewer legal actions are taken when an apology is offered, than when it is not. Additionally, it is entirely possible to deliver a “blameless apology”. Many companies have figured out how to do this. The key is a truly empathetic, not sympathetic employee who personally apologizes. But this must be carefully trained and practiced – it cannot be an “apology guideline” issued to the front line staff.
  3. Acquire and develop a common “answer database” – extracts of which are accessible by customers, front line and back line employees. Often we find that the front line employee properly handles the problem, however the backline employee either does not know the turnaround time quoted the customer, or does not understand current process or is not held accountable for the customer commitment. This database is separate policy and procedure database and distinct from a case management system. It is not an “either or” scenario – you need both. Be careful in choosing your database – All databases are not created equal – we have found an amazing one which has its roots in university systems. With the database we instituted, we have found a FCR improvement of up to 10 points.
  4. Develop internal language guides – they are crucial, especially if you are in an industry with a lot of jargon, like healthcare or technical services. These guides help you eliminate industry jargon from the front line employees and the marketing departments, by giving them alternative language more easily understood by the customer (and the trainee for that matter).
  5. Listen – Really Listen – The number one item the customers say they want when complaining (93%) – is to be treated with dignity. However, only 37% of customers say they experienced this treatment. What do customers really want when they ask for dignity? For the most part, they want to be heard and treated as important to the company. Often, when listening to complaint calls, we hear the company representative “jump in”, interrupting the customer, because they have heard the complaint many times in the last week, and think they know what the complaint is, what caused it, and how to fix it. The representatives’ intentions are always good. They are trying to quickly solve the problem, saving the customer time in having to explain it, and the company money in terms of their productivity. But this good intention usually backfires. Big time. Companies need to reset expectations with their front line representatives, teaching them about what customers really want and translating that into actions that the representatives can take. In this case, letting the customer tell their full story in their own way and in their own time, without interrupting. Then the representative can paraphrase what they heard the customer declared was the problem, and most importantly, tell the customer in a firm and confident voice: “I can help you with that!” In all the calls we listen to, this latter phrase is the reset button with the customer, that makes them feel important and gives them confidence that their problem can and will be resolved.
  6. Use the Rage Study data – regarding what customers really want, to formally design a complaint handling and service recovery program. Remember to include both monetary and non-monetary items from the list of 12 attributes that customers want in the complaint process. Adding both non-monetary and monetary remediation improves the brand loyalty from 23-37% (either monetary or non-monetary) to 73%, when both methods are employed.
  7. Make it easy to complain – Eliminate tree prompts and create a team that can triage and handle complaints. Publish a separate 800 number. Don’t hide it deep down in the bowels of your website. Advertise that you want to hear the complaints. And then act on them. The current high level of rage among customers with complaint handling processes, may ultimately reduce the real number of complaints. If most customers feel they get “nothing as a result of complaining” (63% of complainants), they will stop complaining. That is the most dangerous scenario of all. Customers will just move to your competition instead, and speak negatively about your company to anyone willing to listen. Your company won’t know why the customer left and won’t have the opportunity to remediate the problem.

In the next post, I’ll discuss what we, as customers, can do to reduce frustration and rage when dealing with disappointing customer service.

Aligning Business Model & Culture to Maximize the Analytics Opportunity

In a recent blog post Analytics in Services: Actions versus Talk, we reviewed how companies are applying big data and analytics for both internal and external uses. That review led to a survey and executive panel discussion at the November 2015 Arizona State University Center for Services Leadership (CSL) Annual Compete Through Service Symposium where we further explored adoption rates, challenges, and lessons learned.

Adoption rates

The survey of 42 CSL member-companies and Symposium attendees revealed that roughly 25% have actually deployed initiatives using this technology, 25% have not considered how they will utilize analytics, and approximately 50% are developing a plan or are in pilot. Interestingly, these percentages are consistent whether companies are trying to improve marketing effectiveness and operational efficiency, helping set service levels, or attempting to expand markets and build new sources of services and solutions revenue.

Analytics_adoption_rates

Intent

The survey also asked respondents to describe what they were doing in each of these areas, from which the panel discussed several case studies in some detail. What emerged was an interesting set of objectives that can be captured as:

  • Efficiency – improve operational efficiency and reduce risk.
  • Experience – enhance every aspect of the customer’s experience.
  • Expansion – generate new services-based revenue streams.

As noted in our prior blog, the drive for efficiency has been well documented and the data reinforces that it is the most broadly adopted.

The second area, experience, generated a great deal of discussion and it became clear that this is where much of the energy in the market is focused. Experience encompasses all aspects of the customers’ journey: understanding each as an individual, marketing more effectively, setting and attaining appropriate service levels, providing support proactively, and anticipating future needs. It was evident that for a number of respondents this was the path to revenue growth both in terms of wallet share and market share.

Which leads to the third objective, expansion. A number of technology companies are aggressively pursuing the opportunity to be suppliers of technology, infrastructure, and consulting for analytics. However, a relative few organizations are also leveraging analytics to turn the data they own/access along with their expertise to generate new services revenue streams.

The executive panel was comprised of companies who fell into both of these categories: Siemens, IBM, DuPont Pioneer and Intel.

Challenges

A broad set of inhibitors were cited by the survey respondents and we subsequently discussed during the panel. The challenges fell into three major categories, with some unexpected challenges emerging:

Analytics_Challenges

  • Data

There were two distinct sets of issues identified here. The first regarded the capture, integration, and filtering of data from a rapidly growing array of sources.
The second set of issues centered on data security/privacy/rights/integrity – and the potential financial and brand risks of getting it wrong.

  • Resources & Infrastructure

Not surprisingly, skills in data science and analytics were frequently cited. Not only acquiring a skill set that is not traditionally found in many companies, but also nurturing and retaining those critical resources in a highly competitive market.
The infrastructure necessary to support new analytical workloads and the growing volume of data was something that many respondents cited as a ‘hidden’ cost—or at least one which was not always factored in up front.

  • Business Model

The most frequently cited issues were associated with establishing a clear and compelling business model—particularly in regards to establishing new services revenue streams. The age-old challenge of competing priorities was compounded by the lack of effective means for calculating the ROI for the customer and the concerns over financial risk cited above. As one panelist pointed out, we are entering an age where data is the new currency—and yet there is no accepted methodology for measuring ‘return on data’.

Summary – Ideas to Consider

The executive panel shared their insights and made some compelling suggestions for companies considering leveraging big data and analytics to drive top line growth. Ideas that were discussed in the interactive session with the symposium attendees included:

  • Integrating internal & multiple external data sources combined with your expertise for more value
  • Identifying new markets and buyers for the services offerings based on data + analytics + expertise
  • Developing a ‘skunk works’ first-of-a-kind team to launch and experiment—avoiding the culture trap
  • Bringing on new skills and augment with university and industry programs
  • Considering building a partner eco-system to fill gaps in your infrastructure and skills
  • Establishing credible means for measuring the ROI for both the customer and the business

What You Need to Know about the Impact of Service Crises

Service crises and their impact on companies

Extreme and massive service failures. They are probably the worst nightmare for any service provider. Such crises have a profound impact on customers as the service they seek becomes simply unavailable. The scope of the problems – thousands, to even hundreds of thousands, of customers being hit at the same time – assures wide-scale media attention, damaging the reputation of the company even more. Vivid examples of such failures include JetBlue’s Valentine’s Day crisis in 2007, when over 130,000 customers got stranded; or the BlackBerry service failures in 2011 and 2012, when Blackberry owners around the globe could not access the Internet or their emails for several days in a row.

Whereas a traditional product-harm crisis still offers the possibility to trace the batches of defective goods and engage in recall actions, a mass service crisis does not have this option. All users are experiencing service failure at the same time. This drop in objective service performance (OSP) has an immediate and strong negative impact on the perceived service quality (PSQ). However, it may take much more time for companies to restore their customers’ satisfaction.

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Losses loom larger and last longer than gains

We studied the impact of mass service crises on the perceived service quality for a major European public transport provider. During the observation period, the company experienced several service crises caused by extreme winter weather, which was unprecedented in the recent past.

Consistent with the argument of the Nobel Prize winners Daniel Kahneman and Amos Tversky, we find that negative experiences – drops in service performance – have a much stronger negative impact on perceived service quality compared to improvements in service performance. However, our results also show that the detrimental impact of such crises goes beyond a stronger immediate negative impact. What is even worse for companies is the fact that this negative impact also lasts longer. Losses not only loom larger than gains, but they also linger. Any improvements in objective service performance will only have a short-lived effect on perceived service performance, whereas deteriorations in objective service performance will have a lasting negative effect on perceived service performance.

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The role of history

The ultimate impact of a mass service crisis may depend on the history of a company’s service performance. Customers can be more forgiving if the company has a good track record. On the other hand, an unexpected crisis may be of such an extreme disconfirmation of their (high) expectations that it can result in extreme anger. On the other hand, when the company has a history of bad service, customers may already have become cynical. A new crisis would not add any new information: the company is simply living up to the (low) expectations. But it may also become the final drop for these customers, infuriating them even more.

Our results show that, in case of a “business-as-usual” scenario with a relatively stable service performance, the following picture will emerge: improvements will have short-lived positive effects, and deteriorations will have lasting negative effects. In case of “sustained gains”, an upward spiral of ever better service, a new improvement will result in lasting positive effects (customer delight: the customer gets an even better performance than expected), but a sudden deterioration will have a strong lasting negative effect (extreme negative disconfirmation). When the company is already in a downward spiral (“sustained losses”), an additional deterioration will not have much effect anymore; the damage has already been done. A sudden improvement, on the other hand, will not add much either.

So, what to do?

Even though service companies would love to avoid such mass service crises, they often have little real power to do so, no matter how well prepared the companies are. Restoring the customers’ appreciation of the service quality to the pre-crisis level can only be attained by a continued service performance at a higher than pre-crisis level. A crisis will raise the bar for the future, and improving once is not enough. The customer needs to see that the company succeeds consistently in providing a better service. This is all the more important for companies with a good track record who suddenly face a crisis. Such crisis is an extreme deviation of what customers are used to, and has a strong detrimental effect. Getting back to the old pattern of good and significantly better service is crucial. A silver lining is that when one is already in a downward spiral, an additional negative experience will not further decrease the customers’ judgements in the long term.

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In sum, companies should focus on a stable (and good) service performance level. Such performance level has the best outcomes for customers’ service assessment, and takes much less effort compared to constant adjustments needed in response to peaks and troughs in service performance. A good and stable performance, in turn, is a strong argument for companies in their communication to customers, as it may engender favorable perceptions of the service quality.

This post is based on the article “Losses Loom Longer Than Gains. Modeling the Impact of Service Crises on Perceived Service Quality over Time” which is co-authored by Maarten Gijsenberg (University of Groningen, The Netherlands), Harald van Heerde (Massey University, New Zealand) and Peter Verhoef (University of Groningen, The Netherlands). It is published in the Journal of Marketing Research (Volume 52, Issue 5, October 2015; ).

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MJ Gijsenberg PictureMaarten J. Gijsenberg is Associate Professor of Marketing at the University of Groningen, the Netherlands. He holds an MSc in business engineering, and a PhD in marketing (both from the University of Leuven, Belgium).

His research focusses on the econometric modelling of marketing decisions (timing and size of investments, targeting of actions) and their effectiveness, with special attention to the over-time dynamics of the latter (due to e.g. the impact of both macro-economic and firm-specific crises on consumers’ behavior), and main focus on advertising. His work has been published in the Journal of Marketing Research and the International Journal of Research in Marketing.

He was second runner-up of the 2010 EMAC McKinsey Marketing Dissertation Award, and his research has also been awarded with a Marie Curie Fellowship of the European Commission. Recently, his paper on advertising effectiveness around major sports events was selected by the Marketing Science Institute as one of the “2014 Must-Read Articles for Marketers”.

Analytics in Services: Actions Versus Talk

The INSIGHT Group / CSL CTS Symposium Survey Panel

Ed Petrozelli - The INSIGHT Group

By Ed Petrozelli

By all accounts, the use of big data and analytics is exploding. Ironically, relatively little hard analysis has been done to assess exactly how it is being applied, and to what effect – especially as it relates to services. So, as has been observed by others, we are left to ponder the balance between talk and action.

Internal Focus

Much of the conversation has been centered on applying analytics to an organization’s internal operations in order to increase efficiency and reduce risk. There are a myriad of case studies in functions ranging from supply chain optimization to new employee candidate screening. There are also numerous examples of leveraging big data and analytics to improve the efficacy of marketing in order to increase yields, extend reach and enhance the target customer experience.

Within the realm of services, there are a growing set of proof points for using analytics in setting and managing service levels.  Examples include: service analytics for internal benchmarking of operations to make improvements; customer analytics to identify changes in service delivery; and financial analytics to determine the relative value of customers and segments.

New Services Revenue

There has been less discussion on the promise of analytics as a new revenue source. The end game should be to apply analytics in expanding the top line as much as reducing the bottom line. Of course, there are the suppliers that are in business to sell analytics software and data management solutions; however, I’m referring to companies that leverage these tools, combined with mountains of data and deep pools of expertise, to deliver new services to customers – often with innovative business models.  A few forward thinking companies are doing just that with leading edge applications such as:

  • Energy Services Companies that provide building analytics as a service to owners/tenants.
  • Learning Companies that are piloting personalized digital learning solutions based on student analytics.
  • Agriculture Companies that are harvesting a storehouse of agronomic data to offer farmers improved yields.

What we learned

To further develop insights into how companies are actually leveraging analytics, we partnered with ASU’s Center for Services Leadership (CSL) to survey member companies and registered attendees of the CSL Annual Symposium. This spans a broad spectrum of industries and services offerings. The objective is to better understand the strategic intent behind the use of analytics and the lessons learned so far. A somewhat surprising picture of adoption is emerging that reflects both potential and confusion. Our research has also yielded an interesting set of issues and critical success factors.

To learn more, join our executive panel at the 26th Annual Compete through Service Symposium on Thursday, November 5th, 2015.  Representatives from IBM, INTEL, DuPont – Pioneer, and Siemens will join with me to comment on these findings and also share their experiences and insights regarding leveraging big data and analytics to grow services revenues. Also check out our article, Generating New Services Revenues through Analytics.

How Valuable Are the Net Promoter Score and Other Customer Feedback Metrics?

customermetric_bigBy Evert de Haan and Peter Verhoef

A large and growing amount of firms rely on Customer Feedback Metrics (CFMs) to monitor the customer base and the performance of the marketing department. Examples of these metrics include Customer Satisfaction (CS) and the Net Promoter Score (NPS). Recently, new customer feedback metrics, such as the Customer Effort Score (CES), are gaining traction with a promise to outperform the existing CFMs.

While the positive relationship between customer satisfaction and firm performance, including revenue and profitability, is well documented in academic literature, most findings are mixed for the NPS. In regards to the new Customer Feedback Metrics, such as the Customer Effort Score, third party empirical proof is relatively nonexistent, keeping managers in the dark about the reliability of these metrics. Despite the lack of academic literature and empirical proof, many firms rely on a single metric, specifically the NPS, as their key performance indicator.

Our research team aimed to shed more light on popular Customer Feedback Metrics by investigating the following issues:

  1.  The extent in which different Customer Feedback Metrics are appropriate to monitor the customer base, and
  2. The effectiveness of using multiple metrics as opposed to using one single metric.

The research performed by our team proved that the NPS is as good as Customer Satisfaction in predicting customer retention. We also found labeling customers as Promoters, Passives, and Detractors works well for many firms. The NPS, combined with information regarding Customer Satisfaction, further improves the ability to monitor the customer base. Using multiple Customer Feedback Metrics is therefore highly recommended.

Table 1. Customer Feedback Metrics (CFM)

CFM Measurement
CS (Customer Satisfaction) “All in all, how satisfied or unsatisfied are you with [company X]?” (1 = very unsatisfied, 7 = very satisfied).
Top-2-Box CS The proportion of customers of the firm that gave a score of 6 or 7 on the CS question.
Official NPS (Net Promoter Score) “How likely is it that you would recommend [company X] to a friend or colleague?” (0 = very unlikely, 10 = very likely). Respondents who gave a score of 0–6 are “detractors,” those who gave a 7 or 8 are “passives,” and those who gave a 9 or 10 are “promoters.” Subtracting the proportion of promoters by the proportion of detractors provides the Official NPS.
NPS (Net Promoter Score) This is the average untransformed NPS score (0–10 range) provided by the customer.
CES (Customer Effort Score)  “How much effort did you personally have to put forth to handle your request?” (1 = very low effort, 5 = very high effort).

In our research, we surveyed an extended group of customers from 98 firms across 19 different industries. In this survey we measured three different Customer Feedback Metrics, including Customer Satisfaction, the NPS and the Customer Effort Score. Information regarding these three different Customer Feedback Metrics can be found in the above table.

For Customer Satisfaction and the NPS, we used the untransformed scores as well as two popular transformations. The first transformation, the Top-2-Box CS, indicates the proportion of customers providing one of the two highest scores on Customer Satisfaction at a firm level. In other words, the Top-2-Box CS is the proportion of customers who are (very) satisfied. The second transformation is the official transformation for the NPS; grouping customers into Promoters, Passives, and Detractors. Further detail regarding NPS can also be found in the table above.

Two years after the initial survey measuring the Customer Feedback Metrics, we asked the same customers if they were still customers at the surveyed firm. This allowed us to test how accurately different Customer Feedback Metrics can predict actual behavior of customers. Given the historical strong, positive correlation to overall firm performance and firm value, our team looked at customer retention.

The graph below shows the strength of the relationship between the different Customer Feedback Metrics and customer retention, while controlling for firm- and industry heterogeneity, customer demographics and relationship length. Our research found that all Customer Feedback Metrics are significant in predicting customer retention, since all Customer Feedback Metrics perform better than having no Customer Feedback Metric information (i.e. the bar most to the left in the graph).

Transforming Customer Satisfaction and the NPS do significantly improve the predictions. This is indicated by the higher bars of these two Customer Feedback Metrics compared to their untransformed counterparts. The difference between the Top-2-Box CS and the Official NPS is not significant, so these two Customer Feedback Metrics work equally well in predicting customer retention. When looking at the three bars on the right you can see that combing the Top-2-Box CS with one of the other Customer Feedback Metrics leads to even better predictions. The combination of Top-2-Box CS and the Official NPS leads to the best predictions.

Predictive Strength of Customer Feedback Metrics

Predictive Strength of Customer Feedback Metrics

The Customer Effort Score, although statistically significant, is the least predictive Customer Feedback Metric compared to the other predictive measures. This finding contradicts the promises made by the developers of the Customer Effort Score who stated that it would outperform both Customer Satisfaction and NPS. Although this may be the case in some conditions, on a broader level this Customer Feedback Metric performs quite poorly. Therefore, we highly recommend firms and managers not rush to adopt Customer Effort Score, especially as a single metric, until it has been objectively shown that it is a good indicator of future customer behavior and/or firm performance. Customer Effort Score, as an indicator of future customer behavior and/or firm performance, can be proven by independent (scientific) research, or tested by the firm.

In conclusion, we recommend firms to continue using the NPS to track customers and performance, but also include the Top-2-Box CS in the dashboard of metrics. This dashboard enhancement will enable firms to better monitor and predict customer behavior and firm performance. Furthermore, we recommend firms to not only measure these Customer Feedback Metrics, but also link these metrics to customer behavior and firm performance. Doing so will result in a better understanding of the consequences of changes in the Customer Feedback Metrics, and help to make a more educated decision about which Customer Feedback Metrics to include, or exclude, in the dashboard. This approach can better enable firms to financially quantify the impact of marketing initiatives, which ultimately can help improve the position of marketing departments within firms.

The article The Predictive Ability of Different Customer Feedback Metrics for Retention featured in the post was co-authored by Evert de Haan (University of Groningen, The Netherlands), Peter Verhoef (University of Groningen, The Netherlands), and Thorsten Wiesel (Westfälische Wilhelms-Universität Münster, Germany). It is published in the International Journal of Research in Marketing, Volume 32, Issue 2, Pages 195-206.

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foto evert_smallEvert de Haan is a PhD candidate at the Department of Marketing of the University of Groningen, The Netherlands. In September 2015 he will start as a Junior Professor in Marketing at the Department of Marketing of the Goethe University in Frankfurt, Germany. His research interests concern customer feedback metrics, marketing accountability, the effectiveness of (on- and offline) advertising, the customer’s online journey and the role of mobile devices play in this. He has published in the International Journal of Research in Marketing.

Peter VerhoefPeter C. Verhoef is Professor of Marketing at the Department of Marketing, Faculty of Economics and Business, University of Groningen, The Netherlands. He also holds a visiting position as professor at BI Oslo Norwegian Business School. He obtained his Ph.D. in 2001 at the School of Economics, Erasmus University Rotterdam, The Netherlands. His research interests concern customer management, customer loyalty, multi-channel issues, category management, and buying behavior of organic products. He has extensively published on these topics. His publications have appeared in journals, such as Journal of Marketing, Journal of Marketing Research, Marketing Science, International Journal of Research in Marketing, Harvard Business Review, Marketing Letters, Journal of Consumer Psychology, Journal of the Academy of Marketing Science, and Journal of Retailing. His work has been awarded with the Donald R. Lehmann award for the best dissertation based article in the Journal of Marketing and Journal of Marketing Research in 2003, the Harald M. Maynard Award for the best paper published inJournal of Marketing, and the Sheth Award for long-term impact of the Journal of Marketing in 2013. He is currently an editorial board member of the Journal of Marketing, Journal of Marketing Research, Marketing Science,  Journal of Retailing, Journal of Service Research, Journal of Interactive Marketing, and the International Commerce Review. He functions as an area editor forJournal of Marketing Research and he International Journal of Research in Marketing. He has extensive teaching experience for undergraduate, graduate and Ph.D. students. He is also involved in executive teaching on customer management and is the founder of the Customer Insights Center, University of Groningen. He is department chair of the marketing department.

Extended Service Warranties: Why Are They Purchased?

By: Rajiv K. Sinha

RajivWarranties have traditionally been used as a means to signal to potential customers that the product is high-quality and to allay customers’ anxiety over purchasing a new product of unknown reliability. However, given large increases in reliability as well as the level of commoditization in consumer electronics markets, the value of warranties as a signaling tool is questionable. Adapting to market forces and to managerial concerns, the duration of warranties and the extent of coverage have been rapidly declining. This change has led many firms to switch their focus from offering free base warranties to offering optional extended warranties, or extended service contract warranties (ESC warranties), which consumers must opt into and pay for themselves. In fact, firms have grasped the financial reality that offering extended service contract warranties is extremely profitable. These ESC warranties are lucrative for firms as they all but eliminate the firm’s commitment to free base warranties and essentially allow the firm to profit by making wary consumers share in the risk of product failure. The profitability of these warranties is striking- firms such as Best Buy and Circuit City generated nearly 50% and 100%, respectively, of their annual profits from warranty sales in 2004. Moreover, the extended warranty industry generated almost $25 billion in sales in 2011 alone.

Given that the financial benefits accrue principally for the seller, it is unclear what benefits these ESC warranties could possibly provide to consumers. On the one hand, the traditional warranty literature indicates that warranties are primarily used by consumers as a signal of product quality. Producers with more reliable products have an incentive to provide extensive warranty coverage as a means of signaling quality to consumers. On the other hand, the insurance literature is based on protecting manufacturers from assuming the product failure costs caused by heavy users. Essentially, manufacturers view ESC warranties as a means of signaling low customer quality; those customers who purchase ESC warranties are more likely to overuse or misuse the product.

Thus, the warranty literature generally treats warranties as a signaling tool to reduce overall perceptions of risk by assuaging customer concerns of product quality in uncertain markets while the insurance literature views warranties as a means of screening out heavy users of a product who will likely generate warranty costs for the manufacturer. Given that individuals are unlikely to purchase an ESC warranty unless they can easily imagine something going wrong with the core product, it is difficult to envision ESC warranties as a signal of quality.

Clearly, these different perspectives raise some interesting questions.

  • First, if traditional warranties signal value, then what do ESC warranties, which increase the up-front cost to consumers and assume that something can go wrong, signal to consumers?
  • Second, to what extent does the nature of the product affect willingness to pay for these ESC warranties?

For example, past research has suggested that people are more likely to purchase an ESC warranty for a hedonic than a utilitarian product. This raises the question of why this is the case, particularly when the potential loss of use of a utilitarian product is likely to result in more economic loss than the loss of use of a hedonic product. Moreover, utilitarian products, relative to hedonic products, are more likely to need to be replaced, as they generally serve functions which must be performed as part of a quotidian routine.

Recent research supports the assertion that perceived risk is a very important factor in the decision process, and that it is the consumer’s perceptions of risk that are driving the decision process. However, this relationship is influenced by the nature of the product– for a hedonic product, consumers really want to minimize their exposure to risk and risk-related concerns.

Interestingly, the perceptions of risk engendered by an unfamiliar brand can be mitigated by offering an ESC warranty if the warranty is from a known retailer and is offered before the purchase decision is made. Additionally, if the same offer is made by the manufacturer of an unknown brand it only serves to further highlight the risk associated with purchasing an unknown product leading to a reduction in the perceived value of the product.

In contrast, people who buy extended warranties for durable and utilitarian products (like refrigerators, which rarely fail) are buying a different kind of an insurance policy: they want to avoid feelings of guilt and regret in case of product failure and the subsequent consequences of not having coverage. This is quite different from protecting from product failure itself and may well explain the reasons for ESC coverage, despite the fact that that is seems like an irrational decision at first glance, particularly from a purely economic perspective.

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Dr. Rajiv K. Sinha (Ph. D., Penn State) is a Professor of Marketing and the Lonnie Ostrom Chair in Business at the W.P. Carey School of Business, Arizona State University. His research interests include new product development, technology diffusion, software and music piracy, product pricing, public policy issues related to tobacco and alcohol addiction, Internet based brand communities and marketing strategies for digital products. 

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