Forrester Loyalty Metrics

3. How likely are you to switch from us to another provider for these products or services?  Not at all likely, Slightly likely, Somewhat likely, Moderately likely, Very likely

Customer Experience Management with the Forrester CxPi

Perhaps you could have safely ignored CEM (Customer Experience Management) three years ago, but you certainly can't now:

• Customer experience is growing up; it's been important to firms for long enough that your competitors are differentiating themselves on experience.
• The recession is strengthening the correlation between customer experience and customer loyalty, across all 12 B2C industries Forrester Research studied in its report "Customer Experience Correlates to Loyalty" (published February 17, 2009). 

As CEM comes of age, most businesses will cut other areas disproportionately more than they will cut areas that impact customer experience.  
So customer experience is more important than ever, but how should you measure your customers' perceptions of the experience you provide?  The service quality gap model is more useful for identifying areas that need improvement than for tracking overall sentiment. For this purpose, you might want to consider the Forrester CxPi, which is a succinct measure for evaluating customer experience that you can readily adapt to your own research.
Forrester calculates it customer-experience index using the net score for three key questions:

1. Usefulness - Thinking about your recent interactions with these firms, how effective were they at meeting your needs?  1 - Didn't meet any of my needs to 5 - Met all of my needs
2. Ease of Use - Thinking about your recent interactions with these firms, how easy was it to work with these firms?  1 - Very difficult to 5 - Very easy
3. Enjoyability - Thinking about your recent interactions with these firms, how enjoyable were the interactions?  1 - Not at all enjoyable to 5 - Very enjoyable

The individual indexes for each measure are calculated by subtracting the percentage of a firm's customers that reported a bad experience from the percentage that reported a good experience:  take the percentage of consumers who selected one of the top two choices (4 or 5) and subtract the percentage of consumers who selected the bottom two choices (1 or 2).

To calculate the overall CxPi, Forrester Research averages the net scores for all three questions. An example:



Forrester provides the following guidelines for judging a CxPi:

• Excellent: 85%+
• Good: 75% to 84%
• Okay: 65% to 74%
• Poor: 55% to 64%
• Very poor: <55%

What makes the CxPi an outstanding metric is its strong correlation to loyalty. Depending on industry, Forrester reports a very high correlation to willingness to repurchase, a high correlation to likelihood to recommend, and a medium correlation to reluctance to switch.

The minor disappointments with the index relate to the choice of scales. Unfortunately, the choice of scales is inconsistent: ease of use is a bipolar scale instead of a unipolar scale like usefulness and enjoyability. And scales that label each point have greater reliability and validity, especially among less educated consumers, than scales that label only the endpoints, as done by Forrester here. But those are minor quibbles. While the CxPi was only created in 2007, it has already emerged as a valuable and important index.  

Forrester makes available a complimentary copy of the 2008 results to the Customer Experience Index. This report provides the results for 113 organizations in a dozen different industries, making it an excellent document to use for benchmark comparisons of the customer experience your organization provides.

CSAT, the Public Domain Customer-Satisfaction Question

Many organizations use the following question, often called CSAT, to measure customer satisfaction:

What is your overall satisfaction with our company?
1. Very dissatisfied
2. Somewhat dissatisfied
3. Neither satisfied nor dissatisfied
4. Somewhat satisfied
5. Very satisfied 

This question has the twin advantages of brevity and familiarity; it is recognized and easily answered by most respondents. Any adult who has taken a survey has most likely answered a form of this question before.

CSAT is traditionally analyzed by tracking over time the percentage of "Satisfied" respondents, i.e., the percent who answer 4 or 5. Many organizations are happy to see that 70-80% of their customers are satisfied and feel little sense of urgency to make improvements so that satisfaction exceeds this level. When phrased as above, the satisfaction question ignores significant research into how to structure rating scales to provide the greatest reliability and validity (see the abstract of "The Optimal Length of Rating Scales to Maximize Reliability and Validity" by Jon Krosnick and Alex Tahk, which studied 706 tests). The CSAT question should instead be asked in one of the two following ways, with no numbers presented:

What is your overall satisfaction with our company?

• Not at all satisfied
• Slightly satisfied
• Moderately satisfied
• Very satisfied
• Completely satisfied 

Or:

What is your overall satisfaction with our company?

• Completely dissatisfied
• Mostly dissatisfied
• Somewhat dissatisfied
• Neither satisfied or dissatisfied
• Somewhat satisfied
• Mostly satisfied
• Completely satisfied 

Mere satisfaction alone is not enough: the key top-line number is the percent of respondents reporting themselves to be "Completely satisfied". This is a far more important metric. In the seminal paper, "Why Satisfied Customers Defect" by Thomas O. Jones and W. Earl Sasser, Jr. (covered in this description of the "folk" Apostle Model), the authors report that for Xerox completely satisfied customers (rating of 5) were six times more likely to repurchase over the next 18 months than somewhat satisfied customers (ratings of 3-4).

Your first survey to report this statistic will certainly report a much lower percentage than you had hoped for, giving you a meaningful metric to track your performance against. Use the top-two percentage on the five-point scale for marketing purposes, and to compare yourself to other firms' self-reported numbers, but use the "completely satisfied" percentage to measure the results of your initiatives and innovation.

If satisfaction is just one part of the customer experience and loyalty survey you are conducting, the CSAT question can be an effective measure.

By itself, though, a single question can be very volatile from measurement period to measurement period. As a result, most professionally commissioned customer-satisfaction reports provide a customer-satisfaction index, derived from two to four questions. The American Customer Satisfaction Index is the most famous of these and is useful if the principle concern of your research is customer satisfaction.

Net Promoter Score’s Angels & Demons

There’s something devilish in the air. Wednesday night my wife and I watched a Dr. Who episode involving a demon, Thursday afternoon I attended a webinar where Fred Reichheld said market researchers thought he was the devil, and this morning my son complained that his camping trip meant he was going to miss tonight's premiere of Angels & Demons.

Since you probably don’t care about my family’s viewing habits, let’s turn to Fred’s presentation. Fred showed a slide with his face superimposed onto a cartoon devil, claiming that he has become the enemy of traditional market researchers. Among the criticisms he reported:

Well, presenting an oversimplification of legitimate criticism of NPS is one way to attempt to inoculate your listeners from paying attention to that criticism. I thought it was amusing, but I will play along. In fact, I will play devil’s advocate and give Fred halos instead of horns for these contributions to loyalty research:

That last is important. As he said: “NPS is an 'open source' system: you are all welcome to use it! You don't have to pay me to use it.” (I could have done without his next statement: “Therefore its bad business for market research, and why they call me a liar.”) But if you’ve ever gone to implement the five questions of the Secure Customer Index® or the one question of the Customer Effort Score™ and realized you couldn’t because they are proprietary, you’ll appreciate that there are no such restrictions on the Net Promoter Score.

Oh, and even if you liked the book (Angels & Demons, not The Ultimate Question), my advice is to skip the movie. This review of Angels & Demons said it all for me: "Hanks returns to the dullest role of his career, under the direction of Howard, who takes the material as seriously as a kidney stone on the way out." As for Fred, I’ll leave it to you to decide whether he is an angel or demon.

Customer Effort Score™: A Loyalty Predictor for Customer Service Interactions

Though it was little noted at the time, in December the Corporate Executive Board introduced a new loyalty metric, the Customer Effort Score^TM:

Exceeding customer expectations has long been the measure of success in customer service interactions-89% of customer service executives believe that "delighting the customer" will lead to increased loyalty. New research by the Corporate Executive Board's Customer Contact Council, however, reveals the alarming truth: exceeding customer expectations results in virtually no loyalty gains. In fact, service and support centers have little stake in building customer loyalty at all.

"The probability that a service interaction will drive disloyalty is approximately four times greater than the chance it will create any positive loyalty impression. In other words, as a function, customer service typically plays on the ‘negative side' of the loyalty field," said Matthew Dixon, Ph.D., Managing Director of the Customer Contact Council. "Most service executives are using traditional customer satisfaction (CSAT) or the more recently popularized Net Promoter® Score (NPS) to gauge loyalty in service interactions, but we found these metrics fail to capture the most powerful driver of disloyalty-the amount of personal effort a customer has to put into the service experience." In light of this new understanding, the Customer Contact Council has developed an original metric that is far more predictive of loyalty than either CSAT or NPS. This new metric, the Customer Effort Score^TM (CES^TM), is based on a single question that determines the degree of required customer effort during a service request.

Unfortunately, the actual wording of the single question is only available to Corporate Executive Board subscribers. The Customer Contact Council has published a presentation, "Shifting the Loyalty Curve: Mitigating Disloyalty by Reducing Customer Effort", which provides an introduction to the research. In a blog post, the author of the study provides additional detail about customer effort by service category and industry. The blog Every Experience Counts reports on the metric in a little more detail.

The selection of the CES metric was derived from a survey using a convenience sample of almost 18,000 customers of CEB clients. CES suffers from similar problems to NPS: it's proprietary and the data used for deriving it is not publicly available, making it difficult for third parties to verify the claims about the methodology. That said, I would encourage any contact center managers who are already CEB customers to check out the full report. It requires little effort (ahem) to integrate the Customer Effort Score into existing transactional surveys, which certainly makes it worth piloting.

Most surprising to me was the finding that 89% of customer-service executives look to customer satisfaction as the primary driver of customer loyalty. After all, it was over 13 years ago, Jones and Sasser published their landmark paper "Why Satisfied Customers Defect", introducing the Apostle Model and demonstrating that customer satisfaction and loyalty were orthogonal. Clearly the word is not yet out that satisfaction is but one component that drives loyalty.

Net Promoter Score (NPS) Criticisms and Best Practices

The Net Promoter Score®, popularized by Fred Reichheld in his book The Ultimate Question: Driving Good Profits and True Growth, is one of the simplest loyalty measures. Customers are asked "How likely is it that you would recommend us to a friend or colleague?" and then provide a rating from 0 ("Not at all likely") to 10 ("Very likely").

The measure is called the "net promoter" score, because detractors are subtracted from promoters, to provide the estimate of how many more promoters than detractors the organization has. Detractors are defined as respondents rating their likelihood to recommend 6 or less, with promoters only those who rated their likelihood a 9 or 10 (respondents who selected 7 or 8 are considered neutral). The NPS measure can run from -100% (0% promoters, 100% detractors) to 100% (100% promoters, 0% detractors), with typical measures in the 30-40% range.

 

Traditional customer-satisfaction measures typically omitted willingness to recommend, instead focusing on aspects like perceived value, customer satisfaction, corporate image, and rational and emotional commitment (see the ACSI model).

Avis, HP and IBM are among the many prominent adopters of NPS. The benchmark is popular for its simplicity, and Reichheld claims it correlates to company growth. Critics contend that it doesn't, that its 11-point scale has lower predictive validity than other scales, that the segmentation of promoters/neutrals/detractors is arbitrary and that other questions may be better predictors of growth rates:

• Not the Most Important Customer-Satisfaction Question - "We find no support for the claim that Net Promoter is the 'single most reliable indicator of a company's ability to grow' (Netpromoter.com 2006; Nicks 2006). Although we do not have access to the raw data from which these claims were made, we were able to compare some of the exemplar cases of Net Promoter with the ACSI, which Reichheld (2004) reports does not correlate with growth. Instead, we found that when making "apples-to-apples" comparisons, Net Promoter does not perform better than the ACSI for the data under investigation... The clear implication is that managers have adopted the Net Promoter metric for tracking growth on the basis of the belief that solid science underpins the findings and that it is superior to other metrics. However, our research suggests that such presumptions are erroneous. The consequences are the potential misallocation of resources as a function of erroneous strategies guided by Net Promoter on firm performance, company value, and shareholder wealth." - Timothy L. Keiningham, Bruce Cooil, Tor Wallin Andreassen, & Lerzan Aksoy, "A Longitudinal Examination of Net Promoter and Firm Revenue Growth"
• Doesn't Accurately Differentiate Promoters and Detractors - "The rule-of-thumb score classes proposed by Reichheld (promoters are those respondents who give a likelihood of recommendation of 9 or 10 while the detractors give 6 or less) are not supported statistically, mask important changes and potentially mislead management that there is negative NPS when this may not be the case." - Ken Roberts, Forethought Research Australia. Further, the standard NPS question itself is unipolar (willingness to recommend) but Reichheld's analysis treats it as bipolar (willing to detract vs. willingness to promote).
• Less Accurate than Composite Index of 3 Questions - "In his Harvard Business Review article ‘The One Number You Need to Grow', Reichheld maintained that since his tests showed propensity to recommend to be the single question that had the strongest statistical relationship to future company performance, there was no point asking any other questions in customer surveys... a single item question is much less reliable and more volatile than a composite index." - Customer Satisfaction - The customer experience through the customer's eyes, Nigel Hill, Greg Roche and Rachel Allen, p. 7
• Fails to Predict Loyalty Behaviors - "This research examines different customer satisfaction and loyalty metrics and tests their relationship to customer loyalty behaviors. The goal was to test the robustness of the customer-level analysis conducted by Reichheld and Satmetrix, which served as the foundation of their Net Promoter research. Contrary to Reichheld's assertions, the results indicate that recommend intention alone will not suffice as a single predictor of customers' future loyalty behaviors. Use of multiple indicators instead of a single predictor model performs significantly better in predicting customer recommendations and retention." - "The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Customer Retention, Recommendation, and Share-of-Wallet" (Timothy L. Keiningham, Bruce Cooil, Lerzan Aksoy, Tor W. Andreassen, Jay Weiner). NPS is attitudinal rather than behavioral, measuring how many people say they would be likely to recommend, rather than how many are doing so. A large body of research indicates that claimed intention is a better reflection of present attitudes than future behavior (Bird, Ehrenberg and Barnard).
• Performs Worse than Satisfaction & Liking Questions - The paper "Measuring Customer Satisfaction and Loyalty: Improving the ‘Net-Promoter' Score" by Daniel Schneider, Matt Berent, Randall Thomas and Jon Krosnick counterintuitively demonstrates that "satisfaction" and "liking" are better predictors of recommendations than "likelihood to recommend".
• Performs Worse than Other Scales - Schneider et al also demonstrate that the 11-point scale has the lowest predictive value of any of the scales tested. The authors recommend a 7-point scale with labeled ends and midpoint for the willingness-to-recommend question but also recommend a bipolar scale for a reworded variant.

As a result of this mounting criticism, only 19% of customer-feedback professionals agreed in a March 2008 survey that the NPS is a better predictor of growth than other loyalty questions and indices; 40% were neutral ("Customer Feedback Professionals Do Not Believe the NPS Claims", Hayes).

Despite these criticisms, NPS remains popular because it is well marketed, easy to understand and its model makes intuitive sense: every organization wants more promoters than detractors.

Some suggested best practices for using the Net Promoter Score in your organization:

• By all means, include the willingness-to-recommend question in your surveys.
• Use a customer loyalty index consisting of the willingness-to-recommend question with other questions relevant to your business (see Apostle Model Best Practices for one suggested index).
• Do not use the 11-point scale advocated by Reichheld, which is arbitrary in its assignment of promoters and detractors and has lower predictive validity than other scales. Reichheld shows flexibility as to the actual scale used; his original work showcases Enterprise Rent-A-Car, which uses a five-point scale in their research (treating 5 as promoters). Instead, use the seven-point bipolar scale recommended by Schneider, Berent, Thomas and Krosnick :

How likely is it that you would recommend us or recommend against us to a friend or colleague?

• Extremely likely to recommend against
• Moderately likely to recommend against
• Slightly likely to recommend against
• Neither likely to recommend nor recommend against
• Slightly likely to recommend
• Moderately likely to recommend
• Extremely likely to recommend

• As Reichheld suggests, use a follow-up open-ended question to probe why a respondent selected the choice that they did.
• Calculate the Net Promoter Score primarily for comparison to other firms, but use an arithmetic mean of your responses for internal tracking and benchmarking, as this will provide a more stable measure over time.

If you have time to read one white paper on the Net Promoter Score, read "Measuring Customer Satisfaction and Loyalty: Improving the ‘Net-Promoter' Score". Its authors include Jon Krosnick, the foremost researcher on questionnaire design, and Matthew Berent, Staff Survey Researcher with Intuit, which is one of the firms often cited for its use of NPS.

Customer Experience Excellence: Why, What and How

Yesterday, at the SCORE 2009 conference, I had the good fortune to hear Bruce Temkin of Forrester Research present "The Path to Customer Experience Excellence: Why, What and How". I regularly read his blog, Customer Experience Matters, so I was familiar with much of the material, but where the blog provides bite-size snack, Bruce's presentation was dinner and a show. (Complete with corny-enough jokes that I immediately reused one later in the day!)

Bruce began by walking the audience through a customer interaction that a Forrester analyst Adele had with a consumer-electronics retailer. His framing analogy was that too many organizations make customers roll a rock up the hill, like Sisyphus, only to have the rock roll back down. Adele used the site, but didn't get what she needed. The rock rolls back down the hill. Adele called customer service, but didn't get what she needed. The rock rolls back down the hill. Adele called technical support, but didn't get what she needed. The rock rolls back down the hill. Adele went back to the site and sent an email, but the responding email didn't give her what she needed. The rock rolls back down the hill. 

As a guy who once named his company after a Greek demigod, I appreciated Bruce's take on this story. The two lessons he quoted:

1. "Making customers push rocks up hills does not build loyalty."
2. "Don't mess around with Zeus."

Here is my quick recap of the "Why, What and How" to customer experience excellence, with links to related blog posts from Bruce:

• Why? You could have safely ignored customer-experience management three years ago, but not now. Customer experience is growing up; it's been important to firms for long enough that your competitors are differentiating themselves on customer experience. The recession is strengthening the correlation between customer experience and customer loyalty, across all 12 B2C industries Forrester studied. Perhaps as a result, most businesses will cut other areas disproportionately more than they will cut areas that affect customer experience.
• What? One of the top firms in Forrester's customer experience rankings is CostCo, which tied for #3 after Barnes & Noble and USAA. "Great customer experience is not about Rainforest Café with a dazzling impression: it's about consistently meeting the needs and beating the expectations of customers. Unlike when going to a 7-11, you don't even expect to be able to park near the building at CostCo. But you do expect a broad selection at a great price." [quote from my notes; not verbatim]
• How? To build great customer experiences, your organization needs to follow the three principals of Experience-Based Differentiation:
1. Obsess about customer needs, not product features.
2. Reinforce brands with every interaction, not just communications.
3. Treat customer experience as a competence, not a function.

Bruce wrapped up by pointing attendees to his free booklet, The 6 Laws of Customer Experience: The Fundamental Truths That Define How Organizations Treat Customers. Bruce is one of the leading authorities researching CE today; you'd have to have rocks in your head not to review his findings and work to make life easier for your customers.

Voice of the Customer (VOC) Techniques & Technologies

Bruce Temkin, a principal analyst at Forrester, defines "voice of the customer" as "a systematic approach for incorporating the needs of customers into the design of customer experiences." His blog post "Are you listening to the voice of the customer?" outlines five levels of activities in a VOC program:

1. Relationship tracking. Organizations need to track the health of customer relationships over time...
2. Interaction monitoring. Every customer interaction - from an online transaction to a call into the call center - is important. Firms need a way to monitor how effectively they handle these customer touches...
3. Continuous listening. ...There are many opportunities to hear what customers are saying, such as listening to calls in the call center, reading blogs, reading inbound emails, and visiting retail outlets.
4. Project infusion. Projects that affect customers should incorporate insights about customers. Despite the clear need for this type of effort, many companies lack a formalized approach for infusing customer insights into projects...
5. Periodic immersion. Every so often, it's valuable for all employees - especially executives - to spend a significant amount of time interacting directly with customers or working alongside frontline employees...

Surveys, online communities and text analytics can help you listen to the voice of the customer at each of the five levels that Bruce describes:

1. Relationship tracking. Periodic surveys are an excellent way to keep your finger on the pulse of customers. Many of our users conduct a full census of customers annually or biannually, but to truly listen to the voice of the customer we advocate monthly or quarterly surveys of a random sample of customers. That way you always have a fresh perspective on current customer attitudes.
2. Interaction monitoring. Automated surveys can follow up each purchase, customer-service interaction and renewal. By integrating your CRM system with your enterprise feedback platform, you can constantly listen for the voice of the customer. Coupling this interaction with survey alerts/email triggers is a great way to act on the voice of the customer, too.
3. Continuous listening. Text analytics enable you to eavesdrop on thousands of customers as they comment on your surveys, email your organization and blog and tweet about you.
4. Project infusion. Qualitative and quantitative research into the voice of customer needs to be infused throughout the product lifecycle: integrate such research into the planning, development, implementation and marketing of each product or service.
5. Periodic immersion. Online communities can immerse your employees in the thoughts and attitudes of your customers. To truly become a customer-driven organization, make sure that employee participation in your customer community is broad and deep.

Modern technologies make it easier than ever to listen to customers. Make sure your organization is evaluating such tools, and be sure to check out Vovici's new Voice of the Customer Success Package that can help your organization better listen to customers.

ACSI (American Customer Satisfaction Index) Model: Strengths and Weaknesses

The ACSI Score is just one of five multi-item scales that make up the expanded model of the American Customer Satisfaction Index. Each multi-item scale represents a different aspect of customer attitudes: Customer Expectations, Perceived Overall Quality, Perceived Value, Customer Satisfaction and Customer Loyalty (Customer Complaints, shown in the graphic below, is based on a single question).

The American Customer Satisfaction Index has greater predictive validity than most other customer-satisfaction measures. 

• Macroeconomically, ACSI has been shown to correlate to GDP and PCE (Personal Consumption Expenditure) growth ("The Effect of Buyer Satisfaction on Consumer Spending Growth", Fornell & Rust, 2005).
• Microeconomically, ACSI predicts stock market performance for indices as well as individual stocks and even correlates to CEO bonuses.

Since the ACSI was started in 1994 and is used as a benchmark measuring changes to satisfaction over time, the underlying model has not been changed significantly despite subsequent research into its shortcomings. Most of the following criticisms are from "The evolution and future of national customer satisfaction index models" (Johnson, Gustafsson, Andreassen, Cha; 2001).

1. No WOM/Recommendation Index - By contemporary standards, the model is remiss in that in leaves out any measurement of positive word of mouth. Early word-of-mouth research focused on complaining behavior (Gronhaug and Kvitastein, 1991; Singh, 1988), as does the ACSI model. Starting in 1991 and popularized in 1995, WOM research has shifted to recommendations and customer advocacy (Brown et al., 2005; Christopher et al., 1991; Jones and Sasser, 1995).
2. Complaints are a Driver not Consequence of Satisfaction - When the ACSI model was first constructed, there was little awareness of the effects of complaint resolution on satisfaction. Complaints are in the wrong place in the model (Johnson, et al; 2001).
3. Satisfaction as an Intermediary - The effects of Quality, Value and Expectations on Loyalty are all mediated by the cumulative satisfaction index. In reality, Quality and Value most likely directly affect Loyalty without going through Satisfaction, as this explains how Satisfaction and Loyalty can diverge. Value, in particular, is important when a customer re-evaluates whether to remain loyal (Johnson, et al; 2001).
4. Expectations' Effect on Cumulative Satisfaction - The link from the Expectations index to the Perceived Value index is weak, as is the link to the Customer Satisfaction index. Expectations correlate to Satisfaction less closely as time from initial transaction increases. This index can be safely omitted and in fact is omitted from the revised Norwegian Customer Satisfaction Barometer. 
5. Link from Quality to Value Problematic - This link has no sound theoretical basis (Johnson, et al; 2001).
6. Value and Quality Indices Overlap - By creating a separate Price Index, Value can be removed and better correlations obtained (Johnson, et al; 2001).
7. Uses Partial Least Squares for SEM - See "Customer Satisfaction in a Reduced Rank Regression Framework", Pietro Giorgio Lovaglio, 2004.
8. FID (Fuzzy Influence Diagrams) May Outperform SEM (Structural Equation Modeling) - FID has only recently been applied to the measurement of customer satisfaction and seems to outperform SEM "in solving the problems of nonlinearity, validity and causality" (Na An, Jinlan Liu, Yin Bai, 2007).

The expanded ACSI model would be a great foundation for another academic organization to use to build a modern customer satisfaction and loyalty model, unconstrained by the need for complete backward compatibility.

ACSI (American Customer Satisfaction Index) Score & Its Calculation

The American Customer Satisfaction Index (ACSI) is the most well known national customer satisfaction index model, a type of economic indicator that assesses the overall satisfaction of consumers in a country. The ACSI is compiled by the National Quality Research Center (NQRC) at the University of Michigan. While intended as a macroeconomic measure of U.S. consumers in general, many corporations have used it to measure the satisfaction of their own customers.

The heart of the American Customer Satisfaction Index is a set of three questions that assess satisfaction, each on a different 10-point scale:

Some organizations simply average the three ratings, like this:

(Satisfaction + Expectancy + Performance - 3) / 3 * 100

This produces an overall score from 0 to 100 that can be used as an approximate benchmark to industry results published by TheACSI.org. 

The true ACSI customer-satisfaction score is a weighted average of the answers to each of these three questions, using a proprietary weight for each of the three questions, with different weighting schemes for different industries. For instance, overall satisfaction is typically given a higher weight than expectancy, which is given a slightly higher weight than performance. The State of Ohio uses the following weights:

((Satisfaction-1)*.3885 + (Expectancy-1)*.3190 + (Performance-1)*.2925) / 9 * 100

If you want the precise results for your industry, you will want to commission a custom ACSI research program with ACSI, for $50,000; this will include the precise question wording and survey methodology used for gathering the benchmark data. Otherwise the above question phrasing and calculation can be used for a Do-It-Yourself customer-satisfaction index and benchmark.