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2.8 Customer Satisfaction and Willingness to Recommend

Figure 2.3

Figure 2.3 Ratings

Within organizations, customer satisfaction ratings can have powerful effects. They focus employees on the importance of fulfilling customers’ expectations. Furthermore, when these ratings dip, they warn of problems that can affect sales and profitability.

A second important metric related to satisfaction is willingness to recommend. When a customer is satisfied with a product, he or she might recommend it to friends, relatives, and colleagues. This can be a powerful marketing advantage.

Purpose: Customer satisfaction provides a leading indicator of consumer purchase intentions and loyalty.

Customer satisfaction data are among the most frequently collected indicators of market perceptions. Their principal use is twofold.

  1. Within organizations, the collection, analysis, and dissemination of these data send a message about the importance of tending to customers and ensuring that they have a positive experience with the company's goods and services.
  2. Although sales or market share can indicate how well a firm is performing currently, satisfaction is perhaps the best indicator of how likely it is that the firm's customers will make further purchases in the future. Much research has focused on the relationship between customer satisfaction and retention. Studies indicate that the ramifications of satisfaction are most strongly realized at the extremes. On the scale in Figure 2.3, individuals who rate their satisfaction level as "5" are likely to become return customers and might even evangelize for the firm. Individuals who rate their satisfaction level as "1," by contrast, are unlikely to return. Further, they can hurt the firm by making negative comments about it to prospective customers. Willingness to recommend is a key metric relating to customer satisfaction.

Construction

    Customer Satisfaction: The number of customers, or percentage of total customers, whose reported experience with a firm, its products, or its services (ratings) exceeds specified satisfaction goals.

    Willingness to Recommend: The percentage of surveyed customers who indicate that they would recommend a brand to friends.

These metrics quantify an important dynamic. When a brand has loyal customers, it gains positive word-of-mouth marketing, which is both free and highly effective.

Customer satisfaction is measured at the individual level, but it is almost always reported at an aggregate level. It can be, and often is, measured along various dimensions. A hotel, for example, might ask customers to rate their experience with its front desk and check-in service, with the room, with the amenities in the room, with the restaurants, and so on. Additionally, in a holistic sense, the hotel might ask about overall satisfaction "with your stay."

Customer satisfaction is generally measured on a five-point scale (see Figure 2.4).

Figure 2.4

Figure 2.4 A Typical Five-Point Scale

Satisfaction levels are usually reported as either "top box" or, more likely, "top two boxes." Marketers convert these expressions into single numbers that show the percentage of respondents who checked either a "4" or a "5." (This term is the same as that commonly used in projections of trial volumes; see Section 4.1.)

Example: The general manager of a hotel in Quebec institutes a new system of customer satisfaction monitoring (see Figure 2.5). She leaves satisfaction surveys at checkout. As an incentive to respond, all respondents are entered into a drawing for a pair of free airline tickets.

Figure 2.5

Figure 2.5 Hotel Customer Survey Response

The manager collects 220 responses, of which 20 are unclear or otherwise unusable. Among the remaining 200, 3 people rate their overall experience at the hotel as very unsatisfactory, 7 deem it somewhat unsatisfactory, and 40 respond that they are neither satisfied nor dissatisfied. Of the remainder, 50 customers say they are very satisfied, while the rest are somewhat satisfied.

The top box, comprising customers who rate their experience a "5," includes 50 people or, as a percentage, 50/200 5 25%. The top two boxes comprise customers who are "somewhat" or "very" satisfied, rating their experience a "4" or "5." In this example, the "somewhat satisfied" population must be calculated as the total usable response pool, less customers accounted for elsewhere, that is, 200 2 3 2 7 2 40 2 50 = 100. The sum of the top two boxes is thus 50 1 100 5 150 customers, or 75% of the total.

Customer satisfaction data can also be collected on a 10-point scale. Regardless of the scale used, the objective is to measure customers' perceived satisfaction with their experience of a firm's offerings. Marketers then aggregate these data into a percentage of top-box responses.

In researching satisfaction, firms generally ask customers whether their product or service has met or exceeded expectations. Thus, expectations are a key factor behind satisfaction. When customers have high expectations and the reality falls short, they will be disappointed and will likely rate their experience as less than satisfying. For this reason, a luxury resort, for example, might receive a lower satisfaction rating than a budget motel––even though its facilities and service would be deemed superior in "absolute" terms.

Data Sources, Complications, and Cautions

Surveys constitute the most frequently used means of collecting satisfaction data. As a result, a key risk of distortion in measures of satisfaction can be summarized in a single question: Who responds to surveys?

"Response bias" is endemic in satisfaction data. Disappointed or angry customers often welcome a means to vent their opinions. Contented customers often do not. Consequently, although many customers might be happy with a product and feel no need to complete a survey, the few who had a bad experience might be disproportionately represented among respondents. Most hotels, for example, place response cards in their rooms, asking guests, "How was your stay?' Only a small percentage of guests ever bother to complete those cards. Not surprisingly, those who do respond probably had a bad experience. For this reason, marketers can find it difficult to judge the true level of customer satisfaction. By reviewing survey data over time, however, they may discover important trends or changes. If complaints suddenly rise, for example, that may constitute early warning of a decline in quality or service. (See number of complaints in the following section.)

Sample selection may distort satisfaction ratings in other ways as well. Because only customers are surveyed for customer satisfaction, a firm's ratings may rise artificially as deeply dissatisfied customers take their business elsewhere. Also, some populations may be more frank than others, or more prone to complain. These normative differences can affect perceived satisfaction levels. In analyzing satisfaction data, a firm might interpret rating differences as a sign that one market is receiving better service than another, when the true difference lies only in the standards that customers apply. To correct for this issue, marketers are advised to review satisfaction measures over time within the same market.

A final caution: Because many firms define customer satisfaction as "meeting or exceeding expectations," this metric may fall simply because expectations have risen. Thus, in interpreting ratings data, managers may come to believe that the quality of their offering has declined when that is not the case. Of course, the reverse is also true. A firm might boost satisfaction by lowering expectations. In so doing, however, it might suffer a decline in sales as its product or service comes to appear unattractive.

Related Metrics and Concepts

    Trade Satisfaction: Founded upon the same principles as consumer satisfaction, trade satisfaction measures the attitudes of trade customers.

    Number of Complaints: The number of complaints lodged by customers in a given time period.

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