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Formula: The calculation of this formula is subject to some interpretation; the key issue is how long to wait before a customer is assumed to have stopped buying from the company. In some cases, this may be anyone who has not placed an order within the past month and in other cases within the past year. The correct formu-lation will depend upon the nature of the business. With this in mind, the formula is to subtract from the total customer list those that have been invoiced (or sold to on a cash basis) within the appropriate time period and then divide the remainder by the total number of customers on the customer list:
Total number of customers – Invoiced customers Total number of customers
Example: The customer service department of the Indonesian Linens Company is being inundated with requests from the president to reduce the company’s high rate of customer turnover, which is currently 30% per year. The department man-ager does not have enough staff available to contact all customers regularly, and so asks the controller for assistance in finding out which customers are most im-portant, so that the department can focus on them. Mr. Noteworthy, the controller, conducts an activity-based costing analysis of all customers and determines which 50 customers produce the largest amount of gross margin dollars for the company. The customer service manager gratefully shifts the department’s focus to these key customers. A few months later, Mr. Noteworthy calculates customer turnover both in total and for this smaller group of key customers, using the information in Table 14.2.
The table shows that, although overall customer turnover has not changed, the increased focus on high-profit customers has resulted in greatly reduced turnover in this key area.
Cautions: There may be some customers who only purchase small amounts each year; one may not want to include these customers in the turnover calculation, fo-cusing instead on those that provide a significant level of sales volume. Another variation on the ratio is to determine the top customers who provide the company with the bulk of its profits and only measure the turnover rate among that group. By subdividing customers in this manner, a company can focus its customer re-tention strategy on those who have the largest financial impact on the company.
Total number of customers
Customers not placing order in the last three months
Total Customer Base
Key Customer Base
284 / Business Ratios and Formulas
NET PROMOTER SCORE
Description: It is extremely difficult to monitor customer satisfaction, since this can encompass a variety of aspects of the customer experience, such as initial ser-vice, pricing, product or service quality, warranty service, and so on. One way to summarize all these aspects of customer service into one measure is to track the propensity of customers to recommend the company to their friends or colleagues. This approach uses a simple ten-point scale, where a score of ten represents an en-thusiastic endorsement. Surveying on just this question, rather than the usual long list of customer-satisfaction questions, also results in a higher proportion of cus-tomer responses.
Formula: Conduct a customer survey, in which they are asked on a scale of 1 to 10 if they would recommend the company’s products and services to their friends or colleagues. Then divide the total of all 9 or 10 scores by the total of all 1 through 6 scores. The formula is:
—Number of customers giving score of 9 or 10 on 10-point scale— Number of customers giving score of 1 through 6 on 10-point scale
Customers giving scores of 7 or 8 are considered to be passively satisfied, and so are unlikely to either recommend the company or detract from it. Their scores are therefore excluded from the ratio.
Example: The Samson Hair Loss Clinic specializes in hair restoration, which is a painful and expensive process. It relies primarily upon customer referrals for new business, so it pays particular attention to customer satisfaction with its services. It recently completed a survey of 100 recent clients, where they gave scores on a ten-point scale for whether they would recommend Samson to their friends. The results were:
Scores of 9 or 10 47 Scores of 7 or 8 18 All other scores 35 Total respondents 100
The survey resulted in a net promoter score of 1.3:1, which was derived by di-viding the 47 scores rated at either 9 or 10 by the 35 scores rated below a 7. The ratio indicates that the clinic still has considerable work to do to improve the ex-periences of those customers with low scores (and who may actively turn poten-tial customers away with negative recommendations).
Cautions: This metric assumes that there is a causal relationship between revenue growth and a high net promoter score. There is likely to be one, given the strength
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of social networks for selling some types of products. However, people are more likely to go out of their way to recommend consumer products, such as a plasma television, than they are for more pedestrian products, such as cement. Thus, it makes sense to first test the concept on an individual company basis to ensure that this tool represents a valid way to foster more revenue growth.
If the people being judged by this metric are also the ones collecting the un-derlying data, then survey results may be skewed upward. To prevent this, have a third party collect the survey information.
BROWSE TO BUY CONVERSION RATIO
Description: In most retail establishments, it is impossible to determine how many people browse through the store, and so there is no way to determine the ratio of potential customers to those who actually make a purchase. However, this is a simple and effective calculation for any situation where the store is on-line, since the exact number of browsing customers can be compiled. In this situation, a company has a great deal of interest in the browse to buy conversion ratio, since it can adjust its on-line store presentation to encourage a higher proportion of buyers and get immediate confirmation through this ratio of the effectiveness of its changes.
Formula: Divide the number of buying customers by the number of browsing cus-tomers. This measure can be subdivided into individual pages on a Web site; for ex-ample, the measure can be used individually for the camera, television, and video camera sections of an electronics store Web site. This type of “slice and dice” mea-surement may yield a greater degree of accuracy in determining which parts of an on-line store are most effective in attracting customer orders. The ratio is:
Number of buying customers Number of browsing customers
Example: An outside Web site developer has contacted the International Baby Supply Center, offering to redesign its Web site to attract more paying customers. The company’s lead buyer, Mr. Smythe, decides to structure the deal so that the Web site developer is paid only if the browse to buy conversion ratio improves after the site changes are completed. The developer is willing to modify the Web pages depicting products, but not the home page. Accordingly, the number of browsing customers is measured at the product pages rather than at the home page. The de-velopment contract states that the developer will be paid 10% of the sales from the increased proportion of buyers for six months following installation of the new Web pages. Table 14.3 reveals the before-and-after statistics for the site.
The developer’s efforts have resulted in an improvement in the ratio of 3%. To calculate the amount to be paid to the developer, Mr. Smythe multiplies the 3%
286 / Business Ratios and Formulas
Number of buying customers Number of browsing customers Browse to buy conversion ratio Average sale per customer
10,400 130,000 8%
17,225 157,000 11% $15
difference by the number of browsing customers after the changes are imple-mented, which is:
3% ´ 157,000 Browsing customers = 4,710 Additional buying customers
He then multiplies the increase in buying customers by the average sale per customer of $148, to find the amount payable to the developer. The calculation is:
$15 Average sale per customer ´ 4,710 Additional buying customers = $70,650
Cautions: The number of browsing customers used in the ratio can be subject to a considerable degree of interpretation. For example, it can be summarized from the number of potential customers who access the home page of the site, from the number who access specific product pages, or those who have placed an order but back out just prior to paying. One possibility is to measure the ratio at all of these points in order to determine where in the process the greatest proportion of po-tential customers drop out of the purchasing decision.
Description: Recency refers to the time period between visits by a customer to a company’s retail location. Most stores are not equipped to track the arrival of cus-tomers at a store, except for some retail clubs that issue identification cards to their customers. However, on-line stores can easily determine when customers have ac-cessed the site, and so have reasonable grounds for calculating this measure. An on-line store can use the measure as a target for its marketing efforts. By issuing advertisements, special deal notices, and so on, and then noting any changes in the recency measure, a company can see if its marketing efforts are changing the pur-chasing behavior of its customers.
Formula: Subtract the most recent date of a customer site visit from the date of the last visit date. This number can be summarized and averaged for all customers, or for select subgroups of customers.
The measure can be used for physical retail locations by measuring customer access based on the dates of their noncash purchases; however, this modification
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Last Visit Date
August 13 August 12 August 10 August 17 August 20 August 9 August 30 August 29 August 27 August 23
Prior Visit Date
August 2 August 4 August 1 August 16 August 5 August 3 August 10 August 11 August 13 August 7
11 Days 8 Days 9 Days 1 Day
15 Days 6 Days 20 Days 18 Days 14 Days 16 Days
to the formula will exclude those customers who have only browsed through the store and not purchased anything.
Example: The Christmas Express Company’s marketing manager wants to cal-culate the recency of the customers accessing its on-line store. The information for 10 randomly selected customers is shown in Table 14.4.
The average recency for the information in the far right column of the table is 11.8 days.
Cautions: It may not be that easy to trace the recency of customers at an on-line site, because they may be accessing the site from different on-line service providers, which will give them a different identification that cannot be compared to their identifications from previous site visits. The best way to avoid this prob-lem is to require a site log-in using a company-issued identification so that there is no question about who is accessing it.
DIRECT MAIL EFFECTIVENESS RATIO
Description: Direct mail campaigns have a high product design, production, and mailing cost, so it is crucial to verify the success of these endeavors. A successful campaign usually has a low single-digit response rate and can swing between a profit or loss if the response rate varies by a fraction of a percent. Consequently, a company that engages in this form of marketing must pay close attention to the direct mail effectiveness ratio.
Formula: This ratio can be measured in two ways. Under the first approach, po-tential customers do not place an order at the time of the response to the direct mail campaign, and must be contacted in order to confirm a sale. To measure this type
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