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Contozo Report

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  • 1. CONTOZO'S SURVEY REPORT DEFINITIONS: For the purpose of this report INDEPENDENT VARIABLES are defined as those attributes of the product or service being evaluated (like QUALITY OF THE FOOD, FRIENDLYNESS OF THE SERVER, etc), that affect the DEPENDENT VARIABLES or OUTCOMES (like OVERALL SATISFACTION, LIKELY TO REFER A FRIEND, LIKELY TO VISIT AGAIN) METHODS: A sample of 484 respondents was analyzed. English and Spanish subsets of respondents were merged and all averages calculated. All three outcome variables (OVERALL SATISFACTION, LIKELY TO REFER A FRIEND, LIKELY TO VISIT AGAIN) where analyzed to determine the best set of predictor factors for each of them. A regression analysis was also performed for each dependent variable to define the weight of a hypothetical change on each of the independent variables and its impact on outcomes. In order to define possible patterns, responses with ratings of 1 and 2 in the dependent variables were analyzed using regression. GENERAL RESULTS (ALL RATINGS): AVERAGE RATINGS FOR QUESTIONS: We were A server Server The The menu The Overall Likely to Likely to seated was there was server had an quality of quality of refer a come promptly to take friendly was able excellent the food service friend back the order and to answer choice of was quickly patient all our items excellent when questions taking the order 3.53 3.53 3.63 3.62 3.67 3.77 3.71 3.73 3.68 Compared to other full service restaurants Contozo's is 0.3 points above the average for Quality of food and 0.2 points above the average for Overall quality of service. For all other variables Customer Link doesn't have comparable values as questions differ significantly.
  • 2. REGRESSION ANALYSIS A Best Subset Regression approach was taken. Best subsets regression is an efficient way to identify models that achieve your goals with as few predictors as possible. For example: How to achieve an increase in the LIKELIHOOD TO REFER A FRIEND from 3.73 to 4? One way is to increase the ratings in all independent variables; however, this is not only expensive but usually unnecessary. By identifying the BEST SUBSET we can define the two or three variables that will have more weight in achieving this goal. BEST SUBSET REGRESSION FOR OVERALL QUALITY OF SERVICE The best subset identified involves the following three variables: Quality of the food 0.517 Variable Coefficient Server was friendly and patient 0.242 Server was there to take the order quickly 0.104 The Coefficient is the amount in which the dependent's variable average rating will change if the independent variable changes by 1 unit. Example: If the average rating of the QUALITY OF FOOD increases from 3.77 to 4.77 and all other variables remain the same, the OVERALL SATISFACTION will increase from 3.71 to 4.23 (a positive change of 0.517). Additionally if SERVER FRIENDLINESS average increases from 3.63 to 4.63 and all other variables remain the same the OVERALL SATISFACTION will go from 3.71 to 3.95. If all variables mentioned here increase by 1 the OVERALL SATISFACTION rate will go up to 4.573 (result of adding 0.517+0.242+0.104 to the current average). A similar increase in all other variables combined will have an effect of less than 0.2 in OVERALL SATISFACTION . This is also applicable when the change is not positive but negative. This example can be used to help interpret all other variables. BEST SUBSET REGRESSION FOR LIKELY TO REFER A FRIEND The best subset identified involves the following four variables: Quality of the food 0.328 Variable Coefficient Server was friendly and patient 0.168 We were seated promptly 0.144 Menu selection 0.158
  • 3. BEST SUBSET REGRESSION FOR LIKELY TO VISIT AGAIN The best subset identified involves the following three variables: Quality of the food 0.327 Variable Coefficient Server was friendly and patient 0.246 Menu selection 0.124 We were seated promptly 0.106 WEEKLY CLIENTS Further analysis of the data demonstrated that those who visit Contozo's on a weekly basis represent a population with slightly different expectations. This important population was composed by 102 respondents (21%) of the sample, and averages a total of 2.87 visits a week. According to this data this subgroup accounts for 81.26% of all projected visits to Contozo's in one year. AVERAGE RATINGS FOR QUESTIONS: We were A server Server The The menu The Overall Likely to Likely to seated was there was server had an quality of quality of refer a come promptly to take friendly was able excellent the food service friend back the order and to answer choice of was quickly patient all our items excellent when questions taking the order 3.68 3.82 3.75 3.73 3.85 3.95 3.94 3.93 3.89 This population shows a significantly higher satisfaction with Contozo's service and has different priorities. A regression analysis was performed for this population and the results follow: BEST SUBSET REGRESSION FOR OVERALL QUALITY OF SERVICE (WEEKLY CLIENTS) The best subset identified involves the following three variables: Quality of the food 0.466 Variable Coefficient Server was friendly and patient 0.422 Menu selection 0.296
  • 4. Note that SERVER FRIENDLINESS and MENU SELECTION are significantly more important for this subgroup as compared to the general sample. The coefficient for these two variables is significantly higher than the one observed for the general population, meaning a change in any of them will have a sizable impact on OVERALL SATISFACTION. BEST SUBSET REGRESSION FOR LIKELY TO REFER A FRIEND (WEEKLY CLIENTS) The best subset identified involves the following two variables: Quality of the food 0.303 Variable Coefficient Menu selection 0.190 Seated promptly 0.153 BEST SUBSET REGRESSION FOR LIKELY TO VISIT AGAIN (WEEKLY CLIENTS) The best subset identified involves the following two variables: Menu selection 0.306 Variable Coefficient Quality of the food 0.332 Server was friendly and patient 0.137 USING THIS INFORMATION Customer retention is a priority issue to address for any business. Based on the results of this analysis, this fact is particularly true for Contozo's. Now that the main variables that drive up or down customer satisfaction have been identified the next step is usually to define which variables are easier and less expensive to improve. A recent study done at 384 locations of an important fast food chain in Canada using the same devises used for Contozo's asked clients WHETHER THEIR SERVER SMILED OR NOT. It was shown this variable had a strong influence on OVERALL CUSTOMER SATISFACTION and the ratings where significantly low during the night shift. The client was suggested to reinforce the importance of courtesy by sharing the results with all personnel in contact with customers. Contozo's is presented with a similar situation, where SERVER WAS FRIENDLY AND PATIENT has a coefficient of 0.422 in OVERALL SATISFACTION for frequent customers. An improvement in this variable will probably have a strong and positive influence on OVERALL CUSTOMER SATISFACTION and might entail a lower investment than modifying the quality of the food or adding new items to the menu, the other factors found to have a significant influence. That might be a first step towards quality improvement. For other variables the decision might not be as easy and, although providing a method to calculate return on investment is beyond the scope of this report, the information contained herein can be useful to avoid pitfall projects.
  • 5. A common approach to do this starts by translating a 1 to 5 scale to percentages and then calculating the projected change in a given variable by using the coefficient. The formula used to make the conversion to percentage is (variable average rating – 1) x 25. Example of scale conversion: Current average agreement with the statement LIKELY TO COME BACK of 3.89 is equivalent to (3.89-1) x 25= 72.25% LOW RATINGS ANALYSIS In order to define possible patterns, responses with ratings of 1 and 2 in the dependent variables were analyzed using regression. The results where as follow: Likely to visit again Variable Number of responses with 1 and 2 Overall satisfaction 121 Likely to refer someone 106 111 AVERAGE RATINGS FROM LOW RESPONDENTS FOR OVERALL SATISFACTION We were A server Server The The menu The Overall Likely to Likely to seated was there was server had an quality of quality of refer a come promptly to take friendly was able excellent the food service friend back the order and to answer choice of was quickly patient all our items excellent when questions taking the order 2.48 2.29 2.12 2.14 2.2 2.01 1.28 2.23 2.24 AVERAGE RATINGS FROM LOW RESPONDENTS FOR LIKELY TO REFER SOMEBODY We were A server Server The The menu The Overall Likely to Likely to seated was there was server had an quality of quality of refer a come promptly to take friendly was able excellent the food service friend back the order and to answer choice of was quickly patient all our items excellent when questions taking the order 2.43 2.44 2.36 2.33 2.25 2.42 2.23 1.34 2.16
  • 6. AVERAGE RATINGS FROM LOW RESPONDENTS FOR LIKELY TO VISIT AGAIN We were A server Server The The menu The Overall Likely to Likely to seated was there was server had an quality of quality of refer a come promptly to take friendly was able excellent the food service friend back the order and to answer choice of was quickly patient all our items excellent when questions taking the order 2.51 2.5 2.37 2.45 2.5 2.43 2.45 2.34 1.28 Two procedures were used to define a possible pattern in the data and probably single out variables resulting in such an outcome: a) Stepwise regression and b) Best subsets regression. Coefficients are not provided in the results below as not all values in the scale were used. REGRESSION RESULTS FOR LOW RESPONDENTS FOR OVERALL SATISFACTION The results show preponderance of inconformity with (in order of importance): 1. Quality of the food 2. Variety of the menu 3. A server was there to take the order quickly REGRESSION RESULTS FOR LOW RESPONDENTS FOR LIKELY TO REFER SOMEBODY The results show preponderance of inconformity with (in order of importance): 1. Quality of the food 2. Variety of the menu REGRESSION RESULTS FOR LOW RESPONDENTS FOR LIKELY TO COME BACK The results show preponderance of inconformity with (in order of importance): 1. Quality of the food 2. Variety of the menu Even though it was possible to define the most common factors associated with low outcomes, it is important to note that these customers tended to show very low ratings in most or all of the variables, making it difficult to reliably define priority issues for this group. The incorporation of variables like gender, income, nationality, etc, did not change the tendency shown above.
  • 7. SUGESTIONS 1) Since Quality of the food was present as an important factor throughout the current analysis it is suggested to survey your customers to find potential problems, e.g.: freshness, temperature, presentation, taste, consistency in quality, etc, and then establish an improvement strategy based on specific problems. 2) Reinforce importance of courtesy by sharing these results with ALL personnel who might get in direct contact with consumers, i.e. hosts, servers, cleaning personnel, etc. 3) Although emphasis should be placed on frequent customers’ needs, it is important to remember new customers are the gateway group to increase Contozo's frequent customer base. 4) Menu selection was also an important factor. It has been seen in previous studies that having a wide range of choices is not as important as having consistent availability of the choices offered. Having items on the menu that are not consistently available can create unmet expectations. Input from servers regarding customer’s requests can add invaluable information.