Engineering Management, Information, and Systems Department. Analysis of Statistical Trends Between Design and Comfort at Chili's Restaurants.

Transcription

1 Analysis of Statistical Trends Between Design and Comfort at Chili's Restaurants OWimmom Engineering Management, Information, and Systems Department Southern Methodist University School of Engineering Dallas, Texas 75275

2 Analysis of Statistical Trends Between Design and Comfort at Chili's Restaurants EMIS Senior Design Spring 25 Asif Hussain Kristyn Starr

3 Table of Contents Management Summary. 1 Background and Description...3 Analysis of the Situation... 5 Technical Description...7 Analysis and Managerial Interpretation...11 Conclusions and Critique Appendix...39

4 Management Summary

5 We were asked by Brinker International to carry out a Trend Analysis for one of its major restaurants, Chili's. The data we used for this analysis was off of their Guest Satisfaction Survey (GSS) from Fiscal year 24 Quarter 3 to Fiscal year 25 Quarter 3. There were approximately 2 million entries and our primary tool to carry out the analysis was SPSS, statistical analysis software used in businesses to solve business and research problems regarding data management and analysis. Our primary objective was to determine if restaurant prototypes affected a guest's comfort level and overall restaurant experience. Chili's has 19 different prototypes and all of their stores come under these. Prototypes differ from each other in architecture, interior setup, furniture, themes and decoration. In SPSS we ran Crosstabs procedures which produced Pearson's r values between Overall Comfort and several other relevant variables taken from the GSS. Pearson's r values are used to determine if 2 different variables have a strong correlation (statistical relationship). Through these procedures we determined that there were not any strong correlations between Overall Comfort and the other variables used. One-Way ANOVA tests were used to find statistical significance between the mean values of all the variables with regards to prototypes. Through this we found that there are some prototypes that differ significantly from other prototypes in their mean values. Some prototypes were rated statistically higher than others but due to the lack of benchmarks, we were unable to conclude whether certain prototypes were better than others according to Brinker's standards. 2

6 1 Background and Description 3

7 Brinker International has been collecting data regarding guest satisfaction and they gave us their data from Fiscal year 24 Quarter 3 to Fiscal year 25 Quarter 3. They wanted us to use the data gathered to prove or disprove that the architecture, prototype, affects a customer's comfort. The restaurant we focused our attention on was Chili's. With this data they hope they can easily remedy minor problems in comfort and possibly target a high comfort rating prototype. This would give them an idea of where to alter the poor prototypes and emulate new restaurants against. The GSS asks close to 15 questions ranging from income to overall experience. Guests rate most of the questions on a scale from 1 to 5; 5 being excellent and 1 being bad. Some of the questions asked more depth as to why a guest gave a poor rating on a query. For example one of the questions was directed at the overall cleanliness of the restaurant. If the user rated the cleanliness poorly they were asked as to why; examples of response choices were unclean seats, tables, floors, menus, bar, fixtures/wall, silverware/plateware, and staff's attire. The first step in our process was to decide which questions to keep as our variables in finding trends in the data. We focused on a total of 19 variables. These were the variables we felt directly could affect the comfort of a guest; they were finalized with the help of our contact at Brinker. The next step, before analysis could begin, was to merge the prototypes with the corresponding restaurants since our focus was on the prototypes and not the individual restaurants. 4

8 Analysis of Situation - P^^p 5

9 We approached the problem from a statistical point of view. The database had about 2 million cases of which we could work with in finding trends. It was decided that it was best to use all of them verses a random sample because the random sample could end up heavily weighted towards a prototype and the assured significance with the full database would be lost with a random sample. With the help of SPSS we were able to investigate some preconceived notions we had of the problem. One of these was the idea that the comfort was affected by such variables as cleanliness, atmosphere, server's enthusiasm and restroom status. We decided that using the Crosstabs analysis of SPSS would enable us to prove or disprove our original notions by finding a correlation between comfort and the other variables. The information from the Crosstabs would then be used to help enforce any findings from the One-Way ANOVA. It was decided that a One-Way ANOVA would be the best way to compare the prototypes against each other by using a means comparison of the variables to see which were significantly different.

10 Technical Description 7

11 Our analysis was based off of 1,8, cases from the GSS and contained 18 variables from 4 different categories. The categories were: staff, restaurant environment, comparison to similar restaurant types and other. IOther: I Staff Questions: Acknowledge quickly upon seating Attentiveness of server Beverage served timely Food served timely Enthusiasm of server, promptness of payment Servers' knowledge Welcomed upon arrival. Restaurant Environment Questions: Overall atmosphere Overall cleanliness Overall comfort Restrooms Comparison to Similar: Overall Atmosphere Food Service Overall Experience Operation Hours The analysis was completed using the Crosstabs analysis and One-Way ANOVA on I I I SPSS. The only assumption made to the data was despite being considerably less entries for 3 of the prototypes we felt there were enough that when comparing means it would not affect the outcome. This assumption was further established by running the cases weighted on a One-Way ANOVA. 8

12 An example of Crosstabs output would look like the following: r nunt Crosstab How would you rate the Atmosphere? Total Overall Comfort Total Symmetric Measures Value Asym p. Std. Error Approx. P Approx. Sig. Interval by Interval Pearsons R O Ordinal by Ordinal Spearman Correlation O N of Valid Cases 85 a. Not assuming the null hypothesis. b. Using the asymptotic standard error assuming the null hypothesis. o. Based on normal approximation. In the Crosstab table it shows the various ratings for comfort and atmosphere that were given in the cases examined. The Pearson's R value and Approx. Sig in the Symmetric Measures table are the two values that are the most important. In order to show that there is a significant correlation between comfort and atmosphere you would want the Approx Sig. to be.5 or less and the Pearson's R value to be close to 1. or -1..

13 An example of the One-Way NOVA output is as follows: nr,f.rf AN OVA Sum of Squares df Mean Square F Sic. Bebseen Groups Within Groups Total Dependent Variable: Overall Comfort I S1 Multiple Comparisons Mean Difference 95% Confidence Interval (I) Prototype (J) Prototype (l-j) Std. Error Sig. Lower Bound Upper Bound j.292 j.318 j I-. t ( ) 4. cr5 3.9[ Once again the important value to look at is the Sig. (significance) which should be.5 or less to say it is significant. With the Multiple Comparisons table an asterisk will appear next to the value in the Mean Difference column if the prototypes mean value is significantly different than the one being compared against it. The graph simply gives a visual of the information in the Multiple Comparisons table. 1

14 Analysis and Managerial Interpretation 11

15 First, a Crosstabs analysis was run first to see if there was a correlation between comfort and any of our carefully selected 19 variables. In order for us to say there is a strong correlation between comfort and other variables the Pearson's rvalue would need to be close to 1., meaning that there is a positive relationship, or -1. meaning there is a negative relationship. The maximum rvalue produced was.62. This is too faraway from 1. or -1. to confidently say that there is a correlation between the 2 variables. However, since we were dealing with almost 2 million survey entries comfort is somewhat affected by other variables. This maximum value was for the correlation between comfort and overall cleanliness as seen in Figure 1. 12

16 Figure 1. Correlation Between Comfort and Overall Cleanliness Crosstab Overall Cleanliness Total Overall Comfort Total Symmetric Measures Asymp. Std. Approx. Value Error(a) T(b) Approx. Sig. Interval by Interval Pearsons R (c) Ordinal by Ordinal Spearman Correlation (c) N of Valid Cases a Not assuming the null hypothesis. b Using the asymptotic standard error assuming the null hypothesis. c Based on normal approximation. Although these findings break the hypothesis that comfort is due to other variables such as staff and environment, this does not negate the possibility that the comfort is affected by the structure, also referred to as the prototype. The next analysis run was a One-Way ANOVA test. By using this we hoped to see significant differences in the means of the variables separated by prototype. Each of 13

17 our 19 variables was run separately using prototype as the factor of comparison. The means were calculated from the guests' ratings of I to S for each of the 19 variables. For the ease of documentation and readability each variable is set up with the figure name, corresponding graph, question asked, response and appropriate score, and the highest and lowest ranking prototypes. Figure 2. Acknowledged Quickly* D 1 9 ) I'll Prototype Question: "Were you acknowledged quickly when you entered the restaurant". Responses Yes-I, No-2, Do Not Recall-3 Highest scoring prototypes: II, 7 and 85 Lowest scoring prototypes: 12 Refer to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 14

18 Figure 3. Attentiveness of Server* 43 V 42 a) C/) (1) (I) ci) ci) > 4.- ci) (U ci) Prototype Question: "Rate the attentiveness of your server" Responses: 1-5 (Poor-Excellent) Highest scoring prototypes: 14 Lowest scoring prototypes: 7 Refer to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 15

19 Figure 4. Beverage Served Timely* 45 IEN > W CO 43 42J Prototype Question: "Beverage served in a timely manner?" Responses: 1-5 (Poor-Excellent) Highest scoring prototypes: 6, 9, 12 Lowest scoring prototypes: 7 Refer to Appendix for legend to mixed prototype labels conversion to numeric prototype labels Lr1

20 Figure 5. Food Served Timely* U a) >4. a) (WI) 'I- C CO Q) Prototype Question: "Food served in a timely manner?" Responses: 1-5 (Poor-Excellent) Highest scoring prototypes: 6, 85 Lowest scoring prototypes: 11 * Rclr to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 17

21 Figure 6. Enthusiasm of Server* Ir.CO U) r 43 9-, C Lii C (' Prototype Question: "Rate your sever on being enthusiastic and friendly" Response: 1-5 (Poor-Excellent) Highest scoring prototypes: 14 Lowest scoring prototypes: 7 Refer to Appendix thr legend to mixed prototype labels conversion to numeric prototype labels 18

22 Figure 7. Promptness of Payment* Prototype Question: "Rate the promptness of your payment process" Response: 1-5 (Poor-Excellent) Highest scoring prototypes: 14 Lowest scoring prototypes: 11, 1 Relr to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 19

23 Figure 8. Servers Knowledge* a) ) J Prototype Question: "Rate the knowledge of your sever" Response: 1-5 (Poor-Excellent) Highest scoring prototypes: 12, 7, 85 Lowest scoring prototypes: 11, 1 Refir to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 2

24 Figure 9. Welcomedt L) E42 1) 41 ( 4J ii Prototype Question: "Rate the staff on being friendly and welcoming when you entered the restaurant" Response: 1-5 (Poor-Excellent) Highest scoring prototypes: 12, 14 Lowest scoring prototypes: 11, 1 ReIr to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 21

25 Figure 1. Overall Atmosphere* 142 C) 41 > I.4- ci) ( Prototype Question: "Rate the atmosphere of the restaurant" Response: 1-5 (Poor-Excellent) Highest scoring prototypes: 8, 9, 1, 12, 14, 8 Lowest scoring prototypes: 11 Retr to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 22

26 Figure II. Overall Cleanliness U) U) ('3 a) 4.2 ('3 a) > 4.1 ('3 1) ii Prototype Question: "How would you rate the overall cleanliness of the restaurant?" Response: 1-5 (Poor-Excellent) Highest scoring prototypes: 1, 12, 14 Lowest scoring prototypes: 7, 85 Refer to Appendix for legend to mixed prototype labels conversioti to numeric prototype labels 23

27 Figure 12. Overall Comfortt 4.14 l 1 '-I. I ') Co a) 3.98J ii Prototype Question: "How would you rate the overall comfort of the restaurant?" Response: 1-5 (Poor-Excellent) Highest scoring prototypes: 8, 9, 1, 12, 14, 8 Lowest scoring prototypes: 11 Refer to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 24

28 Figure 13. Restrooms* (I, E I- NO Cr 3.8 r1 C Ct ci) 36 V V V V I I V I I ii Prototype Question: 1-low would you rate the cleanliness of the restrooms? Response: 1-5 (Poor-Excellent; for 'did not visit' was omitted) Highest scoring prototypes: 1, 12, 14 Lowest scoring prototypes: 6, 6, 7, 85 Refer to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 25

29 Figure 14. Compare To Similar _Overall* 4.1 a) > 2 a) 4 a) co CL E39 C- a) Prototype Question: "Compared to other similarly priced restaurants in the same vicinity, would you say Overall is..." Response: 1-5 (Much Worse- Much Better) Highest scoring prototypes: 9, 1, 12, 14, 85 Lowest scoring prototypes: 11, 7 - Refer to Appendix thr legend to mixed prototype labels conversion to numeric prototype labels 26

30 Figure 15. Compare To Similar _Atmosphere* 4. ci) 39 U) 38 CL O L 3.7 'I- a) Prototype Question: "Compared to other similarly priced restaurants in the same vicinity, would you say the atmosphere is.,." Response: 1-5 (Much Worse- Much Better) Highest scoring prototypes: 1, 12, 14 Lowest scoring prototypes: 7 Refer to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 27

31 Figure 16. Compare To Similar _Food* LL (A a) :: (D CO CI E38 = ca 11) Prototype Question: "Compared to other similarly priced restaurants in the same vicinity, would you say food quality is..." Response: 1-5 (Much Worse- Much Better) Highest scoring prototypes: 9, 12, 14, 85 Lowest scoring prototypes: 11, 1, 7 Refer to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 28

32 Figure 17. Compare To Similar Service* 41 a) 5 a) 4 a) 39 a) CL E (5 a; Prototype Question: "Compared to other similarly priced restaurants in the same vicinity, would you say quality of service is..." Response: 1-5 (Much Worse- Much Better) Highest scoring prototypes: 12 Lowest scoring prototypes: 11, 7 Refer to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 29

33 Figure 18. Operation Hours c ii Prototype Question: "How would you rate the hours of operation for this restaurant?" Response: 1-5 (Poor-Excellent) Highest scoring prototypes: 14, 8 Lowest scoring prototypes: 1, 7, 85 * Refer to Appendix for legend to mixed prototype labels conversion to numeric prototype labels 3

34 Figure 19. Overall* (T5 > D C 3.94 ('3 a) 3.92j Prototype Question: "Overall, how would you rate your experience?" Response: 1-5 (Poor-Excellent) Highest scoring prototypes: 9, 12 Lowest scoring prototypes: 11 * Refer to Appendix tbr legend to mixed prototype labels conversion to numeric prototype labels 31

35 IThe questions in regard to overall comfort and overall cleanliness had a paired question asking if the guest had rated it low, why. We found it was important to look that the frequencies of the reasons to the poor responses for the questions regarding overall Icomfort and overall cleanliness. In the GSS a low rating was considered anything less than very good (4) or excellent (5) on a 1 to 5 scale. 32

36 Figure 2. Frequency of why for low overall cleanliness rating 4 Overall Cleanliness Why Rated Poor 3 2 C) a) 1 a) LIE 1 1 L H Overall Cleanliness Why Rated Poor Question: "We strive for excellence. Please tell us the area(s) of the restaurant hat caused you not to rate Very Good or Excellent for Cleanliness." Response: 1- Exterior 2- Dining Room 3- Bar 4- Floor 5- Fixtures/Walls 6- Table 7- Seats 8- Menu 9- Silverware/Plateware/Glassware 1- Table Accessories 11- Staff's Attire 97- Other Most Frequent: Floors, Table, Silverware/Plateware/Glassware were the most frequent reason a guest rated low on overall cleanliness 33

37 Figure 21. Frequency of why for low overall comfort rating 1 Overall Comfort Why Rated Poor C-) 2 7 (D LL H H H Overall Comfort Why Rated Poor Question: "We strive for excellence. Please tell us the area(s) of the restaurant that caused you not to rate Very Good or Excellent for Comfort." Response: 1- Music Inappropriate 2- Noise Level Too Loud 3- Tables/Booths Too Close Together 4- Seating Uncomfortable 5- Lighting Too Dim 6- Lighting Too Bright 7- Temperature Too Cold 8- Temperature Too Hot 9- Table Cluttered 1- Unclean II- Insects in Restaurant 12-Cigarette Smoke 13- Other Most Frequent: Noise Level Too Loud, Tables/Booths Too Close Together and Temperature too hot were the most frequent reasons a guest rated comfort low 34

38 Conclusion and Critique 35

39 We concluded, from our graphs and statistical output, that Prototypes 14 and 12 had high scores and Prototypes 11 and 7.X consistently scored low in the survey. After reviewing the Prototype definitions we discovered that Prototype 14, which most often had the highest mean scores, had recently been renovated and refurnished. It was safe to assume that guests had enjoyed their restaurant experience here and the changes made to the restaurant had improved guest satisfaction. Since Prototype 11 was scoring low consistently we checked the output to see if there were any trends. Most of the low scores for Prototype 11 were associated with the staff and their lack of ability to keep the restaurant in order. There were consistent low scores regarding the server's attentiveness, the speed with which food and drinks were brought out to the guests and the time it took for the guest to receive his/her check and pay for their meal. This could imply that the staffs at these restaurants are not trained well or enthusiastic about their jobs or that the set-up and architecture of these restaurants is so poor that it prevents the staff from being time-efficient and good servers. Prototype 7.X is the same as prototype 7 except that it is expanded. There are only 2 stores with this prototype and only.2% of all cases were from prototype 7.X which could be the primary reason that 7.X consistently scored lower than 7 and most other prototypes. The other reason 7.X may have scored low is because of its location. The people in that particular area may not be fond of the prototype so if prototype 7 was there it may have also scored poorly. Our recommendations for Brinker regarding their Chili's restaurants is that there may be a need to ask more direct and specific questions regarding each aspect of a prototype. Another recommendation maybe for them to carry out some focus groups

40 inquiring about the low scoring prototypes in order to improve their scores or incorporate some of Prototypes 14 and 12's characteristics into their low-scoring stores to improve their ratings. Lastly, Brinker may want to do further analysis on the topic by examining the top 2 boxes of scores as opposed to comparing themeans because at times the means can be somewhat misleading with such a large amount of data. 37

41 Appendix 38

42 I * Prototypes had to be relabeled as only numerical values in order to perform the analysis in SPSS Numeric Prototype Label Mixed Prototype Label I 1 SP M I 55 5.A 6 6.X 7 7.X 85 8.X 39