Esther's Hypotheses 1. There are stark differences between male and female customers and operators. a. We have identified most of the difference between male and female operators (Thanks Juan). However, any time we provide any means or differences between groups, we need to ensure that we also provide SE, T-stat, confidence interval (optional) and P-value to assert out finding statistically. This is a general note throughout our assignment. b. TODO:// Put T-stat and P-values for differences between males and female customers and operaters 2. The obvious other drivers of customer rating are holding times, service times and operator experience. a. Regression model is done. See below 3. Service time is a cost which I would hope to control, if not minimise, but not at the expense of losing a customer. a. Regression model is done. See below. Research Questions Q: What drives customer rating, with a view to improving rating while controlling service times? TODO:// Once we settle on the regression model, we can advise the recommendation Q: Which of the drivers do you identify could have managerial implications? - Done, although the language could be improved. Notes from Final Instructions The main deliverable that I want is a quantitative description of how the variables provided are related - which ones drive which and how strongly. I would like to know how much an extra minute on hold affects customer rating, holding service time constant, but if holding times affect service times I need to know that as well. - Done Service times also are not benchmarked though if an operator consistently handled less than 10 calls per hour, we would ask why. Most are handling more like 15. (MINE://Handling 15 calls per hour means spending 4 mins on each call which results in low rating. This can also have a managerial implication. Based on our analysis calls that take between 4-8 minutes result in highest rating. To be discussed with the team) We would be interested in knowing what might be the effect of increasing the experience level of our operators. - Done If we could reduce holding times by one minute on average then how would this affect the services times and ratings of our male consultants? - TODO:// Out of Scope I definitely would not expect a recommendation on how much M-Tel should spend on reducing holdings times. Executive Summary TODO:// Introduction The M-Tel call centre has been set up to handle complaints since late 2012, they provide support to customers often early in their contracts that do not fully understand their bills. Esther would like an insight into how they can minimise the costs of the complaints section of their call centres without sacrificing the quality of customer service. Our focus will be on how we can better understand what drives the customer rating and how these drivers can be influenced by management. Background & Key Issues/Analysis & Results Males vs. Female Customers Approximately 70% of the customers are males. 786 Male and 334 Female customers called the call centre for 1120 total calls. Male vs. Female Customers 900 786 800 700 600 Total 500 400 334 300 200 100 0 0 1 Hold Time Preliminary analysis of the data indicates that increase in hold time leads to lower ratings. In particular, Males customers are more sensitive to longer hold time as opposed to Female customers. Overall, hold times longer than 2 minutes lead to ratings below the benchmark (ratings: 6) Hold Time vs. Ratings 8.00 7.00 6.00 5.00 4.00 Total 3.00 2.00 1.00 0.00 TODO:// Put it in a graph, t-stat and p-values for each hold group. Need to put SE, t-stat and p-values as it is one of the hypothesis of Esther. These are not matched pairs Service Time Calls that take small time to service score poorly on customer rating whereas calls that take between 4 to 8 minutes, the average ratings are above 7. Service Time vs. Rating 8 7 6 5 Total 4 3 2 1 0 The graph also suggest that the relationship between service time and rating is curvilinear. Customer Gender Female customers systematically give higher customer ratings than male customers. (TODO:// Create SE and T-stat and P-Values) Female vs. Males Ratings 6.8 6.6 6.4 6.2 Total 6 5.8 5.6 5.4 5.2 0 1 19% of the calls take between 2-4 minutes and have an average rating of 3.70. 73% of the calls take between 4-6 minutes and have a 6.55 rating. 8%~ of the calls take between 6-8 minutes and have a 7.10 rating. (TODO:// Graph this info, t-stat and p-values) Operator Gender Female consultants tend to receive higher customer ratings as opposed to male consultants (TODO:// Create SE and T-stat and P-Values) Operator vs. Ratings 6.08 6.07 6.06 6.05 Total 6.04 6.03 6.02 6.01 6 5.99 0 1 Customer/Operator Gender To address the question of whether or not gender plays a role in the interaction between customers and consultants we will measure the average ratings across the different gender combinations. Average Rating and 95% Confidence Interval 8 7.5 7 6.5 6 5.5 5 4.5 4 Customer Gender/Consultant Gender From the chart above we can see that female consultants get better ratings when engaged with female caller and slightly less than average when engaged with a male caller. (TODO:// Create SE and T-stat and P-Values for difference between the groups) Gender & Service Time Calls from Female customers take 25's (9%) more, on average. Also Female consultants spend an extra 37's (13.5%) when dealing with Female customers. Male consultants take 4% extra when dealing with Female customers. (TODO:// Probably best to keep put this section with Regression Model) Experience This graphs shows that consultants with higher experience tends to receive better customer ratings. One key observation here is that there exists a dip in the customer ratings received by consultants during 4 to 6 months of service. The relationship however, looks fairly linear with slight dip as we move towards right. Females are, on average, more experienced than Males, with 17.6 months of experiences vs 15.24 for Males. (TODO:// Graph it, t-stat and P-Value) Total 12 10 8 Total 6 4 2 0 1-3 4-6 7-9 10-12 13-15 16-18 19-21 22-24 25-27 28-30 31-33 Experience & Service Time Less experienced consultants tend to take longer to pick up incoming calls (I think we can remove this observation, does not seem to add value), this is the case for both Males and Females. Also, consultants with more experience tend to have longer calls (Comment:// This is also validated by the regression model) (TODO:// Graph it, t-stat and P-value)(*maybe this is why calls than are shorter than 4 minutes have a lower rating). Attrition Based on the number calls, female consultants seem to stay longer in the job (1 to 2 years), Male consultants seem to quit earlier with the average Male consultant more likely to work only for 1 to 1.5 years. (TODO:// Graph, t-stat and P-value) Drivers for Customer Rating Performance variables The two key performance variables are: Customer ratings Service time (Cost) Variable Diagram Hold Time Rating Experience Service Time Regression Model - Ratings The Regression Analysis suggest the following drivers for the key performance indicator - Customer Ratings: Regression coefficients Coefficien Predictor t Constant -9.32151 Hold -0.05526 Service 0.07295 Service2 -0.00009 HoldServic e 0.00008 Experience 0.26834 Cust_MM 0.79469 Cust_FM 0.80681 Std Err 0.82759 0.00564 0.00663 0.00001 t-value -11.3 -9.8 11.0 -7.0 p-value 0.000 0.000 0.000 0.000 0.00002 0.00738 0.11026 0.14700 4.8 36.4 7.2 5.5 0.000 0.000 0.000 0.000 Cust_FF 0.48492 0.14059 3.4 0.001 The Model has an Adj. R square of 77.3% and explains 77.3% of the variability in ratings and the model error is 1.53. The regression model validates that the key drivers for the Customer Ratings are: Service Time Hold Time Operator experience Gender of the Customer and Operator Service Time The relationship between service time and customer ratings is curvilinear (downward sloping) due to negative quadratic coefficient. There exist overwhelming evidence of that service times impact customer ratings, with t-values for linear and quadratic coefficients being 11.0 and -7.0 respectively. The relationship explains that keeping other variables constant, increase in service time leads to higher customer ratings up to 403 seconds beyond which the ratings start to decrease for every additional increase in service time. Service time has managerial implications as it relates directly to cost and can be controlled. Hold Time There exist strong evidence that hold time affects customer ratings as it has a t-value of -9.8. An extra minute on hold, reduces the ratings by 3.32. However, the effect of Hold time on ratings depends on Service time (t-value: 4.8) and equals -0.05526 + 0.00008 * Service time. In other words, the negative effect of Hold time on rating is offset by additional 0.0048 rating for 1 minutes of increase in Service Time. Reducing the hold time by 1 minute will increase the rating by 3.32, keeping everything else constant. Hold time has managerial implications and can be controlled by expanding the call centre during peak hours. Operator Experience The customer ratings go up by 1.662 for every 6 months of increase in experience. There exist strong evidence that hold time affects customer ratings as it has a t-value of 36.4. This is the highest t-value among all other factors implying that management should undertake efforts to increase their retention rate as that would help achieve the industry benchmark of 6.0. Gender of the Customer and Operator Cust_MF Cust_MM Cust_FM Cust_FF Average 0.00 0.79469 0.80681 0.48492 0.69547 The table above explains that the mean difference in ratings between male customer and female operator is 0.69547. The mean difference is highest when female customers and handled by male operators, followed by the interaction between male customers and male operators and lastly with female customers and female operators. While this is an interesting finding, due to the lack of ability to route calls based on the customer gender limits putting these findings into action. Regression Model - Service Time The Regression Analysis suggest the following drivers for the key performance indicator - Customer Ratings: Regression coefficients Predictor Constant Hold Experienc e Cust_FF Cust_FM Cust_MM Coefficien t 177.905 0.676 Std Err 4.160 0.024 t-value 42.8 28.7 p-value 0.000 0.000 2.508 17.484 10.470 7.066 0.168 3.508 3.687 2.775 15.0 5.0 2.8 2.5 0.000 0.000 0.005 0.011 The Model has an Adj. R square of 48.7% and explains 48.7% of the variability in Service time and the model error is 1.53. The regression model validates that the key drivers for the Customer Ratings are: Hold Time Operator experience Gender of the Customer and Operator Hold Time There exist strong evidence that hold time affects service time as it has a t-value of 28.7. This is the highest t-value among all other factors implying that management should undertake efforts to decrease the hold time. An extra minute on hold, increase the service time by 40.5 seconds driving up cost. Operator Experience The service times goes up by 15 seconds for every 6 months of increase in experience. There exist strong evidence that hold time affects customer ratings as it has a t-value of 15. Gender of the Customer and Operator Cust_MF Cust_MM Cust_FM Cust_FF 0.00 7.066 10.470 17.484 Average 11.673 The table above explains that the mean difference in service time between male customer and female operator is 11.673. The mean difference is highest when female customers are handled by female operators, followed by the interaction between female customers and male operators and lastly with male customers and male operators. Limitations of Methods Ratings o Models only explain limited variability in data. o The model tries to predict a nominal variable with linear numerical model restricting its usability. o The useability of the model to predict is limited but can be used to identify the key drivers. o The number of outliers are more than acceptable. Service Time o Model only explains limited variability in data (48%) o There are 51 outliers (s-residual of plus or minus 2.0) which are unexplained by the model. Recommendations & Conclusion Other Notes Key measures count, mean, standard deviation, standard error, T-scores, P-values, 95% confidence interval, net promotor score, frequencies/percentages, correlations of variables. 1. Graph correlations of variables 2. Detail partial correlations Targets, Benchmarks - best practice examples Report Structure (12 pt font, 2 cm margins) 1. Table of Contents 2. Executive Summary incl. Recommendations (2 pages) 3. Body of the Report (10 pages) a. Introduction to the problem b. Background to the problem c. Explanations of how we formalised and set up the problem d. Analyses and the results of the analyses (descriptions of methodology, summary tables and graphs) e. Recommendations f. Conclusion Typical body sections might be Background and Key issues (largely verbal and diagrams), Problem formulation (probably more mathematical), Analysis and results (probably using tables and plots to summarise), Recommendations and conclusions. The page limit on this section is 10 pages in total, and it should be well structured. You do not need to use the whole 10 pages to get a good mark! 4. Appendices - data listings, minute technical details of the analyses or computer output Relationships Service time vs. Holding time o Scatter plot o Correlation Data Distribution o MM, MF, FM, FF Appendix Male and Female consultants take the same amount of time to deal with calls. COPYRIGHT MATERIAL M-Tel Call Centre data: What does it tell us? In May 2015, Esther Ching is three months into her role as Operations Manager for MTel, a Malaysian telco company. Her main responsibility is managing all the complaint call centres located across India. These centres handle all billing complaints and service difficulties. The main products are landlines, mobile telephones and internet access. She is required to present a report about the performance of her section, as part of the regular performance review. In the original job description her role was to \"control the total costs of the complaints section while maintaining an excellent level of service to our customers likely to ensure long term return business.\" The main cost is the hourly labour costs of the over 300 consultants employed in a typical centre. Including on-costs this works out at $38 per hour but the salary is lower for the first 6 months of employment resulting in a cost of $29 per hour. The consultants range in experience from a few months to three years. To a very large extent, consultants can be rostered on and off to match the busiest periods of incoming calls, in three hour blocks. There are roughly equal numbers of males and females, though nearly all consultants are between 20 and 40 years of age. Esther wants to obtain a snapshot of how customers react to their experience with the complaints centre as well as the efficiency of the consultants in dealing with complaints. Problem Background The complaints section of the call centre has been in operation since late 2012. Prior to that date, this function was outsourced and the call centre only handled sales. Customers are often confused about the details of their M-Tel bills, especially during the first few months of their contracts. The level of confusion is not helped by the high turnover of sales staff who sometimes fail to properly explain the terms of a new contract and can send customers to inappropriate contracts for their specific needs. Customers reach the complaints section of the call centre of M-Tel through an electronic management system (EMS) that sorts the customer's problem into several COPYRIGHT MATERIAL categories. The EMS is also able to have the consultant record some basic data about each customer call as well as detect data input by the customer. Three time stamps are automatically recorded by the EMS. First, the time that the customer joins the complaint centre queue. Second, it records the time that the call is answered by a human operator. Third, it records the time the call is completed. All times are recorded to the nearest second. So it is possible to calculate how long each customer waited before being answered and how long the centre spent dealing with the customer complaint, all to the nearest second. Some customers hang up before reaching a consultant. Every thirty seconds, they are advised of the likely remaining wait time so many will hang up if the quoted time is too long. Others will initially hold and then hang up if the wait is not reducing fast enough or if it has been too long. Two important additional pieces of information are also obtained. First, the gender of the calling customer is also recorded by the consultant. The customer identity is known and on the database but the consultant records the gender by taking note of the name and voice. Secondly, and perhaps most importantly, at the completion of the call each customer is asked to rate their level of satisfaction with the company's complaint handling procedures. They hear the following recorded message at the end of the call. You have taken up your valuable time to contact us about a problem with MTel's billing service. We hope that the problem has now been resolved. We would appreciate you rating your service experience with our complaints service. The consultant you just spoke with will not be aware of your rating. Please give your rating on a scale 0 to 10 with 0 being most dis-satisfied and 10 most satisfied. You can speak your rating into the mouthpiece after the tone. The data. The worksheet EMS data contains data on 1120 calls automatically stored by the EMS from the Malay state of Johor only and over the month of May. This data base is only for customers with billing complaints and excludes those customers who hung up before reaching a call centre consultant. The lost calls comprise only about 5% of connections in the long run and it is believed that these customers will call back at a less busy time. The data also excludes those customers who declined to give a rating. These data comprise around 10% of calls. One could think of this as a 12th possible rating response of the customer in addition to the more informative response of 0-10. The first column headed hold records the number of seconds the customer was kept on hold before reaching the operator. The column headed service records the duration COPYRIGHT MATERIAL of the call in seconds. The column headed rating gives a satisfaction rating from verydissatisfied to very satisfied, coded on the 0-10 scale. Columns Customer and consultant have the gender of customer and consultant with 1=male. In the last column is the number of months the consultants has been in the job, obtained by matching the consultant ID which is recorded by the EMS with their HR records. The problem. Esther has the spreadsheet in front of her - a bewildering array of numbers. She somehow needs to convert these numbers into information, ideally information that leads to a better understanding of the management issues options. She puts out a tender for an analysis of the data base, and you win the job. Her letter scoping the job is below. COPYRIGHT MATERIAL Dear Consultants Churn in the mobile communications industry is high. M-Tel care about our customers and the call centre is one point in our customer relationship where we have a chance to make a difference. Customers are already inconvenienced when they call us. So we can either rescue or confirm a potential customer loss. Our call centre has collected information on a very large database of customers. I have provided you with a database of 1120 calls as discussed. Though there may be some differences across different centres and times of year, you can take the provided data base as a reasonable summary of all our operations. If we are happy with your analysis, we may hire you to repeat the analysis on other databases at a later date. Based largely on anecdotal evidence, but also on my probably superficial examination of the data, we suspect that there are stark differences between male and female customers and operators. The obvious other drivers of customer rating are holding times, service times and operator experience. Of course, from my point of view, service time is a cost which I would hope to control, if not minimise, but not at the expense of losing a customer. So my primary interest is to understand what drives customer rating, with a view to improving rating while controlling service times. Which of the drivers you identify could have managerial implications? I am not just interested in this for scientific curiosity! I want to be able to use the results. You are welcome to contact me with further questions or to seek my reactions to your ideas up until Friday August 26th. Sincerely Esther Ching Operations Manager (call centres) M-Tel Hold Service Rating Customer Consultant Experience ID 84583 84628 84634 84666 84680 84717 84818 84913 84932 84971 85031 85070 85099 85164 85175 85279 85289 85300 85386 85486 85588 85637 85717 85785 85802 85899 85936 86022 86039 86081 86181 86279 86375 86466 86539 86546 86560 86591 86611 86673 86710 86796 86896 86943 87000 87090 87116 87180 87206 87263 90 88 77 94 56 59 134 108 203 71 53 50 42 86 88 41 64 139 51 85 89 42 78 63 45 57 95 90 86 90 99 83 44 36 65 63 114 80 44 71 66 65 29 103 101 68 44 33 82 124 280 305 237 261 285 267 340 315 346 213 229 223 224 313 298 201 257 335 227 345 345 246 236 248 244 295 329 283 261 282 333 199 278 281 288 264 367 293 236 333 257 288 277 290 330 310 197 278 197 394 6 7 1 6 9 7 2 7 3 2 4 4 8 10 1 2 6 3 6 8 10 4 5 3 8 6 7 7 4 9 8 0 9 9 9 10 5 9 4 10 9 4 6 3 6 10 1 9 1 10 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 0 1 1 1 0 1 1 0 0 0 0 1 1 1 0 0 0 0 1 0 0 1 0 1 1 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