Introduction and problem definition
This section gives an overview of the report and the problem that this report is solving. The primary purpose of this report is to provide an analysis of some of the important aspects of customers from TESCO public limited company. The analysis is meant to assist Telco in making important decisions which are data- driven as well as being conscious of the nature of customer base that they have.
TESCO is public limited company leading international supermarket based in the United Kingdom (UK). TESCO Public limited company has a wide base of customers. TESCO public limited company would like to improve their customer experience based on the experiences that they have with the customers in the recent past. As a result, TESCO collected some important information from their customers. The information that was collected for each client included customers’ loyalty, customers’ churn, customers’ purchasing amount and the last time of purchase just to mention a few.
The problem that is being solved in this report is to help TESCO public limited company in predicting their customer’s churn as well as the Recency Frequency Monetary (RFM). Therefore, the focus of TESCO is to be able to predict their customers’ church based on the parameters that they have. Similarly, TESCO is interested in segmenting customers into different categories based on their churn or loyalty. Segmentation of the customers will enable the management to properly prioritize the customers and improve on their customer service as well as experience.
Finally, TESCO public limited company is interested in determining the Recency Frequency Monetary (RFM) of the customers. Recency Frequency Monetary (RFM) method is a technique that is commonly used in marketing and sales field to quantitatively analyze and determine the best customers for given product or business (Vermeeren, et al., 2011). Similarly, Recency Frequency Monetary (RFM) will also help TELCO to evaluate and improve on their customer loyalty scheme that they launched in 1995.
There are several studies that have been done on marketing, customer service, and customer satisfaction. Several scholars have published articles and journals on customer satisfaction; ways of improving customer satisfaction, benefits of improving customer satisfaction and why it is important to improve customer satisfaction (both to the business and to the customer).
Customer satisfaction is the way of quantifying the extent or the degree to which a client or customer is happy with a service, a product or even with experience during the service (Chesnokova, et al., 2013). A study by (Bourne & Paul, 2016) demonstrates why customer satisfaction is a very important concept for business. The study outlines that a satisfied customer is a loyal customer. A loyal customer is a treasure that a business should keep and hide from the rest of the business. The study demonstrates that a loyal customer is very crucial in ensuring that a business is on par with its competitors. However, this article did outline how to identify some of the loyal customers that a business should treasure.
Literature review
A study by (Jung & Yoon, 2013) outlines that customer satisfaction can be used as a tool to stand out of the rest of the competitors. Therefore, a business with stiff competition, it is important that they improve on their customer service and experience so that they can attract more loyal customers (Cheng, et al., 2009). However, this study did not outline the metrics or the techniques that business can use to identify loyal customers as well as keep the existing ones.
Exceptional customer service or experience can help a company to build a brand (Netemeyer, et al., 2010). When a company builds a brand of itself or a product, it will be very easy to reach a wide range of market as well as stand out of the rest of the competitors (Deng, et al., 2013). Similarly, building a brand will enable the business to be sustainable enough thereby fulfilling the concept of going concern. Therefore, it should be a primary objective for a business to offer exceptional customer experience to its customers as well as its prospective customers (Sun, et al., 2013).
A study on the analysis of market- based approach to customer satisfaction reveal that every customer should be treated as if they are “very important persons” ( VIP) (Hur, et al., 2015). The researchers argue that treating customers in a manner to suggest that they are highly valued and very important will improve their loyalty. The loyalty of a customer is one way of getting customers outstanding from the pool of customers. The study reveals that some of the best practices that can make a customer feel very important are when customer service officers make endless efforts to assist the customer in every way they can as well as when the customer service officers (and the entire staff of the business) keep their promises and remain honest. However, the study did not point out some of the important aspects that a business should consider doing to their customers so that the customers can feel they are treated with utmost respect.
The other way of improving customer satisfaction is by constantly measuring the level of customer satisfaction (Deng, et al., 2013). In this dynamic market, a business should make it a habit to constantly measure the level of their customers’ satisfaction. They should get timely feedbacks o that they can strategize and make necessary adjustments to improve on their customer experience. Similarly, constantly evaluating the level of customer satisfaction will enable the business to put the right technology in marketing and offering their services. Putting the right technology in place will ensure that a business is sustainable and is at par or above par with the rest of the competitors. However, this study did not outline how customers can be put into different segments or categories for a better experience. Moreover, the study did not outline some of the best ways and parameters for measuring the level of customer satisfaction.
Methodology and empirical study
The other way of ensuring exceptional customer service and experience is maintained is by knowing the right way to survey the customers (Setiawan & Budi, 2014). There are numerous ways and platforms of surveying the customers to get their feedback on their experience. However, not all the survey methods are suitable or can work for every business (Setiawan & Budi, 2014). The nature of the survey should be determined based on the nature of the products and services. However, this study did not reveal how to suitably determine a survey method.
A study by (Bueshken, 2009) reveals that it is very crucial to keep an eye on what the customers are saying about the business on social media. Similarly, it is important to keep an eye on what the customers are saying about the industry on social media. Therefore, it is very crucial for a business to build and maintain a serious and working online social media platform for better customer experience (Bueshken, 2009). The study further suggests that customer analytics and intelligence can be applied to such information to determine whether there should be an improvement. Similarly such feedback will help the business to know their position compared to the competitors in the industry (Bueshken, 2009)
This section describes the analytical tools as well as the analytical techniques that have been conducted to meet the objectives. Similarly, this section also outlines the research design, the model evaluation metrics, the working data, and the model building process. The analytical tools that have been used are Microsoft Excel and IBM SPSS (Statistical Packages for Social Sciences). IBM SPSS has been used to construct a model for predicting customer churn using binary classification trees. Microsoft Excel has been used to evaluate the performance of the model. Microsoft Excel has also been used to evaluate the performance of the model against the Recency Frequency Monetary (RFM) method.
As mentioned above, the analytical techniques that have been used are the decision trees and the RFM method. Decision trees have been chosen because building a decision tree is an easier and simpler way of making predicting and comparisons using SPSS (Reyes, et al., 2010). Similar, decision trees are crucial in identifying the groups in a data set, demonstrating the relationships among the groups and among the variables and predicting future occurrence of the events. Decision trees also outline the probability or the possibility of the occurrence of such events (Boos, et al., 2013).
Results
Recency Frequency Monetary (RFM) method is a technique that is commonly used in the marketing and sales field to quantitatively analyze and determine the best customers for given product or business. The determination of the best customers for products and services is done based on the examination of the most recent purchases that the customers have made, the frequency of purpose of the customers and the amount of money that the customers spend for goods and services. Customers who are considered to be the best for business are those who often purchase from the business and with significantly large amounts of money (Yin, 2012). RFM analysis uses sales data to create a segment of a pool of customers who would be best suited for the business (Schafer, 2015).
The research designs that have been applied in this study are the descriptive research design and the quantitative research design (Boos, et al., 2013). A descriptive research design is a technique that is applied to provide an explanation or description of an event or a phenomena. In our scenario, a descriptive research design has been used to outline the nature of the customers that TESCO public limited company have. The customers have been divided into either churners or non- churners (Boos, et al., 2013). A prediction model has been produced to forecast the number and nature of the customers that TESCO public limited company expects to have or receive for their goods and services (Boos, et al., 2013).
A quantitative research design is a technique that is used to describe a phenomenon in terms of the number or frequency of occurrence (Boos, et al., 2013). A quantitative research design is applicable when the parameters or the variables at hand are quantitative in nature (Boos, et al., 2013). A quantitative research design has been applied in this research to determine the number of churners and non- churners. Similarly, a quantitative research design has been applied in this research to determine the profit as well as determine the frequency of purchase for different categories of goods.
The data set consisted of the information that was collected from the previous customers that visited the TESCO public limited company (plc). The variables of the data set are shown below.
|
Variable Name |
Description |
|
ID |
The unique ID of customers |
|
Purchase |
Number of purchases during the observation period1 |
|
T.last |
The time gap between customer’s first purchase and last purchase during the observation period |
|
T.active |
The time gap between customer’s first purchase and last day of the observation period |
|
Loyalty |
A binary variable to show membership level: (0) Silver (1) Gold |
|
Service Failure |
Number of service failures during the observation period |
|
Total Profit |
Total profit generated by the customer during the observation period |
|
AP.spent |
Total spending on the Apparel category during the observation period |
|
BH.spent |
Total spending on Bakery category during the observation period |
|
DL.spent |
Total spending on Deli category during the observation period |
|
DY.spent |
Total spending on Dairy category during the observation period |
|
FV.spent |
Total spending on Fresh Produce category during the observation period |
|
GM.spent |
Total spending on General Merchandise category during the observation period |
|
GR.spent |
Total spending on the Grocery category during the observation period LQ |
|
LQ.spent |
Total spending on Liquor category during the observation period |
|
MT.spent |
Total spending on Meat category during the observation period |
|
Socio.Economic |
Socio-Economic status of the customer on a scale from 1(lowest) to 10 (highest) |
|
churn |
A binary variable to show the churn status of the customer in the prediction period2 (0) non-churner (1) churner |
The results of the model for that can be used to predict the churn are shown below. The results have been obtained by use of SPSS. The dependent variable was the churn while the rest of the variables except the customer id are the independent variables. The table below shows the model summary. The summary outlines that various segments of grouping the customers (Hasnelly & Eddy, 2012).
|
Model Summary |
||
|
Specifications |
Growing Method |
CRT |
|
Dependent Variable |
Churn |
|
|
Independent Variables |
T.last, T.active, Loyalty, Service Failure, Total Profit, AP.spent, BH.spent, DL.spent, DY.spent, FV.spent, GM.spent, GR.spent, LQ.spent, MT.spent, Socio.Economic, Purchase |
|
|
Validation |
None |
|
|
Maximum Tree Depth |
5 |
|
|
Minimum Cases in Parent Node |
100 |
|
|
Minimum Cases in Child Node |
50 |
|
|
Results |
Independent Variables Included |
Purchase, Total Profit, GR.spent, DY.spent, T.last, BH.spent, FV.spent, MT.spent, DL.spent, T.active, GM.spent, Service Failure, LQ.spent, Loyalty, AP.spent, Socio.Economic |
|
Number of Nodes |
57 |
|
|
Number of Terminal Nodes |
29 |
|
|
Depth |
5 |
Conclusion and Recommendations
The table below shows the prior probabilities. A prior probability is a probability value obtained from the data set that we had. From the prior probabilities, it is clear that the probability of getting a churner customer is 0.428 while the probability of getting a non- churner customer is 0.572.
|
Prior Probabilities |
|
|
churn |
Prior Probability |
|
0 |
.572 |
|
1 |
.428 |
|
Priors are obtained from the training sample |
|
Misclassification Costs |
||
|
Observed |
Predicted |
|
|
0 |
1 |
|
|
0 |
.000 |
1.000 |
|
1 |
1.000 |
.000 |
|
Dependent Variable: churn |
|
Risk |
|
|
Estimate |
Std. Error |
|
.213 |
.003 |
|
Growing Method: CRT Dependent Variable: churn |
The table below outlines the importance of all the variables as well as the normalized importance.
|
Independent Variable Importance |
||
|
Independent Variable |
Importance |
Normalized Importance |
|
T.last |
.231 |
100.0% |
|
Purchase |
.129 |
55.8% |
|
Total Profit |
.128 |
55.5% |
|
GR.spent |
.123 |
53.4% |
|
DY.spent |
.106 |
46.0% |
|
BH.spent |
.066 |
28.6% |
|
FV.spent |
.054 |
23.3% |
|
MT.spent |
.048 |
20.8% |
|
T.active |
.045 |
19.6% |
|
GM.spent |
.034 |
14.8% |
|
DL.spent |
.033 |
14.5% |
|
Service Failure |
.030 |
12.9% |
|
Loyalty |
.017 |
7.3% |
|
AP.spent |
.007 |
2.9% |
|
LQ.spent |
.007 |
2.8% |
|
Socio.Economic |
.001 |
0.3% |
|
Growing Method: CRT Dependent Variable: churn |
The table below shows the classification of the predicted churns. There are a total of 9238 cases of non- churners while the cases of churners are 2060.
|
Classification |
|||
|
Observed |
Predicted |
||
|
0 |
1 |
Percent Correct |
|
|
0 |
9239 |
2203 |
80.7% |
|
1 |
2060 |
6498 |
75.9% |
|
Overall Percentage |
56.5% |
43.5% |
78.7% |
|
Growing Method: CRT Dependent Variable: churn |
The bar graph of a normalized and importance. From this graph, we can say whether the variables are normally distributed. The data does not portray the characters of a normal distribution.
Using the analysis of a confusion matrix, it is clearly demonstrated here that the accuracy is 78.6%. This result demonstrates the model that we have developed above has predicted 78.6% of the real churners. Apart from the calculations below, this assertion can also be seen in the classification table. According to the records in the classifications table, the model has predicted 78.7% of the churners.
|
Actual |
Prediction |
|
|
0 |
1 |
|
|
0 |
True Negative (TN) |
False Positive (FP) |
|
1 |
False Negative (FN) |
True Positive (TP) |
The cumulative lift chart
The table below has been used to produce a graph of the cumulative lift.
|
Decile |
Decision Tree |
RFM |
Random |
|
0 |
0 |
0 |
0 |
|
1 |
18.34862 |
18.70148 |
10 |
|
2 |
36.83839 |
36.81487 |
20 |
|
3 |
55.11644 |
55.91625 |
30 |
|
4 |
71.58316 |
71.44201 |
40 |
|
5 |
80.52223 |
82.99224 |
50 |
|
6 |
84.61538 |
91.53140 |
60 |
|
7 |
88.26159 |
96.21266 |
70 |
|
8 |
92.23712 |
97.90637 |
80 |
|
9 |
96.23618 |
99.76476 |
90 |
|
10 |
100 |
100 |
100 |
The full output of the model can be found in the appendix section of this report. The appendix provides the actual predicted values as well as the surrogate values.
The table below outlines the output of the comparison of the RFM and the CRT.
|
Decile |
Decision Tree |
RFM |
Random |
|
0 |
0 |
0 |
0 |
|
1 |
18.34862 |
18.70148 |
10 |
|
2 |
36.83839 |
36.81487 |
20 |
|
3 |
55.11644 |
55.91625 |
30 |
|
4 |
71.58316 |
71.44201 |
40 |
|
5 |
80.52223 |
82.99224 |
50 |
|
6 |
84.61538 |
91.53140 |
60 |
|
7 |
88.26159 |
96.21266 |
70 |
|
8 |
92.23712 |
97.90637 |
80 |
|
9 |
96.23618 |
99.76476 |
90 |
|
10 |
100 |
100 |
100 |
Conclusion
The analysis results demonstrate that TESCO can actually pull out their customers from a pool of customers. Pulling out customers from a pull of other customers ensures that better customers experience and service is maintained. From the results, it is clear that the model can pull out 78.6 % of the churners. Therefore, if TESCO will choose to use this model for predicting the churns in the pull of customers, it is possible that they will a significant number of customers (Stock & Bednarek, 2014). The number of churns that they will get will be sufficient to ensure significant profits (Shestakov, et al., 2010).
The comparion of the RFM and CRT reveals that FRM will yield higher income compared to the random (test) group of customer. Therefore, it would be prudent of the management of TESCO to use RFM (Setiawan & Budi, 2014). This will ensure that they manage a large pull of customers so that they keep at par with the competitors. Similarly, it will enable them to easily pull out their customers from the large pool of customers for better customer experience (Otterbring, et al., 2018).
It is recommended that TESCO does further research on the distribution of their customers. The research should focus on comparing the customer bases and income. Moreover, the research should focus on evaluating the level of customer satisfaction and experience in those areas (Nahm, 2013). Conducting such studies will enable TESCO to determine the best ways of distributing their supplies (Lee, et al., 2015). Similarly, the study will help TESCO to make necessary improvements for better and sustainable customer experience (Kapustina & Babenkova, 2010).
It is recommended that TESCO should conduct proper market research aimed at determining the level of customer satisfaction of their competitors (Jacobson, et al., 2009). The research will help TESCO to know their position in terms of customer experience that they offer (Helia, et al., 2018). Therefore, by conducting the research, TESCO will be at a point of making necessary adjustments in terms of their customer service and experience (Hur, et al., 2015). Moreover, the research will be crucial in ensuring the sustainability of TECO public limited company (plc) (Bueshken, 2009).
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