Pilgrim Bank Case Analysis

Transcription

1 Mel Lazo ISDS 754 Marketing Analytics Module Dr. Black 7 December 212 Pilgrim Bank Case Analysis Introduction and Defining the Relationships The business analyst at Pilgrim Bank, Alan Green, was tasked with guiding the marketing team to a better understanding of customer profitability. With the help of his supervisor and the IT department, he has access to an extensive data set of over 3, customers, both old and new. Included are continuous variables for Profit, Tenure, and Satisfaction, categorical variables for Age buckets and Income buckets, and indicator variables for Online customers and BillPay users. In particular, Pilgrim Bank wants to understand the dynamics of profit between Online uses and non-users. For both years, 1999 and 2, the Online customers had the greatest average profit; also there was an increase in the number of Online customers: _9Profit _Profit Table 1 - Summary Statistics by Online Users N Sum Mean _9Online $3,317,624. $ , _Online $3,364, $ , Though the difference was not significant in 1999, it did turn out to be significant in the year 2, probably from the increase in users. In fact, the total Profit from Online customers almost doubled since On the surface, banking online does seem to drive profitability, or at least, should be reason to explore the relationship further.

2 Lazo 2 In order to explain the relationship between a customer and his/her profit, I have decided to view it in the frame of Regression Equations. If we find significant predictors of profitability, it could shed light on factors the marketing team should focus on to attempt and boost annual profit for the bank. For a very holistic, simplified view of this relationship, we can consider to following regression type relationship: The idea of building this regression model is for the purpose of explanation more than prediction. We want to be able to explain what drives profitability. First Attempts at Building Regression Equations We start off trying to explain the drivers of profitability for the year 1999 with a very simple Linear Regression Model using all demographic variables as inputs, including whether the customer was an Online user and if they used Pilgrim s online BillPay service. What we get is the following equation: Table 2 - Regression Attempt 1 Parameter s Variable DF Parameter Standard Error t Value Pr > t Standardized 95% Confidence Limits Intercept < _9Tenure < _9Online _9Billpay < Age < Age < Age < Age < Age < Age < _9Inc < _9Inc < _9Inc < _9Inc < _9Inc < _9Inc < _9Inc < _9Inc < _9District _9District

3 Lazo 3 The above regression equation is showing us how profitability changes based on the difference groups represented by the reference indicator variables. Interpreting these standardized regression coefficients for demographics, we can see for age bins 2 and 3, profitability went down when compared to the other groups and the higher age bins were a bit more profitable. With income, bins 2, 7, 8, and 9 were more profitable than the more moderate income levels. And finally, we can see that District 12 was significantly higher than the other two Districts. Notice, also, that being an Online customer was not significant in driving profitability where BillPay was. For the year 1999, Online may not have been a well-used channel, and with the old channels still in place, any profit it may have brought in was insignificant. Since BillPay users did have higher profitability, those Online users were at least profitability drivers. _9Profit _Profit Table 3 - Summary Statistics by BillPay N Sum Mean _9Billpay $3,73,783. $ , _Billpay $4,5, $ , We can see the same near doubling of average and total profit from 1999 to 2 when looking at customers that participate in the BillPay service. In the next year, can Online banking bring in more profitability if the company focuses on getting customers to use the Online channel more while also getting them to sign up for online BillPay services? Despite the Y2K Computer Bug, you can see above that online BillPay users still increased. We can look at regression for the year 2 to see if Online profitability changed. Regression for the Year 2 I essentially took the same approach for building a regression model as for the year The result is an equation with the following parameter estimates:

4 Lazo 4 Table 4 - Regression Model for Year 2 Parameter s Variable DF Parameter Standard Error t Value Pr > t Standardized 95% Confidence Limits Intercept < _Tenure < _Billpay < _Online _Inc < _Inc < _Inc < _Inc < _Inc < _Inc < _Inc < _Inc < _District _District We can see that the effects of the demographic variables were relatively the same, but now Online users were positively significant in profitability. So gathering these two explanatory regression models, I would recommend to the marketing team that there are huge profit opportunities in the online banking area to warrant incentive programs for customers to become Online users and take advantage of the BillPay service. However, only a small percentage (about 4%) of customers switched from non-online user to Online user in that year so there may be roadblocks to getting customers to switch. We can imagine younger customers being more receptive to online banking and utilizing online BillPay services, so getting new customers to sign up for online banking from the very beginning may yield higher profitability. Factoring in Customer Relationships Of the two models detailed above, neither had a high Adjusted R 2 value, indicating a less than perfect fit. We can account much of this imperfection in the very nature of real-life data, non-normality, and heteroscedasticity, however, we can look at one factor not yet used in the equation that may be of

5 Lazo 5 interest to the marketing team: customer relationship. Can we improve the above models if we include information about the loyalty and satisfaction of Pilgrim s customers? In order to address this question, we have been given the results of a satisfaction survey given to customers in 1999 with the variable _9Satisfaction. We will include this variable into the 1999 regression model and see if there is an improvement in model fit. Table 5 - Regression with _9Satisfaction Parameter s Variable DF Parameter Standard Error t Value Pr > t Standardized 95% Confidence Limits Intercept < _9Tenure < _9SATISFACTION < _9Online _9Billpay < Age Age Age < Age Age Age _9Inc _9Inc _9Inc _9Inc _9Inc < _9Inc < _9Inc < _9Inc < _9District _9District With the inclusion of _9Satisfaction in the model, we see that the Adj. R 2 fit statistic increased from.74 to.3198, a relatively drastic boost in model fit. Including a satisfaction score for the year 2 would most likely yield a similar boost in Adj. R 2. With this boost in fit, we also see the patterns in the demographic variables have changed. Younger- to moderately-aged customers were more profitable and the higher income levels were not actually as profitable. We also see from the _9Satisfaction estimate that highly satisfied customers significantly contributed to customer profitability. An obvious recommendation would be to increase marketing efforts geared towards increasing customer satisfaction.

6 Lazo 6 Customer relationship marketing, however, need not stop at understanding profitability. The implications of strong customer relationships point to increased customer loyalty, which is realized now to be equally important to a business as profitability. An understanding of this customer relationship could also shed light on determining customer lifetime value. Understanding Customer Behaviors Examining the dataset closely, I noticed that there are many customers that did not have data in the year 2, signifying that the customers have left Pilgrim Bank. Retention rates have been monitored by businesses extensively to gauge how well the business can attract loyal customers. Upon analysis of the Pilgrim Bank dataset, we can see that from 1999 to 2, the company has lost 5,162 customers, about a 16.3% churn in this one year. From the information available, I tried to determine the characteristics of customers that churned and those that were retained. Upon analysis, it is easy to see that Pilgrim was able to retain almost 86% of Online customers, and of those, almost 91% of BillPay users were retained. This comparison further advocates for marketing teams to push online initiatives since it has implications for both customer retention and profitability. Running basic t-tests also revealed significant differences in average profit and satisfaction for 1999 for retained customers t-test for _9Profit Method Var. DF t Val. Pr> t Pooled Equal <.1 Satterthwaite Unequal <.1 Table 6 - Corresponding t-tests by Retained Customers t-test for _9Satisfaction Method Var. DF t Val. Pr> t Pooled Equal <.1 Satterthwaite Unequal <.1 These results indicate that retained customers had significantly higher average profits and higher average satisfaction scores, indicating that building strong customer relationships comes from improving customer satisfaction and focusing on the drivers of profitability.

7 Lazo 7 Another important aspect for Pilgrim Bank to look at in understanding customer behaviors is customer acquisition. Customers interests and needs change over time, so carefully monitoring characteristics of a company s newly acquired customers. Pilgrim Bank has taken on 7,178 new customers in the year 2. Compared to customers in 1999, these new customers had more online and users on average. Figure 1 - Online Frequencies between Old and New Customers With only a two year period, it is hard to see major trends in the use of Pilgrim Banks online services, but at face value, we can see that new customers are more likely to be Online users. Over the next few years, with a solid online initiative from the marketing team, Pilgrim Bank can expect to see increased acquisition and improved customer retention.

8 Lazo 8 Tying it All Together with Customer Lifetime Value Together, looking at customer profitability, retention, and acquisition, Pilgrim Bank can begin to look at overall company growth by calculating Customer Lifetime Value (CLV). Conceptually, CLV is the total value that a customer adds to a company. More technically, it is the accumulation of a customer s historic transactions and the sum of the customer s discounted future cash flows (profits). Much speculation has been put into rigorously calculating CLV, and many different versions exist depending on the nature of the company and its customers. Without intimate discussion of Pilgrim Bank and their operations, it will be difficult to look at CLV at a level higher than just rough estimation. We can, though, establish a framework in the context of the Pilgrim Bank Case Study. Figure 2 - Customer Lifetime Value Framework Business Programs Related Metrics Goal Consequence Customer Retention Marketing Team and Customer Relationship Program Customer Acquisition Customer LIfetime Value (CLV) Company Growth Customer Profitability In attempting to estimate CLV calculations, we can look at known CLV calculations and assess the assumption used in calculation. The above calculation assumes constant margin (profit), m j and retention rate, r. Practically, those numbers will vary each year, and calculations would be made on that basis. Also, we can imagine

9 Lazo 9 the discount rate, I, to change over time. As such, this equation is rather simplistic. You will also notice the time index, T j, which also plays into the calculation assumptions. Ease of calculation, there has been instances that this index could be an expected lifetime of a customer, a very large number to estimate infinity, or infinity itself. The choice of which to use is up to the company and how they want CLV calculated. It is also important to note that this equation does not factor in customers historical values, another important part of the CLV calculation. According to a paper by Gupta and Lehmann (25), using the expected customer lifetime can over shoot the true value of this equation, and thus, using infinity would be the best method. Using a bit of calculus and algebraic manipulation, you can see that: for T j. Using this version of the CLV calculation, we can assess Pilgrim Banks company value by Online users and see yet again the value of marketing online services: _9CLV _CLV N Sum Mean _9Online $1,547, $ ,566, _Online $2,584, $ ,178, ,58.98

10 Lazo 1 References Gupta, Sunil; Hanseens, Dominique; and others. Modeling Customer Lifetime Value. Journal of Service Research, Volume 9 (2), 2 November 26, pg Hayes, Bob. Lessons in Loyalty. Customer Loyalty. QP March 211, pg Ogden, David. Mathematical Methodologies for Calculating Customer Lifetime Value. The SAS Institute, 212.