KEEPING CUSTOMERS USING ANALYTICS

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1 KEEPING CUSTOMERS USING ANALYTICS This paper outlines a robust approach to investigating and managing customer churn for those in the business-to-consumer market. In order to address customer retention and loyalty, businesses must first understand customer churn through comprehensive analysis and quantify its impact. This analysis will provide initial insight, potentially identifying specific groups of customers requiring market research or specific communication attention. Depending on the industry and market conditions, a predictive churn model may be required to help identify potential churners. The analysis provides the foundations for developing customer retention and customer loyalty marketing initiatives to minimise the future impact of customer churn. FOLLOW A PHASED APPROACH Essentially there are a number of distinct phases leading to effective churn management. The biggest challenge can be breaking what can seem a mammoth task into manageable pieces. Figure 1: Phases of analytical driven retention The starting point is to quantify the type and level of churn and its impact on the business. Given the existence of a churn issue, the next phase is to understand the key factors driving churn. Depending on the nature of the factors influencing churn, a predictive churn model may be required. Armed with the capability to identify who is likely to churn, retention initiatives can be developed and designed to manage this. The final phase is to measure the effectiveness and refine as needed. KEEPING CUSTOMERS USING ANALYTICS SALLY CAREY DATAMINE LTD 1

2 Quantifying the impact of churn Churn within the business needs to be quantified. Before doing this, a key step is to define what is really meant by churn. This essentially defines the type of customers that you need to manage. Often, both voluntary and involuntary churn exist. An example of involuntary churn is a bank closing a customer s credit card as a result of non-payment. Usually, for churn management, the focus is on voluntary customer churn. The next consideration is whether to focus on both hard and soft churn. Hard churn is when there is a defined event that signifies churn, for example, the closure of an account. However, this approach may be too simplistic, thereby requiring soft churn to be considered. A customer may be defined as having soft churned if they have not transacted with the organisation for a period. The length of time varies depending on the nature of the industry and often on the customer s initial behaviour (namely, transactional frequency). For example, a shorter period would be used if focusing on supermarket shopping (as we shop and eat every week) compared to booking a holiday. Consideration should be given as to whether to focus on a specific customer group. The measurement may be limited to customers with the most valuable product, for example home loans for banks, or to another area such as the busiest time of year for seasonal businesses. Restrictions regarding the interface of existing systems may require a product led approach rather than taking a holistic customer view. This definition phase considers the practical issues within your business and should not be left to an analyst to decide in isolation from the business. Once the degree of churn is known, it needs to be expressed as a financial consequence to the business. This assists with getting the right degree of focus from the business - a 5% churn rate could be costing billions of dollars. To assess the financial impact depends on the value or profitability by product, service, channel and/or customer segment. A number of assumptions based on customer knowledge can be made. For example if the average tenure of a customer is 5 years, the expected future revenue can be built into the assessment. The purpose is to gauge the magnitude of churn on the business, not to provide a highly accurate measure. KEEPING CUSTOMERS USING ANALYTICS SALLY CAREY DATAMINE LTD 2

3 Understanding the key factors surrounding churn To understand more about the type of customers that are churning involves profiling by comparing customers who have churned compared to customers that have not churned. This will use all available information, including service history, behavioural, demographics, channel, value, usage etc. Integrating any specific information collected at the point of disconnecting, for example, reason codes, is useful. The behavioural profile identifies the typical customer patterns of behaviour and interactions with the business prior to churning. Integrate any qualitative market research that is available to help build understanding of the complete customer landscape for the churn events. Developing the predictive model Broadly, the modelling process commences with the collation of all data variables and derived variables as inputs to the model. This is followed by model development and appropriate testing and validation methods. The business owners input is required with the upfront planning (to identify all the variables that could be useful) and the back-end testing, leaving the modelling to an expert, statistical analyst. Testing involves the use of historic data to produce a lift curve and a list of variables within the model together with their predictive power. Once the model has been built and tested, its performance needs to be assessed. This will indicate the overall effectiveness of the modelling process. Using the model on a data set where the status of the customer is known, a table showing the proportion of false positives and false negatives can be produced. The proportion of these needs to be weighed up against the potential profitability of these customers and the cost of the communications. KEEPING CUSTOMERS USING ANALYTICS SALLY CAREY DATAMINE LTD 3

4 Figure 2: Summarising the validity of the model predictions based on historical data. The objective is to optimise the number of accurate predictions that will appear as indicated by a cross in the chart above. Implementing retention initiatives Key findings and recommendations based on the analysis provide the foundation for implementing customer retention and customer loyalty marketing initiatives to minimise the future impact of customer churn. Examples of the type of insight you can expect include the best time to contact a customer based on a moment of truth and a defined demographic profile to help with creative and offer. The combination of likelihood to churn or risk of churn and value provide a powerful basis for marketing action. Figure 3: Focusing in on the most valuable customers and those that are likely to churn. KEEPING CUSTOMERS USING ANALYTICS SALLY CAREY DATAMINE LTD 4

5 These initiatives will need to run for long enough to gather sufficient data to measure their effectiveness. Knowing what s working and what s not There are two key aspects to measure to know whether the model is working as anticipated and to know whether the communications in place are effective. Figure 4: Establish a framework to measure the accuracy of the model predictions independently from the effectiveness of the communication programme. Control group 1 s retention rate is compared with the customers retention rate within the retention program. Both groups have a high likelihood of churning and this comparison measures the effectiveness of the communications. Control group 1 s retention rate is compared with control group 2 s retention rate. Neither of these groups has received a communication and the expected result is that a higher number of customers are lost from control group 1 than control group 2. KEEPING CUSTOMERS USING ANALYTICS SALLY CAREY DATAMINE LTD 5

6 SUMMARY Churn analysis is the first essential step towards implementing effective customer retention and customer loyalty programmes. More specifically, it will: Establish the cost of customer churn to your business and provide justification for appropriate investment in customer retention and customer loyalty initiatives Target retention efforts on high-value customers with a high risk of churn Focus retention efforts on the areas over which your business has most control Combat churn by providing the data backbone for developing predictive modelling for more proactive customer management KEEPING CUSTOMERS USING ANALYTICS SALLY CAREY DATAMINE LTD 6

7 BRIEF AUTHOR BIOGRAPHY Sally Carey Sally is Director of Datamine Ltd, a New Zealand based analytics consultancy that moves its clients beyond guesswork. Sally has over 25 years of B to B and B to C marketing and using quantitative approaches for business decision making. Sally has an MBA from Bradford University (UK) and is a Fellow of the Institute of Direct & Digital Marketing (UK). Sally believes that extraordinary results are achieved by a combination of analysis and intuition, and have been referred to by some clients as magic. Keywords: churn, retention, profiling, modelling, predictive model, performance, data mining, direct marketing, customer insight KEEPING CUSTOMERS USING ANALYTICS SALLY CAREY DATAMINE LTD 7

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