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In the communications sector, customer loyalty is the key to profitability - it costs much less to retain an existing customer than it does to win a new one

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In the communications sector, customer loyalty is the key to profitability - it costs much less to retain an existing customer than it does to win a new one. As part of an organizational business transformation project, XO Communications identified an opportunity to improve its performance in the small- and medium-sized business sector by increasing customer retention, and needed a strategy that would help it achieve this in a cost-effective way. The majority of the customers in this segment spend less than $1,000 per month with XO Communications, so it was not economically prudent to allocate dedicated client service managers to each account. At the same time, it was vital to offer these clients a high level of service and ensure that any issues they had were dealt with quickly and effectively. Instead of a 'brute force' approach - simply hiring more client service managers - the company needs to identify the customers who were most at risk of changing providers, and prompt the client service team to intervene appropriately. The XO Communications team worked with IBM Education Services to build up a high level of competence in-house and transitioned away from the early models built by third-party consultants. This enabled the team to identify the factors that indicate whether a given customer is likely to change providers, and build a sophisticated statistical model that provides a monthly risk assessment for each customer. Customers are prioritized based on the score the model calculates for them, and the client service team deals with the highest-risk customers first: making contact with them, finding out if they have any problems or are dissatisfied with the service they are receiving, and then taking appropriate steps to improve the situation. By providing an analytics solution in-house and giving client managers the ability to identify the customer relationships that need most attention, XO Communications has been able to reduce customer churn without substantially increasing headcount. XO Communications has applied the skills and experience from this project to generate additional returns in subsequent projects. With additional tuning and analysis, the next phase of the customer churn project has produced another $7 million in revenue and the model is 50 percent more accurate than evaluation and deployment. During the process, the team evaluated more than 500 variables to see which had the most influence on customer retention. Ultimately, a model was built based on the 25 most relevant variables. The model assigns risk scores to each customer. The top 20 percent reflects 66 percent of the churn, so the top 10 percent are passed on to the centralized retention team, and the next 10 percent are passed to the field retention team for remedial action. As part of the project, the Customer Intelligence team received comprehensive training from IBM, which helped not only to gain technical knowledge, but also to build up a set of best practices for future predictive analytics deployments. As a result of the expertise gained, the team's churn prediction model has been able to outperform the early third-party model by 50 percent. With the success of this initial project, the team is now embarking on a number of new initiatives using predictive analytics, as well as expanding information delivery with dashboards and business intelligence.

Questions

1. What analytics solution is needed by the company?

2. What are the necessary components to solve the problem? Identify and explain.

3. Why should analytics be used instead of just hiring more client service managers?

4. Given the CRISP-DM approach that makes use of the following steps in providing a predictive analytics solution a. Business Understanding

b. Data Understanding

c. Data Preparation

d. Modeling

e. Evaluation

f. Deployment

5. Give the phases that were described in the case study given.

6.why data understanding and data preparation are important in this analytics solution.