Goizueta Business School
Home Alarm, Inc.:
Assessing Customer Lifetime Value
Marketers … have assembled vast databases identifying their customers and their buying habits. With such information, companies now believe it’s as important to reach the right people as it is to reach lots of people.
- Business Week, September 23, 1991
It was late one afternoon in May and Kevin Starke, the VP of Marketing for Home Alarm, had just returned to his office after listening to a presentation by a team of students at Goizueta Business School. Home Alarm was one of the largest privately held alarm security services companies in the US. The company had grown rapidly over the last 10 years and now had more than 80,000 residential and commercial customers. Home Alarm offered customers a complete range of security solutions, including intrusion detection, fire detection, access control, and video surveillance.
Home Alarm had provided data to the students as part of a class project in which they were supposed to identify the major factors that seemed to be driving customer churn at Home Alarm. Due to its excellent customer service Home Alarm had a much lower customer churn rate than its publicly traded competitors. Nonetheless, Kevin Starke and Home Alarm’s CEO were curious to see whether the students would be able to come up with ideas to further reduce customer churn.
Kevin reflected on the findings the students had presented to the executive team. Many of the results had confirmed what Kevin had known from experience. For example, customers with better credit ratings tended to be more long-term customers. However, one finding had sparked his interest because it was something that he could easily imagine operationalizing on a large scale. The students had found that residential consumers that were signed up for autopay, i.e. whose monthly payments were automatically deducted from a checking account or a Professor Florian Zettelmeyer prepared this case to provide material for class discussion rather than to illustrate either effective or ineffective handling of a business situation. The names and the data used in this case have been disguised to assure confidentiality. Minor changes are made by Professor Tongil “TI” Kim.
Copyright 2008 by Florian Zettelmeyer.
This document is authorized for use by Heshan Liu, from 1/9/2015 to 4/30/2015, in the course: BUS 342/542/542P Marketing Intelligence and Customer Insights - Kim (Spring 2015), Emory University. Any unauthorized use or reproduction of this document is strictly prohibited.
credit card, were less likely to cancel their service than consumers who received a monthly statement and paid by check.
The implication was clear to Kevin: perhaps new residential customers should be strongly encouraged to sign up for auto-pay; it might even be worthwhile trying to convert existing customers to auto-pay. Regrettably, while the students had presented evidence that customer on auto-pay were less likely to churn, the students had not been able to quantify how important the effect was. In particular, Kevin wanted to know: how much more profitable is a customer on auto-pay than one who does not use auto-pay? The answer to this question seemed key to Kevin because it would tell him how much he could spend on salesperson and customer incentives in order to sign up customers for auto-pay. Before he brought the idea to his CEO, Kevin wanted to make sure there was enough money in auto-pay to make it worthwhile for the company.
Kevin knew he had to calculate the added value of customer with auto-pay. However, if what the students had said about churn was true, he knew that it would not be enough to compare the current revenue of the two groups. Since the effect of auto-pay was to retain customers longer, any difference in the value of the customers would only become apparent over time. Clearly, the right approach was to calculate the LTV of a typical customer with...
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