Management of eBusiness/eCommerce
Big Data and Analytics
Case Study: Rover Mobile
SCS2812 Management of eBusiness / eCommerce - Module 6 Case Study
Rover Mobile: A Hypothetical Case Study
You are the eCommerce Manager at Rover Mobile, which includes responsibility for online customer acquisition and retention. You’re recognized throughout your organization for your mastery of web analytics, and the ability to use analytics to drive and manage eCommerce performance.
One of the most pressing needs at Rover Mobile is to reduce customer attrition, especially with highvalue customers. High attrition, or customer churn, is typical in the mobile industry, as customers switch providers to chase better deals and packages. You’ve heard of other mobile companies using advanced business analytics/ customer intelligence using multiple online/offline data sources to micro-segment their customer base, and to serve these micro-segments with the best marketing offers to increase retention and loyalty. Your research indicates that other mobile service providers, such as Fido, have used advanced analytics to increase its retention by 10%. You’ve discussed this opportunity with your boss, the VP of Marketing, and she agrees that you investigate the opportunity for Rover Mobile, and create an executive summary of the opportunity for the Management Team.
About Rover Mobile:
Rover Mobile is a relatively new regional player in Canada, known for innovative products, great service and low pricing. The management team is lean, aggressive, and growth-oriented. Decisions are generally made if “the numbers look good”.
Because Rover is a lean organization, its IT department is often overburdened with projects. Most of the department’s focus is on web development and operations, customer billing, management reporting, and supporting internal technology infrastructure.
There is currently no advanced analytics capability within the organization. Rover does not employ any data scientists, nor does it have a data quality/governance function. Data sources are often siloed, making management reporting difficult and non-real time. Any existing analytics performed are
“descriptive”, not “predictive”.
Relevant Customer/ Financial Data
Current # of Active Customers: 150,000
Current average revenue per customer: $500/ year. Current net profit is 15%.
Current annual attrition: 25% (Retention rate is therefore 75%)
Cost to acquire a new customer: $500/year