Wharton Customer Analytics Initiative (WCAI) is the world’s preeminent academic research center focusing on the development and application of customer analytics methods. Through an innovative “crowdsourcing” approach, WCAI enables academic researchers from around the world to help companies better monetize the individual-level data they collect about customers through the development and application of new predictive models.
THE
PROBLEM
STRENGTHEN CUSTOMER RETENTION
Every year, millions of Americans benefit from the flexibility of rent-to-own agreements. These programs offer an easy and affordable way for people to furnish their home with high-ticket items, such as kitchen and laundry appliances, furniture, televisions, and more. The business is a boon to consumers and manufacturers alike, but raises unique challenges for suppliers, who must anticipate demand for capital-intensive goods. In an effort to optimize their own product and pricing offerings, and strengthen customer retention, the country’s leading rent-to-own retailer enlisted the help of Fuel Cycle and WCAI to analyze two key data sets: customer surveys and transaction data, in service of uncovering true consumer preferences and possible drivers of customer default.
THE
SOLUTION
LEVERAGING AGILE RESEARCH METHODOLOGIES
Across industries, researchers often face the challenge of unstructured data sets, information that companies accumulate over the course of multiple years and different software systems. Fortunately, the Fuel Cycle Exchange (FCX) marketplace offers a variety of tools and solutions for bridging existing data sets, as well as gathering new customer feedback, through panels, surveys, online communities, and more. Through FCX, WCAI was able to augment the retailer’s existing transaction data with new Conjoint.ly surveys. These discrete-choice questionnaires help assess how consumers value different product types and pricing options, such as the ideal size for flat-screen TVs, or preferred trade offs between weekly rental rates and overall term length.
Leveraging the full breadth of the Fuel Cycle platform, WCAI identified several key differences between stated and actual consumer preferences, including older customers’ proclivity towards higher rates. Their analysis also found possible predictors of default, including new customer status and, perhaps counterintuitively, higher incomes over $100K. These findings led to actionable insights around higher prioritization of existing customers, who are less likely to default; more stringent screening of new customers; and targeted promotion of high-end goods to older consumers.
Key Takeaways
5811 CUSTOMER AGREEMENTS
254 CONJOINT.LY SURVEY RESPONSES
12 PREDICTOR VARIABLES
Methods Used
CONJOINT, LOGISTIC REGRESSION, VARIABLE ANALYSIS
LOOKING
FORWARD
GATHERING ONGOING CUSTOMER INTELLIGENCE
Retailers face many moving pieces in the emerging omnichannel landscape. By harnessing the voice of the customer, WCAI identified clear opportunities for the nation’s leading rent-to-own player to improve their product and pricing efforts, and cut down on default rates. But these findings only scratched the surface, with both teams discussing future plans to gather additional customer data through the Panel and Community surveys available through FCX. As consumer behavior continues to evolve at a rapid clip, such real-time insights have proved vital in ensuring that the client retains their leading position in the rent-to-own space.
