One of our customers, a top rent-to-own company, faced a challenging problem. There was a serious disconnect between the customer acquisition targets of the local stores and cash flow planning being done at corporate headquarters. This disconnect made it impossible to accurately forecast cash flow and in turn led to mistimed promotions and poor inventory planning decisions.
The rent-to-own market is a steadily growing market segment in furniture and appliances in the US. The $8.5-billion rent-to-own industry — or RTO — continues to improve its business, customer service and pricing – becoming a viable and attractive option to consumers the American economy. The unique rent-to-own transaction sprang up in the 1970s in response to a growing consumer need for acquiring the use of household products without incurring debt or jeopardizing the family’s credit rating. Rent-to-own customers come from all walks of life, desiring consumer durable goods in their homes without the long-term financial obligations associated with credit sales. What distinguishes rent-to-own from a retail credit sale is the term “rent.” There is no interest charged to consumers, no credit checks involved and customers can return the merchandise at any time for any reason without penalty.
The customer demographics vary from store to store but the overall market is predominantly: white, incomes of $15,000 – $36,000, females, ages 35-54, have graduated high school, and live in a home they are buying or own. They key issue that these customers bring to the rent-to-own marketplace is that they do not always carry a contract to term because they can return the merchandise at any time for any reason and there is no penalty in doing so. For the RTO company, this results in not knowing future cash flows even though contracts are in place. So, a store with $5,000,000 in monthly signed contracts today does not know how many contracts they have to sign this month to ensure $5,000,000 in monthly revenues next month.
Using Halo’s unique multivariate analytical capabilities, our customer is now able to better determine monthly sales goals for new customers to reach maintenance and growth revenue goals on a store by store basis. Tied to this better cash flow process is inventory and promotions to drive the types of customers and contracts that have longer lasting power – resulting in lower customer churn and higher profitability.
When you have a lease-based market, it’s exceptionally hard to know your expected cash flows both long term and short term. By effectively incorporating product mix, seasonality, geography, type of lease and other key variables using predictive analytics, companies can be more effective in maximizing cash flows while minimizing their variability. It’s how to know your cash flow when you don’t know your cash flow.