Our client is a provider of hotel-inspired services for multifamily communities. When engaging with a new community, their sales team needed to focus and personalize their marketing efforts on the residents most likely to become customers. They needed a data-driven way to identify the residents most likely to become high revenue customers.
We aggregated multifamily community data and historical revenue. We employed data-science-driven clustering analysis to identify common traits of high revenue customers and trained a model to predict sales outcomes for new communities. Our analysis confirmed that users of our client’s services were much more likely to re-sign the lease, conferring significant savings to multifamily community owners on churn, leading to an acceleration of sales.