While the site selection process has historically been an elegant art form, the power of A.I., machine learning, and predictive analytics continue to demonstrate that the future of retail leasing is a combination of art and science. Retailers need to understand store performance at finely granular levels only data analysis can provide.
A.I. makes it possible to quickly filter through the hundreds of thousands of demographic, psychographic, competitive, financial, traffic, and other variables that can impact store performance. Through machine-learning algorithms and over 175,000 variables at the census-block level, you can find the best combinations that will create the most revenue for your next store.
Accurate Models, Better-Informed Decisions
Traditional brokerage firms rely on linear modeling approaches. These just aren’t enough, on their own, to support fully informed site selection decisions. Machine learning-driven and predictive models eliminate underperforming sites and allow us to help you minimize financial risk, limit inefficient CAPEX, and expand average unit sales volume.
Future store performance can be forecasted using historical data and a geospatial database, comprised of massive mobile data that tracks consumer behavior in real time.
At Locate, we’re passionate about discovering actionable insights from mountains of retail data. We build accurate site forecasting and predictive models, customized for your brand, based on subsets of the aforementioned variables combinations. For example, take a look at a sample prediction report:
Elegant Data Analysis
AI helps us process and analyze large retail data sets to empower our site selection process. In addition to our technology, we’re the only firm with a team of brokers able to leverage this data to forecast future store performance highly accurately.
If you’d like to build a site forecasting model customized for your brand, fill out the form below.