REAL-TIME PERFORMANCE DATA FOR RETAILERS
Reliable retail analytics start with incredibly accurate retail footfall counter.
Get a true customer count by excluding employee traffic, grouping buying units, and eliminating other traffic sources.
Provide a clear, accurate conversion metric by quantifying true selling opportunities only.
Dwell & Occupancy
Analyze dwell zones and store occupancy to determine most popular areas of the store and optimize store layout.
Optimize staff levels based on traffic analysis to improve conversion rate and the customer experience.
Measure marketing effectiveness by determining the cost per shopper and the impact of campaigns on store traffic.
Decrease service wait time and improve customer experience by matching check-out personnel with customer traffic.
Close the gap between ecommerce and physical customer analytics by measuring employee engagement and customer behavior such as dwell time, shopping patterns and demographics.
Predictive Retail Analytics
Optimally align future operations by utilizing Vea’s high accuracy traffic forecasting algorithm. From sales and promotions to the impact of Black Friday Traffic, let us help you find a model that predicts your future retail store traffic.
WHY USE VEA RETAIL ANALYTICS?
Make better decisions to optimize your store’s operating efficiency. Using Point of Sale data can only tell you what happened in a day, but not how it happened. Combining a customer counter with Vea Retail analytics software can help you to understand your store’s true opportunity. Find the answer to the question, “How effective is your store at capitalizing on the opportunity presented?”
WHO BENEFITS MOST FROM RETAIL INTELLIGENCE WITH SENSOURCE
View the overall performance of the retail chain, compare performance goals and benchmark in-store analytics.
Compare pre and post campaign traffic and set traffic goals to gauge marketing effectiveness.
Compare performance to goals of stores in a region. Reward top per forming stores and plan visits to store in need of improvement.
Use historic retail footfall to predict future trends and peak times. Staff according to predicted traffic to ensure adequate coverage.