The retail industry has propelled its profits and customer experiences by using location intelligence for critical decisions. The high adoption of mobile devices across all demographics gives retailers who use location data valuable insights into consumer behaviors offline. Spectus bridges the gap between offline and online intelligence by presenting a comprehensive view of the consumer. Retailers can now provide a more engaging brand experience by understanding and applying human mobility analysis to their retail business strategies.
Why a data clean room for Retail?
According to Forrester, 90% of data and analytics decision-makers believe it’s a priority to increase the use of data insights in business decision-making. Spectus enables retailers with full transparency into their data to build the custom solutions they need in a cost-effective, secure environment for maximum developmental flexibility while minimizing data-specific risks.
Fundamental Retail Use Cases
- Visits & Footfall – understand your retail traffic patterns and influences from surrounding areas that may be hindering potential at certain locations
- Site Selection – quantitative analysis of potential retail locations based on competitive POIs, traffic patterns, socio-demographic data, and historical trends
- Customer Experience – increase engagement from loyal or new customers with timely communications and value-added merchandising based on traffic and other data points
Build a holistic view of your customers’ behaviors to improve their brand experience
Transform the way your team leverages human mobility data
Spectus is built for retail revenue growth
True omnichannel
Merge offline with online data for a realistic, unbiased view of consumer behavior and provide a more meaningful brand experience
Unyielding privacy
Mitigate risk knowing no PII or granular data can be accessed or shared using patent-pending differential privacy techniques
Brand loyalty
Increase brand awareness and engagement by delivering more value to the customer – when and where they prefer
