Footprints AI

Step 1: Indoor Shopping Behavioral Profiling:

Footprints AI collects customer behavior data from indoor shopping environments using ambient connectivity and mobile sensors.

The collected data is processed using machine learning algorithms and building information mapping to generate indoor paths.

The algorithms also create affinity profiling, behavioral clusters, and predictions for individual shoppers.

Step 2: Online Behavioral Profiling:

Footprints AI collects user identity and profiling data from all digital channels to create a comprehensive 360-degree shopper profile view.

Step 3: Offline-to-Online Fusion:

Footprints AI's proprietary AI technology combines offline and online customer data to create a comprehensive view of customer behavior.

Step 4: Monetization:

Retailers can monetize their offline-to-online customer data through retail media.

Step 5: Prediction and Targeting:

Retailers can use Footprints AI to predict, target, and sell based on customer behavior, both online and in-store.

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We enable our retail clients to:

New Profits: New stream from Retail Media or boost current media offering.

Time-to-Market: Retail Media offering in 3 months, not 3 years.

Minimum Costs: Minimize R&D costs and focus on your data and your media offering.

Improve Return on Ad Spend: 5-8x better retail media performance of your own media investment.

Know Your Customers: Better know your customers beyond transactions and in-app registrations.

Expand Your Retail: Discover your next most profitable geographies & communities to engage.

This results in lower costs, faster time to market and increased profits, giving our customers a competitive advantage in the Retail Media Network market.

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For retail brands, especially CPG, but also automotive, financial services and other B2C brands, we generate valuable insights into consumer shopping habits, their predictive behavior, and their marketing channels of most engagement which they can use to increase efficiency and Return on Ad Spend on their media investments.

For retail brands this translates into:

· Improved customer acquisition costs with up to 50%.

· Driving 5x-8x more efficiency with close-loop ads and just before shopping ad campaigns with short term results and attribution of ad-driven sales.

· Increase in-store sales with short term ROAS results.

· Self-service and fully programmatic.

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Behind the scenes, our software has a cutting-edge AI model for understanding and predicting shopping behavior. It does so by seamlessly acquiring data from Wi-Fi, smart sensors, and other connected infrastructure already in place and fusing it for creating behavioral profiles of customers. Our proprietary AI technology combines offline and online customer data to create a comprehensive view of customer behavior.

This means, the engine uses first-party data, such as information collected from smart sensors in indoor connectivity infrastructure, to match physical shopping habits with digital profiles. The result is a single customer view for all users, including the previously anonymous ones, that allows retailers to understand their customers' current and future shopping habits, including their searches, visits, and purchases, both online and in-store.