The startup offers a predictive shopper insight platform that employs A/B testing, supermarket simulations, and automated in-store observations to enhance in-store marketing strategies. This technology provides actionable insights for trade marketing and category teams, reducing uncertainty in planning and optimizing product placement and packaging.
Funding
$320K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Retail marketing campaigns often fail to achieve their objectives due to subjective decision-making or suboptimal guidelines in planning and execution. Pre-testing in-store marketing strategies has traditionally been slow, expensive, and inaccessible, leading to uncertainty in optimizing product placement, packaging, and overall campaign effectiveness.
Solution
Shopnosis provides an AI-powered platform that enables brands and retailers to pre-test and optimize in-store marketing campaigns using real shopper behavior data. The platform leverages computer vision and machine learning algorithms trained on millions of in-store shopper observations to analyze shopper behavior at scale. By simulating real shopping trips and purchases, Shopnosis benchmarks marketing executions against key performance indicators (KPIs) such as visibility, engagement, and conversion. The platform delivers actionable insights and improvement recommendations, allowing users to optimize their marketing before going into production and maximize return on investment.
Target Audience
The primary target audience includes trade marketing teams, category teams, brand teams, and agencies responsible for planning and executing in-store marketing campaigns for FMCG brands and retailers.
Features
- AI-driven analysis of in-store marketing campaigns based on millions of shopper observations
- Computer vision algorithms to track shopper behavior, including eye movements and product interactions
- Predictive insights on campaign performance, including visibility, engagement, and conversion rates
- A/B testing capabilities to compare different marketing creatives and strategies
- Benchmarking against industry best practices and successful in-store campaigns
- Identification of key elements to improve marketing effectiveness
- Support for various in-store marketing types, including displays, aisle fins, and hangers