Symphony RetailAI, the leading global provider of AI-enabled revenue growth management solutions and customer-centric insights for retailers and CPG manufacturers, has announced today a new partnership with InContext Solutions, the global leader in 3D simulation software and virtual shopping insights for retail. This partnership supports Symphony RetailAI’s agile merchandising initiative and allows retailers to identify, design and test category and store layouts in an AI-enabled virtual environment. It also has proven to significantly reduce the time and expenses traditionally associated with these processes, while increasing the accuracy and customer value.
“Historically, retailers have found investment in store-layout optimization to be prohibitively expensive,” said Pallab Chatterjee, CEO Symphony RetailAI. “However, by virtualizing the process of category- and store-planogram design and testing, Symphony RetailAI and InContext Solutions are able to help retailers create agile store layouts that keep pace with rapidly changing market conditions. In addition to saving time and money building new store environments, retailers see instant and measurable impact through improved category growth and customer outcomes, visualized by easy-to-use overlay analytics on performance.”
The new partnership is a logical outgrowth of Symphony RetailAI’s focus on enabling retailers to respond to today’s need for constant agility in the marketplace. Retailers require the ability to examine granular trends and market influences, localize assortments and respond quickly to these pressures. Traditional methods of physically evaluating and setting up macro- and micro- layouts for stores are becoming liabilities that impede responsiveness to customer demand for new experiences in store. Now, Symphony RetailAI’s space planning solutions are enhanced through the advanced VR technology of InContext Solutions’ ShopperMX platform, enabling retailers to streamline the space planning process and drive more effective use of physical store real estate. The resulting outcome is 5-10% improvement in category performance.
“Using machine learning to intelligently optimize trade promotion spend and model new layouts with virtual simulation testing presents unprecedented efficiency and confidence for retailers when it comes to incremental resets or significant format redesigns that impact category and store profit,” said David Rich, Executive Chairman and CEO of InContext Solutions. “We are excited about the opportunity to deliver a unique solution to our combined customer base; a robust planning and optimization solution that enables an end-to-end experience from demand planning to store execution. In doing so, we’re extending the proven value of our solutions and providing a service unavailable elsewhere in the market.”
The most significant improvements to traditional store planning processes include:
- Accuracy – AI-enabled shelf scores provide a highly optimized cluster or store specific assortment and arrangement that has been validated by shoppers.
- Cost – Digital prototyping is much more cost effective than physical sets and traditional shopper testing, all planning activities happen in a virtual environment that can be built, reconstructed and manipulated at any time – substantially reducing costs.
- Speed – Real-time predictive analytics and shopper validation reduce the time and cost of traditional research or in market testing.
Through this partnership, optimized planograms developed using Symphony RetailAI Store Planning and Optimization are seamlessly integrated with InContext Solutions’ ShopperMX 3D Content & Curation platform to make multi-user virtual reality testing possible. By creating a unified feedback loop between the AI-enabled planning solution and VR testing process, users can refine the planogram before updates are made to physical stores – increasing speed and reducing costs. This integration brings first-of-its-kind capabilities that further extend Symphony RetailAI agile on-demand merchandising solutions while reducing time to market for these decisions.
