ShelfWatch effectively comprehends the environment in which SKU’s are merchandised. It provides actionable insights and creates a virtuous feedback loop which helps CPG companies in their perfect store execution.
Image Recognition technology increases sales force productivity, improves shelf condition insights and helps drive incremental sales.
Gartner Research,
“Image Recognition can help consumer goods manufacturers win at the retail shelf” - Ed Porter, Tuong Nguyen
ShelfWatch gives a complete picture of your perfect store execution by calculating different KPIs that can be customised as per requirement. These include:
On-device blur and angle detection: ShelfWatch’s mobile app takes images to assimilate analysis on product placement and visibility on the shelf. It also provides smart features like blur detection and angle or eye-level alignment while taking images.
Off-line Mode: Images can be clicked even in a no-internet zone without hindrance and can be uploaded once an internet connection is available.
Real-Time Reporting: ShelfWatch gives near real-time KPI feedback using image analysis and deep learning that are directly transferred to ShelfWatch cloud for detecting POSM and SKUs.
Integration: ShelfWatch easily integrates with multiple SFA and DMS apps. All our salient features such as real-time image quality check and real-time shelf insights work perfectly in the integrated solution.
Corporate Dashboard: ShelfWatch’s detailed Insight dashboard provides competitive analysis by count, presence, shelf area covered by SKUs and POSMs, brand presence across stores, and geographical breakdown over a map overlay.
Customised Reporting: Every brand has unique visibility compliance standards and reporting needs. Using Power BI dashboard to create customised dashboards in partnership with brands helps ensure high relevance and usage within company organisation.
Supervisor Portal: It can benchmark the performance of your sales rep and help them improve their KPIs. It can also monitor store-level issues and send message alerts to sales teams.
Quality Feedback: Image Despite training, oftentimes field users make errors in taking pictures, which can affect important measurement KPIs. ShelfWatch App has an image quality assistant, where it alerts the user if an input image has issues like - blur, glare, steep angle, wrong category etc.
Image Stitching Guides: ShelfWatch app has a stitching assistant feature which guides the user to take sufficient overlap images (for stitching them together to give one complete image), thus ensuring that no product is missed or double counted.
Empowering Global CPG & Retail Ecosystem with Data-Driven Decision Making
2 weeks with >90% accuracy from the start
>8 weeks, claims quick setup while delivering below 70% data accuracy
Effortless to track and train unknown SKUs via Saarthi portal
Lack processes for detecting and training AI on unknown SKUs, leading to ad-hoc handling and delays
Consistent accuracy of 95%, even with occluded/rotated SKUs, similar-looking SKUs, or low-light environments
Struggles to achieve 95% accuracy in non-ideal conditions or demands complex photo-taking guidelines
Detection within 48 hours; competitor SKUs are proactively monitored in Saarthi portal
Struggles to accurately detect POSM materials, as the AI training process is longer than the POSM's market presence
Highly compatible, currently processing over 3M photos per month in traditional trade channel alone
Most pilots in traditional trade have failed to achieve data accuracy above 70%
ROI-focused, strong emphasis on customer success, supported by a 150+ member team; NPS of 8.0 in
quarterly surveys
Struggles to provide adequate customer support while serving multiple clients due to small team sizes and non-scalable approaches
2 weeks with >90% accuracy from the start
>8 weeks, claims quick setup while delivering below 70% data accuracy
Effortless to track and train unknown SKUs via Saarthi portal
Lack processes for detecting and training AI on unknown SKUs, leading to ad-hoc handling and delays
Consistent accuracy of 95%, even with occluded/rotated SKUs, similar-looking SKUs, or low-light environments
Struggles to achieve 95% accuracy in non-ideal conditions or demands complex photo-taking guidelines
Detection within 48 hours; competitor SKUs are proactively monitored in Saarthi portal
Struggles to accurately detect POSM materials, as the AI training process is longer than the POSM's market presence
Highly compatible, currently processing over 3M photos per month in traditional trade channel alone
Most pilots in traditional trade have failed to achieve data accuracy above 70%
ROI-focused, strong emphasis on customer success, supported by a 150+ member team; NPS of 8.0 in quarterly surveys
Struggles to provide adequate customer support while serving multiple clients due to small team sizes and non-scalable approaches
case study
At ParallelDots, privacy and security are our top priorities. We adhere to leading industry standards and are dedicated to ensuring the security of your data with comprehensive governance throughout the entire platform.
“ParallelDots allowed us to gain visibility into how our products are placed on shelf across different regions and retailers. The data and insights provided by ShelfWatch helped us take quick remedial action. We are able to prioritize execution for low performing stores and drive productivity across our field force.”
RB Health,
Customer Activation Manager
