In this case study, you'll learn how synthetic computer vision helped Instore Power Provider (IPP) overcome the common pitfalls of traditional image recognition to cement its leadership position in the retail automation space.
Founded in 2006, Instore Power Provider (IPP) is a European field marketing agency and a leader in the industry. Focused on cutting-edge solutions, they offer a suite of technology-driven retail execution services from Point of Sale (POSM) management to merchandising and automated shelf auditing, supporting both Consumer Packaged Goods (CPG) brands and retailers.
IPP has been providing CPGs with services for more than 15 years and prides itself on consistently staying up-to-date with the global, social, and technological changes affecting the modern retail space. Because of this, they are a trusted partner of leading brands such as Heineken, P&G, Nestle, Pepsico and more.
Catalin Bratu is IPP’s CTO. Today, Catalin is in charge of the company’s innovation and technology adoption. He has accumulated extensive experience with various retail automation technologies, including image recognition. Catalin’s ambition is to elevate IPP to the status of No.1 trusted store execution partner in the CPG space and expand its operations to a regional level.
The Challenge: Outdated Tech and Competitive Markets
Recently, IPP have found themselves at the centre of a two-sided challenge: On the one hand, CPG brands are scrambling to maintain in-store performance in the wake of global economic downturns, with increased levels of inflation and long-term recessions still looming on the horizon. On the other hand, retail solution providers are struggling to keep up with the advancements in digital automation that could, in theory, empower CPG brands to improve their in-store execution and weather these difficult market conditions.
This issue particularly applies to the field of traditional image recognition — while the technology has become a cornerstone of shelf auditing and automation, it relies on manually annotated data, causing several drawbacks for retail-based applications:
- It is costly and prone to human error
- It is not designed to scale across locations or large and dynamic product catalogues
- Adoption and real-world deployment are a long process
IPP had been eyeing traditional image recognition for a while but also became increasingly aware of its limitations. After performing trials with some popular providers, they concluded that its adoption could not be scalable and/or commercially feasible.
"We found out the hard way that many retail-based image recognition solutions rely on outdated tech and implementation, despite being positioned as go-to’s for Field Agencies and CPGs. This simply wouldn't cut it for us." - IPP
In short, they were on the hunt for a new approach to retail-based image recognition. They wanted a solution that would overcome the pitfalls of manually annotated data and significantly improve their shelf stocking and auditing services. When they reached out to us, they were interested in how our synthetic data-based technology could help them to achieve this goal.
"It became clear that moving away from the constraints of traditional image recognition would put us far ahead of the curve in terms of what value we could provide for CPGs and retailers alike. We saw the Neurolabs ZIA as a huge opportunity for us." - IPP
The Solution: A New Generation of Image Recognition
At Neurolabs, we're computer vision experts first and foremost. That means our technology and solutions stay native to the digital space, where they can profit from higher fidelity and flexibility. Rather than relying on a collection of manually annotated real-world images, our platform turns SKUs into digital 3D models for our image recognition algorithms to automatically learn from and reference.
This approach enables a faster, more accurate, and more robust adoption of new SKUs into a product catalogue, and also greatly improves scalability and cost efficiency.
Practically, this means that with our platform you can:
- Complete a real-world deployment in less than 3 weeks — the market standard is 2-3 months
- Onboard a new SKU in 1 day or less — this is 4x faster than the market average
- Achieve 95% accuracy for SKU-level detection from the outset and increase to above 98% for specific categories.
Moreover, our technology ZIA is built to scale from day one:
- Out of the gate, we provide access to over 100k pre-saved SKUs on our platform
- Our system is designed to create full product catalogues for thousands of SKUs in less than 12 weeks
Despite our technology’s complexity, we keep adoption simple and streamlined. Below is a 4-step overview of how ZIA turns real-world SKUs into digital models and how we use these to power image recognition:
Step 1 - Data Collection
Using our platform, our clients can either upload an SKU’s printing label or a simple 6-picture sample of the product in question. This content can be sourced directly from CPG brands or via so-called brand banks.
Step 2 - 3D Assets
Using this digital content, we generate a digital twin of each SKU. These high-fidelity 3D models reflect the real features of each product, including sizing, material, reflectivity, and more.
Step 3 - Synthetic Data
Next, we automatically populate a variety of digital scenes with our newly adopted SKUs. Rather than using volatile real-world settings, we use these adaptable environments to train our image recognition algorithms.
Step 4 - Trained Algorithm
After training, our final algorithms can accurately detect the digital SKU’s real-world equivalents in a variety of store settings, lighting conditions, and more. From here on out, our clients can choose which metrics and KPIs to track and analyse.
Real-World Deployment
We proposed a joint trial to showcase our technology ZIA's prowess and ease of use in a real-world setting. Together with IPP, we decided to focus on a single product category (detergent) containing a catalogue of 455 SKUs in total — the in-store proofs would be preceded by three weeks of digital onboarding.
We aimed to highlight four key aspects of synthetic data-powered image recognition:
- Production-level SKU detection accuracy for the SKUs in scope
- Shelf KPIs looking at facing count, out-of-shelf rate (OOS), share-of-shelf (SOS), and shelf-planogram compliance
- Standardised KPI collection process across multiple stores
- A faster, more efficient shelf auditing turn-around for field representatives
"We were genuinely surprised at how quickly we got things going. Having the Neurolabs team available for support every step of the way was one of the most notable differences to previous providers we had trialled." - IPP
After only three weeks of joint tech validation, we tested IPP’s new image recognition across 77 in-store locations — of those three weeks, we only needed several days to add all 455 SKUs to our system. During this time, the IPP field force captured ~1,300 images per day. We performed weekly assessments of our image recognition, which performed at a 97.6% accuracy rate. We also stayed fully available for technical support, addressing any issues within 24h.
Using synthetic data for image recognition provides IPP with a drastic and, most importantly, sustainable market advantage. Our solution created immediate value across the entire product life-cycle, not only benefitting IPP but drastically improving the possible services offered to their core clientele: retailers and CPGs, who today are more reliant on perfect store execution than ever.
IPP will be rolling out a large-scale deployment of our image recognition technology in 2023 — their initial focus will be a collaboration with a well-renowned global CPG brand and encompass a product catalogue of 1700 SKUs.
We will remain at IPP’s side to ensure a frictionless deployment for both IPP and their partner. Our flexible platform will enable both parties to easily adapt to new situations and environments, tackle complex challenges and easily expand product catalogues in a matter of days. They will also be privy to the constant improvements made to our algorithmic models, ensuring a lasting top standard in the accuracy of their analytics.
Next Steps: The Future of Retail Automation
We are excited to be partnered with companies like IPP to pioneer a new generation of shelf auditing and automation. While traditional image recognition cannot keep up with the advancements of modern retail automation, synthetic computer vision keeps up with the possibilities in this field and even expands them. We are confident in saying that early adopters will cement their foothold in the retail automation space for years to come.
"With Neurolabs ZIA, we've been able to unlock an entirely new set of opportunities for IPP and our clients. We would recommend their platform to any business that’s serious about their image recognition and shelf auditing." - IPP
Want to experience the advantages of synthetic data firsthand? Book a demo here.
You can also take a closer look at how our technology works and why it beats traditional image recognition solutions.
At Neurolabs, we are revolutionising in-store retail performance with our advanced image recognition technology, ZIA. Our cutting-edge technology enables retailers, field marketing agencies and CPG brands to optimise store execution, enhance the customer experience, and boost revenue as we build the most comprehensive 3D asset library for product recognition in the CPG industry.
