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Consumer Packaged Goods (CPG) companies are striving to remain competitive in a rapidly evolving market. Yet, only 54% of CPGs polled this year have adopted image recognition (IR) tools to get ahead. This may be because their past experiences with traditional image recognition software have left them disappointed or sceptical of new solutions. For example, they may have invested in technology from established vendors, costing them a lot of money, time, and effort manually importing reference images and annotations into the software. Or, they may have found the solutions highly complex to deploy with less than optimal results in terms of data accuracy and time-to-market. Additionally, the resources poured into training staff on the new tech took Field Marketing Agents (FMAs) away from their core role of boosting company profits by increasing sales figures. However, new generation image recognition technology that’s powered by synthetic data, like Neurolabs ZIA, is changing the game. By utilising synthetic image recognition, the need for manual input and their associated time and costs in training image recognition learning algorithms is eliminated. This technology can also easily integrate with existing Sales Force Automation (SFA) solutions and company processes, removing the hassle of onboarding and training teams to use and deploy new technologies in their daily work. In this article, we will explain how plug-and-play synthetic image recognition is helping CPGs boost revenue by empowering CPGs to collect more effective data-driven retail execution insights. Integrating synthetic IR tools with SFA streamlines processes SFA solutions provide a wealth of tools to enhance CPG performance and sales strategies. Yet, a study by Accenture found that 80% of industry CTOs admit they find it difficult to generate value with their existing technology solutions. With next-gen image recognition tools entering the market, CTOs must look to modernise their operations or risk losing their competitive edge. Combining synthetic image recognition technology with existing SFA software can improve data quality and process efficiency, enabling CPG brands to make better decisions and react faster to market developments. For example, plug-and-play tech solutions like Neurolabs ZIA allowed Sagra Technology to quickly upgrade auditing processes through its ease of integration with their existing Emigo SFA System. Let’s take a closer look to see how: Neurolabs ZIA integration case study: Sagra Technology With Neurolabs ZIA's synthetic computer vision approach, we could deploy an IR solution for Sagra Technology in one week. This allowed them to hit the ground running in taking their auditing solution to market – a task that would've taken the brand months if they were using traditional IR methods to conduct their vital checks. Our Neurolabs solution significantly reduced the IR training time to run audits smoothly. For example, after a brief training window (5 days), ZIA identified all 25 SKUs in their project catalogue despite the only visible differences in products being the number of medicine doses contained within each packet. After the initial ZIA deployment, Sagra Technology tested our solution's effectiveness and found that the synthetic computer vision algorithm could detect SKUs with 98.3% accuracy within just five seconds. This case study shows that CPGs field reps can integrate synthetic data to streamline their operations and make data-driven decisions with high accuracy and speed, giving them a competitive edge. Plug-and-play tech provides richer data insights Integrating synthetic image recognition APIs with business intelligence and data visualisation tools can provide deeper and more actionable insights, leading to better decision-making and increased sales. For example, synthetic image recognition technology can be used to enhance competitor analysis by identifying key visual elements in rival brands (such as pricing and facings). From here, brands can analyse how this information can be adapted to make brand messaging more appealing to audiences. Synthetic data offers several critical advantages to retailers’ IR solutions. The first of which is that it’s easier and less time-consuming to generate training data. For example, Neurolabs' ZIA contains 100,000 SKUs preloaded into its database. This means that companies can process large amounts of data quickly, identifying patterns and trends that may have taken longer to spot through traditional competitor research methods. As a result, retailers can take a more data-driven approach to improving their sales. No additional user training is required with synthetic image recognition technology Synthetic image recognition technology automates gathering retail images and annotating them with the necessary data to train their algorithms. So, no additional staff training is required to prepare the image recognition learning algorithms. Plus, all synthetic data is created artificially within the training algorithms generating real-world simulations of retail settings. No time is wasted taking photos and uploading images to a centralised platform. Additionally, Neurolabs ZIA is easy to use and has an intuitive interface, making it a breeze for teams to onboard the technology and deliver fast results. With this in mind, businesses can focus on their core competencies of increasing productivity and subsequent sales. If you’d like to learn more about the advantages of synthetic data over real data, check out our dedicated article where we go into more detail here. Neurolabs ZIA is your CPG's ideal fit solution for boosting company revenue As technology for CPGs advances, ensure you maintain a competitive advantage by utilising powerful plug-and-play image recognition technology like Neurolabs ZIA. Its computer vision capabilities enriched by synthetic data can significantly reduce the need for manual input and training in your operations and provide you with vast amounts of data you can use to improve your decision-making. Plus, our solution seamlessly integrates with your existing tools, no matter the platform, minimising technology onboarding time while enhancing your data analytics' overall quality and accuracy. Interested in learning more? Book a demo with us today and see how Neurolabs ZIA can transform your CPG brand. 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.

Traditional Image Recognition is like trying to play football/soccer in running shoes. You can do it but you won’t perform at your best. You’ll find it harder to get a reliable touch of the ball, and while you’ll be able to take part in a game, eventually you’ll become frustrated at the limitations. You’ll feel you could’ve done more with less effort. And you’ll probably end up buying football boots straight after the game. We already know how high the bar is for CPG brands, traditional IR is already struggling to meet them, and won’t be able to do so at all soon. Limited datasets and difficult integration that hinders rapid expansion of SKUs. Human biases seeping into datasets, reinforcing societal biases, which lead to incorrect outcomes. The need to hire more people, at great expense, to ensure the process has a chance of running smoothly. And these are only a few of the many direct and indirect issues that crop up. However, AI has provided an opportunity for you to make the lives of CPG brands easier, with less effort. This in turn will make your working life a more stable, enjoyable experience. In this blog post we’ll explain why AI will help you become the go to person for CPG brands when they need someone to ensure their supply chain and field marketing operations run smoothly. What is Synthetic Data? What are its Benefits? To put it simply, Synthetic Data is generated by computer algorithms rather than real world scenarios. It can be used to train Synthetic Computer Vision (SCV) models to detect real world objects. They can be used to simulate store environments, and customer/SKU interaction. Once you start using Synthetic Data, for your IR, you'll come across the following benefits: Cost Effective Easier to scale More accurate Takes less time to implement into your systems The infographic below shows the contrast between the two types of image recognition. How Traditional Image Recognition Holds You Back As a FFM or FMA, you’re generally judged on the following tasks: Shelf KPIs (Share of Shelf, Out of Stock, etc.) In-store location of products Planogram compliance (shelf location, # of facings, # of SKUs, missing/inaccurate shelf tags) Quality of in-store displays and promotional materials execution Competitor adjacencies and activity Pricing compliance These are made a lot harder by the limitations of traditional IR. For example, if a CPG client wanted to quickly scale their SKU onboarding it would be a drawn out, inefficient, and inaccurate process. The delays can contribute to CPG brands having to push back their marketing schedules, causing them to miss quarterly targets. It gets even worse if you’re trying to do this all manually, it’ll be a much slower, inaccurate and more difficult process as a result. This ratchets up the pressure on you, which is already high. Another example, the lower level of accuracy of traditional IR means that Shelf KPIs won’t be what they should be. As a result, OOS will be more likely to occur, and bias from data capture can negatively influence in-store marketing campaigns. So now, CPG brands are turning up the heat, because their following metrics are falling as a result of the issues with traditional Image Recognition: Footfall Store revenue ROAS ATV The data isn’t fit for purpose. The products aren’t in sync with consumer trends. The marketing activation is ineffective. Why Synthetic Image Recognition Helps CPG Brands But if you were to use Synthetic IR, you wouldn’t have these issues and CPG brands would enjoy the following benefits, all because of you: Increased brand loyalty Increased brand advocacy Higher AOV/ATV Higher LTV Higher per store profitability Customers on shop floors or at home for ecommerce will now find that it’s much easier to get the items they want, when they want, at the prices they want. Why? Because the high level of accuracy of Synthetic IR means that the dreaded OOS (from a consumer perspective) is less likely to happen, as it’s much easier to track SKU availability in real time. The technology enables these processes to be completed accurately, and at scale. Utilising Synthetic Data enhances retail execution, which makes it much easier to provide customers with personalised experiences across the retail spectrum. This leads to a more harmonious business relationship between you and CPGs. Neurolabs Case Studies Of course, we understand your scepticism of us saying it, our company is based on technology after all. Here are our case studies showing real world evidence of the power of Synthetic Data fueled Image Recognition. Sagra - Find out how AI improved their shelf recognition process. IPP - See how we transformed IPP’s Shelf Auditing. Akcelita - They came to us and were able to do exactly what we’re talking about in this blog post - improved Image Recognition for their CPG clients. ITAB - Read about how they scaled their Shelf Monitoring Solution. Auchan Retail - See how they got rid of OOS. Uvesco - We helped them solve On-Shelf Availability with Synthetic Computer Vision. See what Synthetic Data and Image Recognition Can Do For You Get in touch with us today to request a demo/find out more information. 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.

The Consumer Packaged Goods (CPG) industry is experiencing rapid growth, as consumer demand for everyday products, such as food, beverages, household items and toiletries, have remained high, even in times of financial crisis (with rising inflation and worldwide supply chain issues). This means retail execution is more important than ever. To illustrate, the global CPG market will reach $2,460 billion by 2028, representing a Compound Annual Growth Rate of 3% from figures in 2022. The CPG space is also fiercely competitive. For example, CPGs must defend their profits from new players entering the market, and the increasing prevalence of Retail Private Labelling. For example, since Q2 2022, Retail Private Labelling profits in stores like Aldi and Target have increased by an average of 2%. Therefore, for CPGs to maintain growth and edge ahead of their competition, brands must gain richer and more accurate store-level insights faster and more cost-effectively. The shop floor represents the final frontier in the sales process. So, companies invest heavily in Retail Execution (REX) tools such as Image Recognition (IR) technology to deliver the comprehensive store insights they need to generate more sales. Theoretically, one photo file uploaded to an image recognition tool can reveal multiple KPIs such as planogram compliance, pricing, and promotional information, etc. Image recognition is also capable of delivering these results in a short time frame (typically in a few minutes, if not seconds). Unfortunately, however, this isn't the reality with CPG's experience with traditional image recognition. A recent survey found that 49% of CPG brands feel that their data and insights are not leveraged to the fullest extent. This may be due, in large part, to the unreliableness of the technology, as planning accuracy was ranked as retailers’ second biggest priority for improving their brand in 2023. The drawbacks of traditional image recognition are caused by the laborious process of manually acquiring images and annotating data, which is time-consuming and often yields inadequate amounts of data. In addition, once the IR is developed, environmental factors like lighting and narrow aisle widths in certain store formats can impede data accuracy, hindering CPGs' abilities to make the necessary improvements in their retail execution strategies. To counteract these pitfalls, new generation image recognition technologies leveraging synthetic data, such as Neurolabs' ZIA, can help CPGs gain confidence in their data once more and help them utilise insights more effectively. Read on to find out how. Gaining better CPG data and insights starts with optimising retail execution processes Customer experience is central to all successful REX strategies. Therefore, CPG brands must ensure that products are readily available, shelves look orderly and well-presented, and their goods are competitively priced. For more information on meeting customers' high expectations on the shop floor, read our post on building the 'Perfect Store' strategy. Unfortunately, CPGs using traditional IR technologies face some challenges. Namely, these technologies rely on real-world data to be developed, making them vulnerable to disadvantages posed by adverse retail environments, such as poor lighting and layout, which can impede data accuracy. Gathering large amounts of real data to train IR learning algorithms can also be a resource-intensive and time-consuming process that may be prone to human error. Enter Synthetic Image Recognition. With synthetic image recognition, CPGs can benefit from more reliable and accurate data, leading to greater efficiency in auditing processes and, ultimately, optimal guided decisions in store execution that lead to increased sales. The following section will explain how this can be achieved through our solution. How synthetic data generation provides better data-driven insights for CPGs Synthetic image recognition uses computer-generated digital twins of the products, generated directly from product packaging labels or artwork, to train machine learning algorithms. It then analyses shelf images to identify the products within them and their proper placement. With this in mind, here are some examples of how synthetic IR can help provide better data insights for CPGs: Improved data quality Synthetic IR produces highly-accurate insights, streamlining the product detection process. The outcome; CPGs get better quality data (with fewer errors and more accuracy), enhancing retail decision-making. For instance, Neurolabs’ ZIA has cloud connectivity which lets field marketing agents (FMAs) on the shop floor see real-time insights on store shelves. With ZIA data insight accuracy doesn't deteriorate over time because the data is generated using computer algorithms, which can be easily tweaked and updated to reflect changes in the real world. Subsequently, synthetic data generation also helps FMAs quickly see what issues need to be fixed, such as low stock availability. In contrast, traditional IR data struggles to maintain real-time accuracy because the data is based on fixed features extracted from a limited set of real-world examples. Traditional IR training data can also quickly become outdated as new environmental variations emerge in the real world. As such, this explains why so many users may have had a poor experience with traditional IR as it can be slow to adapt to new retail scenarios. Diversity of training data Synthetic image recognition offers a significant advantage over traditional IR because it can generate diverse data sets for training algorithms. It utilises synthetic computer vision to recreate product variations, environments, and lighting conditions without human input. This means synthetic data generation is proficient in creating a greater variety of training data sets, providing CPGs with various insights to analyse and test that wouldn't be feasible with a real-data approach. This level of functionality allows ZIA to “imagine” how an SKU would look like on various shelf environments prior to the real SKU being on the shelves. Subsequently, the ability to create diverse training data ultimately leads to a robust, high-performance technology with high accuracy guaranteed. In addition, this level of accuracy is achieved very fast (a few hours/days) because Neurolabs’ ZIA can automatically generate high volumes of training data in hours, giving the tool a distinct edge over traditional IR. Low complexity in integrating Synthetic Image Recognition Synthetic image recognition technology can adapt and scale with the ever-changing needs of CPGs. This is because solutions like Neurolabs’ ZIA stores all data in the cloud and can integrate with existing Sales Force Automation (SFA) tools. As a result, it's easy for field agents to enhance their solutions with a powerful and readily available technology – namely, synthetic image recognition. To illustrate, CPGs can update master product catalogues and artworks within their SFA. Neurolabs’ ZIA can access this data and automatically update the product recognition models in record time. In contrast, updating SKU changes with traditional IR technologies typically takes days to complete. The benefit of this type of functionality ensures FMAs working on behalf of CPGs have the most up-to-date data sets to help them perform their daily tasks and stay ahead of competitors. Neurolabs ZIA: enhancing data-driven retail execution through Synthetic Image Recognition Neurolabs’ ZIA also offers dedicated customer support to ensure that CPGs and FMAs get the best insights from its synthetic data generation to ensure retail execution is optimised to the highest standard. Overall, Neurolabs ZIA offers a reliable and scalable technology, so companies can worry less about the IR data gathering process and focus more on building their brand . If you want to learn more about how Neurolabs’ ZIA can bring the gold standard of image recognition technologies to your CPG brand, you can find out more here or alternatively, contact us for a demo today. 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.

In the rapidly evolving world of image recognition (IR), there is a need for a fresh perspective on its adoption and how next-generation IR can help overcome challenges Consumer Packaged Goods (CPGs) brands and Field Marketing Agencies (FMAs) face in their daily work. This article aims to shed light on the truth behind the narrative surrounding outdated retail execution technology (such as traditional IR solutions) and explain how Neurolabs presents a new and innovative way of doing things for those who have struggled with less-than-optimal technology in the past. Why legacy IR technology has been a let-down According to the 2023 POI State of the Industry report, 54% of companies use image recognition technology. However, while they may say they're using it, it doesn't mean they enjoy it. This may be because Image recognition technology, in its older format, has been an industry-wide let-down, which has meant that many stakeholders deploying traditional IR solutions are struggling to remain or become competitive. CPGs and FMAs need ways to maximise sales. But those who have yet to adopt IR technology are hesitant to try it due to the perceived high cost, time investment, and potential inaccuracy of the results. Additionally, traditional IR is very complicated to use. Many companies believe it is difficult to scale if CPG’s choose to expand their offering. It can also take weeks or months to deploy the solution. Then, of course, companies need to pool time and resources into the technology’s maintenance and accuracy as time progresses. In many cases, when companies do adopt IR tech, it ends up being incredibly disappointing. Image recognition software over-promised and under-delivered Ten years ago, image recognition was successful in other industries (e.g. security and surveillance, robotics and industrial automation). Hence it was assumed that it would work equally well within the CPG sector. However, the CPG space is entirely different due to the sheer amount of similarities between products and their tendency to change extremely frequently. Under these conditions, legacy IR creates more problems than it was supposed to solve. For example, the technology is not easily scalable in terms of expanding the product catalogue across multiple retail locations. For example, a traditional IR solution vendor can promise object detection accuracy of 95%+ in small scale trials. However, this level of detection, in many cases, cannot be replicated and sustained over time across larger scale deployments. It also takes too long to implement (around three to six months for around 300-500 SKUs), giving rise to CPGs and FMA's deploying additional resources to get the system up and running. As a result, these issues have generated a lot of scepticism from the industry, slowing down the drive to innovate new methods for solving the problems. Many CPGs are left saying, "We tried it but didn't get the results we wanted." Or, "We didn't get it to work, so we abandoned the project." But what if it did work and did deliver results? Reluctant image recognition adoption Many companies use old IR tech as a "best of the worst" solution to their retail execution software needs. It's frustrating, but the other option (manually adding SKU and shelf-level KPIs to retail execution software) is considerably more tedious. Nevertheless, for many companies, the manual way (i.e. counting number of facings and inputting the number in the SFA tool) was and continues to be perceived to be more reliable than traditional IR solutions. In addition, other factors are holding companies back from adopting better IR solutions. These include: The cost of change: Companies fear changing up their systems due to the presumed impact it will have on budgets. Lack of awareness: Most companies are unaware of how sophisticated IR technology has become. They're stuck in this idea that IR doesn't work effectively and is unreliable. Set-up and maintenance: Many CPGs and FMAs believe integrating new tech will interrupt current workflows, take too long to implement, consume a lot of internal time for training, etc. Maintaining a high level of performance is a scary ambition for many companies. Until now, there have been few viable alternatives to the manual process/legacy IR. Adopters may use legacy technology through gritted teeth or revert to old, manual methods. When the IR tech under-delivers, it causes frustration and changes the perception of all IR solutions. For instance, unsatisfied IR customers may see IR tech as ineffective and wasting time and money. Because the technology under-delivers, it is seen more as a "nice to have" rather than a "must have" solution. Therefore, making the business case to invest in newer IR solutions becomes very difficult; few employees will champion it internally with decision-makers, and as a result, this half-hearted adoption often leads to unsatisfactory outcomes for all involved. Legacy image recognition is holding the industry back The tension between CPGs and FMAs can be palpable, as both search for better IR tech to aid their performance. Despite the pressure, the fact remains that viable solutions have been hard to come by; leaving them locked in a standstill of disappointment and doubt. To that end, legacy technology is seen as not able to solve problems but instead causes them. For example, it can take months to introduce an IR model to a new catalogue of SKUs. Plus, companies need timely and up-to-date analytics that they can rely on. However, adopting a changed SKU into a legacy IR system can take days. Accuracy, implementation time, scalability and costs are all key factors that must be addressed. Synthetic IR is the antidote to legacy IR Our IR technology, Neurolabs ZIA, offers a cutting-edge solution to remedy many of the issues experienced with outmoded IR technologies. Its use of synthetic computer vision and data enables our IR technology to learn faster and with higher accuracy as it leverages a larger and more diverse data pool. With our onboarding process, onboarding a new CPG customer takes a mere day, SKUs minutes, and a high degree of product detection a matter of hours - all with little to no training. Here's how our synthetic IR technology works: Furthermore, you can easily streamline product catalogues across multiple retail locations, empowering CPG brands to grow and scale their offering. This is because our technology offers a much more cost-effective IR solution than traditional legacy technology, as it doesn't require CPGs to cover the costs of gathering real data to train IR learning algorithms. While other IR solutions are available, ZIA is the only one powered by synthetic data - ushering in a new era of image recognition and expanding the possibilities within the retail sector. To learn more about synthetic image recognition and how ZIA can transform your retail execution, click here. 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.

The rapid advancement of AI technology is transforming our world, moving from the stuff of science fiction to becoming an integral part of our everyday lives. From the emergence of tools such as ChatGPT, Bard and other generative AI technologies, we have witnessed a cultural phenomenon in recent months of industries looking to innovate their offerings with these new technologies. In the Consumer Packaged Goods (CPG) space, in particular, brands are looking to AI to transform retail execution (REX) processes to make them more streamlined and optimised to modern data-driven working practices. This is where Neurolabs' very own AI-driven shelf auditing assistant ChatCPG comes into play. It sets out to redefine how CPGs, Field Marketing Agencies (FMAs) and Sales Force Automation vendors (SFAs) move from insight to action during REX operations. This article will introduce Neurolabs’ ChatCPG and explore why this new AI technology is a must-have for brands that want to maintain a competitive edge. What is ChatCPG? ChatCPG is a game-changing solution designed to empower brands and marketers in mastering perfect store execution. This revolutionary, AI-driven shelf auditing assistant technology covers every aspect of store planning and auditing, from ensuring shelf compliance to analysing product availability, prices, competitors, and more. Instead of painstakingly manually analysing predefined dashboards, ChatCPG harnesses the power of image recognition (IR) data captured from in-store visits and applies an intelligence layer on top of the data. From here, it utilises natural language processing technology to provide immediate, detailed and accurate responses to user questions in plain English. For example, suppose a user wants to learn more about the recent performance of items in a region rather than devise a complex series of filters to compare performance. In that case, they can simply ask ChatCPG a question such as “How was shelf performance last week in stores across London?”. With ChatCPG, you can get instantaneous store insights without manually sifting through complex dashboards. With ChatCPG, you can access our highly effective generative AI in retail technology to provide fast and reliable answers to most of your shelf auditing questions. It really is a "must-have" AI tool in the CPG industry that makes everything faster, simpler, more personalised and insightful for the busy CPGs, FMAs or SFAs. Why is AI in retail and ChatCPG such a big deal? What makes ChatCPG so revolutionary is that it fundamentally changes how users, such as Perfect Store managers and field sales managers, etc., engage with shelf auditing data infinitely more dynamically. Unlike transitional solutions that rely on a more static approach to leverage insights on REX, ChatCPG takes data analysis to a whole new level. Instead of analysing REX through the predefined dashboards, ChatCPG harnesses the power of image recognition (IR) data captured from in-store visits and applies an intelligence layer on top of the data. From here, it utilises natural language processing technology to provide immediate, detailed and accurate responses to user questions in plain English. In essence, the technology paves the way for end-users to consume insights in an innovative way and rapidly respond to a wide range of data-led queries. The benefits of ChatCPG ChatCPG offers numerous benefits across various activities, such as shelf compliance, product availability, price analysis, competitor analysis, and promotional display compliance. These benefits include; Makes granular data insights more discoverable ChatCPG is built on Neurolabs’ ZIA and designed to use its output as input data to power the generative AI in retail. This means that it is tailored to automatically understand ZIA's shelf auditing and retail execution language and interpret shelf KPIs such as out-of-stock, planogram compliance, the share of shelf, shelf availability, etc. Unlike traditional methods that require manual coding, ChatCPG eliminates the step where the SFA partner needs to hard-code and predefine vital REX metrics. With ChatCPG, users are able to define their own KPIs on the fly, allowing for a more dynamic approach to market and product analysis. In practice, this means that users can more effectively communicate and ask questions using natural language, enabling them to ask more in-depth questions than a standard analytics dashboard could provide. This, in turn, empowers users to be more strategic in their analysis with seamless access to all the information they need to back up their decisions. Subsequently, ChatCPG makes it easy to uncover REX insights quickly and gain a competitive edge. Instantaneous access to REX insights ChatCPG offers users a remarkable advantage in both speed and accuracy when it comes to leveraging the insights gathered from shelf auditing. By using ChatCPG, users can quickly see the results of shelf audits, saving them valuable time by eliminating the need to manually search through databases for answers. Additionally, ChatCPG provides more accurate results compared to relying solely on human observation and interpretation of spreadsheet data. This is particularly important, as human interpretations can be prone to errors due to factors like time constraints, team members' lack of data literacy, or training limitations. This is vital as REX teams prioritise speed and efficiency in ensuring that the data collected can be acted upon promptly. Additionally, they require accurate and in-depth non-real-time data for strategic analysis and data science work. For instance, users can quickly inquire about product availability using ChatCPG, allowing the generative AI to swiftly identify stores with stockouts. This removes the need for manual checks, providing users with immediate and precise information they can use for real-time in-store checks. It can also help with non-real time planogram and promotions analysis to spur long-term growth. It can integrate with your existing REX technologies If integrated with existing SFA tools (which connect multiple databases), ChatCPG can help highlight determinate causes of common issues like low stock availability across different store locations. To use an illustrative example, users can ask, "Are stock levels in our Covent Garden store always running low on certain items?" and, provided your SFA tools can access historical, point-of-sale information, ChatCPG could provide the historical data related to that location to help you draw insightful conclusions from data. This compatibility benefit means ChatCPG can help you optimise processes to become more cost-efficient, as it can offer marketing advice and helpful tips on effective REX strategies. Artificial Intelligence in CPG industry: Simplify your shelf auditing The transformative power of generative AI paves the way for greater efficiency and innovation in a wide range of industries, including CPGs. For shelf auditing specifically, technologies like Neurolabs ChatCPG can significantly streamline and simplify processes. In addition, our cutting-edge AI-powered tools can enhance the quality of your shelf auditing experience, retail execution (REX) and trade promotion (TPx) strategies by improving the speed at which users can get accurate answers about their data. This is vital, as it can address some of the challenges in managing REX and TPx. By integrating powerful image recognition tools with AI capabilities, ChatCPG empowers CPGs to make informed decisions and uncover valuable insights that may have been previously inaccessible due to time or data visibility constraints. Simply visit our dedicated ChatCPG page and sign up to be among the first to access this revolutionary tool that unlocks new efficiency levels in your stores. Experience the benefits of ChatCPG firsthand and elevate your shelf auditing practices by joining our waitlist today. 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.

Catalogue management refers to the process of managing the content of a brand's catalogue of products. As the name suggests, catalogue management includes creating catalogues, setting up product categories, entering product information, setting pricing, and controlling the catalogue's content. The catalogue is the foundation for developing IR (Image Recognition) technology because, ultimately, the catalogue dictates what the IR technology should be capable of recognising. As a result, catalogue management in the context of IR is vital because without an accurate catalogue, building IR technology is a fruitless exercise, as IR tools cannot extract insights related to the product catalogue of interest. For IR to work effectively, associated imagery must be available for every SKU product to train the IR tech. Traditionally, training IR tools to identify individual SKUs has been a complex and laborious process, given the amount of real imagery needed. Therefore, maintenance and management of catalogues is a real headache. Although there is no foolproof solution to guarantee 100% product catalogue management, there are some considerable advantages when taking a synthetic data approach. In this article, we’ll explore how Neurolabs ZIA and its use of synthetic data can help make CPG catalogue management a breeze. Choosing the right data approach to Image recognition in catalogue management Currently, there are two main approaches to catalogue management, these include: Approach one: The use of real data in product catalogue management Traditional IR technologies use real imagery to upload to the catalogue. Imagery can be photogrammetry images (i.e. images from various sides of the product) or just one to two product catalogue images (i.e. front-facing) Using traditional imagery puts the onus on the CPG brand as they need to supply the imagery, which is sometimes challenging or unfeasible as this approach is slow and expensive. However, in some cases, image recognition solution providers will send an agent to gather this data on the CPG's behalf. This service, of course, will be priced into your solution package. Approach two: Scraping catalogue information from online sources Alternatively, if you or your IR solution provider cannot gather real data to train image recognition learning algorithms, it can gather catalogue information from online sources such as e-commerce websites. The problem with this approach is that it is often incomplete, as there is no guarantee that solution providers can find the information they need to identify SKUs for full catalogue coverage accurately. As a result, many CPGs could struggle to find the time and resources to 'fill in the gaps' by updating their catalogues with real data. This, in turn, impacts the cost-efficiency of seeking a traditional IR solution for catalogue management. Issues with maintaining catalogue accuracy with online data collection Trusting unverified online sources to generate vital catalogue data may not be the wisest move for CPGs looking to push ahead of their competitors. The information available online can be inaccurate for many reasons, such as: Online data sources can be insufficient in training IR for SKU recognition in diverse retail environments E-commerce websites may only include front and back images of products on a plain background, which is not enough to train the traditional IR algorithms for production-level performance. For IR technology to work correctly, the tech needs several different angles of an image, and ultimately, the IR tech needs to be capable of recognising SKUs in any environment. Typically, traditional IR vendors deploying web scraping techniques for catalogue management often use online image recognition technologies like Google Lens to visibly search for SKUs. However, this method is less robust than solutions offered by dedicated image recognition providers. Google (the world’s largest search engine) naturally provides the underlying technology for Google Lens, which functions well under “ideal” conditions, such as perfect lighting, product placement and when the test image closely resembles the product catalogue imagery. However, retail environments are highly diverse. For example, product arrangements and overall conditions on the shop floor, such as bad lighting, partial occlusions etc., can vary. Therefore, the online images used by Google to train its one-shot algorithms can often fail in real-world SKU detection scenarios. E-commerce SKU imagery and annotations may contain poor quality information Additionally, traditional IR solution providers using web research techniques for catalogue management depend on their sources displaying correct online product information, which, of course, is virtually impossible to guarantee if a third party has provided the images. For example, SKU images may be of poor quality; they may have distracting product image backgrounds, graphics overlaid on the product packaging, poor image resolution etc. Moreover, traditional IR vendors may also need to use tools like Google Translate to gather data. This may yield inaccurate results when product copy (used for image annotations) is translated into their desired language. Overall, insufficient information hinders IR vendors' ability to construct IR learning algorithms. Synthetic image recognition and data optimises CPG catalogue management Next-generation image recognition solutions like Neurolabs ZIA use synthetic data and synthetic computer vision (SCV) to train image recognition algorithms. In practice, we make onboarding and catalogue management as simple and streamlined as possible. All CPGs need to do is provide us with the following: SKU artwork - At Neurolabs, we can leverage your existing SKU artwork assets (print label, typically a pdf file) to produce the synthetic data required to build your master catalogue. This is our preferred method for data gathering as it saves CPGs the most time and effort. However, if these artwork resources are unavailable for whatever reason, don’t worry; we’ve got your back. ZIA Capture App - Our dedicated app empowers anyone with an iPhone to scan and onboard an SKU in less than 30 seconds. From here, in just a few minutes, your products can be represented as 3D models to train our synthetic IR algorithms. So for a typical use case of 50 - 100 SKUs, you can onboard your entire catalogue in less than two hours and have the synthetic IR training model ready for deployment in less than a day, achieving 96%+ accuracy from day one. This is a game-changer for the industry! These robust data capture methods enable you to start training the synthetic IR technology to detect products before items hit the shelf, giving you a competitive edge. In addition, our image recognition datasets include an extensive database of 100,000+ SKUs, making catalogue management a breeze. Furthermore, our technology integrates into your existing end-to-end Sales Force Automation (SFA) solution via our API. ZIA’s effortless integration enables you to significantly reduce the time it takes to onboard our synthetic data sets and image recognition technology, streamlining your catalogue management processes. Neurolabs ZIA streamlines product catalogue management With our next-generation synthetic image recognition technology, you can streamline catalogue onboarding and management, ensure a high degree of accuracy in your product catalogue management practices and move ahead of your competitors. If you want to learn more about how Neurolabs ZIA works and how it can boost your company's bottom line, download our free eBook now. Alternatively, if you want to see for yourself how effective our solution is, get in touch with us today to book a free demo. 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.

Image recognition (IR) is fairly ubiquitous within the Consumer Packaged Goods (CPG) space, with 54% of companies utilising some form of IR. However, research shows that 46% of companies have either never used IR or have stopped after trying it in the past and been left disappointed with the cost, speed and lack of scalability. The issues with traditional IR are well documented, with many often citing a lack of accuracy, the slow speed of execution and the exuberant cost involved in setting up and maintaining the technology. However, what if we told you that IR doesn’t have to be like this? What if there were another way to enjoy all of the benefits of IR without the aforementioned pitfalls? Throughout this article, we will introduce you to a groundbreaking new IR technology that is set to take the world by storm. So don’t let your CPG brand miss out on the most powerful and innovative image recognition solution on the market today! A picture perfect store strategy The ultimate dream of any CPG is to deliver the perfect store strategy. Consisting of the ‘Six P’s’ known as Product, Pricing, Place, Promotion, People and Process, the perfect store strategy relies on IR to provide CPGs with the above information and factors they need to achieve their goal. Unfortunately, due to poor accuracy, speed, scalability and high costs, traditional IR has been less than ideal in delivering reliable results that will allow many CPGs to realise their goal of delivering the perfect store strategy. Thankfully, however, a certain new technology on the market has proven far more accurate and effective at turning a dream into reality. Introducing Neurolabs’ ZIA Utilising synthetic data and an advanced AI learning model known as synthetic computer vision modelling, ZIA (Zero Image Annotations) is the next generation of IR technology. ZIA’s groundbreaking IR tech delivers superior accuracy, quality and scalability compared to traditional IR and also at a cost-effective price point that works for CPG brands of all sizes. Compared to the outdated and inefficient traditional IR model, ZIA is a revolutionary, eye-opening approach to image recognition that just simply works. Here are just a few of the benefits that ZIA provides: The most accurate image recognition solution As demonstrated in our work with tech-firm Sagra Technology, ZIA has the ability to pinpoint even the most minute details from seemingly identical products. As such, our solution is capable of producing highly reliable results with a visual detection accuracy of 95% from the outset. Depending on the type of product your brand sells, our technology can achieve over 98% product detection accuracy, and in Sagra’s case 98.3%, making it one of the most consistently accurate IR solutions available. Seamless global scalability ZIA is the perfect partner for any brand looking to scale up its business and add new products to its line of SKUs. To get started, all you need to do as a CPG is upload your product artwork and our synthetic computer vision model will handle the rest. One of the biggest problems with traditional IR is that it is reactive, as it can only conduct image recognition on already launched products. However, with ZIA, our solution is entirely proactive. ZIA doesn’t rely on real data (images) for its training data. Therefore, CPGs can upload SKUs to our system by simply submitting their in-production artwork. Due to this proactive approach, a CPG can also make tweaks to their inventory over time, making it incredibly effective at providing IR for its new products or any promotions as and when they launch. With ZIA, CPGs no longer need to wait for the item to be on store shelves. Instead, they can add new SKUs and have them be a part of their IR solution as soon as the artwork is ready. Additionally, CPGs are even able to achieve image recognition on products before they launch. A cost & time efficient solution As stated previously, ZIA stands for Zero Image Annotations, and our technology definitely lives up to that name. Our state-of-the-art, AI-based, synthetic computer vision tech means that teams no longer need to painstakingly annotate data to train their IR algorithms which, being a manual process, takes considerable time and cost. In addition, because of its reliance on synthetic data, ZIA also solves one of the biggest issues with traditional image recognition, degradation. As outlined in the second law of thermodynamics — the total entropy of a system either increases or remains constant. In terms of image recognition, this means that each time the product is placed in a new environment (e.g. a new shop or different lighting conditions) or undergoes a design change, the success rate (accuracy) of the traditional IR algorithm is essentially being degraded. Over time, this means that the IR will become less accurate, and will be less likely to detect products. Unlike traditional IR, ZIA’s visual accuracy doesn’t degrade over time as the algorithm has already been trained to identify products within numerous environments and scenarios. As such, ZIA saves time and money while delivering a consistently high level of performance and accuracy. Ease of integration At Neurolabs, we are aware that many CPGs may already have heavily invested in image recognition technologies. However, with ZIA, our solution can integrate with any existing Sales Force Automation (SFA) tools. Think of it as a plug-and-play upgrade! Expert support, every step of the way While there's no complicated and drawn-out onboarding process with ZIA, we understand that you still might have questions about how best to use the platform. As a result, CPGs who use our platform will get access to a knowledgeable and dedicated team who can help solve any queries you may have and get you up and running within a week. Simplify your IR journey with Neurolabs’ ZIA The benefits listed above are just a taste of what is possible when you switch to ZIA. As a genuine, generational leap for image recognition, ZIA is a truly accurate, streamlined and stress-free IR solution that is far more capable than virtually any other IR solution on the market. Thanks to our highly accurate solution, a CPG of any size can boost its sales and revenue when they use ZIA. However, don’t just take our word for it. Why don’t you get in touch with one of our team today to see it for yourself? Request a demo of ZIA here! 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.

Image recognition (IR) tech has been around for some time, and when it was first introduced it sounded promising. From simplifying stock counts to streamlining in-store audits, image recognition set out to be an essential tool for optimising retail execution and enhancing the customer experience. However, there remain certain drawbacks to the use of IR as we know it. For instance, exactly how it is trained has a ripple effect in terms of its effectiveness, and how reliable it really is are critical concerns that retailers must navigate. Perhaps for these reasons, according, a recent POI report, only 54% of CPG’s are currently utilising image recognition. Computer vision is essentially the “brain” of image recognition, so the data used to train the technology is vital. Generating real data (i.e. photos and manual annotations) to train traditional IR technologies, especially in retail contexts, is expensive, time-consuming to collect and not scalable. Therefore, brand and field marketing agencies are increasingly turning to synthetic computer vision (SCV) to aid in this process. SCV uses computer-generated digital twins of SKUs and retail settings to train its image recognition algorithms. It represents the next evolution of IR technology. The use of SCV and synthetic data stands to transform how CPG brands, Field Marketing Agencies (FMA) and Sales Force Automation (SFA) companies use and deploy image recognition tech. So, are you ready to try a better approach to IR? Read on to learn about the advantages of switching your image recognition from real data to synthetic data. The Five Advantages of Synthetic Data in Image Recognition Synthetic data refers to computer-generated data that is used to train machine learning models for image recognition tasks. This data can be generated programmatically and can be used to create a wide variety of images, including objects and scenes, without the need for expensive and time-consuming data collection processes. By using synthetic data, you can unlock new potential within your image recognition, including: 1. Greater accuracy Synthetic data provides greater accuracy than real data, and results are more reliable. Synthetic data also reduces the restrictions on what you can do with your IR tech. For instance, when connected to an SFA solution, synthetic IR can improve planogram compliance, shelf compliance, product availability, price analysis, competitor analysis and promotional display compliance to name a few. Synthetic data offers a distinct advantage over real data when it comes to training algorithms. Generating vast volumes of diverse data with synthetic data creates a more robust training data set, which helps to ensure that product detection accuracy does not degrade over time. This also allows algorithms to handle even the most challenging store environments, with difficult lighting conditions or deformations of product packaging. 2. Low complexity to set up and use the technology Gathering real data to train IR algorithms is not scalable, because it is costly, time-consuming and labour-intensive. By contrast, synthetic image recognition creates hyper-realistic 3D digital twin of your products and begins training the algorithm in a matter of minutes. This revolutionary approach to image recognition is transforming the industry and our first of a kind technology, ZIA (Zero Image Annotations) is training SCV algorithms to detect products from just their packaging label. This reduces the complexity of setting up and using the technology, as the 3D digital twin and the virtual scenes of the shop floor becomes the source of all your training data, meaning you don’t have to take pictures of a physical object multiple times. Instead, the SCV model can detect the product in 360 degrees, removing the need for human input throughout the entire operation. With real data, you can only generate reactionary methods of IR, meaning you can only begin training a traditional computer vision model once the product exists on shelves. As a result, product analysis is delayed. However, synthetic IR allows you to expedite the process and generate an analysis before a product has even launched, simply by using the packaging label/artwork file. 3. Robust performance across different store situations and environments Environmental factors like lighting, product distortions and even messy surroundings can alter the performance of traditional IR models that rely on real data. This is because they’re often trained on sterile marketing photos that assume ideal conditions. So, traditional IR models can struggle to accurately detect a product when it is on a busy shelf, deformed in some way or has less than ideal lighting compared to when it is by itself or alongside the same SKU. By using synthetic data to build virtual scenes, computer vision algorithms can detect SKUs in a multitude of different environments, and within minutes. In turn, products are detected even in more challenging environmental conditions (which is typically the norm for real-world stores). Detecting deformities allows for more accurate results as products might be damaged by consumers or during transit, and traditional IR will likely not be able to detect the product as a result. Alternatively, synthetic computer vision solutions can mimic both ideal and imperfect lighting situations as well as other sources of error, like product damage and mess. For example, ZIA has a robust performance track record that can detect products even if they are in a different state than when they were first uploaded onto our system. In practice, this means that our model is trained to detect a product that has been crushed, crumpled or deformed in a variety of ways. Best of all, since a digital twin isn’t real, the product can be deformed endless times without creating any waste. This means it’s good for the environment and your bottom line. 4. Faster product onboarding Traditional image recognition technology using real data needs a considerable amount of imagery and time to be trained before it can be used. However, synthetic data allows for faster onboarding as the algorithm programmatically generates training data from just the product artwork/label. As an example, with ZIA, a customer can be onboarded up to four times faster than the market average. ZIA also has a database of over 100,000 existing SKUs that the SCV algorithm has already trained with that you can leverage from day one. This speed enables CPGs to onboard a high volume of SKUs in a matter of hours and days rather than weeks or months. In turn, FMCG companies can speed up their market launch, ensure perfect store execution and get ahead. 5. Lower cost of set up and maintenance Finally, synthetic image recognition technology is more cost efficient and faster to use than real data image recognition since you need to collect and label large datasets manually. Additionally, synthetic data can be generated at scale, allowing for more extensive and varied datasets - which would be impossible to achieve at scale with real data. Also, a unique and competitive feature of synthetic image recognition is that it allows SKUs to be pre-loaded, meaning that new packaging and seasonal promotions can be detected as soon as they appear on the shelves, instead of having to react to them afterwards. Access synthetic data solutions, and more, from Neurolabs Interested in learning more about the potential of synthetic data for your retail execution? Request a demo today or download our ebook, where we explore how Synthetic Computer Vision is changing the game when applied to retail shelf auditing! 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.

As a Consumer Packaged Goods (CPG) brand, we’re sure you’re well aware that you can have great products and an established reputation in the industry but still fall short when it comes to standing out from your competitors and increasing revenue. As with many other industries, technology presents enormous growth opportunities for brands and businesses, and the CPG space is no exception. Enhanced technologies allow CPGs to improve their operations and gain fantastic insight into customer bases, optimal distribution channels, and more. Image recognition software is one such enhanced technology that is currently revolutionising the consumer packaged goods industry. Image Recognition (IR) is a type of AI technology that essentially acts as the eyes of a computer, allowing it to “see” and contextualise real data and imagery of SKUs and retail shelving. Due to its impact, image recognition investment in the CPG sector is expected to increase at a Compound Annual Growth Rate (CAGR) of 22% from $2.08 billion in 2022 to $10.23 billion in 2030. Image recognition can maximise retail store execution (REX) efficiency It should come as no surprise that customers have a negative perception of stores that regularly run out of stock. According to an older study by the Harvard Business Review, 21% to 43% of people will end up purchasing an item from another shop if their favourite store runs out of their favourite products. More recently, data has shown that CPG retailers missed out on approximately $82 billion in revenue in 2021 due to low product availability. As a result of these massive missed sales opportunities, CPGs invest a lot of time and resources into developing a Perfect Store strategy. Retail shelf auditing procedures play a huge role in executing a ‘Perfect Store’ strategy as they are used to optimise product placements, displays, and point-of-sale locations. However, it’s important to note that manual retail shelf audits take a lot of time to complete, and the results can often be inaccurate, giving rise to increased instances of out-of-stocks. Thankfully, image recognition tools digitise processes, making retail shelf auditing faster and more accurate for retail planners. For example, tools like Neurolabs’ ZIA use synthetic computer vision technologies to detect SKUs from shelf images. ZIA and other modern image recognition tools can also extract in-depth and accurate information on shelf KPIs like inventory levels, packaging promotion information, and more. This means that, equipped with ZIA, a field rep could finish a store visit 8x faster, allowing for more store visits, while at the same time extracting richer and more reliable shelf data. Neurolabs’ ZIA platform can also help CPGs and Field Marketing Agencies (FMAs) to break away from the outdated tech and implementation that many retail-based image recognition solutions rely on. Our previous work with European FMA, Instore Power Provider (IPP), demonstrated that our solution can complete a real-world deployment in less than three weeks, making us considerably faster than the market standard of two to three months. Furthermore, our state-of-the-art platform can onboard a new SKU in one day or less, making it four times faster than the market average. The enhanced capabilities of our ZIA platform allows you to execute an IR approach that is not only faster to onboard, but also more accurate and robust in its output. For example, in our aforementioned case study, IPP achieved 97.6% accuracy when detecting products. Meanwhile, our collaboration with Sagra Technology achieved over 98% SKU detection accuracy in just seven days. As a CPG, enhancing your SKU detection capabilities with a modernised image recognition tool such as Neurolabs’ ZIA can have numerous benefits. One of the biggest bonuses of ZIA is that it helps to reduce the time in-store for field reps tasked with maintaining/ensuring perfect store execution compliance. IR can help you increase sales A recent survey in the US found that 80% of consumers seek out brands that understand their needs. In addition, 80% of shoppers will also try out a new product if it is offered at a discount. Therefore, CPG brands looking to gain a competitive edge should take advantage of price matching and AI tools that can help them gather and analyse competitor data and customer insights. Enhanced image recognition tools like Neurolabs’ ZIA, for example, incorporate data sets such as competitor pricing at SKU-level, helping CPGs make data-driven pricing decisions. In addition, Neurolabs ZIA also supplies SKU information as JSON files, allowing FMA teams to integrate their existing third-party SFA tools to help them improve customer engagement metrics. One beverage company studied by Bain and Company found that 40% of its spending on pricey merchandising items like tables, chairs, and umbrellas went to outlets without the growth potential to warrant such investments. However, when the company supplied field reps with enhanced analytics tools (outlining insights such as optimal product placement guidance, sales reward systems, etc.), the company was able to make its products more enticing to customers and boost profits by 5%. Neurolabs ZIA can help you stay ahead of market trends and tech The speed, scalability, accuracy and efficiency of synthetic image recognition tools like Neurolabs’ ZIA enable CPGs to stay ahead of retail trends and direct competitors. ZIA is easy to deploy and has 100,000 SKUs pre-loaded in its database. Furthermore, ZIA also integrates with your existing SFA tools to make the onboarding process as quick and straightforward as possible, with no training required for in-store field reps. Interested in learning more about Neurolabs’ ZIA and the synthetic computer vision model that powers it? See how we can help drive your business forward and understand more about the power of synthetic data in our latest eBook. Alternatively, please feel free to contact us directly for a personalised demonstration. 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.

With trends in the Consumer Packaged Goods (CPG) industry rapidly evolving, new and established brands face the difficult challenge of keeping up with the pace of change. Among the stark changes for CPGs are the innovations in the image recognition (IR) field and the evolution of such with the catalyst of artificial intelligence (AI). While AI is not yet living up to the predictions of the 1980s (we're looking at you, Skynet), it's most certainly being used practically in CPGs to help players to understand their customers' needs, improve operational efficiency, and, most importantly, boost profits. With that in mind, in this article, we will look at three real-world applications of artificial intelligence tools that you'll see much more of in the CPG space in 2023 and beyond. AI trends in the CPG sector to look out for Piggybacking on the latest trends may find you sitting at the tail end of the successful work of other brands. However, it need not be. For CPG brands to maximise their customer base, they must ride AI trends to stay relevant in the market. Ignoring them can not only be detrimental to their growth but also damage their reputation from their negative outlook and lack of action when it comes to innovation in the space. At Neurolabs, we are confident that the following applications are going to become more prevalent: Personalised recommendation engines Personalised recommendation engines collect and categorise user data, their purchases, ratings, and relationships with other users in more detail, allowing CPGs to offer personalised services or products according to consumer decision-making. Why? Well, according to McKinsey, 71% of consumers demand personalised messaging from brands and companies and giving the consumer what they want is the age-old secret to success. A further study also by McKinsey found that 83% of customers say that they want their shopping experience to be personalised in some way. Research by the global management consulting firm shows that effective personalisation can increase store revenues by 20 to 30%. With CPGs, however, this poses a challenge. Whereas in e-commerce, personalised recommendations are common, brick-and-mortar retailers struggle to offer the same level of service. For CPGs, AI innovations are making this challenge more than achievable, allowing companies to reconsider how they connect with consumers at each and every touchpoint. For example, through Geofencing, virtual barriers that trigger a response when customers cross defined thresholds, retailers can send different recommendations when customers pass by an aisle. Not only can this offer opportunities for digital point of sales, it can also enhance a customer shopping experience through personalised recommendation engines, bringing attention to items, services and products tailored to their needs, that they may otherwise have missed. Personalised recommendation engines can fuel tried and tested marketing for businesses too. Currently, 7% of CPGs are using location-based marketing campaigns, a practice that personalised recommendation engines can add further depth. Instead of basing recommendations on geographic locations alone, AI can offer a more complex personality breakdown, tailoring location-based marketing to a more bespoke and personal range of products. Optimising price/loyalty programs Discount and loyalty programmes are an old-hat tactic for CPG businesses, reigniting customers with familiar brands through targeted sales - usually on a seasonal or time-lapsed basis. But, while these strategies are unsurprisingly popular, they are not always cost-effective. For example, while eight out of ten customers are enticed by a discount, 72% of marketing promotions fail to break even. However, this does not mean that they are not cost-effective in the long run, as they improve a customer's familiarity with a specific store or brand. What's more, moving forward, CPG teams can optimise their in-store promotions considerably through the use of AI. For example, historical sales data can be gathered to train machine learning algorithms to help it identify customer segments to target with discounts and customer loyalty reward programmes. This means there is more opportunity to target the right people and, more importantly, the people who are most likely to repurchase and close the gap on the revenue lost through original discount programmes. Furthermore, this data can also be used to track competitor pricing and even forecast future sales results to help CPGs plan their visual merchandising more effectively. The use of synthetic data in IR Recently there has been an increased demand for technology, such as Image Recognition, to improve the efficiency of retail store execution. Specifically, IR can be used to automate the current manual shelf auditing process. Due to its reliance on traditional IR, the shelf auditing process in the CPG sector is slow to execute and often yields inaccurate findings, causing large dissatisfaction with the CPG brands. This is because traditional image recognition tools rely on real-world data consisting of images of products and retail shelving to help CPGs manage inventory and merchandising. However, there is a complex-sounding but easily integrated solution for CPG businesses experiencing these challenges, and it's that of Synthetic Computer Vision (SCV). Modernised image recognition software uses SCV technologies to improve retail auditing and planogram compliance speed and accuracy. SCV creates 3D digital twins of Stock Keeping Units (SKU)s and then uses machine learning algorithms to replicate CPG shelving and store layouts. Utilising the technology in this way is helping Field Marketing Agencies (FMA)s, and CPGs assess Key Performance Indicators (KPI) like on-shelf availability, placement, and pricing. ZIA, however, goes one step further than traditional Image recognition. With Neurolabs’ ZIA, a CPG can also create bespoke reporting of KPIs such as competitor pricing, allowing businesses to make better decisions that align with their goals. For instance, improving sales, increasing revenue and removing friction in the customer experience are just some of the added benefits that come with using Neurolabs’ ZIA. The key advantage of SCV tools like Neurolabs' Zero Image Annotations (ZIA) is that its powerful AI can integrate and enhance existing Sales Force Automation (SFA) software, meaning no lengthy onboarding or time-to-market delays for in-store field reps and immediate results for CPGs. It also ensures at minimum 95% SKU-detection accuracy from day one of solution implementation. Neurolabs' ZIA is helping CPGs catch up with developments in AI With its potential to speed up image recognition in shelf auditing, increase accuracy in planogram maintenance, and enhance the customer experience, now is the time to start looking at faster-to-deploy solutions like Neurolabs' ZIA. To learn more about its applications for both FMAs and CPGs, look at our latest eBook. Alternatively, get in touch with us now for a free demo. 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.

Consumer Packaged Goods (CPG) brands can benefit greatly from the implementation of a Perfect Store Strategy, as it enables them to increase their visibility in the marketplace and drive sales growth. However, despite the fact that many Consumer Goods companies strive to achieve a 'Perfect Store' strategy, research reveals that only 44% are satisfied with the execution of their in-store sales. A Perfect Store is designed to make it quick and easy for customers to find the products they are looking for. Each CPG company defines the Perfect Store differently, as specific markets and distribution channels have different needs. With that said, all Perfect Store strategies encompass the six P's: Product, Pricing, Place, Promotion, People and Process. This article will serve as a complete guide to executing a Perfect Store strategy and explain how modern Image Recognition (IR) technology can significantly enhance the success of said strategy. The benefits of building a Perfect Store Strategy Customers pay little attention to the six P’s that make up the Perfect Store. However, these six tenets (Product, Pricing, Place, Promotion, People and Process) are vital for focusing CPG's efforts to bring about the following benefits: Improved presence in-store Research shows that 70% of shopping decisions are made in-store, indicating that most consumers are easily swayed by enticing displays and price points rather than consciously seeking out specific goods they've decided upon before heading out to the shops. This means that CPGs can easily influence their customers' purchasing behaviours by developing a Perfect Store strategy that improves the display of goods. For instance, placing a point-of-sale display of drinks next to tills can encourage more impulse purchases in warmer seasons. Significant sales uplift Perfect Stores go above and beyond to provide the best shopping experience for their customers and, as a result, attract customer loyalty and significant sales uplift. For example, a beverage business in South America found that its ‘ideal’ stores had a market share over 30% greater than outlets where no Perfect Store strategy was in place. Greater ability to monitor and improve store performance If you're a CPG Sales Director, implementing a Perfect Store strategy is a superpower, allowing you to measure performance and gain a strategic advantage. With in-store data, you can identify stores that aren’t performing well and make changes in order to increase sales. The six P’s of implementing a Perfect Store strategy To maximise success of your CPG, it's essential to craft a comprehensive Perfect Store strategy that incorporates the six P's. Here are some tips to ensure success: 1. Product To ensure customer satisfaction and maximise sales, CPG brands should create a plan to deliver a Perfect Store that exceeds customer expectations. Leveraging customer research, competitor data, and market trends, you can gain insight into customer preferences and measure your performance against competitors'. Utilising image recognition to analyse Shelf Compliance, Product Availability, Price Analysis, Competitor Analysis, and Promotional Display Compliance will provide actionable data to inform and optimise your positioning and strategy. With this approach, you can ensure an adequate and competitive product assortment that will boost customer satisfaction and increase revenue. 2. Pricing Price optimisation for CPG brands is a key factor for success. With the assistance of IR technology, CPGs can maximise their profits by better understanding the pricing dynamics of their products and their competitors' products. Image recognition technology can give an accurate picture of the various price points of a product across different markets and retailers. This data can be used to understand the shopper's sensitivity to price, measure brand performance at the current price point, and calculate the optimal price point to achieve your desired objectives. Moreover, IR technology can help CPGs identify the most profitable price points to offer across their portfolio while also meeting shopper targets. 3. Place Consumers are well-acquainted with spotting their favourite items in certain areas of a store, making in-store placement a key factor for both visibility and the likelihood of purchase. Therefore it's paramount to take into account factors like the store layout, aisle positioning, primary shelves, and secondary placements such as displays to make sure that your products catch the eye of your target customers. Again, this is where image recognition provides valuable insights, giving you data on shelf compliance, product availability, price analysis, competitor analysis, and promotional display compliance. 4. Promotion Point-of-Sale (POS) marketing is a core aspect of success in the retail space, with an estimated 76% of purchasing decisions being made in-store. With the aid of modern IR technology, such as Neurolabs ZIA (Zero Image Annotations), new SKUs can be recognised prior to their release date. This proactive approach allows CPG brands to measure the efficacy of product promotions and track the performance of their items across different outlets, enabling them to make informed decisions about product placement and promotion strategies. Ultimately, this provides CPGs with the means to maximise the effect of POS marketing and secure long-term success. 5. People To ensure your employees can deliver a Perfect Store experience, it is essential to empower them with knowledge of the six P's and equip them with the latest shelf auditing technologies. Our advanced IR technology can provide your team with a reliable, highly accurate tool to expedite their shelf auditing duties and save time. Our image recognition solution has an impressive accuracy rate of 95% from the outset, increasing to over 98% in certain categories. With such advanced technology, your employees can carry out their shelf audits quickly and accurately, ensuring your store's Perfect Store experience is delivered to the highest standard. 6. Process For maximum efficiency in executing your Perfect Store strategy, it's essential that you streamline your processes. Neurolabs ZIA is the modern IR technology that can onboard new SKUs in less than a day, using an API to integrate with your existing end-to-end solution or any Sales Force Automation (SFA) software. This onboarding time is significantly less than the market average of five to seven days using traditional IR methods. Moreover, to ensure the Perfect Store strategy is implemented successfully, Neurolabs ZIA offers precise and dependable image recognition and data analytics. It also utilises synthetic data for training and learning, meaning there will be no degradation in product detection accuracy over time. Build your Perfect Store with help from Neurolabs' ZIA Creating a Perfect Store strategy starts with setting clear goals and eliminating time-consuming, error-prone processes. Moreover, it involves investment in powerful technologies that represent the gold standard in the SFA industry. Synthetic IR helps CPGs obtain 100% retail audit compliance by leveraging machine learning and artificial intelligence (AI), giving FMAs more scope to gather and analyse shop floor data more efficiently. Neurolabs' ZIA solution can complete category checks and receive findings in seconds. Plus, it can integrate with existing SFA tools to help minimise onboarding time and ensure your Perfect Store strategy is executed seamlessly. To learn more, look at our latest eBook or request a demo with us today. 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.

AI is revolutionising all industries at the moment. According to recent research, worldwide AI revenues will increase from $643.7 million in 2016 to more than $36.8 billion in 2025. In retail, AI-driven IR (Image Recognition) technologies can transform the brick-and-mortar shopping experience, making it more customer-centered and seamless with capabilities such as personalised product recommendations and cashier-less payments. But naturally, many Consumer Packaged Goods companies (CPGs) and Field Marketing Agencies (FMAs) may be skeptical of investing in new image recognition tech. They may ask themselves, are the solutions accurate enough to streamline inventory auditing? Does its inclusion boost sales and add more value to the end customer? In this article, we will address some hesitations CPGs and FMAs may have about IR technology and outline how tools like Neurolabs' ZIA can enhance retail execution. Myth One: IR technology isn't reliable Image recognition technology is used to ensure the following is executed perfectly in retail stores at all times: Arranging inventory to attract as many buyers as possible Optimising store distribution channels to avoid overstocking and low-availability With that said, the CPG industry views traditional IR methods negatively due to its tendency to generate inaccuracies in the data sets because of its reliance on manual processes. That is to say, traditional IR requires real-world data (i.e. photos and manual annotations) to train its algorithms. However, modern Image Recognition technology uses synthetic (computer-generated) rather than real-world photos and data to train its algorithms, increasing the speed of shelf auditing execution and the accuracy of SKU detection results. Neurolabs' ZIA solution, for instance, can obtain 95% accuracy in SKU-level recognition from the moment it's deployed thanks to its synthetic Computer Vision (SCV) technologies, speeding up the process and quality of shelf auditing. Additionally, Neurolabs ZIA allows new SKU packaging design files to be uploaded and trained within the learning algorithms before products hit the shelves. This gives CPGs a competitive advantage because the synthetic IR can detect the new product before its delivered to stores, all without the need for real data input. Myth Two: IR doesn’t speed up retail execution FMAs have a hectic schedule and must visit multiple stores daily to maintain compliance KPIs. With traditional IR technologies, they have to take photos of shelves, upload them to a central management system and then wait to receive feedback on their tasks. However, research from Stanford on Planogram compliance found that manual retail auditing tasks like these carry a mistake rate as high as 20%. Therefore, FMAs and CPGs need an IR solution that can guarantee fast and accurate feedback in order for them to perform their duties efficiently. Traditional IR takes time to execute and can't maintain high accuracy in SKU detection because it requires human input to train the algorithm using real data collection. Synthetic data solutions, such as Neurolabs' ZIA, use Synthetic Computer Vision (SCV) to build a 3D Digital Twin of each product, eliminating the problems associated with poor photo quality. The technology can quickly onboard new SKUs with its vast training ability, providing better performance, data, and results over real-data collection. The solution can also easily streamline product catalogues across multiple retail locations, providing fast and accurate reporting of stock levels and other KPIs, such as out-of-shelf rate, shelf share percentage, and competitor price comparison. Additionally, Neurolabs’ ZIA delivers instant access to comprehensive and actionable insights, providing CPGs and FMAs with a real-time JSON (lightweight data file) that’s easily integrated with existing data and reporting systems. The data is stored in the cloud and can be accessed and exported from anywhere, with no restrictions whatsoever. Myth three: IR is too risky of an investment Both CPGs and FMAs are under pressure to increase sales, and the way to do this, in most cases, is to gather and analyse large volumes of high-quality, real-time customer data. Insights such as shelf share percentage of products or competitor pricing hugely benefit CPGs in helping them ensure compliance with retailers and improve their revenue with best-in-class retail execution. However, Traditional IR performance and accuracy is not up to par due to its reliance on real data which is time-consuming, inaccurate and costly. This is a problem because when the field rep works with inaccurate data, they are unable to meet their performance expectations and make the necessary improvements in-store. As a result, CPGs need guarantees that any new IR technology investment will deliver reliable and accurate insights in a timely manner. There really are no efficiencies that can be made to this outdated approach. But the good news is that solutions like Neurolabs’ ZIA can boost your current solution's IR performance with synthetic data. Synthetic Data Powered IR is revolutionising retail shelf auditing. Compared to traditional methods, it offers faster time-to-market, new SKU uploads, and shorter training times for the algorithm. Moreover, it is much more accurate and retains this consistency, unlike traditional IR which decreases in accuracy over time. It is also remarkable in its capacity to easily and cost-effectively scale across multiple retail outlets whilst also unlocking new capabilities such as uploading products for detection before their shelf release date. Neurolabs' ZIA technology integrates with any Sales Force Automation (SFA) solution used by FMAs and their field reps via API. This means that field agents can upload existing SKU data and shelf imagery, and the system will automatically stitch these images together to compile a complete and accurate planogram. There are more benefits to the upgrade, too, including speed of execution, better quality data, better accuracy, cost-efficiency, scalability, etc. To illustrate, the updated IR system takes less than one week to deploy for up to 1000 SKUs. It provides: Product/SKU recognition Share of shelf Planogram compliance Price recognition Thanks to its synthetic computer vision technologies, Neurolabs’ ZIA can scale quickly, accessing hundreds of thousands of virtual SKUs by default, no matter how many items, shelves, or stores your business has. Neurolabs' ZIA can enhance your IR capabilities Neurolabs delivers the next generation of IR tooling to help streamline FMA workloads and CPG store auditing and planogram capabilities. Download our latest eBook below to learn how Neurolabs' synthetic computer vision works. Alternatively, get in touch to find out how our technology can reassure any doubts you might have about the capabilities of image recognition for retail. 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.