What is your role in handling the team and technology at Trax?
I’m the MD of EMEA at Trax. I lead a team of highly skilled individuals to deliver results for Trax customers in the retail space. Our customers are some of the world biggest Consumer Packaged Goods Businesses (CPGs) and Grocery Retailer. My team is comprised of operational experts dedicated to our key customers, account management and marketing and the team is supported by our technology and R&D teams in Israel.
Trax technology is a leading computer vision platform that combines automation, AI and cloud computing to provide unprecedented insights into the retail shelf. The platform analyses photos taken by IoT fixed cameras, Trax mobile applications or crowd-sourcing and stitches the images together to create an entire digitised picture of the physical shelf. From this, using the platform, CPGs and retailers can leverage insights into ‘on the shelf’ product performance and understand what happens at the point of selection.
How has the Retail industry progressed in the last two years?
The biggest change we’ve seen in the industry is customer expectations. Digital transformation has meant that e-commerce is now booming, and consumers have come to expect the instant experience of shopping online wherever they go.
However, while the e-retail giants like Amazon have come in and changed the game for retailers, traditional stores have not kept up with this innovation. Bricks and mortar retail is still maintaining the same standards of practice they have been for years, and are now seeing the consequences of not changing with the times. Hundreds of stores have closed over the last two years because they have not introduced new ways to shop – they just do not provide customers with the shopping experience they want.
How do you see the pace of evolution of the retail industry further accelerating with adoption of AI, Machine Learning and Computer Vision?
Bricks and mortar stores are now coming round to the fact that things need to change if we’re to see high streets and shopping malls survive. We’ve seen AI, Machine Learning and Computer Vision change online retail dramatically, with brands now able to do everything from analysing the shopper behaviour online and serving ads for products to get the products from warehouse to door in just a few hours. Learning from these capabilities, we’ve seen new advances in technological applications for the physical retail space too, meaning stores can innovate better and therefore evolve to compete with online giants.
In the next few years, I anticipate that there will be a rise in adoption of these technologies in the physical retail world that will enable retailers and CPGs to transform how they do business. This will allow stores to ultimately create a more convenient and enjoyable experience for customers.
What is the current state of Image Processing technology in Marketing, Sales and Advertising?
Retailers and CPGs understand that the way the products are merchandised, directly influences shopper purchasing behaviours. However, Shelf Auditing ensures that the shelves are full and the products are correctly mechanised which has remained a very basic task for many years. The current process is that the stores or sales reps (for CPGs) manually check how products are merchandised on the shelf but this is laborious and inefficient, a very costly process, and, of course, prone to human error.
However, this process is changing. With Trax, images are used to get instant and accurate insights into the shelf and how products are performing. In reducing audit times with the use of our image processing technology, sales reps have more time to improve merchandising and store staff have more time to spend with shoppers.
How does Trax help digitize Customer Journeys?
The average shoppers’ attention in a store is held for 7 seconds; that’s absolutely no time at all. Therefore, it’s important to capture what happens at the shelf for understanding shoppers’ behaviour. With retailers, Trax uses fixed IoT cameras as part of the computer vision platform to gain insights into what is happening on the shelf. The data provides insights into why products are chosen – for example, a product may not be performing well because it is on the bottom shelf, or because there are similar products around it that are currently on offer or because it is simply not on shelf.
In the future, we’ll also see insights from Trax be able to map out the entire customer journey incorporating fixed cameras, robotics and sensors. Once these are in place, stores will gather far more information on inventory and shopper behaviour, and have a greater ability to enhance the shopping experience and compete with online rivals. This technology will also help shoppers to navigate a store via an app, and identify which products are available and where, and then filter them by preference – such as only being guided to gluten free products. The technology will also enable a shopper to see which stores have certain products in stock before deciding where to shop.
Which businesses have been the fastest to adopt Computer Vision, IoT and Image Processing technologies?
At Trax, we work with many big name CPGs that have been quick to take advantage of the innovation we provide. For example, we work with beverage giants like Coca Cola and ABInBev, companies that use Trax to understand how promotional offers are working in-store to ensure that they have reduced out of stock (OOS) levels. This gives them a competitive advantage, understanding what they need to do to ensure that their products are performing better than other brands on the same shelf. And this works on a global scale too – utilising Trax data, these companies are able to understand which region products are performing best in and amend strategies based on this accurate knowledge.
How does Computer Vision unlock opportunities in the Mobile-driven Retail markets?
Smartphone penetration necessitates an omni-channel strategy and digitisation has transformed many traditional areas like music and travel, and it is just a matter of time before the brick and mortar environment is digitised to optimise and enhance the shopper experience.
Mobile commerce is becoming a key player within the retail market, it unlocks the possibility for CPGs and retailers to stay even closer to consumers, offering the potential for people to make purchasing decisions at any point. By using a multitude of social and in-store data points, such as previous transactions and customer in-store interactions, retailers and brands will know more about what the shopper wants from their preferred brand of coffee to what hair product suits their style and use that data to roll out more effective, personalised and interactive marketing campaigns.
Compared to the US, how do you see the markets in EMEA and APAC dealing with the disruptions in Digital Retail landscape?
Across the globe, retailers and CPGs understand that to compete and compliment with on-line grocery, they must embrace with technological change. Our customers throughout the world are now quickly understanding that digitising the physical world of retail goes hand in hand with the digitization of retail at large.
What are your predictions on the role of AI, Machine Learning and Robotic Process Automation for retail?
The pace of innovation in retail shows no sign of slowing. The industry is seeing a wave of innovative applications for these emerging technologies that are creating new core capabilities for retailers.
One is availability-management. Retailers will continue to move away from the flawed process of only managing what’s in the store. Increasingly they will focus on what’s actually on the shelf, in the “eyeline” of the consumer. This will not only go some way in eliminating out of stocks but will also give rise to information-powered shopping: where retailers can guide the individual consumer using individual insights derived from a Machine Learning engines.
Radio-frequency identification (RFID), image-recognition, IoT and blockchain are allowing retailers to see what is in the store and where the product came from – and the potential of these technologies is growing by the day. Image-recognition and Machine Learning are now being combined with granular data that allows for product-level decision making at the micro level something that past analytics tools could not provide.
The core to seeing these new applications become reality is a real-time feed of what is currently on the shelf. At Trax, we’re seeing that fixed-camera solutions are the lowest-cost way to collect real-time in-store data at scale. Pairing this with the rapidly expanding Machine Learning capabilities that we are leveraging will enable unique insights into consumer shopping habits on a level we can’t even imagine.
What advice you wish to give to all the MarTech-and AI-Ops professionals looking to build a career in Retail and related industries?
Use your knowledge of technology to bring innovation into the industry. Retail cannot survive if it stays the way it is, and that goes for every role within the industry everyone from actual manufacturers to sales reps needs to be ready to embrace technological change because innovation will be the key to saving bricks and mortar stores.
Thank you, Martin! That was fun and hope to see you back on AiThority soon.
Martin is a strong, visionary Leader with experience in building, transforming and accelerating companies in start ups, transitions, realignments and sustained growth. He also possesses creative ability in operations, sales, marketing, and business developmental, with strategic vision, operational excellence and commercial acumen.
Trax is the world leader in computer vision solutions for retail. Trax offers best-in-class in-store execution tools, market measurement services and data science solutions that are fundamentally transforming how in-store retail data is being collected, viewed and analysed. With Trax, consumer goods manufacturers and retailers can improve product availability, reduce distribution gaps, identify category opportunities and increase their sales immediately. Our unique technology uses a combination of image recognition, deep learning and data science to turn shelf images captured with mobile devices, fixed cameras or robots into real-time actionable insights. These recommendations and predictions not only help customers measure and monitor their in-store execution performance but also unlock revenue opportunities in the marketplace.