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Three real-world applications of AI you'll be seeing a lot more of


 

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.


Example of geo-fencing showing a phone being notified of a promotion

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.


Example showing a customer being targeted with a promotion based on their likes

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.


Image showing a photo of a real bottle of Coca Cola and a 3D generated model of the same bottle
An example of a real photo of a bottle of Coca Cola and a 3D Digital Twin of that same bottle.

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.


The Future of Retail Shelf Auditing Ebook - Download eBook
 

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.


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