by Jen Gaeto, Executive Creative & Strategy Director, Equator US.
Leading Equator’s award-winning US studios, Jen is a design and branding expert with 20+ years’ experience in consumer packaged goods. Jen is passionate about eliminating banal and formulaic design, as she works closely with clients and develops her teams of designers, artworkers, and account handlers alongside in-house food stylists and photographers to deliver packaging solutions which precipitate unprecedented on shelf standout and business growth for private and national brands across the US.
Gaeto: AI Can Help, Not Replace
Artificial intelligence (AI) is one of the major disruptive themes set to influence the future of the packaging industry. With a revolution almost certainly at hand, where should food and drink brands focus their efforts to ensure they don’t lag behind the rest of the FMCG industry?
Recently, there’s been a buzz around AI, ever since AI-enabled chatbot ChatGPT went ‘viral’ and sparked a fresh wave of discussion as to AI’s utility and its capacity to impact our personal and working lives. Given AI’s potential to transform virtually all industries, it’s little wonder that businesses are growing wary of finding themselves on the back foot when it comes to integrating AI into their processes. After all, AI could provide companies with a significant competitive advantage, and, as time passes, see those floundering to work with its applications ultimately fall behind.
So what is ‘artificial intelligence’ and how does it work? As McKinsey’s explains: “AI is the ability of a machine to perform cognitive functions typically associated with human minds, such as perceiving, reasoning, learning, interacting with the environment, and problem solving.”
In-depth windows into customer behavior and preference
AI’s ability to harness vast swathes of data to provide real-world insights – turning many decisions once made at ‘gut-level’ into data-driven ones – is one of its major benefits. Firstly, AI systems can build sophisticated models of audience behaviour, providing brands access to valuable information about customer preferences and trends, with the ability of effectively segmenting them. For example, retailers looking to roll out or redesign tiered own-brand ranges, from entry price point to premium, could employ this kind of in-depth analysis to inform their packaging decisions, shape their value propositions and target their marketing efforts.
Relatedly, machine learning – a subset of AI – means computers can identify patterns in data and ‘learn’ from them to make improvements. For packaging companies, this iterative approach could be used in the design process. AI-led A/B testing, for example, could allow brands to identify the formats, designs and messages that perform optimally among consumers, without sinking vast amounts of time, energy, and people power into the process. The advantages of this shorthand methodology would be therefore manifold.
Creating meaningful connections with customers
Standing out from the crowd by providing fun new ways to engage with customers isn’t easy. However, when it comes to creating campaigns that consumers respond to, AI can be a priceless tool, as demonstrated by drinks brand Snapple, which created the ‘Snapple fAIct Generator’ to celebrate the ‘Snapple Real Facts’ that have featured on its packaging for 20 years. AI-powered tool, which can be accessed via QR codes on the bottles, can create facts about any given topic, prompted by users entering an adjective and a subject. It’s a brilliant way of utilising AI in packaging to deepen the connection between brand and customer.
AI can also be used to improve the omnichannel experience; specifically, through predictive analytics – the use of past data to predict future events. In a retail environment, this can take the form of gathering and analysing consumer data through things like loyalty cards and user motbile applications. It enables brands and retailers to understand what consumers want as well as how and when they want it, allowing them to hone their marketing attempts accordingly and ensure consistency across multiple touchpoints, whether online or in-store.
Another interesting, albeit controversial, angle for retailers is the use of advanced AI technologies that are trained to read facial expressions. In 2022, researchers from Australia’s Queensland University of Technology identified new ways for retailers to use AI together with in-store cameras, tracking facial expressions and using that information to inform store layouts.
This kind of technology can also be used to gauge shoppers’ reactions to products. According to researcher Dr Kien Nguyen, “Emotion recognition algorithms work by employing computer vision techniques to locate the face, and identify key landmarks on the face, such as corners of the eyebrows, tip of the nose and corners of the mouth.
“Obvious actions like picking up products, putting products into the trolley, and returning products back to the shelf have attracted great interest for the smart retailers… Other behaviours like staring at a product and reading the box of a product are a gold mine for marketing to understand the interest of customers in a product.”
For now, privacy issues may hamper the use of so-called emotion recognition algorithms, but it is certainly an area for retailers and brands to monitor.
Improving sustainability in the supply chain
In addition to improving the customer experience, AI also has the power to supercharge sustainability, as machine learning algorithms can generate more eco-friendly alternatives to packaging designs. An obvious example is Amazon’s PackOpt tool, which the retailer has used since 2018, to save roughly 60,000 tons of cardboard annually.
AI algorithms can also be used to monitor and track products throughout the supply chain. Recently, the Co-op joined up with Polytag, a labelling company that specializes in providing AI-based waste collection and circular recycling solutions, to trial a unique UV tag and QR code combination on its own-brand bottled water packaging. The aim is to gain greater oversight of these products as they move through the recycling chain.
Ultimately, for brands and retailers in the FMCG space, the best way to use AI is not – as some fret – to replace humans, but to help them make strategic decisions. In many cases, this means letting AI do the heavily lifting when it comes to making sense of large datasets, and letting humans act on its insights. The whole purpose of AI, after all, is to improve the human experience.
