If you’ve ever shopped online, you’re likely to have encountered recommendation engines — software that uses your previous purchases and other data to suggest products you might want to buy. In its early stages, these tools were basic, suggesting endless lists of detective novels to anyone who bought a single book from Ian Rankin, for example.
However, with continuous advancements in technology utilising AI, machine learning and behavioural science, these systems have become increasingly advanced and efficient. Modern-days retailers are now in a position to accurately anticipate individual customers’ purchasing patterns and the products that might attract them the most.
Additionally, AI is being employed to generate personalised marketing campaigns, create products and services aimed at specific customer groups, improve the shoping experience, and make delivery services more streamlined.
According to Richard Hepworth, a consulting partner at EY Ireland, there has been a surge of investment into integrating AI tools from various organisations to augment operational efficiency and bolster consumer experiences. “The retail sector is seeing diverse applications of AI throughout the supply chain. This includes enhanced product development, streamlined distribution and pricing, personalise marketing campaigns and promotions, targeted employee training, effective customer service and engaging shopping experiences,” says Hepworth.
Hepworth also points out that many brands are exploring the benefits of AI in their operations. Retailers and department stores are investigating how AI could potentially enhance customer experiences in-store. This includes using AI to analyse patterns in customer traffic and interactions, leading to optimised store layouts and product placements, ensuring that popular products are easily accessible. Service providers are also focusing on AI to improve their delivery services, which directly affects retail operations. AI aids in streamlining delivery routes and predicting optimal delivery times, which is paramount for online retailers in maintaining customer satisfaction.
Paul Prior, digital chief at Three Ireland, asserts that access to personal information is not necessary for retailers to enrich the customer experience and augment sales probabilities. Instead, he suggests using recommender systems that trace an individual’s actions and the corresponding behaviour of similar consumers. Prior notes that the larger question is how many people display like behaviours to provide useful forecasts of a person’s future actions.
Prior mentions that retailers already possess a certain level of knowledge about a customer, gathered from their previous interactions with the retailer’s website. The wealth of information available now extends far beyond this, with data detailing the likelihood of purchases among other things. This type of generic data, which can be obtained commercially, can be used further to train artificial intelligence in predicting behaviour, devoid of any personal information.
Different sources are utilised to amass data, says Hepworth. These could include customer information such as buying history, browsing habits and personal preferences; inventory specifics like stock availability, product flow and demand trends; transactional data inclusive of payment methods, transaction values and frequency; and behavioural data garnered from interactions with websites, applications, and in-store sensors.
The role of technology remains one facet of the picture, explains Prior. The core remains understanding human psychology and neuroscience. He gives an example that humans inherently crave social interaction. With the advent of digital age, initial attempts to humanise chatbots did not sit well with people.
On a personal level, technology can boost the chances of a successful sale by tracking a customer’s engagement on the website, he notes. It can monitor the duration of a customer’s visit, identify if they are facing difficulty locating a certain product and foresee if they are potentially considering alternatives.
Early recognition of these signs can trigger timely interventions, according to Prior. For instance, if a customer is evaluating a new phone, they might be interested in its camera features. Understanding this can help make useful suggestions that cater to their needs.
This is increasingly significant, given the shift in consumer behaviour, he points out. Nowadays people have lesser patience and spend less time reading content to find what they need. A generative AI can quickly manufacture highly pertinent content and present it to customers. He explains that there is a considerable amount of work done behind the scenes to expedite this process.
Indeed, there will be inevitable concerns about privacy amongst individuals. Hepworth highlights that tighter reins are being put on the use of Artificial Intelligence through new legislation such as the AI Act implemented by the EU in Ireland recently. A clear and specific General Data Protection Regulation (GDPR) is already effective, ensuring privacy, control, and safety of data. He emphasises that many firms adhere to robust governance policies over the AI experiences they provide and the consumer data they handle.
Hepworth also suggests that customers can be proactive in guarding their personal details. She encourages customers to scrutinise privacy policies of companies to comprehend how their information is utilised and shared. She further advocates for strong, unique passwords to safeguard online account info and encourages regular account checks for any unauthorized happenings. Importantly, she reminds that GDPR’s primary principle is consent, urging consumers to leverage opt-in/opt-out choices for data gathering and promotional communication, making sure they dictate what is shared to enrich their interaction with trusted brands.
Prior echoes this sentiment, pointing out the main objective is enhancing the customer experience. He elaborates that the technology allows companies to only promote products that customers might find relevant, hence delivering an experience that meets their anticipations.
This eventually leads to a highly personalised experience. He shares his epiphany that individual requirements vary greatly – what he wants from a customer journey may greatly differ from anyone else’s. The vital lesson here, he explains, is the understanding that he is not a representation of others.