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AI personalisation begins to dominate ecommerce

AI hyper-personalisation begins to dominate

Brands and retailers know that the cost of acquiring and retaining customers continues to rise. This is not just about inflation, channels raising advertising rates, and search platforms such as Google opting to move away from cookies, instead it’s about the cost of operating on so many channels to catch consumers that are perfectly comfortable moving from one to another.

On top of the cost comes a rash of regulatory and technical challenges. Data privacy laws are becoming more complex, and the fines are real. Companies like Apple believe privacy is a fundamental human right and are working to prevent any kind of digital identity stitching across environments. The loss of third-party identifiers, with the move away from cookies, has impacted marketing performance and made it harder for brands to reach and re-engage potential customers.

In tech, digital acceleration has led to an explosion of more siloed, disconnected customer data, making it hard to know the customer at all times. Vendor positioning is rampant, and most brands are confused about what they own and wish to streamline their MarTech stack.

In 2023, because of the tough trading environment, retailers will first have to keep a closer eye on their operational costs, and AI has a potential role to play in important areas including personalisation, site optimisation, marketing, inventory management, logistics, omnichannel product discovery, customer service, fraud prevention and so on. Where costs cannot be reduced, they will be looking at ways to make these activities more productive. This will include equipping staff with more powerful tools and, even more critically, the data to drive them.

AI has emerged as an effective way to improve the performance of all these areas by supporting human intelligence. Deep machine-learning-based insights can reveal what just happened and ideally what action should be taken next as the shopper moves through their buying journey. Demand for AI has exploded — the number of businesses adopting AI technologies has grown by 270% in the last four years, says Gartner, and around 62% of consumers and business buyers are open to the use of AI to improve their experiences, according to Salesforce.

AI personalisation on the consumer side

On the consumer side, AI provides the technology and advanced insights to help create an exceptional, hyper-personalised experience through the integration of assets and processes that often work independently — namely, content, recommended products and services and customer support interactions, which in turn can increase customer retention. The tide is also turning on consumer dominance of control of their data.

In retail, AI analyses social, historical and behavioural data so brands gain a much more accurate understanding of customers and anticipate their behaviour, which enables them to provide highly relevant content, increase sales opportunities and improve the customer journey.

In a recent study by McKinsey, 79% of respondents stated that integrating AI into marketing and sales has increased business revenue. Enterprises were able to generate at least 20% additional revenue thanks to AI-based business strategies.

Therefore, the choice for businesses is no longer whether to implement AI but rather when and how quickly and how they will manage the internal cultures that may slow down the adoption of automation.

AI is essential to the building of more effective personalisation through a brand’s customer data platform (CDP). Using a CDP platform, brands can harness customer data to create meaningful personalised experiences. Linking data from disparate systems to see all the ways customers interact with the brand provides a much-needed unified view, which will help decipher a customer’s intent, especially as the channel explosion continues at pace.

Users of already-dated CDP solutions can build complete and accurate profiles for insights analysis and granular segment building based on orders, behaviours, custom attributes, calculated insights and other intent data. Once data is unified in a CDP, insights are used to build segments from the unified datasets. But by its very definition segmentation is not personalisation, as PPS has already overtaken that technology many fold. It is unique and personal to every individual, rather than being merely a part of a larger collective.

The benefits of a predictive personalisation solution (PPS) have been identified by McKinsey as being able to generate a 20x higher ROI than all other forms of digital marketing combined.

PPS is AI-driven robotic marketing, specifically taking predictive personal product selections to each individual as each one has been identified to be most likely to purchase from it. The average basket size multiplies, as in turn does customer lifetime value, and essentially lifetime loyalty. But hidden beneath the figures are also some staggering benefits, such as a need complete eradication of your RoR. People don’t send back products they want, as speculation and purchasing on a whim, dissipates.

Retailers slow to adopt new AI technology will find themselves denigrated as consumer experience from other retailers proliferates. 80% of consumers are more likely to purchase when the consumer’s relationship with the retailer is personalised, and 60% are more likely to become repeat customers, says Twilio Segment in a 2021 study.

At some point, AI will underpin almost all operational activities for brands and provide an additional boost to marketing teams by removing the need for onerous and repetitive data analytics. In 2023 the imperative will be to expand the use of AI and find more and more test cases that will demonstrate success not just with marketing and personalisation but to the wider enterprise and business architecture as a whole.

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