No one wants to be irrelevant online: How AI drives individual relevancy

No one wants to be irrelevant online: How AI drives individual relevancy
Posted by Attraqt | 5 July 2021

In retail stores, the shop assistant worth their weight in gold was the one that sized you up and tailored their sales pitch perfectly. They knew your behaviour through basic profiling, your needs with a few questions and your desires as they observed you browsing in-store. The pandemic hit. Product discovery shifted online, en-masse. Personalisation is now a vital tool for e-commerce.

Why? Because the endless aisle on countless websites and the limitless options online leads many shoppers to exhibit choice paralysis. They need help; they need recommendations that appeal to them directly – an audience of one. The good thing is that the tools that brands can now use have come of age. Artificial intelligence, complex algorithms and data analytics have made sure of that.

We also live in an age of great expectations. Complex searches on Google, Amazon, Netflix or Facebook now provide a very sophisticated, individualised and customer-centric response. Relevance matters. Consumers now demand that brands understand them. Context matters as well. Understanding the customer’s particular needs or intent at any point in time – so-called ‘in the moment’ experiences – is vital.

If I am an adult male and I’m searching for a size 35 women’s sneaker, the search engine should now understand that it is not for me but for my wife, daughter or friend. In recent years, the idea of ‘if you bought X, you might like Y’ popularised by Netflix has moved on, it has now reached much greater levels of sophistication. Relevance and context can now be considered in granular detail, as can real-time shopper intent.

Artificial intelligence and sophisticated algorithms trained on the right data set can make a real difference to personalisation allowing profile-based and contextual targeting, better product and content recommendations and fine-tuning the brand offering. This can hugely influence customer behaviour.

It pays off too, according to Forrester, 77% of consumers have chosen, recommended, or paid more for a brand that provides a personalised service or experience. If brands get personalisation right they banish irrelevancy, instead they can deliver highly relevant digital experiences at scale, retain and delight customers and do so over time – this affects the bottom line and growth trajectory.

Yet, personalisation isn’t easy to get right. Globally, only 7% of companies have deployed insights-driven personalisation strategies that use sophisticated AI to differentiate themselves in the market, according to research by Forrester. One of the reasons is the metric by which personalisation is measured. Many look at whether it drives conversion and revenue directly. However, they fail to gauge customer loyalty, customer satisfaction and customer lifetime value. All are key metrics if you are a brand thinking about connecting the entire online journey.

Personalisation in ecommerce isn’t something that’s buried in a marketers computer and only shared with the IT department, either. It should not be just the realm of the marketing department, which is where it often sits. Personalisation for online shopping has to be a core competency that is inter-departmental and spans the entire business, involving multiple divisions and responsibilities. It involves merchandisers, sales teams, product buyers and specifiers, content creators and data specialists.

This makes sure that there are no data silos – product tagging and descriptions are aligned and what customers search for is reflected in what is on offer – and continues to evolve. Personalised recommendations work when every person in the business is aligned to providing not only a customer-centric experience but one that is highly relevant and allows contextual searches.

Because machine learning can detect individual patterns by comparing these against a database of customer journeys. This means a typical marketer’s segmentation is useful in some cases but the likelihood it will be less relevant for the individual is higher.

Today’s marketers tend to focus on how they optimise campaigns and the ROI of these. They are mainly driven by events; Christmas, Black Friday and others. Or they are led by products; new collections, sale, promotions and the like. But as marketing resources are finite, AI is able to augment efforts by optimising the individual journey or the “long-tail”. However, optimising the long tail requires a different skillset. Instead, marketers and organisations on a whole need to work with algorithms and data analytics in order to consider and appeal to the individual. This needs a whole new set of skills and a fresh mindset.

These days, brands must have two different strategies and competencies, one for short-tail queries, where the mass of searches occur. These are generalist, short search queries that can have a curated product offering, with some degree of personalisation and automation. Versus the long-tail. This involves much more sophisticated searches and therefore varied offerings. Relevance, context and shopper intent must be understood and catered for directly.

It is also a challenge for marketers to move beyond segmentation and persona-based shopping. It is easy to personalise for groups of shoppers that can be easily identified, collectivised and understood. We always think of segments because businesses have limited resources to personalise offerings for each and every individual shopper journey. Yet the latest advancement in AI and deep learning now allow us to optimise responses around an individual. And then scale it to meet commercial and shopper needs.

Right now, individual relevant experiences matter. An algorithm cannot foresee Christmas or Black Friday. Of course, you can train an algorithm to work this out, but then it is too late, the event has passed. This is where human interpretation and intelligence also come into play. Marketers need to make sense of customer journeys and shopper intent in a much wider cultural or trends-based behavioural context. This supplements what artificial intelligence can do and is informed by it.

This is why AI orchestration is so important. Controlling how AI-powered search and product discovery delivers personalisation is a vital process. Algorithms need training. Making sense of what customer data to use is vital, so is human oversight.

Personalisation for ecommerce is an incredibly powerful tool if used correctly. In an era of product overload, choice paralysis and a mushrooming of ecommerce making sense of what to buy and from whom will be crucial. Those brands that help their customers navigate product discovery in a proactive and relevant way will win. No one can afford to be irrelevant.

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