Misuse of AI can destroy customer loyalty: here’s how to get it right

Once just a marketer’s buzzword, the concept of a ‘multichannel experience’ is now industry standard for consumers. Shoppers want to be able to browse a product in-store, complete the purchase online at its best price and availability, and then have it immediately shipped to a desired location.

They expect native functionality, consistency, and a flawless experience throughout the process, regardless of the channel. Increasingly, due to mass adoption of Netflix, Amazon and Spotify, consumers also expect personalised recommendations tailored to their individual taste and preferences. A recent report found that 83% of customers expect brands to serve them a fully-personalised digital experience.

However, the successful implementation of personalisation is easier said than done. While nine out of 10 UK companies surveyed by Adobe believe AI equates with future business success, only 30% are happy with the role AI currently plays in personalising their customers’ experience.

All too often, customers are either bombarded with suggestions for redundant products they’ve or receive recommendations eerily tailored to private parts of their lives. For instance, retail giant Target accidentally exposed a teen girl’s pregnancy to her father due to the historical data it had collected from the consumer, who had begun to buy certain products indicating a potential pregnancy.

And it’s not just creepy ads; just because your data suggests a targeted ad or marketing campaign, doesn’t mean it’s the right thing to do. In a particularly extreme example, an article in Brandwatch references an elderly person in an assisted living home receiving a Christmas basket from a local mortuary.

As personalisation integrates more seamlessly into our lives, the line between enticing and scaring off the customer grows ever more blurred. Often brands might be doing more harm than good with some of their personalisation tactics, which can severely damage their reputation and customer relationships. According to a new report, 75% of consumers state they find certain forms of personalisation ‘creepy’, and consumers are four times more likely to dump a brand after a single bad experience.

As personalisation has become an operational necessity, how can businesses find the ‘sweet spot’ of creating lasting loyalty while steering clear from the ‘uncanny valley’? Here are three tips to get it right.

Brands must become customer & data-obsessed

Before retailers consider implementing a personalisation strategy, they must not only strive to understand the affinities of the end user but must also understand that there is a human being at the receiving end of all this effort. Companies can have the best AI system in place, but if the personalisation strategy is not human-centric, and as a result efforts misfire, they may find themselves in a worse position than had they not implemented the system at all.

In order to succeed, retailers need to become customer-obsessed. Who is it that is being targeted and what are these individuals’ likes and dislikes? Retailers need to track every single customer touch point across all channels, merge that information with relevant customer data from other systems, automatically interpret all information to determine affinities, and then store it in a central place, creating a single, unified profile for each customer.

Since May 2018, retailers have another hurdle to overcome: the General Data Protection Regulation (GDPR), which grants individuals more control over their personal information. GDPR has laid a new groundwork of individual rights which retailers simply need to obey. For instance, the ‘right to erasure’ forces companies to delete all existing data of a user, and ‘the right to object’ stops all permission-based personalisation efforts for that user. However, GDPR is no threat to personalisation. It should instead be considered an opportunity to refine your marketing content to deliver the most value to the unique user experience.

The power of machine learning

To be truly effective, companies need to apply machine learning to the vast amounts of data at their disposal. While previous approaches to personalisation were based on traditional rule-based principles, segmenting customers by predetermined criteria, machine learning algorithms can ingest and analyse ‘big data’ and then make suggestions suited to each individual user. Instead of giving the computer specific rules to follow, it’s programmed to learn as much as possible about your consumers in order to select the experience most likely to appeal to them.

For instance, a basic machine learning platform might analyse shoppers’ browsing behaviour and address and leverage that information to make statistically-supported, best-effort inferences about other characteristics of that user. The ability to easily do this – and more – at extraordinary computational scale enables retailers to deliver far more engaging interactions than traditional rule-based approaches could ever deliver.

The importance of in-the-wild testing

Once retailers have built up a picture of their customers with help of machine learning, they need to create effective engagement strategies using transparent messaging. Brands can decide how and when to engage with their users, and which messages and experiences to deliver throughout the entire customer journey. However, the key to ensuring a seamless customer experience is having a rigorous testing process in place that ensures the testing remains human-centric.

The problem is that regular testing approaches – traditional QA labs, for instance – seem simply outdated in the age of global, omnichannel commerce. Relying on the traditional approach often results in critical issues going undiscovered, because even the best in-house QA team cannot accurately reflect the complex ways that real customers interact with technology. As a result, it becomes increasingly important to put the consumer at the centre of all testing by using real people on real devices. At Applause, we call this ‘in-the-wild’ testing.

In-the-wild testing means real people – your customers, your competitors’ customers, your prospects – in the very markets you serve to provide actionable feedback on any part of the digital and physical customer journey. This type of testing perfectly complements machine learning-driven personalisation efforts, as it enables you to promptly validate proposed technical or advertising strategies with real customers (testers) b efore it gets into the hands of your wider customer base. With an effective approach to in-the-wild testing, you can capture results that far better reflect the realities of your real users’ experiences than is ever possible with just an in-house QA lab.

The emergence of personalisation is one of the most exciting developments in retail technology. Once a unique competitive advantage over others, it is now becoming the norm and has reshaped how brands engage and communicate with their consumers, building lasting loyalty along the way. However, as personalisation integrates more seamlessly into our lives, retailers need to find the’ sweet spot’ between satisfying the customer’s demands and steering clear from the uncanny valley. The only way to ensure this is by rigorously testing using a human-centric approach.

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