Hyper personalisation: 8 examples

Customer Experience

Hyper personalisation: 8 examples

Personalisation has changed e-commerce. Greater means of data processing and better algorithms have enabled companies to provide a more granular, individualised service to customers.

Companies are investing more and more into getting ahead of their competitors

This race has been sometimes termed an ‘arms race’ of personalisation: the need to invest in and innovate to provide a greater level of unique experiences for the consumer.

Expectations from the consumer side have changed and they want a more personalised experience than ever before. According to a recent McKinsey & Company report, three-quarters of consumers switched to a new store, product, or buying method during the pandemic. This is no surprise considering that 71% of consumers expect companies to deliver personalised interactions and 76% get frustrated when this doesn’t happen.

This has given rise to the slightly more refined ‘hyper-personalisation’. Whereas classical personalisation merely expresses that some amount of tailoring is being done: the use of algorithms, machine learning, and other advanced forms of analysing data is considered the next level.

Below, we’ve had a look at eight companies that excel in anticipating and meeting the needs of their customers to illustrate hyper-personalisation and give a sense of the scope of possibilities.

Naked Wines

Wine subscription service Naked Wines saw a huge increase in orders during the pandemic. This has renewed pressure internally to revamp its customer experience and incorporate more personalisation.

To do this, Naked Wine has focussed on the way it communicates with its customers: using customer feedback, it can generate personalised emails featuring a wide array of conditional content, like wine recommendations, calls to action determined by buying behaviour, or invitations to tastings or group events that match their history with the company.

This cements a sense that customer opinion really matters in driving their relationship with Naked Wine: especially important for a business whose customers can be seriously discerning about what they choose to drink!

Netflix

Keeping people engaged is Netflix's number one priority, and so its ability to show users intriguing content they might otherwise miss is key to its ongoing success. To do this, the company's personalised recommendations system draws from many sources. One is customer ratings, which feeds into an algorithm to determine what content is shown and to who.

Netflix also incorporates what is internally called the 'implicit signal' into the algorithm: how the users interact with a show or movie: how long they watch it, whether they rewind, fast forward or stop watching and, if so, at what point. It also includes data on time, location and on which device users watch its content. This does not merely change what gets recommended: it informs the streaming service's original programming, as was the case with House of Cards and The Crown.

Care/of

American beauty company Care/of is a bit more direct in personalising its customer experience.

Arriving at the site launches a quiz, which is processed to develop a regimen of vitamins and supplements. Customers are invited to follow this plan while the site logs data as they progress. As users advance, they pass encoded landmarks which trigger recommendations, giving the impression of a deeply involved process of monitoring and assistance.

Given how serious about their health customers who come to Care/of are likely to be, having the feeling they are being closely supported on their health journey is a strong selling point for this business.

Zalando

While it is common among fashion brands to use cookies to tailor repeat visits, the German fashion retailer Zalando takes things a step further by connecting the dots for the consumer. Upon repeat visits to the website, customers are given the option ‘to pick up where you left off’, allowing them to start fresh with the website or resume previously abandoned items.

As a major e-commerce company, Zalando has consistently invested in its personalisation algorithms, like the opening of the Dublin Tech Hub in 2015. They have innovated in the space as well: its Algorithmic Fashion Companion, or AFC, utilises machine learning (another term that gets thrown around lots — it’s somewhat similar to the way the Met Office predicts weather) to suggest completed outfits for individual shoppers.

Stitch Fix

A new approach to retail: the online personal styling service, Stitch Fix, makes shopping for new clothes and trying new styles fun.

Using recommendation algorithms and data science to personalise clothing items, customers kickstart the process by taking a detailed survey to determine their style, preferences, body type, measurements, and brand preference. From the data collected, stylists pick five products to send to the customer.

With customer’s only paying for the items of clothing that they keep and heavy emphasis on product feedback, Stitch Fix gains a stronger insight into what is working, what isn’t and why allowing them to offer better recommendations in the future.

Amazon

Amazon is famous for its seamless shopping experience and the many ways in which it provides this service.

It might be helpful then to talk about two specific ways it does this: collaborative filtering and predictive analytics. Amazon’s rating and feedback systems connect with one another (‘collaborate’) to push high-quality, popular items to shoppers that may have been previously unaware of them. Shopping behaviour – even offsite – is used to drive recommendations at every point of the shopping journey, from the homepage to the product page – with the ‘frequently bought together’ tag – to recommend the most relevant items and encourage cross-selling.

Secret Escapes

British travel company Secret Escapes showcases an example of how hyper-personalisation is possible in a comparatively simple way, without the assistance of complex algorithms and machine learning. The company uses keywords to customise its landing page for a more flavourful, unique experience with the brand.

For example, if you, as a consumer, discover Secret Escapes on a search engine having typed in the keywords ‘city breaks': you are greeted with the linked homepage. Personalisation can be seemingly incidental while still refining the brand even for people who have merely stumbled upon it.

Grammarly

It is common among businesses whose selling point is self-improvement (e.g. fitness, learning a language) to provide regular feedback to users to help motivate (indeed, not just self-improvement: Kindle and 'Books', Apple's native ebook app, feature reading goals).

Almost by definition, this feedback needs to be personalised. The Ukrainian writing assistant Grammarly uses the opportunity to provide something more useful to its customers. The company collects data throughout a customer’s usage of the app. This provides a chart and statistics of success as well as progress and compares users (positively) with one another: 'You were more productive than 80% of Grammarly users', for example. There are also suggestions for improvement.

Our Conclusion

“Marketers in this new digital-first age need to be data-driven, but without forsaking the human and emotional elements of their marketing,” said Bloomreach Chief Strategy Officer, Brain Walker. “Our Customer Data Platform has done a tremendous job of bringing those all-important, real-time insights in a marketer-accessible format to them so they can perfect the customer experience, ensure it is relevant and timely, and not have to stress about crunch numbers.”

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