From brand love to self-love – how personalisation is changing both customer experience and product teams

This blog is part of our book: Data-driven digital commerce – turn data into revenue. Download the full copy of the book in the green box to the right or at the end of this blog.

The author is a Columbia Road expert Juuli Kiiskinen.

Personalisation will not only change the end-user experience, it will also change the requirements for internal teams. Winning in personalisation requires a lot of cross-functional work from technical product teams to the teams in charge of the brand. Taking personalisation to the level of what has been written on post-its in “dream stage” sessions is now a reality. The question is, are the individuals and teams ready to evolve, especially if they need to reinvent themselves to stay relevant in the future?

Automatically generated video ads – exactly how users like it

While Europeans are still trying to figure out GDPR-compliant ways of gathering data and thinking about the ethical side of artificial intelligence, the folks at Alibaba are already monetising AI and data at full speed. The digital maturity gap between different parts of the world seems to be growing rapidly.

In 2019, Paul Wu, Senior Director of User Experience at Alibaba, gave a thought-provoking presentation at the Business to Buttons event. He introduced the audience to an internal Alibaba project called Aliwood, an AI system that is used in several different ways to enhance ecommerce sales.

According to Mr Wu, Aliwood can automatically generate video ads based on an ecommerce product page and user preferences. The system gathers information from a product page, such as product images and unique selling points, creates a storyline and adds music on top of the video. The soundtrack is not just any music, but is actually based on consumer preferences and the style of the product. In addition to analysing complex product information and creating an audiovisually harmonious video ad, the AI system also helps with content distribution.

Another famous example of personalised content is Netflix. It’s no secret that Netflix's content, such as the thumbnail images of series like Stranger Things, changes based on users' preferences.

The dynamic duo of data scientist and content designer

Building personalised recommendation systems, such as the one Netflix uses to match the right content at the right time with the right users, doesn't come for free. Personalisation doesn't only change the user experience – it also changes team structures and roles.

Designers and developers will continue to have their hands full building and developing internal processes such as user flows, new features and overall scalability of digital services. They will not have time to focus on personalisation, as finding the proper methods for matching individual users with the right content and functionality doesn't happen overnight. Personalisation should not be a side project – achieving a positive return on investment requires commitment in terms of time and money. One way of levelling up with personalisation is to make room for a dynamic duo consisting of a data scientist and a content designer.

While developers and designers will make sure that the frames of the digital services and products are in place, data scientists and content designers will focus on individual needs. This dynamic duo studies data, creates hypotheses and runs tests to find patterns that can serve users in a more personalised way to boost digital business. As developers and designers become system builders, data scientists and content designers will become system utilisers.

Even though the roles and titles would not be set in stone, it’s clear that more people with new skills will be needed for a brand to succeed financially in the personalisation game.

What does “brand” mean when everything becomes personalised?

Supervising brand consistency has become a job of its own within more prominent organisations. The "brand police" department has tried to control and monitor every single corner of the tangible part of the brand, from the visual look of sales presentations to the tone of voice in videos and the colour palette of the website.

In a way, it's no wonder that brands are guarded with such care – they are the magical dust that gets people to buy from your company at a higher price, even if the product is pretty much identical to that of your competitors.

Varying content such as thumbnail images and copy based on user preferences is just the tip of the iceberg when it comes to personalisation. Soon, we’ll likely see a scenario where anything from the user interface and features to the tone of voice and assortment can be modified to match individual user preferences.

As a result, the teams in charge of the brand are also about to face a new era. When companies and brands start to adapt their presence to meet individual users' preferences, eventually they will begin to look and feel more and more the same to each individual.

Will we see an evolution from people gathering around brands towards brands gathering around individuals? The leap from carefully guarding brands towards letting them become totally driven by individual preferences is likely too risky. The probable scenario is that a spectrum of personalisation will be allowed as long as brand and marketing have defined the boundaries.

Another path is to trust the algorithms with everything. One company that has let algorithms determine its existence is the ecommerce giant Wish. In 2021 Lenny Rachitsky, a former Product Lead at Airbnb, sat down with Christian Limon, former Head of Growth and Advertising at Wish to learn more about the company. The Twitter thread Rachitsky wrote about their chat revealed that Wish's strategy is as extraordinary as the products it seems to advertise and sell:

@lennysan (Lenny Rachitsky): "Wish's superpower is leaving no room for taste or opinion. It's what happens when a machine builds a company based on data. The founder didn't plan to sell cheap goods to low-socioeconomic customers, but that’s where the data took him." Twitter, 30. Jan. 2021

"Until you work at a place like Wish, you don't know what data-driven is. Everyone else is data-driven when it's convenient, when it agrees with your opinions. Wish is great at ignoring their own emotions. It's data-driven with as much intellectual honesty as possible."

You don't need to be a fortune teller to predict that the steps companies will take in the future will mimic what the forerunners and big players such as Alibaba, Netflix and Wish are doing today. Yet, how drastic a change algorithms and personalisation will bring, no one knows.

The best thing you can do today to prepare your team for the upcoming changes that data, personalisation and algorithms will generate is to make sure that everyone is open to change, whether it's about roles, teams or control.

Training people to accept constant change is something that even AI is not yet able to do. Therefore, the gap in digital maturity between companies might – after all – not be as big as it seems.

This blog post is part of the handbook Data-driven digital commerce – turn data into revenue. The aim of this handbook is to help a sales, marketing, IT or ecommerce leader to formulate an overall understanding of the critical themes for turning data into concrete business impact. The book includes interviews with global thought leaders on the topic as well as industry experts from companies including Supermetrics, Singular Society and Kesko.