Against the backdrop of heightened customer expectations, brands need to go beyond traditional e-commerce sites, blanket marketing emails and generic social campaigns. Instead, retailers should look to AI to help them win across digital and in-store.
As we head into the latter half of 2024, competition in luxury goods markets remains fierce. Consumer expectations around high levels of personalization continue to rise and brands are recognizing the ever-increasing importance of their digital presence, including on the web and via social media.
A recent report from Deloitte into the Swiss watch sector, for example, noted that social selling is set to become a “key sub-channel for the industry”. It also found that 45% of brands identified developing or strengthening their omnichannel strategies as a strong priority, while 41% want to develop their e-commerce or digital channels.
Against the backdrop of heightened customer expectations, brands need to go beyond traditional e-commerce sites, blanket marketing emails and generic social campaigns. Instead, smart businesses are looking to artificial intelligence to help them win across digital and in-store, by offering hyper-personalized experiences, fitting of a luxury brand.
Why now is the time to invest in hyper-personalization
Luxury businesses are optimistic about growth prospects in many of the world’s markets. The aforementioned Deloitte report found that executives were particularly positive about opportunities in India, the Middle East, and parts of Asia.
The report also rightly notes that while inflation does not generally affect those buying high-end luxury products, the fact that it is falling in many of the main luxury goods markets is a good sign for those selling products in the sub-£500 range.
The coming months are therefore the perfect time for luxury businesses to double-down on AI-driven hyper-personalization. Let’s look at what this involves.
The two pillars of AI in hyper-personalized customer experiences
There are two parts to the solution. Firstly, luxury brands need to be leveraging AI to mine new customer insights from their data. AI can spot patterns, anomalies and other hidden knowledge in large and/or unstructured bodies of data. From these findings, AI can then predict what customers are likely to want to do next at key points in their journey.
This is where the second AI pillar then comes in: generating hyper-personalized customer experiences at speed, in response to customers’ interactions. Luxury brands can use generative AI to produce content such as text, images, audio, and video, near-enough on-the-fly, to craft highly personalized journeys.
These can be incorporated into every stage of your customer-facing offering, creating a stronger brand experience right from the initial marketing, through to the sale, and then afterwards to drive loyalty and repeat purchases.
The blend of AI to segment your target market down to the individual level, coupled with the generative AI capabilities to produce the experiences, mean it is now possible to target in ways and at scales that were simply impossible before.
How do you get started?
At this point, you would be forgiven for feeling both excited by the opportunity, and unsure of how to take your next steps. However, the process does not need to feel overwhelming. As with so many things, dividing the challenge into smaller chunks is the key. Our approach is to segment it into people, process, data, and technology.
People: Multi-skilled teams drive success
When you mention AI, businesses tend to think ‘we need data scientists.’ But if you have tried to hire multi-skilled data scientists, you will know these specialists are few and far between. Demand for their skills far outstrips supply, meaning you are unlikely to meet your AI-driven growth targets if you rely solely on building a full team of data scientists.
Instead, craft multi-skilled data science teams with domain knowledge, covering three main skillsets: investigators, builders, and story-tellers.
Investigators include statistics and probability specialists, who can make your data talk. Builders are the AI, machine learning and MLOps engineers, who create the scalable code and pipelines that underpin your AI-based solutions. The story-tellers turn the findings into actionable business insights and customer experiences.
Process: Get comfortable with failing fast
Not all the AI use cases you investigate will prove fruitful, and you don’t want to waste money on those that aren’t going to deliver value. So instead of going deep in one or two areas, trial a range of potential use cases and ‘fail fast’. Cut the unsuccessful ones, and only invest significant time in those that show value. This will ultimately accelerate growth.
Data: Build your single source of truth — pragmatically
At the core of most successful customer experience AI initiatives is a well-governed, single source of truth (SSOT), bringing together data from a variety of systems. Smart organizations build this single view pragmatically. Instead of trying to create it in one go across the entire business, pull together the data you need for the use cases you have identified. As these are successful and the scope of the AI work expands, so you add more data to the repository.
Technology: Reuse, reuse, reuse
Focusing on multiple AI-driven customer experience use cases simultaneously means you cannot afford to build everything from scratch. This would be far too time-consuming and labor-intensive, and not everything you invest in will ultimately be of value to the business.
Instead, build on existing AI tools, models, automations, and accelerators. Customizing something that already exists represents a far more efficient use of your team’s time, meaning they will be free to do more of the high-value work that is both interesting to them and useful to your business.
Your route to hyper-personalized success
AI offers exciting opportunities to deliver on luxury consumers’ expectations of hyper-personalized interactions with your brand. Where it might previously have been impossible to achieve this level of granularity at scale, the technology now exists.
And by taking this four-pillar approach, smart brands are able to embed AI into every stage of their customer journeys. This, in turn, is enabling them to set themselves apart from the competition and capitalize on the opportunities present in today’s markets.
