AI for hyper personalization: 4 steps to upgrade your customer journey

With generative AI taking over the world, everyone including journalists, colleagues, random LinkedIn connections, and even influencers have something to say about the topic, regardless of their understanding of the tech behind the trend. Experts project that companies fully using AI technologies to enhance operational effectiveness could increase cash flow by 118%. So while it’s clear that this technology has a real impact on business results, what does it actually do? The challenge is sifting through media buzzwords and hype to determine which applications will actually create a unique and engaging customer experience.

How can companies efficiently implement AI-powered tools at each step of the customer journey to enhance the consumer experience and drive sales? If your industry isn’t known for digital innovation, don't worry. We gathered best practices from across industries to show you different ways to user hyper personalization.

Discovery: Craft personalized interfaces

Reports for years have stated that customers are increasingly expecting personalized products and experiences due to the widespread digital transformation of all industries over the last decade. In fact, “71 percent of consumers expect companies to deliver personalized interactions. And 76 percent get frustrated when this doesn’t happen,” according to McKinsey’s 2021 Next in Personalization Report. No matter if you’re selling laptops, sneakers, or household sinks—customers expect your digital touch points to be equally as innovative as the tech giants.

The optimal distribution between human and AI recommendations in an algorithm remains undecided, but what’s clear is that as consumer demand for hyper personalization continues to rise, the days of letting only humans choose the “hottest product” suggestions will be gone. Platforms like Netflix and YouTube already do this, showing each user different and unique homepages based on their likes while still highlighting the general worldwide trends. However, while personalized experiences are easy to create for long-term customers, how can we use AI to introduce new customers to your brand while offering a tailor-made experience?

Brands today use AI machine learning (AI/ML) to learn who each customer is from the moment they land on the company’s website. Businesses integrate this technology through multiple UX options to help their AI learn about each user:

• 3rd-party data algorithms: When consumers agree to third-party cookies companies like Shein use data points from social media and other sites to personalize each person’s shopping experience.
• Pop-up feedback: To learn about customers as quickly as possible, brands can add mini-surveys. TikTok uses this to update each user’s For You Page algorithm by asking their opinion of the last video with the following options: I like it, Neutral, I don’t like it.
• On-page tracking: Beyond tracking clicks, searches, and time spent on certain pages, brands can add interaction opportunities. Netflix allows users to “like” or “dislike” programs while clothing retailer H&M uses the person’s style preferences and “favorite” items to customize the homepage.

These features help train AI/ML algorithms faster so customers can receive the most personalized experience possible. The level of user information available depends on each country’s data laws. Regardless of your business regulations, we strongly urge companies to uphold strong data processing ethics and provide clear cookie permissions and processing information on their sites. This way, customers have the power to determine which of their data figure into their personal algorithms.

Interest: Navigate with virtual assistants

Once consumers know about your brand, make sure they can find what they’re looking for, otherwise they’ll leave. Customer support chatbots have come a long way since Microsoft’s Clippy of the late 90s / early 2000s which offered a few preset choices for users with programmed responses.

Today’s chatbots use AI Natural Language Processing (NLP) capabilities to respond to any demand, remember past interactions, and study data patterns to continuously learn about users. Big tech is leading the adoption of these technologies with Microsoft announcing they reinvented Bing Search by integrating ChatGPT-powered results for more complete answers and a chat experience. Following this, Google announced their own integration of generative AI in Search in order to provide more expansive context to each search request and make complex purchase decisions faster. These technologies allow modern chatbots to provide high-quality, individual feedback for each user, making every interaction feel like a private consultation.

Innovative generative AI tools like ChatGPT are transforming how chatbots support web visitors. Luxury goods group Kering launched Madeline, the AI personal shopper for KNXT, their “cutting-edge fashion space”. This shopping assistant encourages users to add as much context as possible before suggesting high-end fashion items that align with the customer’s needs. Similarly, Expedia’s AI assistant drastically simplifies the travel planning process by evaluating 1.26 quadrillion variables (i.e. hotel location, data availability, room types, price ranges, etc.) before suggesting custom travel plans for the customer.

By optimizing how quickly and easily customers can find the products or services they want, they will be more satisfied with their experience and therefore more likely to be repeat customers.

Intent: Envision the purchase via digital twins

While AI-powered chatbots simplify the search process, customers still need to be convinced to click “buy now”. Before making the purchase, they would compare the price against competitors and read 10 pages of product reviews to make sure other buyers were satisfied. But the central question remains the same: would this product fit their needs?

AI digital twin technology adds a hyper personalization layer by allowing customers to test products before making a purchase. Two of the most impactful applications of this technology—remote try-on experiences and AI-led analysis—reduce purchase hesitation and minimize returns.

Virtual try-on technology

Household retailers realized the opportunity that the augmented reality (AR) craze presented and they invested in mobile applications like IKEA Place which virtually places furnishings in real rooms. A blend of AI and AR features, this app also provides home furnishing tips based on the user’s curated spaces and the context of each room. Since then, IKEA has released its newest tool which uses AI and computer vision technologies to recognize the geometry of indoor spaces and the objects inside them. This upgraded system lets users design their spaces more accurately and even remove existing furniture to imagine what a total redesign would look like. With apps like this,customers can be confident their potential purchases will fit in their homes—no assembly required.

The beauty, cosmetics, and fashion industries can similarly benefit from extended reality technology that combines the digital and physical worlds. By giving customers access to a digital fitting room where they can virtually test hairstyles, try-on products, or trial different makeup looks, customers know what to expect from their purchases and won’t be disappointed with the results. Zalando’s new virtual fitting room uses machine learning and computer vision to consider a customer’s height, weight, and build. It then creates a 3D digital avatar that shows how the product would suit them and if it will run big or small. By providing this sizing advice, Zalando reported a 10% drop in size-related returns.

Custom AI/ML analysis

Beyond testing products that the buyers are interested in, the cosmetic and pharmaceutical industries are applying AI technology to make science-based recommendations on what items customers should purchase. To do this, their tools use visual markers to digitally analyze elements of the user such as their skin quality, skin tone, hair type, and more. L’Oreal’s SkinConsult AI from Vichy Laboratories tracks seven major signs of aging to deliver a personalized skin analysis with recommended product prescriptions. This data-based recommendation provides customers with the security and proof that they need to make a purchase, knowing that it was a choice curated for their exact needs.

Loyalty: Keep customer engagement going

If you’ve made it to this point and successfully convinced customers to buy your product or service, congratulations! That’s half of the battle. Now, you need to keep your customers happy, especially since gaining new customers costs anywhere from five to seven times more than retaining existing ones.

Creating engaging, unique experiences has always been central to inspiring customer loyalty, and accessing more data on each customer with AI-powered features creates new sales opportunities. The ColgateConnect mobile app uses AI gesture-tracking technology to track how long each user is brushing their teeth and whether there are any unbrushed or under-brushed teeth zones. The app learns about how they brush, getting to know their habits and routines better than anyone else. The app then provides continuous feedback like how they can improve their brushing speed, angles, or coverage. In addition to helping users learn about their health, Colgate pushes relevant product suggestions to each customer based on their health needs. It then further encourages users to make purchases by rewarding them with smile points after each brushing session.

Companies that create platforms that customers are excited to use regularly learn more and more about their users. AI/ML-powered features help brands study individual users and larger user patterns so businesses can refine the user experience at every step of the customer journey.

Conclusion

AI–based tools have real and efficient applications throughout the retail customer experience. From homepage recommendations to ChatGPT-run bots, digital avatars, and gamified mobile apps, there are opportunities for hyper personalization at every step of the customer journey.