“Here’s a treat just for you.”
“We saw these and thought of you.”
You’ve probably seen these messages in your inbox, scrolling through social media, or browsing a website. This type of content isn’t random; it’s hyper-personalized marketing messages strategically used to gain customers’ attention.
Personalization is the foundation of a successful DTC marketing plan, but it’s not enough to just have a first name and email address anymore. To gain competitive advantage, brands need to incorporate hyper-personalization into their customer acquisition and retention strategies.
This blog will explore hyper-personalized marketing and its benefits, 4 proven AI-driven personalization strategies, and examples of brands currently winning big with personalization. Let’s dive in!
What’s The Difference Between Hyper-Personalized Marketing and Personalized Marketing?
Hyper-personalization takes customer engagement to the next level by utilizing advanced technologies like artificial intelligence (AI) and machine learning to analyze real-time data and customer engagement with a brand. Unlike standard personalization, which tailors marketing efforts using basic demographics or past purchase history, hyper-personalization delves deeper. It assesses behavioral patterns, browsing history, real-time activity, and even geolocation data to create experiences that feel uniquely tailored to each individual.
The difference lies in the level of customization—hyper-personalization is fluid, adapting to each customer’s evolving preferences, making the interaction feel more personal and timely.
Until recently, standard personalization was the ultimate goal for many DTC brands. They would group their target audience by their likes and dislikes, create promotional emails, or utilize remarketing ads to bring users back to their cart.
Today, standard personalization isn’t enough to break through the clutter and noise. DTC brands need to get to know their customers on a deeper level, going beyond basic likes and dislikes, to understand what content and products resonate with them.
What Are The Benefits of Hyper-Personalization?
With over 70% of consumers expecting personalization in their transactions with a brand, hyper-personalization needs to be the foundation of your marketing plan. Hyper-personalization offers numerous benefits for both DTC brands and consumers, including:
- Enhanced Customer Experience: Hyper-personalization enables brands to provide customers with highly relevant, tailored content, offers, and product recommendations. By catering to individual preferences and behaviors, brands can make every interaction feel more personal, increasing customer satisfaction and creating a more enjoyable shopping experience.
- Higher conversion rates: Hyper-personalization enables brands to present the right product or offer at the right time, significantly increasing the chances of conversion. Whether through personalized emails, dynamic content, or behavioral retargeting, offering products that align with a customer’s needs and interests can lead to higher engagement and increased sales.
- Improved customer insights: By collecting and analyzing customer data across various touchpoints, hyper-personalization provides brands with deeper insights into customer preferences, behaviors, and purchasing patterns. These insights can be used to optimize marketing strategies, refine product offerings, and better meet customer demands.
- Lower customer acquisition costs: By delivering more relevant and personalized messaging, hyper-personalization increases the likelihood of converting leads into customers. Additionally, it enhances customer retention, which is often more cost-effective than acquiring new customers. Satisfied, loyal customers are also more likely to recommend the brand, further lowering acquisition costs through word-of-mouth marketing.
However, considering new privacy laws and first-party data restrictions, many DTC marketers aren’t confident that hyper-personalization is even possible. But there’s hope. 83% of consumers say they are willing to share their data to receive the benefits of a personalized experience. If your customers are willing to share their data, you don’t want to let them down.
4 AI-Driven Personalization Strategies
AI plays a crucial role in empowering DTC brands to implement hyper-personalization at scale. Machine learning algorithms sift through massive volumes of customer data in seconds, including a customer’s demographic and past behavioral data. These insights help brands identify patterns, predict future behaviors, and optimize their time more efficiently.
4 AI-driven personalization strategies are elevating the digital customer experience for DTC brands.
1. Dynamic Content Personalization
AI enables brands to customize the content a user sees based on their behavior and interests. For example, DTC brands can show different landing pages, product recommendations, or even website layouts based on what a user has browsed or interacted with. Suppose a customer is searching for shoes on an online store. In that case, AI can modify the entire homepage to prioritize footwear-related content—displaying shoe categories, personalized recommendations, trending items, and promotions. If that customer shifts to looking at jackets, the content dynamically adjusts to showcase outerwear.
2. Personalized Email Campaigns
AI helps craft highly targeted email campaigns, where product recommendations and offers are tailored to each customer’s history, preferences, and real-time activity. Brands can use predictive analytics to send timely emails, such as restocking reminders or personalized discounts on a customer’s favorite products. For example, a cosmetics company may send an email after a couple of months to remind customers to restock their products before they run out.
3. Behavioral Retargeting and Personalized Ads
The average person scrolls past upwards of 10,000 ads daily, so if you want your content to stand out, you must get creative with it. AI helps track customer interactions across various touchpoints and retarget them with personalized offers. DTC brands see increased engagement and higher conversions by delivering relevant ads or promotions to users to meet their current needs. AI helps deliver the right advertisement, at the right time, to the right users.
4. AI-Powered Chatbots
Chatbots aren’t a new technology, but they have evolved significantly over the years. In the early years of chatbots, consumers were restricted to preprogrammed questions and answers, actually hurting the customer’s experience rather than helping them. AI has drastically improved the customer experience. Now, most chatbots can understand a variety of customer queries, recall past conversations, and browse through previous orders in seconds. AI-powered chatbots better mimic human interactions, so it doesn’t feel like you’re chatting with a robot but rather a member of your customer service team.
Predictive Personalization for Subscription Services
One of the most successful applications of hyper-personalization is with subscription services like Stitch Fix, an online personal styling service. Here, AI-driven models predict what clothing items a customer might like based on past purchases, feedback, and individual preferences. This predictive personalization goes beyond simply recommending similar products—it curates entire subscription boxes tailored to the customer’s unique style.
Stitch Fix utilizes generative AI in a variety of ways, but the most important use is through clothing recommendations (it is a styling service, after all!). AI is used to create a clothing selection based on the client’s feedback, style, fit, and preferences. But the company doesn’t solely rely on AI for everything. AI then shares these recommendations with a human stylist, who then curates the final pieces for the client.
Predictive algorithms enhance the overall customer experience by ensuring that recommendations become more accurate over time. The more data the system collects, the better it understands each user’s preferences, helping drive repeat business. This kind of personalization not only boosts customer satisfaction but also reduces returns and increases the lifetime value of customers.
But remember, AI is a balancing act. This type of innovative technology can sift through thousands of data points and products in seconds, saving companies time and money, but AI isn’t human. AI systems should be used to streamline mundane and repetitive tasks to free up time for more creative work. With something as personal as style, brands still need the human perspective and expertise.
Omnichannel Experiences: Seamlessly Connecting Touchpoints
For DTC brands, offering a consistent and personalized experience across all touchpoints—web, mobile, and social—is critical. Today’s consumer expects a seamless transition when switching between different channels, whether they’re browsing on a mobile app or checking out products on social media.
Brands like Sephora excel in omnichannel engagement by offering a cohesive experience across their online and in-store platforms. Whether browsing online or visiting a physical location, customers enjoy the same level of personalized service, with recommendations and purchase history synced across channels.
Starbucks also excels at hyper-personalization by using real-time data to send unique offers to customers based on their preferences, activity, and past purchases. For example, if you frequently order cold brews, you may get an offer to earn extra stars (points) by purchasing more cold brews.
The key to successful omnichannel hyper-personalization lies in ensuring that data is synchronized across all channels. AI-driven platforms help brands track customer interactions and personalize engagement no matter where the interaction takes place, driving a more engaging and connected customer experience.
Hyper-Personalized Gifting With Nift
Nift helps DTC brands send hyper-personalized offers to customers that are primed and ready to purchase. Brands like Rocksbox and Blenders Eyewear utilize Nift’s powerful AI algorithm and first-party data to find the right customers with the highest probability of purchasing for the first time.
Learn more about Nift advertising for DTC brands here, or contact our team to request a demo.
About the Author
Mark McMaster is Vice President of Performance Marketing Solutions at Nift Networks, a rapidly growing marketplace reshaping how consumers discover and engage with brands. Nift delivers personalized gifts to consumers, AI-matched to their individual preferences. These gifts are offered through premium consumer apps such as TripAdvisor, Afterpay, and Tinder, expressing gratitude during key life-cycle moments. Brands, including True Classic, HelloFresh, Chewy, and SiriusXM, partner with Nift to gain exclusive access to millions of highly engaged customers. Mark’s team identifies new brands that can benefit from Nift as a new user acquisition platform and helps these brands tailor their approach to delight Nift’s consumers and earn their loyalty while meeting CPA or ROAS goals.
Mark brings more than 20 years of experience in marketing with a focus on digital solutions for performance goals. He began his tech career at Google, where he was one of the first vertical marketers tasked with translating evolving online behaviors into effective search advertising. At Google, he managed teams serving some of Google’s most prominent global clients and mid-market and SMB growth initiatives. His final three years at Google were in a product strategy role, improving YouTube’s video solutions for app-based, eCommerce, and other performance marketers, growing the business from a small portion of YouTube’s ad revenue to a $2B+ global business.
Snapchat recruited Mark to launch a performance marketing sales and service team, which became the foundation of the platform’s fastest-growing vertical, eCommerce. Over five years, Snapchat’s performance teams onboarded thousands of customers and a team of over 100 sales executives, analysts, and customer support leads. Creative strategists helped drive success for diverse categories across Snap’s ad products and unique augmented reality (AR) shopping experiences.
Mark is active in the startup world and the eCommerce industry. He co-founded the sports app Takes and was an early advisor to Frenzy, an AI technology platform that optimizes merchandising and product-search for Shopify merchants. He holds a degree from Northwestern University in Marketing and studied Journalism, Political Science, and Humanities as an undergrad at the University of Kansas.
