Data-driven personalization is a marketing approach that uses customer data to provide more relevant consumer experiences.
When done well, the practice minimizes wasted marketing spend and increases conversion rates. In a Deloitte case study, for example, one leading apparel retailer saw a nearly 40% lift in conversion rates and almost doubled their return on ad spend (ROAS) by incorporating first-party data into their personalization strategy.
Here’s how data-driven personalization works, including three examples and four tips to help you improve your customer experiences with data-driven personalized campaigns.
What is data-driven personalization?
Data-driven personalization is the process of using customer data you’ve gathered over time to tailor shopping experiences to individuals. Businesses use it to target consumers with the types of outreach the data suggests are most likely to drive them to a desired outcome. For example, you can personalize email marketing content, send targeted SMS promotions, or display custom product recommendations at checkout based on the browsing and past purchase behavior of individual shoppers.
How does data-driven personalization work?
Data-driven personalization is a three-part process: A business gathers consumer information, uses it to better understand its customers, and personalizes experiences based on what it learns. Here’s how it breaks down:
1. Data collection
Your business gathers demographic and behavioral data from customers. Common sources include your website data, email newsletter engagement, form fills, and social media channels.
This information is known as first-party data, which means that your business collects and owns it. It also means that you’re responsible for data privacy compliance. Some privacy laws require businesses to obtain consent before collecting customer data, and you may need additional permissions to engage in specific marketing practices. You can use a consent management platform to request necessary permissions for the consumer’s location.
2. Data analysis
Your business analyzes collected data with the goal of identifying the experiences most likely to resonate with groups of customers and individual shoppers.
You can use your data to identify key consumer segments, their preferences, decision drivers, and engagement patterns, and anticipate what they’ll do next. You can also establish data signals that will trigger specific personalization automations later on. You might decide to send personalized reminder emails in response to cart abandonment, for example.
AI assistants like Shopify Sidekick can help with this step—it can analyze customer data with plain language prompts and look for opportunities to personalize the experience on your site.
3. Personalization
Your business tailors outreach for individuals or segments, optimizing messaging, content, and specific offers based on individual preferences and decision drivers.
For example, you might create two distinct marketing emails for your two main customer segments and then use generative AI to further tailor emails for individual consumers, including product recommendations based on browsing history and curated blog links that relate to those products.
Examples of data-driven personalization
- Crown Affair’s personalized product recommendations
- Harlem Candle Co.’s VIP email marketing strategy
- Revenge Of’s behaviorally triggered pop-ups
Here are three real examples of data-driven personalization to inspire your personalization efforts:
Crown Affair’s personalized product recommendations
Elaine Choi, founder of haircare company Crown Affair, uses an interactive website quiz to collect first-party customer data and generate personalized product recommendations for each participating user. Customers share details about their specific hair type, and the site generates a product bundle based on their answers. The company also stores quiz information, using responses to build deep customer profiles that can inform future personalization efforts.
“We have a quiz and people can tell us about their hair, and then through that we offer different bundles that might cater to them,” says Elaine.
This personalization strategy requires active customer participation, and it works because the brand incentivizes customers to share information by offering them something that’s useful to them. “We don’t want to burden the customer with requests for their data,” says Elaine, “so we asked ourselves, how do we create value if they do give us the data? We do this exchange of information, and we spit out something that feels personalized.”
Harlem Candle Co.’s VIP email marketing strategy
Harlem Candle Co. uses data-driven personalization to tailor their email marketing content for different consumer groups, using purchase history and customer engagement patterns to focus their marketing efforts on their most valuable customers.
“It’s amazing to be able to create segments for your VIPs, those people who open up every email and add stuff to cart and check out almost all the time,” says CEO Teri Johnson. “When you launch a new product, you know those customers are going to buy. You can give them early access to things because they’re so loyal, and it makes them feel more special, like they’re not just anybody.”
She also makes a point of recognizing long-term loyalty explicitly, saying, “Some customers have been with me since 2016, and we’ll build email flows that acknowledge that in language.”
Revenge Of’s behaviorally triggered pop-ups
The comic book shop Revenge Of uses website pop-ups to personalize messaging based on real-time behavioral signals. The brand uses the Fomo app, which dynamically tracks on-site consumer activity and responds with messages designed to create a sense of urgency and encourage conversion.
“Fomo not only reminds a customer what’s in their cart, but it tells you ‘Someone in Sacramento bought this comic,’ or ‘There are only four of this item left,’” Cofounder Joe Myers says on Shopify Masters.
How to implement data-driven personalization
- Unify customer data across channels
- Build custom customer segments
- Prioritize high-impact automations
- Use AI predictions to help personalize offers
Follow these four steps to implement a data-driven personalization strategy that supports your business goals:
1. Unify customer data across channels
Effective data-driven personalization takes high-quality data—ideally from all sales channels. But consolidating information across various channels is a common challenge, as data silos prevent businesses from making the most of their first-party data. According to one 2025 survey, 77% of chief data officers believe that data silos are detrimental to data-driven decision-making.
The lifestyle brand Aviator Nation experienced this challenge firsthand, as their legacy setup of separate platforms for ecommerce and in-person retail prevented the company from fully understanding its customers and led to a buildup of customer frustration.
Switching to Shopify’s unified ecommerce platform allowed the company to access complete customer histories, which improved its personalization efforts. Unifying data sources also turned acquisition via various channels into a segmentation opportunity instead of a barrier to understanding customer needs.
“We’ve come to realize that each of our customers has a unique relationship with the brand,” Curtis Ulrich, the brand’s director of ecommerce, said in a Shopify case study. “Whether they met us at a retail store or they discovered us at a music festival or they came to a spin class, all of those are unique data points that we can understand about the customer.”
2. Build custom customer segments
Customer segmentation, which is the process of creating groups of customers with shared characteristics, is foundational to any personalization strategy. The haircare brand Ouai segments customers by historical fragrance preferences, for example. To tease an upcoming launch, the brand decided to target customers who had already purchased a product in a specific fragrance with an exclusive preview campaign.
“We’re going to do something personalized and high-touch for all the customers we know love the scent,” says Ouai CEO Hannah Beals. “It’s about finding ways to reward and incentivize different types of customers who engage with the brand in different ways.”
You can use Shopify’s customer segmentation tool to create custom customer segments based on the data captured from your online store. The platform allows users to create rule-based custom filters that group customers with similar characteristics and enables dynamic segmentation, which automatically updates segments to reflect the most current information.
3. Prioritize high-impact automations
Take segmentation a step further by individualizing communications using marketing automations, starting with high-impact options like welcome emails and abandoned cart reminders. Data from Klaviyo shows that these two email flows bring in more revenue per recipient (RPR) than any other.
You can use Shopify Messaging to personalize subject lines and email content and run multiple automation flows for email and SMS. It also allows you to edit workflow templates and create custom automations that meet your business needs.
4. Use AI predictions to help personalize offers
Artificial intelligence (AI) is changing the face of personalization, allowing marketing teams to collect and process larger volumes of customer data and extract increasingly sophisticated customer insights. One application is predictive segmentation, which involves using AI to group customers based on the probability of future behavior.
Shopify’s predicted spend tier feature is one example: The tool analyzes past interactions with your store to group customers by spending potential, sorting them into high, medium, and low predicted spending tiers. You can use this information to tailor marketing outreach based on each group’s unique decision-drivers and purchasing patterns.
Wallet retailer Ridge takes another approach, using AI to enable a volume-based personalized ad strategy. The company uses generative AI to pump out up to 500 ad variants per day, relying on platforms like Meta to micro-target customers through their own algorithm and serve the best-fit ad variant for that individual.
On an episode of Shopify Masters, CEO Sean Frank describes this strategy as “shots on goal.” Although it’s a data-driven approach to personalization, Ridge doesn’t own the relevant data, so they can’t cross-apply it to other customer experiences on their owned channels.
Instead, it relies on ad platforms to serve customers with relevant content according to the platforms’ internal data signals. Sean sees it as the future of advertising. “We’re going to see more personalized advertising,” he says. “What you see and what you like is going to be totally different from what I see and what I like, and Facebook will deliver those ads in the moment.”
Data-driven personalization FAQ
What does data-driven mean?
An approach is data-driven if it responds to information gathered from a real-world source. Data-driven personalization uses customer information to understand what customers need and deliver relevant experiences.
What data is used for personalization?
Data-driven personalization makes use of a range of data points, including browsing history, purchase history, abandoned carts, geographic location, and demographic information.
What are some examples of personalization?
Sending a happy birthday email on a customer’s birthday, dynamically tailoring website content to customers based on browsing history, and targeting VIP customers with exclusive promotions are all examples of personalization strategies.
