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Data-driven marketing uses information about customer preferences and purchasing behavior—rather than intuition alone—to inform marketing decisions.
Collecting data is the easy part. Actually knowing how to use it can be a challenge.
In a 2026 survey from marketing platform Funnel, 72% of in-house marketing teams said they have a lot of data, but don’t know how to turn it into actionable insights.
In this article, you’ll learn how to create a data-driven marketing strategy with tips from small business owners and marketing experts.
What is data-driven marketing?
Data-driven marketing is the process of using customer analytics to create relevant promotional campaigns.
Unlike traditional marketing, which relies on market-wide studies to predict which campaigns your audience will respond to, a data-driven approach uses actual customer behavior to enable personalization and segmentation.
Benefits of data-driven marketing
Analyzing data can help you build stronger campaigns. Here’s how:
Enables personalization
Data allows you to create personalized marketing campaigns, which can lead to more sales. In a 2026 Hubspot survey, 93% of marketers said that personalization increases leads and purchases.
A classic example of personalized marketing is including a subscriber’s first name in an email subject line, but other common data-driven strategies include personalized product recommendations, retargeting ads, birthday discounts, and abandoned cart emails.
Challenges assumptions
Part of a data-driven approach is pivoting when the numbers don’t support your original hypothesis.
When Andrew Benin started Graza, he wanted to create an olive oil that was high-quality but reasonably priced—something in between the inexpensive oils available at every grocery store and the fancy kind found at specialty stores. Since Andrew saw Graza as an everyday olive oil, he didn’t consider marketing it as a gift around the holidays.
“We had pitched, ‘Graza’s not for gifting. Graza’s for everyday use,’” Andrew says on an episode of Shopify Masters. But the numbers told a different story: order volume went way up during the holiday season.
“Q4 brought us to our senses. We were like, ‘Holy moly, people are really gifting this product like crazy,’” Andrew says. Graza quickly shifted their approach, offering gift boxes and upping their inventory around the holidays.
Informs budget allocation
With data-driven marketing, you can create a smarter marketing budget focused on return on investment (ROI).
Jordyn Casaus, director of digital and integrated marketing at hair care brand Crown Affair, uses performance data gathered from Shopify Analytics, Google Analytics, and Triple Whale to allocate her sales and marketing budgets.
“It’s always based on performance,” Jordyn says. “It’s always based on data—how we are making those decisions to either pour more dollars into or less dollars into [a strategy].”
This data-driven approach can also help get buy-in from stakeholders. “When reporting back to finance and executive, it’s always, ‘Hey, this is doing really well, so we need to lean into it… Or, ‘This isn’t doing really well, so I’m going to pull back some here,’” Jordyn says.
Challenges of a data-driven approach
While a data-driven strategy can be beneficial to your brand, there are also a few potential challenges:
Information overload
Too much customer data can be overwhelming. One way to combat information overload is to find the right software to keep you organized.
Shopify’s Analytics is a useful tool, since you can customize your dashboard to show only the most important details. You can choose from pre-made reports or create your own. The data will appear in both graph and table format to help you visualize data in different ways.
Short-term focus
If you focus too much on short-term performance metrics and real-time data, you may miss the bigger picture.
“The No. 1 mistake [people make with data analysis] is that they’re too short-sighted,” says Neil Hoyne, Google’s chief strategist, on an episode Shopify Masters. “They look at the metrics of what happened today, but in reality, consumers take time to build that connection with a product.”
For a bigger-picture view, Neil recommends tracking customer lifetime value (CLV), which measures how much a customer will spend over the course of their relationship with your business.
Unnecessary data collection
Just because you can collect data doesn’t mean you should. Unnecessary data collection can contribute to the problem of information overload, and customers might find it invasive.
“Don’t collect information just for the sake of collecting it,” Neil says. “Think about how you might use it to personalize your emails and customer experiences or deliver better value to them.”
It’s also important to protect customer privacy. Companies should only ask for relevant data about the shopping journey, and avoid using data for purposes other than enhancing the customer experience.
If you have a Shopify store, your website comes with built-in customer privacy features, such as an automated privacy policy, a cookie banner so customers can consent to you collecting their data, and a page where they can opt-out of sharing data.
How to implement a data-driven marketing strategy
Follow these steps to create a data-driven marketing strategy:
1. Develop hypotheses
For Neil, data-driven marketing starts with developing hypotheses, not reading analytics reports.
“Far too often, I see entrepreneurs go in and say, ‘Here’s my analytics report. Let’s see what’s in it,’” Neil says. “And then they’ll stare at and be like, ‘Well, traffic is up in Brazil. Why is that? I don’t know. What do I do with that?’ And you can’t do anything with it.”
Neil recommends writing down a list of hypotheses before diving into the data. These are testable theories that can be proven wrong or right.
To come up with hypotheses, Neil suggests asking yourself: What could my business do differently to better connect with our customers?
2. Identify the data you need
Once you have a list of hypotheses, the next step is to figure out what data will allow you to prove or disprove your hypotheses. You might have easy access to this data in your Shopify Analytics dashboard or a default report, or you may need to create a custom website analytics report or collect new data, such as through customer surveys.
Here are a few examples of data that business owners used to prove or disprove hypotheses:
- Order volume over time. Tracking order volume over time can help you understand seasonal trends and adjust your marketing strategy accordingly. Original Duckhead founder Morgan Cros used data from Shopify’s Analytics to discover that her umbrella business was year-round, not seasonal as she had assumed.
- Return on ad spend. ROAS is a popular metric for assessing the effectiveness of paid ads. Ryan Bartlett, cofounder of apparel company True Classic, tracks new customer ROAS using Triple Whale to support his hypothesis that Facebook ads help new customers convert.
- Post-purchase surveys. Kopari Beauty has a post-purchase survey asking customers how they found out about a brand. If they name a specific influencer, VP of Marketing Toral Patel uses that data—along with other metrics like earned media value (EMV) and social media engagement—to assess the impact of influencer marketing.
One way to find the data you need is to ask Sidekick, an AI assistant built into your Shopify admin. Sidekick has full access to Shopify’s Analytics and can customize existing reports, generate new data explorations, and display data visualizations based on plain-language requests.
3. Use data to inform marketing campaigns
Now that you’ve found the right data, you’ll see if your hypotheses were correct or incorrect, and you can use that information to inform your marketing efforts. What this looks like will depend on your hypotheses.
For example, after sales data disproved Andrew of Graza’s hypothesis that his olive oil wasn’t for gifting, the company updated their website to show gifting options on product pages.
In other cases, you might use the data to increase or decrease spend on an existing campaign. Ryan of True Classic checks new customer ROAS for his Facebook ads daily. If the number is low, he knows he overspent.
4. Iterate
One of the hallmarks of data-driven marketing is continuous testing and iteration. After you create or change a campaign, take time to analyze the results and make improvements.
At Crown Affair, Jordyn has a weekly reporting meeting where she and the brand’s director of sales look at key performance indicators (KPIs) like average order value (AOV) and revenue by channel. They compile the data in a single, cohesive report to share with department heads.
This weekly check-in allows her team to constantly iterate and improve their marketing based on data.
Data-driven marketing FAQ
What is the difference between traditional marketing and data-driven marketing?
Traditional marketing is often based on assumptions about the target audience, and it generally relies on broad strategies and trial and error. Data-driven marketing activities harness specific, concrete information about customers to focus and hone strategies.
What are the benefits of data-driven marketing?
Data-driven marketers can tailor campaigns to be more relevant to specific audience segments, which can improve marketing return on investment (ROI).
What is an example of data-driven marketing?
An example of data-driven marketing is Graza creating a hub for shoppers purchasing their products as a gift after realizing that people commonly bought the items for others.
