Introduction
Collecting first-party customer data is a top priority for most marketers, especially as third-party cookies and other data sources are regulated or blocked by leading tech platforms. But collecting data is only the first step. Knowing how to use first-party data in marketing is just as important. The data you gather actually needs to be applied to marketing programs to make an impact on customer experience and conversion.
And as our data-driven marketing strategy webinar poll below shows, putting data to use can be a real pain point. Even for marketers who are already running programs and collecting data on marketing automation platforms. Nearly 37% of our “Drive More Demand with Data-Backed Strategies” attendees said applying data to marketing was their #1 challenge.
While the concept of building a data-driven marketing program can sound intimidating, it doesn’t need to be overly complicated. There are many ways to layer in data at different points along the customer journey. Doing so helps you to refine messaging, improve conversion rates, and segment customers in more sophisticated ways.
Put simply, using first-party marketing data means learning from how your customers are responding to your current campaigns. Then, using those insights to improve in the future. And it’s not an all-or-nothing proposition. It’s a journey that every team takes one step at a time.
With that in mind, let’s explore a few specific ways your team can get started.
TL;DR: First-party data—collected directly from your audience—is one of the most valuable assets for marketing in a privacy-first world. Applying it effectively can boost personalization, improve campaign performance, and increase conversions. Start small with accessible data like email engagement, PPC results, lead scoring, and organic traffic insights, then refine your approach step-by-step. Success depends on collaboration across teams to create a data-driven culture.
What is First-Party Data?
First-party data is information a business collects directly from its own customers or audience through interactions with its channels, products, or services. Because it comes straight from the source, it’s typically more accurate, relevant, and reliable, making it valuable for creating personalized experiences, improving marketing strategies, and maintaining stronger privacy compliance compared to data obtained from external parties.
Different Types of First-Party Data
First, consider the types of marketing data you can put to use in your campaigns. Some data will be more complex and may require help from your data team to access and analyze, and some data will be readily available within your existing mar-tech stack.
We’ll be sharing some specific how-tos for certain kinds of marketing data in the following section, but the general principles apply across channels and sources. So keep in mind how you can apply these data-driven practices to the types of data you’re already collecting and feel comfortable working with, such as:
- Website engagement data (form fills, clicks, traffic sources)
- Social media engagement metrics
- Email engagement data
- SMS engagement data
- NPS/CSAT survey scores
- PPC and paid social performance data
- Purchase history and product use
Now let’s dive deeper into a few of these first-party data types, and how you can put your insights to use.
4 Ways to Use First-Party Data in Marketing
1. Apply Email Engagement Data to Improve Campaigns
Email engagement data is usually straightforward and easy to access, which makes it a solid starting point for applying data to your campaigns. Here are a few specific ways to apply this data:
- At a high level, you can use email engagement data to evaluate campaigns in aggregate across major segments to see which audiences are most fruitful for your company. For example, you may find that manufacturers engage with your content at a significantly higher rate than businesses in the automotive industry. This may mean it’s worth taking a step back to look at your industry-specific value propositions, or even whether automotive is an audience worth targeting.
- Look at engagement data across content types to see what’s resonating with your audiences. If you notice that checklists tend to produce great clickthrough rates and lead to more qualified opportunities for your sales team, make sure checklists are a regular part of your content creation calendar.
- Notice when specific pieces of content get positive engagement with certain customer segments, and see if that high-performing content can be tweaked or repurposed for other audiences with similar needs. Change the stock photo or update the headline to ensure the content is relevant, and see how it performs with a new segment.
- Monitor your opt-out rates, and if you notice an uptick, make sure you aren’t over-messaging or sending irrelevant content to your audience (unsubscribe rates tend to average around .5%, but that can vary by industry, so use your historic data as a benchmark). Evaluate your email frequency and content mix to make sure you aren’t over-indexing on hard sales or marketing fluff instead of useful, valuable information. And make sure your welcome email sets clear expectations—and that you’re sticking to them in your campaigns.
Ready for more? Check out some other email metrics that you can use to diagnose opportunities in your campaigns.
2. Use Paid Social and PPC Data to Identify Winning Messages
Paid social and PPC data has the advantage of being very clear and relatively quick to accumulate, making it a fruitful source for testing content and messaging. Get started by:
- A/B test headlines in paid social ads, and repurpose the winners in your organic social posts, email subject lines, email headers, or even blog post titles.
- Test landing page templates in PPC campaigns, then ensure the winning template is used for your campaign pages and across your automation programs.
- Test visual content or videos in paid social campaigns, then apply the winning infographic/photography/illustration to your landing pages, website, or email campaigns.
- Test campaign messaging and value props in PPC or paid social before applying to direct mail, display ads, or other (more costly) paid channels.
Explore more ways to use paid social and search data in our beginner’s guide to analyzing Google Ads metrics or with these tips on building PPC landing pages that drive conversions.
3. Implement Lead Scoring to Segment Your Audience
Lead scoring is an objective system that helps your marketing and sales teams automatically rank and prioritize leads based on their behavior. Actions such as visiting your pricing page or watching a product demo video would net a high lead score and indicate a prospect is likely to buy, while behaviors like downloading an ebook or registering for a webinar are still in the earlier stages of the buyer’s journey.
Generally, lead scoring is used to help route the best leads to sales. Using an objective scoring system that your sales and marketing teams develop in partnership allows your sales team to focus their time and efforts on the warmest leads, and avoids jumping the gun on a sales conversation with prospects who are still learning about your services or products. If you haven’t implemented this system into your marketing automation platform yet, creating a lead scoring program is one of the most impactful ways to use data to help structure your marketing-to-sales handoffs.
And if you’re already using a lead scoring system to help route prospects to sales, you can also leverage that data to optimize your marketing campaigns. The same behaviors that impact lead scoring can inform your customer segmentation and engagement campaigns. For example, you can segment your audience by low-scoring leads that haven’t moved forward in their journey. Those lagging leads could have a negative impact on your overall reporting or even email deliverability, so you could try enrolling them in a long-term nurture sequence or re-engagement campaign to spur their interest, or choose to remove them from your database altogether.
Learn much more about this best practice in our five-step guide to building a lead scoring program or by checking out our on-demand webinar in lead scoring best practices.
4. Apply Organic Traffic Data to Optimize Content Strategy and Conversion Rates
The organic traffic data in Google Analytics is a treasure trove of information about your audience’s appetite for content. While you may not be able to tie these data points to specific customer segments like you can with marketing automation data, you can gain valuable insights to inform your overall content strategy.
- Look for your pages and blog posts that show high pageviews and organic entrances (metrics that tell you about traffic—the higher the better) coupled with low exit and bounce rates (metrics that evaluate engagement—the lower the better). These pages are your high performers—they attract traffic and retain your audience’s attention. As long as these pages are ranking well for relevant keywords that relate your product or area of expertise, they’re likely great candidates to promote in your engagement campaigns.
- On the other hand, if you see pages that have low pageviews and organic entrances along with low exit and bounce rates, you may find that you have hidden gems. These posts may not be ranking well on search, but the people who do find them seem to stay engaged and want to explore more of your content. These may also be great candidates for campaign promotion. They could probably use a little attention regarding on-page optimization (so make sure you have internal links pointing to this page and see if the meta tags need an improvement).
- If you have pages that are performing relatively well but have high exit rates, that means your readers aren’t sure where else to go on your site once they’ve reached the end of your page. Make sure you have good internal links to other relevant content on your site or other clear CTAs that help your visitors go deeper with your brand, like a newsletter signup or ebook download.
- Conversely, if you have pages that see great engagement in your campaigns, like high email clickthrough rates, PPC traffic, or social shares, but have low organic entrances and pageviews, it’s time to look at your keyword optimization for those posts. You know the subject matter is solid, so evaluate what topics these posts could be ranking for and do a little keyword research to find relevant terms with decent volume (long-tail keywords may be a great fit for content performing well in the lower part of your funnel). Then, set about optimizing your page for the new terms. You may need to update title tags, headers, and meta descriptions, and don’t forget to internally link to the page from other related pages on your site.
Ready to dive a little deeper? Learn what it takes to optimize your brand’s organic search visibility.
The Final Step: Get Your Whole Team On Board
Bottom line: using first-party data in your marketing programs happens step by step, one data source at a time. If you’re just getting started, begin with the types of data you can most easily access, work with and understand.
As you find success, don’t forget to share your wins with the rest of your marketing organization. Data application throughout the customer journey requires collaboration across channels and teams, from strategists to SEO specialists to analysts. So trumpet your wins, brainstorm with your colleagues, and get your marketing org on board to build a data-driven culture!
Summary
This article explores how marketers can turn first-party data into actionable insights that drive better customer experiences and higher conversion rates. It explains what first-party data is, outlines common types such as email engagement, paid campaign results, lead scoring metrics, and organic search analytics, and details four practical ways to use these insights.
By starting with the most accessible data sources and applying them to refine messaging, segment audiences, and optimize content, marketing teams can make incremental improvements. The key to long-term success is fostering cross-team collaboration and building a culture where data-driven decision-making is the norm.