The phasing out of third-party cookies is forcing marketers to adapt to a new digital landscape.
Several web browsers, including Apple’s Safari and Mozilla’s Firefox, have long been blocking third-party cookies. Moreover, Google Chrome, the world’s most widely used browser, is gradually discontinuing its support for third-party cookies, with a planned phase-out scheduled to commence in mid-2024.
Marketers are expressing growing apprehension regarding their readiness for a cookieless future. For several years, marketers relied on cookies to track user behavior across the web and create highly targeted advertising campaigns. Without cookies, marketers will need to look at alternate ways to understand consumer intent and effectively deliver personalized ads.
In response to the limitations imposed on cookie usage, marketing departments are directing their efforts toward gathering first-party data. A survey conducted by Twilio reveals that 89% of surveyed B2C brands are gearing up to implement first-party strategies.
What is First-Party Data?
First-party data, the data you own, is derived directly from your audience’s interactions with your brand and is a treasure trove of insights. It offers a firsthand glimpse into user behavior, preferences, and interests, making it an ideal resource for understanding purchase intent at the individual level as well as at the aggregate level to understand patterns and trends in overall consumer intent.
8 Ways to Use First-Party Data to Understand Consumer Intent
1. Track Cart Abandonment
Cart abandonment is a significant signal of potential purchase intent. Monitor and analyze instances where consumers add products to their carts but do not complete the purchase. Leveraging this data, you can spot trends in overall intent and interest, as well as identify individual intent to send personalized follow-up emails offering incentives to encourage conversion.
2. Analyze Website Behavior
Analyze user behavior on your website. This data can reveal valuable insights such as the pages users visit, the time spent on different sections, and the actions they take, such as clicking on product listings or adding items to their carts. Patterns in these actions can unveil essential intent signals, revealing not only what products consumers are most interested in but also the stages of their decision-making process. Are they conducting thorough research, comparing options, or demonstrating a strong purchase intent? Use these insights to fine-tune your marketing strategies, such as implementing cart abandonment recovery emails or enhancing product recommendations.
3. Assess Product Views
Pay close attention to the products users view or research on your website. Frequent visits to specific product pages, comparisons, or prolonged time spent exploring particular items indicate a heightened interest in those offerings. Utilize this data to tailor product recommendations and promotional content.
4. Monitor Search Queries
On-site search queries provide valuable insights into user intent. Analyze what users are actively searching for on your website. These queries can reveal specific product preferences, pain points, or information needs, guiding your content and product strategies.
5. Email Engagement Data
For email marketing, first-party data can offer deep insights into purchase intent. Analyze email open rates, click-through rates, and engagement with specific email campaigns. Users who consistently engage with emails or click on product links may be signaling a readiness to make a purchase.
6. Survey and Feedback Data
Sometimes, the best way to understand consumer intent is to ask directly. Implement user surveys and feedback forms on your website. Inquire about their interests, preferences, and purchase intentions. This data can provide valuable qualitative insights that complement quantitative analytics.
7. Loyalty Program Data
Over 83% of consumers say belonging to a loyalty program influences their decision to buy again from a brand. 75% of consumers in loyalty programs will buy more products from the companies they partner with. Loyalty program data is rich in purchase intent signals. By analyzing transaction histories, point redemption patterns, and customer responses to personalized offers within loyalty programs, brands can discern when and why customers are inclined to make purchases.
8. Predictive Analytics
Utilize predictive analytics to forecast purchase intent. By analyzing historical first-party data, predictive models can identify patterns that indicate which users are more likely to convert in the future, allowing you to target them with tailored campaigns.
Conclusion
As the marketing landscape continues to evolve, the ability to decipher consumer intent through first-party data remains a cornerstone for building lasting customer relationships, driving conversions, and staying ahead in a competitive market.
