With increased risks, scrutiny, and privacy concerns surrounding the use of third-party data, businesses are seeking smarter approaches to maximize the potential of their own data. This means looking inward and making the most of first-party data to not only drive conversions but also create more tailored and engaging customer experiences.

Today, data is the cornerstone of effective marketing strategies. With the growing limitations of third-party data, the focus has shifted towards the goldmine of insights that lie within a company's own data vaults. When combined with the capabilities of Artificial Intelligence (AI), first-party data becomes a dynamic force capable of refining targeting and refining customer interactions.

In this blog, we'll explore how first-party data and AI converge to reshape marketing initiatives. From refining data collection practices to leveraging AI's predictive strengths, we'll provide practical insights to help you see instant improvements in the effectiveness of your own first-party data utilization. 

What is first-party data?

First-party data refers to information collected directly from individuals or customers by a company or organization. This data is typically gathered through interactions with the company's own platforms or services, and it’s typically considered the most valuable type of data for businesses because it’s both accurate and trustworthy.

Some examples of first-party data include:

  • Website Analytics: Information about user behavior on a company's website, such as page views, click-through rates, and time spent on the site.
  • Customer Surveys: Feedback and responses collected through surveys or feedback forms provided by the company.
  • Transactional Data: Details about purchases, including what was bought, when, and for how much.
  • User Account Information: Data provided by users when they create accounts, including names, email addresses, and preferences.
  • Social Media Engagement: Information about how users interact with a company's social media accounts, including likes, comments, and shares.
  • Email Marketing Data: Information gathered through email interactions, such as open rates, click-through rates, and conversions.
  • Customer Support Interactions: Details of conversations or interactions with customer support representatives.
  • Mobile App Usage: Data collected from interactions with a company's mobile application, similar to website analytics.

The advantage of first-party data is that it's obtained directly from the source, which means it's likely to be accurate and reliable. Additionally, because it's collected by the company itself, there are typically no privacy concerns or legal restrictions associated with its use.

In contrast, third-party data is obtained from external sources, such as data brokers or other companies. While it can be valuable for insights and targeting, it may not be as reliable or tailored to a specific business's needs, and depending on how it’s collected, it can present a significant legal compliance risk to the companies that use this data.

For this blog, we’re going to focus on first-party data collected in a mobile app’s event stream

Everything a user does on a mobile app is captured in real time in an event stream.

Event stream data is a continuous flow of real-time information generated by user interactions, system events, or other activities within the app. These events could encompass a wide range of actions, such as button clicks, form submissions, location updates, in-app purchases, error reports, and more.

For example, for an eCommerce app, event stream data may contain things like product views, add-to-cart events, and purchase events, but it can also include seemingly less important events like viewing your account and sorting or filtering search results.

In raw form, event stream data looks like this:

Everything a user does inside of an app is stored along with time stamps and additional data. Event stream data is a first-party data gold mine.

Left in raw form, this data can be difficult to work with, but there are several methods that can make this data much more useful.

First-party event stream data offers several distinct advantages for marketers compared to third-party data

Here are just a few advantages:

  • Accuracy and Trustworthiness: First-party event stream data is collected directly from users' interactions within the app, making it highly accurate and trustworthy. In contrast, third-party data can be less reliable as it's sourced from external providers and may not always be up-to-date or reflective of specific user behaviors.
  • Real-Time Insights: Event stream data provides real-time insights into user behavior and interactions. This allows marketers to respond quickly to changing trends and adapt their strategies in near real-time. Third-party data may have a delay in reporting, which can limit the effectiveness of timely marketing campaigns.
  • Deeper User Understanding: Event stream data provides a granular view of how users interact with the app, allowing for a more detailed understanding of their preferences, habits, and engagement patterns. This level of detail can be invaluable for creating highly targeted and personalized marketing campaigns.
  • Customization and Personalization: With mobile event stream data, marketers can create highly tailored and personalized experiences for users. They can segment users based on specific behaviors and preferences, allowing for more effective targeting and messaging. Third-party data may offer less customization, as it's often more generalized.
  • Compliance with Privacy Regulations: First-party data, including mobile event stream data, is typically obtained with user consent and is, therefore, more likely to comply with privacy regulations and best practices. This reduces the risk of legal and regulatory issues associated with using third-party data.
  • Reduced Dependency on External Sources: Relying on mobile event stream data reduces the reliance on external data providers, which can be cost-effective and provide more control over the quality and accuracy of the data used in marketing efforts.
  • Enhanced User Experience: By leveraging event stream data, marketers can deliver more relevant and timely content to users, enhancing their overall experience with the app. This can lead to higher retention rates and increased customer satisfaction.
  • Improved ROI and Conversion Rates: The precision and timeliness of mobile event stream data can lead to more efficient marketing efforts. Marketers can allocate resources more effectively, resulting in higher return on investment (ROI) and improved conversion rates.

Most of the time, event stream data is just collected in a Customer Data Platform (CDP) like Amplitude or Segment, or it's stored in a data warehouse where it’s periodically queried for specific requests or analysis, but if used appropriately, event stream data can be proactively activated with AI to create incredibly rich personalized user experiences.

How to use AI to drive more personalized experiences from your own first-party data

When it comes to creating more personalized experiences with first-party data, most companies take a one-to-one approach — for example, if a user abandons a cart, an app will send an abandoned cart notification to that specific user, reminding them of the specific item they’ve abandoned — but this is just table stakes. It doesn’t take advantage of the full capabilities AI can offer when combined with this data.

In contrast to this one-to-one approach, feeding all of this data into an AI model can uncover deeper patterns within your data, which can help you drive more effective product improvements and personalization — Even providing more personalized for users who haven’t yet performed any actions on your app!

Here’s an example of the type of information you can uncover by performing an analysis of your complete first-party data:

Here’s what this chart shows:

  • Each of the dots is an event. We’ve greatly paired down the number of events shown in this diagram for readability, but this analysis can be easily performed on hundreds of events.
  • The size of each dot is the number of times the event was performed. The bigger the dot, the more times it was performed during the analysis period.
  • The lines connecting the dots show how many times one event followed another. The thicker the line, the more often this path was traversed. You can think of this as a step in a “user journey.”
  • The proximity of the outer dots to the inner dot (“Complete Checkout”) shows how close that event typically is to a checkout event. A user who opens the app or churns is not likely to complete a checkout. A user who views their order item is much more likely to check out.

So, what can we learn from this analysis?

Well, by looking at the larger relationship between all of these different events, combined with an individual’s unique characteristics and actions, we can build a decent prediction of a user’s next actions and their likelihood to convert (or check out, in this case) or even try to encourage actions that are more closely associated with our conversion events.

Also note that while there is some universal truth, these results will vary from app to app. What this means is that the insights you can gather from your own data will be much more accurate and valuable than what you could expect from third-party data. 

What’s true for one app’s customer base could be vastly different for another.

Similarly, we can run these events through a propensity model to predict specific user outcomes based on whether or not a user performs an event.

For example, an analysis of an eCommerce app’s first-party event stream data showed that users who use a gift card for their first purchase are highly likely to churn:

How to read this chart: Users who perform the event “giftcard_place_order” on their first day are only 27% likely to still be using the app after 30 days. Notice, however, that using a gift card has a much more negligible effect for a user who has been using the app for a month.

We can use this information to automatically segment new users who checkout with gift cards and drive them to our loyalty program by offering them free points, for example, as participation in loyalty programs has a much higher propensity for retention:

See here for more examples specific to eCommerce and here for more examples specific to subscription apps.

In other words, we’re not just using our first-party data for each user to provide more personalized experiences for that individual user. We’re effectively using patterns detected from using all of our first-party data to make more rich personalized experiences for all of our users…and these experiences are much more effective, relevant, and not to mention less expensive than purchasing and using third-party information (There’s literally no way to do this analysis from third-party data. Your events only live on your app!).

How to do this propensity analysis on your own first-party data

Doing a propensity analysis on your data isn’t necessarily hard — You could likely tap your own in-house data science team or find a freelance data science resource who can run one of the standard models for you.

Creating a system that can absorb and allow you to quickly act on this data is the challenge.

If you’re an app with event stream data, you’re in luck — we specifically designed the Aampe system to ingest all of your event data (as well as your user properties and even your CMS data) via a simple APIs. (No SDK) and use them all effectively to engage your users:

This allows you to use advanced functionality — like triggering by propensity — without even requiring a change to your current MarTech or messaging software stack:

We can even do a 30-60 day lookback window, so we can start delivering your users more personalized results by day one, without you having to use any third-party data whatsoever.

Maximizing your personalization strategy with first-party data and AI

As we’ve shown, by applying AI to your first-party user interaction/event stream data, apps can unlock a wealth of insights that go beyond surface-level personalization.

AI models can uncover deeper patterns and behaviors within your data, paving the way for smarter product enhancements and tailored user experiences, and this holistic approach doesn't just benefit individual users — it enhances the overall experience for your entire user base.

So, what insights are hiding in your first-party data?

To learn more about optimizing your app's first-party data and AI integration, reach out at hello@Aampe.com or click that big, orange button below. Your users deserve an experience tailored just for them — let's make it happen.