Omnichannel personalization with Profile API
mParticle's Profile API allows you to leverage comprehensive, omnichannel user profiles to create unique customer experiences for your users, anywhere you can make an HTTPS request.
Personalization is key to competing in today’s crowded marketplace. In fact, according to research, 80% of consumers are more likely to make a purchase when brands offer personalized experiences. But, effective personalization requires a single hub for your user data and insights, and the infrastructure to make those insights available at every point of customer contact.
Through universal data collection and best-in-class identity resolution, mParticle has been helping customers build the most comprehensive and accurate view of their users ever since our founding. With the addition of Profile API, these insights are now available to drive one-to-one personalized experiences across all touchpoints, over the entire lifetime of your user journey.
The value of a comprehensive user profile goes far beyond marketing. Your user data can be leveraged to provide engaging, personalized experiences for your users that:
- Increase engagement and satisfaction
- Drive new feature adoption
- Increase user trust
- Reduce churn risk
- Grow lifetime value
With Profile API, you can build a personalization engine that allows you to deploy a coherent personalization strategy across every touchpoint, not just in your messaging campaigns. Your user insights are available to power tailored customer experiences anywhere you can make an HTTPS request, including across web, native apps, brick-and-mortar locations, email, and customer support channels.
Use case example
A common marketing use case for user profiles is identifying lapsed and churn-risk users. Using data collected from your users' previous engagements and purchases, marketers can create at-risk user audience segments with mParticle to launch targeted win-back campaigns through email, push messaging and social campaigns. With Profile API, this user information is now available in even more places, making it easy to expand the reach of your win-back efforts and maintain consistent messaging across every channel. With Profile API, your win-back campaign can be launched across:
- Your app, through alerts, overlays or pop-ups.
- On a support call, by integrating Profile API into an Interactive Voice Response flow
- In your brick-and-mortar stores by integrating your Point-of-Sale(POS) system.
- And even through a voice assistant, like Amazon's Alexa.
Effective personalization across the lifetime of a customer requires a multipronged approach; basic personalization can happen from the very first customer visit, but more sophisticated, higher-value personalization methods require more data and more effort. Profile API helps you deploy the right strategy for each customer, at each stage of their journey.
“First visit” personalization strategies are often based around purchased third-party demographic data. For example, you may know that a user is male, in the 18-34 age range, and currently in Omaha, Nebraska. This is not enough information to drive “one-to-one” personalization, but it does allow “one-to-few” personalization, based on actions by other users with similar demographic information.
However, as you develop a history with your customers, you will begin to accumulate first-party data, including:
- Viewing history
- Support interactions
- In-store visits
This first-party data can power more sophisticated, “one-to-one” approaches to personalization:
- User segmentation - Identify and personalize for lapsed users, at-risk users, high-value users, category preferences.
- Recent history - Personalize based on the category of recent views and purchases
- Machine Learning - Develop unique recommendations for each customer, score users on their propensity to become high value, or to churn.
mParticle user profiles persist across a user’s entire journey, so they are the ideal data hub for bringing together multiple approaches to personalization. Start with third-party data to provide a great experience from the very first visit, and work up to more sophisticated strategies as you build history with your users.
Use case example
An on-demand movie platform needs to populate a carousel of titles for its main screen. Using mParticle’s Profile API, they can create a personalization engine that deploys the best possible strategy for each user, according to what data is available on the profile.
|Strategy||Data requirements||Value||Likely used|
|Return recommendations based on what users of the same gender and age-range watched.||Low||Good||First visit|
|Return recommendations based on what users of the same gender and age-range watched.||Medium||Better||First 7 days|
|Return unique recommendations for the user, based on ML analysis of all the user’s activity, including content watched, summaries viewed, sessions completed and abandoned, searches, etc.||High||Best||8+ days|
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