Predict their next move. Make yours first.
Know who’s likely to act, what to do next, and which customers share the traits and behaviors behind your most valuable audiences.
Turn predictions into measurable growth.
01
Drive more conversions
Focus spend on the customers most likely to act, so more budget goes toward near-term conversions.
02
Protect revenue from churn
Surface rising churn risk early enough for retention programs to intervene while customers are still reachable.
03
Grow repeat revenue
Prioritize the purchase, upgrade, or renewal most likely to increase customer value.
04
Scale proven audiences
Expand from a valuable audience without falling back on broad targeting that weakens relevance.
Static segments explain the past. Predictions shape what's next.
Most audience strategies stop at what already happened—a past purchase, a missed renewal, a current tier. mParticle models what comes next before the outcome is decided.
Act before the outcome
Behavior starts shifting before customers act. Predictions surface that change while there’s still time to influence it.
Move beyond broad rules
Future Behavior scores every customer against the outcome you want to drive, replacing broad rules with individual likelihood.
Keep the marketer in control
Review the prediction, set the threshold, and decide what becomes an audience and gets activated.
Advanced models. Simple execution.
A past purchase. A missed renewal. A current tier. Most audience strategies stop at what already happened. mParticle models what comes next before the outcome is decided.
Evaluate before you activate
See model strength, score distributions, projected impact, and key tradeoffs before activation, so you know when a prediction is ready to use.
Predictions in plain language
Work with readable scores, recommendations, and predictive attributes directly in Audience Builder. Filter and activate without writing SQL or interpreting raw model output.
Built on current customer signals
Scores stay connected to the resolved customer profiles that power activation. As behavior changes, audience eligibility updates without a separate prediction pipeline.
Predictions that go beyond the score.
Turn first-party behavior into clearer priorities, more relevant actions, and audiences you can scale.
Future Behavior
Score likelihood to convert, churn, purchase again, upgrade, or renew, then focus campaigns on customers most likely to act.
Next Best Action
Compare possible actions or offers for each customer, then choose the next step most likely to drive the outcome.
Similar Customers
Start with a valuable audience and find more customers who behave like it, increasing reach without weakening the original signal.
Activate predictions across your channels.
Use predictions across paid media, email, push, SMS, personalization, and warehouse tools through mParticle’s 300+ integrations. Build once and apply the same intelligence wherever customers engage.
Your questions, answered.
Future behavior predicts how likely each customer is to take a specific action, such as purchasing or churning. Next best action recommends which action to take for each individual. Similar customer predictions rank how closely each customer resembles a reference segment you supply.
No, mParticle’s predictive capabilities are designed for marketers. You define the conversion goal and the time frame, and mParticle handles the modeling. There is no pipeline to build and no model to maintain, which is what lets a marketing team run predictions without a data science queue in front of them.
Predictive attributes are machine-learning scores that live on the customer profile alongside behavior and demographics. You define the outcome, and mParticle analyzes thousands of behavioral signals to score which customers are statistically most likely to get there. Once generated, they behave like any other user attribute.
A new prediction can take up to 24 hours to calculate, and shows as calculating until values exist for every relevant customer. After that, predictions refresh automatically on a weekly schedule, so scores keep pace with behavior instead of aging out.
As a score and a percentile. The score is the likelihood of the action, from 0 to 100 percent. The percentile ranks each customer against everyone else scored. Both sit on the profile, and percentile is generally the better choice for audience building because it stays stable as the underlying population shifts.
Historical event data, and enough real conversions inside your chosen time frame for the model to learn from. Predictions most often fall short when the conversion window is too narrow to include a meaningful number of converters, so a longer lookback usually produces a stronger model than a short one.
Anywhere you use a regular attribute. Add them as audience criteria to target customers by likelihood, query them through the Profile API to personalize an experience in the moment, or forward them to any connected destination. See Segmentation for how predictions combine with rules-based audience logic.
Act on what customers will do next.
See how Predictions turns the signals in your data into clearer audience decisions and measurable campaign results.