Likelihood to Purchase
Likelihood to Purchase (LTP) predicts how likely a visitor is to make a purchase within a specific timeframe by analyzing behavioral, transactional, and engagement data.
For example, if a customer browses jackets twice in a week and adds an item to their cart, a high LTP score could trigger a personalized email offer to encourage conversion.
Why use Likelihood to Purchase?
- Boost campaign efficiency by targeting high-LTP segments with personalized offers and minimizing discounts for users who are already likely to buy, directly raising return on ad spend.
- Prioritize retargeting and outreach, focusing resources on users with moderate or declining LTP to recover at-risk conversions.
- Enable dynamic content and triggered automations (e.g., SMS, push, email) based on real-time LTP scores, ensuring the right channel and timing for each shopper.
LTP vs. Conversion Rate vs. Purchase Intent
| Metric | What it Measures | Calculation/Source | Actionability |
| Likelihood to Purchase | Probability that a specific user will buy soon | Predictive algorithm (historical behavior) | Highly granular |
| Conversion Rate | % of visitors who complete a purchase/action | Actual conversions / total visitors | Broad metric |
| Purchase Intent | Strength of a user’s expressed desire to buy | Behavior/engagement signals, self-report | Variable: used for segmentation |
FAQs
Most platforms use machine‑learning models that analyze browsing behavior, past purchases, and engagement events to estimate purchase probability. These predictive models update dynamically as user behavior changes. Each platform weighs signals differently, so reviewing how your provider’s LTP model works is important. Learn more about how Insider One’s LTP model works in its Predictive Segments: Likelihood to Purchase documentation.
Marketers use LTP segments to optimize targeting and incentives: users with low LTP might receive stronger offers or content to encourage a purchase, while high‑LTP users often need fewer promotions. This helps maximize ROI and protect margins. For strategic guidance, see Insider One’s Use Cases for Predictive Segments, which covers how to deploy LTP in different campaign scenarios.
First‑time visitors typically don’t have enough behavioral history for accurate LTP predictions, so many systems classify them as “new” until more data is collected. As they engage (e.g., browse, add to cart), the LTP model updates and assigns them a score over time.