What Is an OCT Model and Why Should You Build One?
- Alaina Molnar
- Feb 4
- 2 min read
If you run a lead generation business, you already know not all leads are created equal.
Some never respond. Some stall out. Others convert quickly into high-value deals.
Yet in many ad accounts, every lead is treated the same.
An Offline Conversion Tracking (OCT) model fixes that. It assigns predicted value to each conversion so platforms like Google Ads can optimize for business impact, not just lead volume.
Why Lead Value Matters in Paid Media
Ad platforms only optimize based on the signals you provide.
In ecommerce, value is obvious; a purchase has a dollar amount. In lead generation, value often appears days or weeks later. Without additional signals, platforms can’t distinguish between:
Low-quality leads that never convert
High-value leads that turn into meaningful revenue
When conversion values aren’t uploaded, platforms optimize blindly, often prioritizing cheap leads over good ones.
OCT bridges this gap by telling the algorithm: “This type of lead is worth more. Find more like it.”
What Is an OCT Model?
An OCT model uses historical conversion data to assign relative value to new leads at the moment they convert.
Instead of waiting for revenue to occur, it:
Identifies attributes correlated with higher or lower value
Uses those patterns to estimate value for new conversions
Feeds those values back into ad platforms
This enables value-based bidding and allows campaigns to optimize toward quality, not just volume.
What You Need to Build an OCT Model
You only need two ingredients:
Outcome Data (Y Variable): Actual downstream value, such as revenue or profit, tied back to past conversions.
Predictive Attributes (X Variables): Characteristics available at conversion time that may correlate with value, such as:
Geography
Time of conversion
Product or service selected
Customer or deal characteristics
These attributes must be known at conversion time and unchanging, so the model can be applied in real time.
Can I Use Linear Regression?
Traditional regression models are great at predicting total value, but they often produce very similar predictions for most conversions.
That’s a problem for bidding algorithms.
The goal of an OCT model isn’t perfect forecasting. It’s clear signaling:
Which leads are worth more
Which are worth less
Where bids should be pushed or pulled aggressively
The Solution: A bucket-based OCT model. They create stronger value separation, making them far more effective for paid media optimization.
The Value of an OCT Model
An OCT model bridges the gap between lead volume and business value.
By assigning predicted value to conversions:
Ad platforms learn what actually matters
Campaigns optimize toward revenue, not just leads
ROAS-based bidding becomes viable for lead generation
If you have downstream conversion data and care about profitable scale, an OCT model is one of the highest-leverage improvements you can make to your paid media strategy.
Want to talk about the steps to build an OCT model?
