Training Facebook to bid on your best leads

Facebook Ads has become the gold standard of paid acquisition because of Facebook’s powerful targeting algorithm. Retailers, for example, feed transactional data into Facebook’s algorithm to train its bidding engine. Then, Facebook optimize bidding for consumers who are most likely to buy from that retailer. The nearly instantaneously feedback loop enables fast iteration on paid acquisition strategies. You should never be bidding more on a lead than what they are worth to your business.

Facebook Ads have some limitations, though, which can make it less powerful for B2B companies. Facebook only holds onto data from the past 28 days. This means that purchase data from sales cycles that take longer than 28 days cannot be fed back into Facebook’s algorithm. Lastly, Facebook’s algorithm learns faster when events happen sooner, which again works against longer sales cycles.

Madkudu’s AI is training Facebook’s AI.

This raises a bit of an issue for SaaS companies. Most are spraying and praying ad dollars. They can only optimize on clicks instead of lead value, because they are unable to identify and optimize for high-quality leads.

Fast-growing SaaS companies like Drift are doing things differently. They feed MadKudu data into Facebook’s algorithm, enabling them to optimize bidding against leads which MadKudu would score high. In short, Madkudu’s AI is training Facebook’s AI.

Translating MadKudu data for Facebook Ads

The goal is to feed transactional data to Facebook that it can use to optimize bidding against leads that we want. MadKudu’s predictive score identifies a lead’s value at the top of the funnel. We just need to capture that lead data as early as possible and send it in a way that Facebook understands.

There are two main attributes that Facebook is looking for to train its AI – an individual and its “value.” For eCommerce, that typically means feeding a purchase back to Facebook; however, we need to adapt our value a bit.

MadKudu is good at predicting the amount that a lead will spend based on historical deal data. This helps us differentiate between self-serve and enterprise leads, for example. Of course, not all leads will convert (even the very good ones), so in order to create our predicted value to send back to Facebook, we can adjust the predicted spend by the likelihood to convert (two variables MadKudu generates natively for all leads). The result is the following:

Lead Value = % likelihood to convert  x Predicted Spend

If a lead has a 10% chance to convert at $30,000 in ARR, we can send Facebook a “transaction” worth $3,000 as soon as the lead gets generated. Now we can send data to Facebook nearly immediately to train its model. We’re training Facebook Ads algorithm to value the leads, without having to wait for the sales cycle to close.

To pass the information between MadKudu and the Facebook Pixel, we used the MadKudu FastLane, which Drift already had on their website. It’s a simple line of javascript that turns any lead form into a dynamic customer fit-driven lead capture device. The same mechanism that helps Drift convert more leads into demo calls is also training Facebook’s algorithm, no extra coding required.

The Impact: 300%

For Drift (in a test run in partnership with Lightning.ai), the impact was clear and immediate: a 300% increase on conversion from Facebook. With MadKudu Fastlane sending transactional data back to the Facebook Pixel, Drift is enabling Facebook to only spend on leads that MadKudu scores well, which Drift already knows to work well in predicting conversion to customers.

By building its growth & marketing foundation on top of MadKudu as a unified metric for predicted success, Drift is able to extend MadKudu to its paid acquisition (in addition to their bottom of funnel use cases). Connecting MadKudu to the Facebook Pixel only takes a few minutes via the MadKudu FastLane.

Get in touch to learn more here.