Predicting Conversion Rates to Improve Success | Pecan AI

Conversion Rate Modeling

Predicting campaign conversion rates is the holy grail for many marketing leaders. With predictive conversion rate modeling, marketing leaders have the ability to predict the health of their marketing campaigns. That enables them to contribute more to the health of the business.

Prior to being able to predict conversation rates, it is important to first understand conversion rate modeling. Conversion rate modeling is the calculation of the number of successful conversion divided by the number of total conversion opportunities. The total of conversion opportunities includes those that succeeded and failed.

With proper conversion modeling, businesses can recoup millions of dollars in wasted ad spend. Importantly, they can also drive higher quality inbound revenue generation opportunities.

conversion rate modeling

Modeling and predicting campaign conversions is not the only way to drive business impact. It is also important to model time to conversion. By reducing time to conversion, businesses can optimize their strategic plans and advertising campaigns. They can shorten prospects’ buying cycles, pulling deals forward into the current quarter.

No matter how you approach it, conversion rate modeling should play a crucial role in how businesses operate. That will be especially true once Google finally decides to pull the plug on third-party cookies.

Predicting Conversion Rates

According to Google, “We’re in the midst of a measurement evolution, and global ecosystem changes are challenging marketers to be forward thinking and privacy focused.” This challenge is where machine learning and advanced AI frameworks like predictive analytics can provide solutions.

Leveraging predictive analytics, businesses can create proactive measurement strategies that forecast conversion events. They can also fill in data gaps related to conversions caused by changes in online tracking and measurement. For example, many marketers have experienced these gaps due to the changes brought about with Apple’s iOS 14 and SKAdNetwork.

The changes that started with iOs 14 limited the data that ad platforms can report for attribution and measurement purposes. However, predictive analytics is exactly the solution needed when marketers seek to fill the missing conversion data gaps.

reactive to proactive, day 2 analytics differences

With predictive analytics, brands can predict the 30-day value of a conversion event on day 2 of a campaign. Through predictive conversion rate models, you can drive more conversions. In addition, you can significantly reduce the time-to-conversion rate. These types of optimization can dramatically improve user LTV, reduce churn, lower CPI, and provide other benefits to your critical KPIs.

It’s time to get a competitive edge by better understanding and improving your conversion rate modeling. Get in touch to find out more about Pecan’s low-code predictive analytics platform. We’d love to show you how it provides accessible AI for real business impact.

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