{"id":9428,"date":"2020-04-16T15:13:40","date_gmt":"2020-04-16T22:13:40","guid":{"rendered":""},"modified":"2025-06-11T07:59:26","modified_gmt":"2025-06-11T14:59:26","slug":"ai-builder-now-supports-numerical-prediction-preview","status":"publish","type":"post","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/power-platform\/blog\/power-apps\/ai-builder-now-supports-numerical-prediction-preview\/","title":{"rendered":"AI Builder now supports numerical prediction (Preview)"},"content":{"rendered":"

Overview<\/h1>\n

AI Builder prediction models now support a preview capability to predict a number. Now you can use AI builder intelligence to predict things like product ratings, price estimates, time to completion, and so on.<\/p>\n

In this post, we demonstrate how numerical prediction works by building an example of an end to end scenario where we\u2019d use AI Builder to help an online business to optimize purchase conversion rate.<\/p>\n

Example<\/h1>\n

Let\u2019s say our online business is quite competitive, and there\u2019s a need to improve purchase conversion. How can we increase the percentage of customers visiting our website who then make a purchase?<\/p>\n

One approach is to identify customers visiting a product page that have a high probability to exit the page before completing a purchase. Using AI builder prediction modeling, we can identify this customer as \u2018likely to drop\u2019, and then trigger a Power Automate flow to send a coupon to this customer. This targeted interaction enabled by AI Builder prediction modeling can help convert site visitors to customers.<\/p>\n

Identify target field<\/h2>\n

For this scenario, we\u2019ll use the \u00a0Online Shopper Intention\u00a0<\/strong>entity. This entity contains historical online shopper behavioral data from the past year. There are two fields which are particularly related to the issue we want to predict.<\/p>\n

ExitRates<\/strong>: Probability that a user would leave the current webpage.<\/p>\n

BounceRates<\/strong>: Probability that a user would navigate away from the current website after viewing only one page<\/p>\n

Let\u2019s use ExitRates<\/strong>,<\/strong> as users who viewed more pages before they exit are probably better candidates to convert to customers.<\/p>\n

Create prediction model<\/h2>\n

First, let\u2019s create a new prediction model from AI Builder section of Power Apps. More information about how to do this: Creating a prediction model<\/a>.<\/p>\n

\"\"<\/p>\n

We use my\u00a0Online Shopper Intention<\/strong>\u00a0entity and the\u00a0ExitRates<\/strong>\u00a0field. Note, numerical prediction is still in preview, so numerical fields are annotated with a \u2018Glimmer\u2019 in the Field drop down menu.<\/p>\n

\"\"<\/p>\n

Next, we\u2019ll exclude BounceRates<\/strong> from the training fields as it might have a high correlation with exit rates.<\/p>\n

\"\"<\/p>\n

Here, let\u2019s skip adding a filter as this data should be sufficient to train the model.<\/p>\n

Once all that is done, it is time to train the model. For this model, we have a linear performance score of 83. Linear performance scores measure closeness between predicted data and actual data. It can be between 0 \u2013 100%, with higher values indicating the predicted value is closer to the real value. \u00a0Generally, a higher score means the model should perform better. However, be wary of perfect scores, as this can indicate an overfit model<\/a>.<\/p>\n

\"\"<\/p>\n

Publish and use your model<\/h3>\n

As part of publishing, we actually create three outputs:<\/p>\n