This article covers the principles of how you can use bpm’online to predict values in lookup, detail and section records.
In bpm’online, predictive analysis enables the prediction of target events based on large volumes of historic data and current facts. It is used for increasing the speed and accuracy of business decisions, relieving the users from performing routine operations and improving the overall efficiency and performance.
Predictive analysis in bpm’online is implemented via a set of algorithms – machine learning models. In the [ML models] section, you can create, train and use your custom machine learning models to predict values for virtually any object in bpm’online.
Starting from version 7.14.0, all out-of-the-box machine learning models (such as lead scoring in bpm’online sales and marketing and predictive case routing in bpm’online service) have been updated to enable their configuration in the no-code UI. You can identify the new models in the list of [ML models] section by version number “(7.14.0)” in their names. For users who initially deploy version 7.14.0 and up, these models are enabled by default. Users who upgrade to version 7.14.0 will still be using original out-of-the-box models. To enable the updated machine learning models, go to the [ML models] section and select the [Prediction enabled] checkbox for the needed model.
To use the functionality of predictive data analysis in bpm’online on-site, perform the corresponding preliminary setup. See thearticle for more details.
Currently, bpm’online can predict the following values:
Lookup value prediction – configuring this prediction model will enable bpm’online to predict lookup field values based on existing data. For example, you can create a model that will predict the most likely category of an account.
Numeric value prediction – enables calculating an estimate of a numeric field. For example, predicting the budget of a lead based on the type of customer need and the customer’s company size, country and industry.
Predictive scoring – determines scores for bpm’online records based on historical and current data. For example, you can create a model that will rate the quality of your leads based on their budget and successful hand-off to sales.
Bpm’online gives you complete control as to what records are predicted and when. Once the prediction model is created, use the [Predict data] process element to add machine learning to your new or existing business processes ().
Bpm’online devotes a lot of resources to predict field values, especially when the process involves a substantial number of record values. We do not recommend running predictive scoring for multiple records simultaneously, since this may influence bpm’online performance. The best solution is running the operation for each separate record (e.g., when adding or modifying the record).