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.
The predictive analysis functions are only available for bpm’online cloud users.
Currently, bpm’online can predict the following values:
1.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.
2.Numeric field value prediction model (link to article). This model enables bpm’online to give you an estimate in numeric fields. For example, predicting the budget of a lead based on the type of customer need and the customer’s company size, country and industry.
3.Predictive scoring model. Configure this prediction model to have bpm’online automatically rate the quality of your records. 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 (link to article) 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. To avoid this, we recommend setting up models in a way that would simultaneously process a small number of records (e.g., only run models for new records or when a record is modified).