Predictions

Predictions are codeless AI-powered tool built on the top of Synerise Analytics.

Key features:

  • Predictions built on data already stored inside the Synerise platform
  • No coding required
  • No need of manual feature selection or creation
  • Fully customizable from the user’s perspective
  • Predictions are saved as events which ready to use further on (for example in the Automation module, the Communication module, and so on)
  • Easy way to discover top contributing factors to the entire model but also to single predictions

How long does the system calculates a prediction?
It depends on the system overload, but to give you an example how long you may wait, a prediction for a 2 mln customer group takes about 2 hours.

Requirements


  • To make use of this feature contact the Synerise Support in order to switch it on for your business profile
  • Create an expression that will be used to define what kind of information you want to get from prediction

Procedure


To configure prediction, you have to perform the following steps:

  1. Go to Image presents the Prediction icon > New analysis.
  2. Define the type of the prediction
  3. Select customers for whom the prediction is to be performed
  4. Select what kind of information you want to get from prediction
  5. Select additional data for the prediction model
  6. Configure the final settings

Define the type of the prediction

Prediction types
Prediction types

You can select one of two prediction types:

  • Customer regression is suited for perfoming the analyses that return the numerical results. It’s best used in cases such as:
    • Predicting the amount of money spent by particular group of customers in the defined time range
    • Predicting the amount of items purchased in the defined time range
  • Custom classification is suited for performing the analyses that return the true/false (or 1/0) values. It’s best used when you want to get to know:
    • Will a customer belong to a particular group of customers?
    • Will a customer leave in the next 30 days?

Select customers


Selecting an audience for prediction
Selecting a group of customers to make prediction

Select the audience for which you want to prepare a prediction.

  1. In the Audience section, click Define.
    • To select an existing group of customers, select the Segment tab (default) and select the groups. If you select more than one, the dependency between them is described by OR, which means that to receive an email, a customer can be only in one of the selected segments.
    • To define a new group of customers, select the New audience tab.
    1. In the New segment field, enter the name of the group of customers.
    2. Follow the procedure described here.
  2. Confirm by clicking Apply.

Select area of prediction


Selecting an expression to define the scope of prediction
Selecting an expression to define the scope of prediction

Select the expression based on which the prediction will be made. For example, if you select an expression that predicts Email OR in last 30 days, you will get prediction for OR for the next 30 days.

  1. From the dropdown list, select the expression you previously prepared.
  2. Confirm by clicking Apply.

Select model inputs


Selection of features to support predicting model
Selection of features to support predicting model

Select the features that the system will use to support the prediction process. A feature is a variable or an event that can be used by AI engine to make a prediction. The list already contains predefined features.

  1. Click Add feature.
  2. From the dropdown list, select one of two options:
    • Manually - You can pick features on your own.
    1. On the list, select the checkboxes next to the features you want to include.
    2. Confirm your selection by clicking Add.
    • Automatically - All features are added to the list (recommended).
  3. After making a selection, click Apply.

Feature analysis

Selection of features to support predicting model
Analysis preview in the form of a line chart

The unit of the feature depends on the type of the feature. For the features that are events, the unit is an occurrence of an event.

Assuming that a feature is an event, then:

  • Count - The number of the event occurrence
  • Min - The minimum number of the event occurrences
  • Max - The maximum number of the event occurrences
  • Missing - The number of customers for whom the feature is not counted
  • Mean - The mean occurrence of the event

Settings


Selection of features to support predicting model
Analysis preview in the form of a line chart

In this section, define the frequency of recalculating the prediction and settings of the event that is generated for a customer for whom the prediction was made.

  1. In the Model configuration section, select the number of days after which the prediction is recalculated.

  2. In the Time shift field, enter the number of days for which you want to make a prediction (calculated from the current date).

  3. Use the slider to define the number of features displayed in the parameters of the event generated for a customer for whom the prediction was made.

  4. In the Name field, enter the value of the scoreName parameter of the event generated (snr.prediction.score) when a prediction is made.

    WARNING:

    You can use the following characters:

    - a-z - only lower case
    - 0-9
    - special characters: `.` and `_`
    

    generic scoring event
    The preview of the prediction generation event
  5. Click Apply.

  6. To save:

    • as a draft, click Save.
    • and calculate, click Save & Calculate.
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