Creating lookalikes predictions

In this article, you will get know how to set up your first Lookalikes prediction. Lookalikes let you compare two segments of customers and find those who display similar qualities of performance. This can be used, for example, to target a marketing campaign at customers alike to those who reacted well to a previous iteration of that campaign.

Prerequisites


  • Enable the Lookalikes predictions. It usually takes several hours to initialize the Predictions module and perform necessary calculations.
  • You should know how to create segmentations or have a segment of customers ready for use as the source.

Configure a prediction

Select the model type

  1. Go to Image presents the Prediction icon > New prediction.
  2. In the Select prediction type window that appears, click Lookalikes.
  3. Click Apply.

Select source and target

The Audience section of the prediction configuration include the definitions of two segments.

  • The source segment contains the “model” customers. This can be, for example, a segment of customers with the best reaction to a past marketing campaign.
  • The target segment contains customers who you want to compare with the source segment and find the ones who are similar to the source group.
  1. In the Audience section, click Define.
  2. In the Source segmentation sub-section, click Choose segmentation.
  3. Perform one of the following actions:
    1. Select an existing segmentation (you can use the search field).
    2. Click Create new and create a new segmentation.
      Instructions for creating segmentations are available here.
      The segmentation will also be available for later use in the list of segmentation.
  4. In the Source segmentation sub-section, choose or create a segmentation in the same way as the target.
  5. Click Apply.

Additional configuration

By default, the calculation is performed once and the results are presented on a 5-point scale.

  1. If you want to leave the settings as default, the predictions is ready to calculate. Continue to saving the prediction.
  2. If you want to change the settings, in the Settings section, click Change.
  3. To schedule a recurring calculation:
    1. Select the Set up recurring prediction calculation checkbox.
    2. In the input field, enter the number of days between calculations.
  4. To change the scale from 5-point to 2-point, select the 2-point scale radio button.
  5. Click Apply.

Save the prediction

To save the prediction:

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

Results

The calculation takes approximately 1 hour per 1 million customers in the source segmentation. When it ends, the results are saved as events in the profiles of all customers of the target segmentation. The score_label parameter of the event shows the scale of similarity on a 2- or 5-point scale.

You can use these events, for example, to build a new segmentation for use as the target of a marketing campaign.

Understanding Lookalikes prediction events

The outcomes of all custom predictions are saved as snr.lookalike.score events in the Synerise platform. Thanks to that, the predictions events are available in the Analytics, Communication or Automation modules. The schema of a prediction event is standardized. The attributes are explained in the table below the figure.

snr.lookalike.score event
snr.lookalike.score event
Attribute name Description
action An event name for the prediction - snr.prediction.score
added by An entity that generated this event. In the case of prediction events, it is always Synerise.
modelId A unique identifier of the prediction
score_label Prediction output: two- or five-point scale label. You can select the scale in the settings of the prediction.
score The result of the prediction (based on the expression which was selected as a target while preparing a prediction).
percentile Prediction percentile
clientId Customer’s identifier
time Time when a prediction was generated, as a Unix timestamp
configurationVersion Allows to verify whether two consecutive calculations ran with the settings (only for debug)
modelVersion Model version allows to verify whether two consecutive calculations ran on the same model parameters (only for debug)
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