Create a decision dataset in Analytics

  • Updated

Decision events (or impressions) are special events triggered when Optimizely Experimentation "decides" that a visitor is bucketed into a certain experiment and variation pair. See Data specification for information.

Create a decision dataset to integrate your Experimentation data. This helps you centralize and analyze your experiment results along with additional information in your data warehouse. 

  1. Go to Data > Datasets > + New Dataset > Source Dataset.

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  2. Select your data from the connection you previously configured in the Create a connection section on the Pick a Source page and click Confirm. Use the search box to find your tables in the warehouse. 

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  3. Give your dataset a name and click Save.save-dataset.png

  1. Select Decision Stream from the drop-down list in the Semantics tab.

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  2. Complete the Decision configuration by configuring the following fields:
    • Actor dataset – The actor corresponding to the identifier used for experiment variation decisions, which is typically a user. The decision dataset and the event dataset must have a many-to-one relationship with this actor dataset.
    • Experiment ID – The experiment ID used by Optimizely. ID is preferred, but if it is not available, leave it blank and populate the experiment name or the rule key. 
    • Rule key – The rule key assigned by Optimizely to identify experiments. This is required only if the Experiment ID is not available.
    • Experiment name – The experiment name used by Optimizely. This is required only if the Experiment ID and the rule key are not available.
    • Variation – The variation ID used by Optimizely. The variation ID is preferred, but if it is unavailable, you can select the variation name or variation key as an alternative.
    • Timestamp – The time at which the decision was made.
    • Is holdback (optional) – The boolean column that indicates decisions that should be excluded from the experiment.
    • Custom partition time column (optional) – The option you use when the decision table is partitioned by a timestamp or date column other than the decision timestamp specified. When specified, each query on the decision dataset includes a filter on the custom partition time column in addition to the decision timestamp column. The before and after skew settings let the filter on the custom partition time column to be wider than the time range selected by the user. For example, with the default settings of 1 day before and after, a user-specified time range of November 10 to 15 would be expanded to November 9 to 16. opti-decisiondataset-4.png
If you use external decision datasets to analyze non-Optimizely experiments, the experiment ID, variation ID, user ID, and timestamp are mandatory.

See Create new datasets for information.