Step 1​: Choose Pipeline Type

Select ‘Create New Pipeline’ from within your Cortex account. Make sure that the “Batch | Real-Time” toggle is set to “Batch”, and choose the Future Events pipeline type.

Step 2: Define Events

Future Events pipelines are used to predict the probability that some event happens in the future for each of your data points. Defining that event means providing four pieces of information to Cortex: (A) happens (B) or more times where (C) within (D) days.

• (A) Event Type: The type of event that your pipeline should predict. These event types are based on the data being ingested into Cortex.
• (B) Frequency: The minimum number of times the event must occur in order to count as a conversion. The default value is “1”, i.e. you are only predicting that a user will take this action once in the specified time range. However, if you are looking to predict if users will do an event multiple times, e.g. Predict how likely each user is to make 5 purchases, then you can increase the event frequency.
• (C) Event Conditions: Conditions (if any) under which the event must occur in order to count as a conversion. These event conditions are based on the data being ingested into Cortex.
• (D) Timeframe: The future window of time over which the event(s) should be predicted. For a batch Future Events pipeline, this window is specified as a fixed number of days. Common values here are 7 and 28 days if you are looking to predict behavior over the next week or month.

You can build a Future Events pipeline to predict any event that you can describe in this way using your data. Add any number of conditions to an event, or chain any number of distinct events together using AND/OR/IF operators. No matter how complex, your events will always read like an English sentence.

Below are a few examples based on e-commerce use cases:

• “Purchase happens 1 or more times OR Rent happens 1 or more times within 7 days”. This pipeline predicts the probability that each user will either purchase or rent within the next week. The OR clause may be useful if you plan to use the predictions to power a marketing newsletter which promotes movies available for both purchase and rental.
• “Purchase happens 1 or more times where Category is equal to Shoes AND Purchase happens 1 or more times where Category is equal to Pants within 14 days”. This pipeline predicts the probability that each user will purchase both shoes and pants within the next two weeks. The AND clause may be useful if you plan to use the predictions to power a marketing newsletter which promotes a both shoes and pants.
• “Purchase happens 1 or more times IF Login were to also happen 1 or more times within 7 days”. This pipeline predicts the probability that each user will purchase within the next week, assuming that the user first logs on and is active within that week. The IF clause may be useful if you plan to use the predictions to dynamically vary each user’s onsite experience. You don’t want the prediction about a user’s likelihood of purchasing to reflect their likelihood of being active in the first place, since the user is already active by the time you’re deciding which experience to show. Read this post for more info about these types of conditional goals.

Step 3: Define Groups

Sometimes you are looking to make a prediction for every user in your system, while other times you want a prediction only for a certain group of users. By default, Future Events pipelines are set up to make a prediction for All Users. If you are looking to predict for All Users simply click Next to proceed to the next step.

If you are looking to limit the prediction group (for example, exclude current subscribers when predicting who is likely to subscribe), you can either include a specific group or exclude a specific group. Like events in step 2, the user attribute values available come directly from the data being ingested by Cortex.

Note that having an Include or Exclude group will also limit the model training for only those groups of users, ensuring that the behavior of users outside of this group doesn’t negatively affect the model.

Step 4: Specify Settings

Specify settings such as your pipeline’s name, schedule, tags, and more.

Every time your pipeline runs, Cortex goes through the end-to-end process of generating fresh predictions from the latest data that’s been ingested. If you’d like to power automation based on predictions that are always up-to-date, make sure your pipeline is set to run repeatedly. If you’re just testing things out or building a pipeline for one-time use, your pipeline should only run once.

Step 5: Review

The final step is to review your pipeline and ensure all settings look accurate! If anything needs updated, simply go ‘Back’ in the workflow and update any step.

Once you hit “Start”, your pipeline will automatically begin training. Depending on how much data is flowing through your account, it could take up to a few hours for your pipeline to finish running.

Step 1​: Choose Pipeline Type

Select ‘Create New Pipeline’ from within your Cortex account. Make sure that the “Batch | Real-Time” toggle is set to “Real-Time”, and choose the Future Events pipeline type.

Step 2: Define Events

Future Events pipelines are used to predict the probability that some event happens in the future for each of your data points. Defining that event means providing four pieces of information to Cortex: (A) happens (B) or more times where (C) within (D) days.

• (A) Event Type: The type of event that Cortex should predict. These event types are based on the data being ingested into Cortex.
• (B) Frequency: The minimum number of times the event must occur in order to count as a conversion. The default value is “1”, i.e. you are only predicting that a user will take this action once in the specified time range. However, if you are looking to predict if users will do an event multiple times, e.g. Predict how likely each user is to make 5 purchases, then you can increase the event frequency.
• (C) Event Conditions: Conditions (if any) under which the event must occur in order to count as a conversion. These event conditions are based on the data being ingested into Cortex.
• (D) Timeframe: The future window of time over which the event(s) should be predicted. For a real-time Future Events pipeline, this window is triggered separately for each user by an action or intervention. Specify both the length of your window (in seconds, minutes, hours, or days), and the event which identifies your intervention.

You can build a Future Events pipeline to predict any event that you can describe in this way using your data. Add any number of conditions to an event, or chain any number of distinct events together using AND/OR/IF operators. No matter how complex, your events will always read like an English sentence.

Step 3: Specify Settings

Specify settings such as your pipeline’s name and tags. Unlike with a batch pipeline, you don’t need to specify a schedule, since your pipeline’s predictions will be made in real-time and on-demand.

Step 4: Review

The final step is to review your pipeline and ensure all settings look accurate! If anything needs updated, simply go ‘Back’ in the workflow and update any step.

Note that because a Real-Time pipeline must gather additional data from users’ current browsing session, it won’t begin training right away. Instead, it will enter a “Data Collection Period” until enough data has been gathered to initiate training. Click here to learn more about this period, and how to begin sending real-time data to your pipeline.