Aggregates are custom customer attributes that are calculated from already existing data. They help you calculate aggregated information for every customer. An example of aggregate would be calculating a customer's lifetime value: In your report, you would use
Running aggregates are custom event attributes, which is the main difference to aggregates. They help you calculate aggregated information for any event. Running aggregates can be used in almost any analytical part of Bloomreach Engagement.
|minimum / maximum
|Returns the smallest / biggest value from the dataset.
|Sums values in the dataset. For example,
sum > purchase > total_price will return the total spend.
|Count the number of data points. For example,
count > purchase will return the number of purchases.
|Returns the average of all values of that specific event.
|first / last
|Returns the value of the first / last occurrence of that event.
|Returns either 1 or 0. For example, if a customer never purchased before, then
exists > purchase will return 0 and vice versa.
|Returns the most common value, such as the most viewed item.
|This counts distinct values in a data set, such as how many different categories a specific customer viewed.
|Lists all the unique values within an aggregate.
most commonaggregate type, in case there are two (or more) equal outcomes, Bloomreach Engagement picks the first one that occurred in the event history.
When using decimal numbers, make sure to denote them by
12.34) as otherwise,
avgor other mathematical aggregates will not work properly
Grasping aggregates and running aggregates and the difference between them might be a bit tricky at the beginning. That's why we created this short video that explains the topic for you. We do recommend watching it as you will be using (running) aggregates very often in Bloomreach Engagement.
It can be created in two ways:
- Go to
Data & Assets>
- Aggregates/running aggregates can also be created when selecting customer attributes/events for your analysis wherever it is possible. If you wish, you can also save them for later use (so next time it appears in the list) by clicking on the save button as shown below.
The process of creating an aggregate is simple:
a. Select the calculation operator (sum, first, last ...)
b. Select the event with which you want to calculate the aggregate. For example
Count > purchase calculates the number of purchases for all customers.
c. You can specify the metric further by adding an attribute of the event. For example
Sum > purchase > total_price will calculate the total revenue generated by each customer.
Running aggregates contain one additional setting, which is deciding whether to include or exclude the event itself from the calculation. This setting is relevant when the anchor event is identical to the event from which the running aggregate is calculated. See the 2 Use Cases about calculating revenue and conversion rate by country below for a better understanding.
You can skip any number of values for the
most common aggregates. For example, you can use the aggregate "first purchase timestamp > skip 1", which will give you the timestamp of the second purchase of that particular customer.
For distinct aggregate, it has 100% precision if there are less than 10k unique values. If there are more values, it has around 97% precision.
The Grouping feature organizes events based on specific attributes, allowing you to track specific event changes for each customer. It only takes the first or last value, for example, the most recent update. It's handy for industries like travel or hospitality when you need the most recent update on event attributes.
Keep in mind, though, that you can only group events using attributes in the Data Manager schema, ensuring alignment with available data fields.
Here, customer John has booked several flights. Each booking event has an ID and the final destination values.
Events sharing the same ID indicate they're part of the same trip but with John altering the final destination.
|Updated final destination
Grouping by attribute allows you to see the latest destination by grouping based on an attribute shared by the bookings. This case applies an event filter for ‘bookings’ and groups by ‘IDs’.
Then, instead of looking at all the events, the feature only looks at the last one and finds the newest information for each booking.
Here, customer John has purchased several times. Each purchase has its unique ID and a ‘purchase_status’ value. As shown below, John decided to return a purchase with the ID 789.
Grouping by attribute allows you to find out John’s real lifetime value by considering only purchases with the ‘purchase_status’ = ‘success’, automatically excluding returns.
The Grouping feature which is applied before the filter, ensures that only purchases that haven’t been returned are counted in.
- Create an Aggregate:
- Define aggregation, event type, and filters.
- Enable Grouping:
- Select an event attribute for grouping.
- Choose to aggregate values from either the First or Last event in each group.
- Save the Aggregate:
- Use this aggregate as a normal one.
Aggregates can be used for personalization through Jinja, as well as for analytics.
Aggregates can be used for filtering and segmenting customers or in expressions. They are usually used in campaign filters. Check out these examples to see what kind of insights are available through aggregates. Notice every analysis related to a customer.
How many purchases does a customer have in total?
How many times customer visited our page in the last 30 days?
What is a customer’s total lifetime value?
What is the average customer purchase when using loyalty points for purchase?
What was the referrer of a customer’s first visit to the webpage?
How many emails has the customer received from us in the last 14 days?
What was the last product the customer viewed?
If you don't track
country as an attribute of the
purchase event, then you need to retrieve it from the
session_start event using a running aggregate. For every purchase in the past Bloomreach Engagement will look a the
last session_start > country that preceded that purchase and will attribute that purchase to the country which is tracked there.
Note: Changing the
including/excluding operator does not make a difference here. Read the next use case for further explanation.
The metric (conversion rate) is, in this case, a formula that uses two types of events – session_start and purchase – meaning that the report cannot be broken down by an attribute of one, or the other. As mentioned above, the attribute ‘country’ is tracked for event session_start, but usually not for event purchase.
The running aggregate you will need for this report looks like this:
Here, it is crucial to switch from excluding to including. We essentially have two anchor events in this case –
purchase. The running aggregate will look for the country of the
last session_start both before every
purchase event and before every
session_start event, including that very event. In other words, the
including/excluding setting matters here because the running aggregate is calculated from one of the anchor events -
The report will look like this:
You may have noticed that, in this case, you have to insert the running aggregate into Rows, before inserting the Metric. Once you insert a formula that uses two types of events under Metrics, you won't be able to choose an event attribute (or running aggregate) under Rows anymore. So make sure to start with Rows in such cases.
- Travel Companies: Track new events for updates on trips, flights, or accommodations.
- Insurance Companies: Maintain a record of the latest state of contracts.
- Count Purchase Aggregation: Create an aggregate for each user to get the number of purchases. Without this feature, you would also count purchases that have resulted in a return (Success - Return). However, with 'Group by' you can exclude such purchases like this:
- Count Purchase
Status = Success
- Group by ID and aggregate only the most recent events (Last events). This ensures that purchases marked as "Success" are genuinely the latest ones, and any subsequent "Return" status events are excluded.
- Count Purchase
Updated about 1 month ago