A trend in Bloomreach Engagement is an analytical tool that enables you to visualize how often a particular event occurred over time by plotting the event on a timeline.

You can watch this video to get an overview of the basic features of trends. However, if you prefer reading, everything will be covered in this guide too.

How can trends empower you in understanding your data

The main benefit of using Trends is their simplicity of use. You only need to specify the events and time period. Then, the tool will display a chart that helps you visualize the time progression of the events. The usual use cases are depicting how page traffic, website visits, or the number of purchases develop over time. However, sometimes you can also use them for more advanced analyses, such as visualizing how the number of purchases and the number of purchased items change together over time, as can be seen in the picture in the following section.

How to Create a Trend Analysis

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1. Creating a New Trend Analysis.

a. Navigate to Analyses -> Trends.

b. Click on the + sign next to the Trends item in the drop-down menu.

c. You will now see the edit window with a new, empty Trend analysis.

2. Adding Events

a. Click on the big + sign in the `Event selector` box.

b. Choose the event that you want to make a chart from.

c. Press Preview to generate and view the chart.

3. Editing Events

a. Hover your mouse pointer above an event in the Event selector box.
A menu with pen and cross icons will appear.

b. Click on the pen icon to select a different event.

c. Click on the cross icon to delete the event.

Other Settings

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To learn more about Date, Customer, and Event filters, please read the Filtering data article.

Event/Customer Toggle

On the right side above the 'Event selector' box, you'll find a toggle that allows you to switch the chart's y-axis between the number of events on a given day or the number of unique customers.

To display event attributes in time, please use Reports.

Cloning Trends

You can clone Trends into other projects and accounts. Read more about it in Cloneable data mapping.


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