You can export any saved audience if you want to use the data for further analysis or actions outside of the BIMobject platform.
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To export your saved target audience simply click Export on a saved audience group. This will export your saved audience into a .xlsx or .csv file.

If you chose to download your exported audience as a .xlsx file, it's already 100% compatible with Excel. To properly open a .csv file in Excel and avoid conversion losses, read this article on Microsoft's support page.

While the default target audience export may cover most of your reporting and investigative needs, you can work even faster, smarter, and smoother with the new advanced data export. With the additions in this feature, you can enrich your CRM system, improve your BI reporting quality and speed up your leads qualification process. Set rules, sit back, and gain a holistic understanding of how your BIMobject audience engages with your products.

Set schedules and receive exported data weekly or monthly directly to your email inbox.

With the advanced export feature enabled, your audience exports will contain all of the information included in the default export, but you will also receive a breakdown of product download and email campaigns performance for the exported audience group. With this data, you can easily understand performance both from a regional and professional perspective - allowing you to analyze exactly what products and files the users have downloaded from your product catalog. You will also be able to see what users have opened your email campaigns and clicked through to your designated call-to-action, enabling you to follow your most dedicated users and ensure you're spending time cultivating the right users across your audience.

The advanced export feature also gives you the possibility to export data by over a custom date range, back as far as the earliest data available for your audience on the platform. The file will be sent to your email inbox once it is ready to be downloaded.

Select custom dates in your export and the file will be sent to your email inbox

The basic export gives you one file. The advanced export gives you two — an audience file and a campaigns file, which share a UserID column so you can join them. Every file arrives as a spreadsheet, or as CSV when the data runs past what a single worksheet can hold.

One row per person in the audience group, most recent activity first. There is no product-level detail and no campaign data here — that is what the advanced export adds.

You can add up Downloads in this file. Each person appears once, so the column sums cleanly. The advanced audience file works differently — see the warning under it.

Column names differ slightly between the two exports. This file writes UserId and ZipCode; the advanced files write UserID and ZIPCode. A template or import mapping built against one will not match the other.

One row per person, per product, per day they downloaded it. Someone who took three products on the same day gets three rows. Someone who took the same product on four different days gets four. People who follow your brand but have never downloaded appear once, with the download columns left empty.

Don't add up the TotalDownloads column. It repeats a person's whole period total on each of their rows, so summing it multiplies their activity by however many rows they have. Remove duplicate UserIDs first, then add up.

TotalDownloads counts files, not products. A product taken as both RFA and IFC counts twice here. Your Insights dashboard counts products, so its figure is lower.

One row per person, per campaign they received. It covers every email campaign your brand sent during the export period, narrowed to the people in this audience.

Reading the engagement columns. Delivered, Opened and Clicked stack rather than sitting side by side. Someone who clicked shows TRUE in all three. To find people who opened but didn't click, filter for Opened = TRUE and Clicked = FALSE.

Both files carry UserID, so you can join on it to put campaign engagement next to download behaviour for the same person. Rows with a blank UserID are people who've opted out. They're counted in your totals, but you can't identify or join them.

In this section we will take you through an example of how you can use the advanced audience export feature to create a dashboard in Microsoft Power BI which can serve as a single point of collaboration for your commercial organization.

Note: all organizations differ from one another, and this example will not be applicable for everyone - it is merely meant as a demonstration of the potential this tool offers.

If your organization uses the Google suite, this example will still be relevant for you, but the process of importing data into Looker Studio and constructing relationships will naturally look different from what is demonstrated here.

The first step in any visualization endeavor is to get your data ready. In this case that will mean identifying one or more saved audience groups that you want to export. Just follow the instructions earlier in this article to download a custom date range export.

When you've downloaded your exported data, it's actually ready to visualize as it is. However, to simplify some operations in Power BI we'll make one quick adjustment: replacing TRUE/FALSE values with 1/0. The main benefit of this is that it helps with visualization formatting, as the binary data type allows for easier sums. If you like your data categorical, feel free to skip this one.

To perform this transformation, simply select any column that contains TRUE/FALSE values and hit CTRL+F to bring up the "Find and Replace" dialog.

In the dialog, input to Find any entry that says "TRUE" and Replace it with 1. Then repeat to Find any entry that says "FALSE" and Replace it with 0. Repeat until you've transformed all applicable columns in your file.

There are plenty of other transformations you could make to clean up your data, such as consolidating names, first names and company names to only lower characters (see LOWER function). However, for the purposes of this tutorial you are now ready to go to step 3 and import your data into Power BI. Note: If you're particularly interested in file type distribution across your downloads, please consult the optional transformation later in this article for help.

In this video we'll show you how to properly import your data file into Power BI and get everything ready to start visualizing.

In this video we'll go through the basics of visualization in Power BI, and create some simple yet interesting charts.

In this final video we'll take a look at the finalized dashboard we started making in Step 2, and also go through a slightly more advanced example of creating a collaborative funnel to understand when and how to engage with your BIMobject audience.

Main benefit: The export file currently contains a field called FileTypes, which indicates all file types that were downloaded in a single download record. For visualization purposes this can become messy, to we'll break out each download with multiple file types into separate records for each file type.

In the Product downloads tab of your export file, select the FileTypes column and go to Data > Text to Columns

In the wizard that opens, select Delimited in the first step, choose Comma as your delimiter and finally click Finish.

After these steps, you should end up with something like this, depending on the number of different file types downloaded.

As you can see, the single FileTypes column has been split into several columns, with a single file type value in each. Make sure to trim any excess spaces in the resulting columns using the TRIM formula before moving on to the next step. For the final part of this transformation, we need to unpivot these columns.

Go to Data > From Table/Range. This will open the Power Query Editor.

In this view, scroll all the way to the right and select all columns that resulted from the split, including the FileTypes column

Then go to Transform > Unpivot Columns > Home > Close & Load. This will create one "Attribute" column and one "Value" column. You can go ahead and delete the "Attribute" column and rename the "Value" column into FileType again for consistency.

You should now have a sheet where the same download record exists on multiple rows, with different file types: