The new Face/Off chart is many things:
A way to compare how segments perform over time, like "average daily usage per subscription tier" or "time-to-value between industries"
Like having a BI tool inside your customer platform
Like staging a Face-off between segments, without the "missing faces" but including the rivalry 🤺
In essence, it's a really powerful way to compare performance over time at a granular level. Some use cases:
"Average number of tickets per industry" in a Support team review meeting
"Product usage by ARR segments" to a Management Report
"Asset performance" in a Custom Company Profile, which now allows you to compare multiple Assets within the same company
"Compare the Top 5 customers within our SaaS segment with the rest of the SaaS segment, in terms of product usage" (going neatly hand-in-hand with our Top/Bottom list filters!)
Let's take a look at how it works! Depending on how you prefer to consume, either read on step-by-step here or watch the 4min video by our Product Manager Daniel below. 🏎
Face/Off and Top/Bottom lists: understanding your portfolio one level deeper
A basic example: do the largest or smallest customers produce most tickets?
The Face/Off chart allows you to pick a metric (Company, End-user, Asset, or Project) and break it down by specific records (companies, end-users, assets, projects) or your own custom filters (like "Top 20% Companies by ARR" or "SaaS businesses"). This analysis will allow you to better understand your portfolio performance, and where to invest more of your time and resources.
A basic Face/Off chart has a couple of parameters that you can follow step-by-step
Select the "Face/Off" chart under "Trend charts" in a Page or a Custom Company Tab (note: in the video it says "Metric Comparison" but the name is Face/Off)
Select the metric you want to compare segments/records on:
First, select the "Model" (e.g., Company)
Second, select the relevant model-level "Metric" (e.g., Number of Tickets)
Start building the "lines" (called Series) that you want to compare - you can add as many as you like based on varying parameters:
"Build by": this defines what kind of line you want to build, either looking at "All" available records (e.g., All Companies), a "Filter" (using the filters from the data module), or a specific record (e.g., Apple, or Anna Doe)
"Operator": this sets the calculation logic for the line - do you want to look at the sum, the average (all) or the average (values)
In the video below, we use Face/Off chart to understand if our biggest or smallest customers produce the most tickets. This is also a great combinatory use case of Face/Off with the Top/Bottom list filters!
An advanced example: these top 20%, how do CSMs feel about them?
After seeing that the Top 20% largest customers produce the most tickets, you might want to check out how usage and CSM score correlates in this top 20% segment. This utilises some deeper parameters in the Face/Off chart which you can access by pressing the "Show more" arrow under any series.
"Split by": this is the property on which you want to split your group of companies
It could be any type (and on End-user/Asset/Project analysis you can even split by Company properties), but categorical fields (like Tier) work well
If you select a "Split by" which produces a loooot of lines (e.g., "Name"), then you can use the "order by" and "limit" features next to it
"Order by" / "Limit": these two parameters go together, and become relevant if you want to reduce the number of lines in your chart and only look at a subset
"Limit" defines how many "lines" you want in the graph
"Order by" defines which lines you want to look at by ordering the records that go into the chart, which often is done through a simple alphabetical ascending/descending.
👑 Pro tip: if you choose to split by ID (e.g., CompanyID), then you can also "Order by" any relevant property, e.g., "Show me the top 15 companies by ARR" or "the top 10 companies by Usage"
📌 Note: as always with metrics, this shows the split based on current data. For example, if you want to see "Activity by Phase" then you will see the activity of the companies, over time, that are currently in each phase. Company A might have been in Onboarding from Jan-Mar and then went to Adoption - that means Company A's data will fall under the "Adoption" line from Jan-today.
Thus be careful using this with parameters that you expect to change over time, like Phase. If this is your use case, then reach out to a CSM to review how we can solve it via another route.