Adobe has long provided calculated metric templates in the metric builder, denoted by the folder + magnifying glass icon in the left rail. These templates are also now available directly in the left rail of Analysis Workspace. They include some common calculated metric definitions that users create, and can be used as is or as a starting point for a more advanced metrics.
Calculated metric template example
The easiest way to add new calculated metrics to a freeform table in Analysis Workspace is to highlight 1 or 2 metrics, right-click and select "Create metric from selection".
Metrics within metrics - You can use calculated metrics to create new metrics. For example, the denominator below is a calculated metric itself.
Average videos consumed by a Video UV
Applying segments to metrics - you can apply any segment to your collected events (or OOTB metrics) to create completely new custom metrics.
Orders for a specific product SKU
Can't find a segment you want to build a metric from? The easiest way to create a new segment is to drag in a dimension directly to the metric builder. A hit segment will be created for you by default, and can be modified as needed. The benefit of creating a segment in this way is that 1) you don't have to leave the metric builder, and 2) the created segment will be local to the calculated metric, and won't clutter up your left rail.
There are 10 different attribution models that can be applied in Calculated Metrics, if fully provisioned for Attribution IQ (if not, 4 models are available). This allows you to compare attribution models, create hybrid models, or apply attribution to your important conversion metrics such as revenue or orders per visit.
Participation is helpful for attributing content on your site or app to downstream success. The participation model gives 100% credit to each touchpoint leading up to a conversion. Page is a common dimension to do a participation analysis against. Dividing by visits removes traffic variability so that every page can be assessed fairly. When sorting descending, lower visited pages that drive conversion will bubble to the top of your analysis.
Pages with higher participation rates mean they are more likely to drive downstream conversion
Engagement scoring is the process of assigning numerical “scores” to key digital activities. Scoring helps you understand how varying engagement levels affect conversion and customer loyalty. Scores can be setup via calculated metrics (non-permanent approach) and used throughout your analysis as a secondary success measure, and indicator of valuable revenue opportunities.
Note: Engagement scoring can be taken even further with processing rules (permanent approach) that populate a counter eVar that aggregates a score for each visitor. The score eVar can then be classified into high/medium/low engagement groups. Additionally, there are several methods for intelligently determining the score weights, such as through propensity scoring in Data Workbench.
Intelligent Alerts can be setup for calculated metrics to tell you when a metric is behaving unexpectedly compared to historical trends. Alerts can be set to trigger on above expected, below expected, % change by, or anomaly detection (intelligence) thresholds.
Calculated metrics can be used in alerts
Approximate Count Distinct returns the count of dimension items for a selected dimension. You can then use the metric in any report to understand the count of one dimension against values of other. For example, the count of unique customers or unique products by Marketing Channel.
Deriving Rolling averages using Mean - Rolling averages help to benchmark performance against recent trends. You can use the Mean function, coupled with rolling date range segments, to create rolling averages that will function irrespective to the panel date range selected.
Rolling 7 Day Average metric
Various rolling averages highlighted in a single table
Benchmarking using Cumulative Average - Cumulative will sum data across rows in a table. Cumulative average will average data across rows in a table. Both fluctuate as you move down the table, whereas "rolling average" is fixed to last X days. Dividing a metric against the cumulative average or rolling average gives you a daily performance benchmark.
Cumulative Average metric
Cumulative Avg, with a daily benchmark, compared to Cumulative & Rolling Avg calculations
Calculating top metric-drivers using If, Quartile and Greater Than - Quartile is an alternative to Percentile and can be used to return the minimum value, 25th percentile, 50th percentile, 75th percentile, or maximum value. For example, you can derive a metric that focuses on just the top 75th percentile of revenue or application completes.
Metric that focuses on the 75th percentile of revenue (quartile = 3)
3rd quartile (i.e. 75th percentile revenue), highlights the top revenue-driving items at any breakdown level
Improved sorting using If, Percentile and Greater Than - 'If' statements allow you to set a floor and/or ceiling for any given metric. This is helpful for reports where you want to sort on metrics such as bounce rate & conversion rate, but don't want to see low volume dimensions. Percentile in this function ensures the floor is relative to actual behavior, rather than just an arbitrary threshold that you've set.
Bounce rate metric that is only > 0 for the top 70th percentile of pages
Improved Bounce Rate metric
Calculating standard deviation using Z-Score - Z-score is the number of standard deviations an observation is from the mean and can be + or -, indicating whether it is above or below the mean and by how many standard deviations. This can be helpful for highlighting dimension items that are above the mean, such as marketing channels that drive above average revenue.
Z-score requires 1 metric input
Z-score used to highlight top revenue-driving marketing channels
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