Tables - Essential Use Cases

Tables present and compare multiple data points concisely, making them ideal for a range of key analyses. Certain use cases that previously would have required multiple complex dashboards can now be simplified with tables.

Here are a few examples of what tables can do:

• Feature engagement
• Site monitoring
• A/B testing
• Campaign performance

Feature engagement

User Persona: Product, User Experience (UX)

Analyzing user behavior within critical flows, such as a login funnel, is essential for identifying friction points and improving feature engagement. Previously, analyzing a complex flow like the login funnel required building a dashboard with individual metrics for each step. Segments and dimensions like device had to be applied or analyzed separately, making direct comparison across these variables cumbersome and time-consuming.

Setting up your table for feature engagement

A single table for feature engagement allows you to compare multiple user segments across a variety of metrics and dimensions. To achieve a consolidated view for analyzing feature engagement:

• Add Columns: Create metrics or funnel metrics that correspond to the features you want to measure and add them to the table. Consider use the metrics division operator to see what percentage of users interacted with a feature relative to a control.
• Compare Users: If you're comparing different types of users, select user segments you wish to compare.
• Group By: Break the data down further by device type or any other custom property.

Site monitoring

User Persona: Product, Operations (Ops), Engineering

Proactive site monitoring is crucial for identifying and addressing issues that impact user experience and business critical flows, such as checkout. Previously, monitoring site performance involved analyzing multiple segments and metrics in separate views, manually segmenting by time- and device-based dimensions. 

Setting up your table for site monitoring

A table set up for site monitoring will usually be grouped by time intervals that draw your attention to anomalous spikes that can arise. To set up your table for site monitoring:

• Date: Set to Past 24 hours (or a more narrow window) if you're planning to do watch your data live.
• Group By: Choose Hour as your first dimension for a time-based approach to monitoring. 
• Add Columns: Add columns that are indicative of site performance, which include things like checkout successful rate, error counts, and percentile-based performance metrics.
• Rows: On the Hour row, change the number of Rows to a higher value to avoid pagination.

A/B testing

User Persona: Product, Marketing, User Experience (UX)

Evaluating the performance of A/B tests is essential for understanding the impact of changes and making data-driven decisions. Before tables, comparing the performance of different experiment variants required creating and analyzing separate segments or dashboards for each variant, making it cumbersome to get a consolidated view of how different groups interacted with subsequent site elements or campaign goals. This fragmented approach hindered quick, comparative analysis of overall campaign success.

Setting up your table for A/B testing

To effectively compare the performance of different experiment variants, a table can provide a clear, side-by-side view of key metrics. Here's how to do it:

• Compare Users: Choose the segments that represent your control and test, respectively.
• Add Columns: Choose various metrics that are related to the success of the experiment.
• Group By: Add built-in and custom properties to add color to your analysis.

Campaign performance

User Persona: Product, Marketing

Understanding campaign performance is vital for optimizing marketing spend and user acquisition strategies. Before tables, analyzing campaign effectiveness, whether comparing segments within a single campaign or across multiple campaigns, typically involved creating numerous individual reports or complex dashboards. This made it difficult to get a unified, comparative view of key metrics like conversion rates or revenue per user across various UTM parameters.

Intra-campaign performance: measuring within a single campaign

In this example, we look at the performance of a single campaign grouped by users of different behavioral cohorts based on the number of touches (measured by number of sessions). We can quickly see in the data that the conversion rate (CVR) is much higher for users with two touches than for those with only one touch.

Setting up your table for intra-campaign performance

To analyze the performance of different behavioral segments within the same campaign, set your table up like so:

• Segments: Start by creating segments that represent your different behavioral cohorts. If setting up segments by number of touches, use Total sessions within User Filters.
• Compare Users: Select the segments you created that represent your behavioral cohorts.
• Group By: Choose dimensions relevant to your campaign's internal segmentation, such as utm_medium, utm_source, and utm_campaign. This allows you to drill down into specific channels and origins.
• Add Columns: Include core metrics that correlate to activity, conversion, and revenue to gauge engagement and value.

Inter-campaign performance: comparing multiple campaigns

In this example, we dive deep into the performance of multiple campaigns grouped first by utm_source and utm_medium. This gives us a way to quickly compare different campaigns within the same source/medium grouping.

Setting up your table for inter-campaign performance

To compare the performance of different campaigns against each other, here's how to do it:

• Group By: Utilize utm_source, utm_medium, and utm_campaign as your primary grouping dimensions to differentiate between various campaign sources and types. Experiment with changing the order of your grouping dimensions.
• Add Columns: Add metrics that help you measure activity, conversion, and revenue.
• Rows: Manually expand your row groupings to see more data at once.