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Data Visualization and Filtering

  • By Optimizely Education
  • Published: Jul 22, 2025
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Outline

Welcome

In this module, you’ll learn how to visualize your data in Optimizely Analytics and apply filters to tailor your analysis. This lesson covers the different visualization types available, how to customize visualizations and filters, and how to apply parameters for deeper insights.

After completing this module, you should be able to:

  • Define what is Visualization

  • Identify all chart types used in Optimizely Analytics

  • Understand the use cases of Filtering

  • Apply Aggregation functions and Segmentation for analysis

  • Define and apply Parameters based on different metrics

What is Visualization?

The visualization module in Optimizely Analytics is a core component that allows you to run, view, and customize data and charts. It's designed to help you interact with your analytical data and present it effectively. This module is consistent across all exploration templates and some elements are included in the dashboard.

Analytics offers a variety of chart types to help you visualize your data. Each of the chart types are listed down below:

  1. Single Value - This chart type displays a single value used for identifying the highest values and analyzing aggregated metrics.
  2. Table - This presents a single metric but within a table format.
  3. Line Chart - Displays data in two-dimensional chart with an X and Y axis visualizing trends and changes over time.
  4. Bar Chart - Uses vertical rectangular bars of varying heights to represent and compare discrete groups.
  5. Horizontal Bar Chart - Displays data as horizontal bars, with the bar length indicating the magnitude of the value.
  6. Pie Chart - A circular chart divided into sectors, illustrating numerical proportion. Each sector represents a proportional part of the whole.
  7. Funnel Chart - Visualizes the progression of users through a multi-step process, typically a conversion funnel.
  8. Bar Funnel Chart - Displays data across multiple stages in a process in a bar chart layout.
  9. Bubble Chart - Displays data points as bubbles, and an additional dimension of data is represented by the size of the bubble.
  10. Sankey Chart - Visualizes the flow or movement of users, resources, or data between different categories or stages.
  11. Breakdown Chart - Displays retention over time, using both elapsed time and calendar time. It’s available only for the Retained measure and supports segmentation by cohorts and attributes.
  12. Pivot Chart - Dynamic chart generated from a pivot table in a multi-dimensional view

Here’s a detailed demo guide covering visualizations and its underlying properties: https://optimizely.navattic.com/a1a0v2t

You can refer to Visualization documentation to get comprehensive understanding on visualization and filtering properties utilized by Optimizely Analytics.

What is Filtering?

Filtering is a powerful capability that enables you to refine and segment data for more precise insights into user behavior and experiment performance. By defining specific conditions, you can create granular views that reveal patterns and trends across your datasets. The filtering layer supports a wide range of options—including conditional, group, aggregate, block, property, value, and time-based filtering.

You have the following filtering options based on your analytics and data processing needs:

  1. Entity filter - Facilitate conditional filtering in data definition and analysis

  2. Property filter - Used to specify conditions inside blocks within cohorts

  3. Time filter - Used for filtering events based on the time of occurrence of each event

  4. NetScript filter​ - Define filtering conditions with NetScript instead of a regular expression tree-based interface

Watch the demo to understand how filters are created and applied:  https://optimizely.navattic.com/92a0mal

What are Aggregation Functions?

Aggregation functions are used to summarize data from a property or block. They apply a specific mathematical or statistical operation to input values, often organizing the results into groups, to provide a concise overview of your data.

These functions are crucial for transforming raw data into meaningful insights, especially when analyzing experiment results or user behavior.

The following is a list of aggregators available:

  • avg – Compute the average of the input values.
  • count – Count the total number of input values, including duplicates.
  • unique count – Count the number of unique input values.
  • first – Compute the first input value, ordered by event time.
  • last – Compute the last input value, ordered by event time.
  • max – Compute the maximum input value.
  • min – Compute the minimum input value.
  • sum – Compute the sum of input values.
  • percentile - Compute the value below which a chosen percentage of your data falls.

What is Segmentation?

Segmentation allows you to divide your audience or data into smaller, more specific groups based on shared characteristics or behaviors. This process helps you gain deeper insights into how different visitor segments interact with your digital experiences and how they respond to your experiments.

​There are two types of segmentation:

  • Segment by Cohort section allows you to filter results using cohorts—custom subsets of your dataset defined for exploration. Cohorts support more advanced segmentation by combining property-based conditions with behavioral filters, enabling deeper analysis of specific user groups or patterns.
  • Segmentation using attributes allows you to group events by specific attributes—columns in your dataset that represent qualitative or categorical traits, such as user country, subscription tier, or account status.

Take a look at this short demo below on segmentation:  https://optimizely.navattic.com/bry0a3o

What is Parameter?

Parameters are a versatile mechanism used to fine-tune visualizations within your data explorations or modeling artifacts. They provide a way to make your analyses more dynamic and interactive.

Parameters are available in the following explorations:

  • Funnel
  • Path Analysis
  • Event Segmentation
  • Retention
  • Impact
  • Visual Exploration

Parameters are also in the following modeling artifacts:

  • Cohorts
  • Derived Columns
  • Metrics

Types of parameters

There are four different types of parameters that you can create listed below:

  1. Column Values
  2. Custom Values
  3. Time Range and
  4. Time Grain

The interactive demo below provides a guide on how to apply parameters: https://optimizely.navattic.com/26a0p79