UX Research Case Study: Types of Filters in SaaS Products
This research case study aims to explore the types of filters commonly used in SaaS products and their significance in enhancing the user experience. Filters play a crucial role in allowing users to efficiently navigate and analyze data within SaaS platforms, ultimately improving productivity and decision-making. In this case study, I will define each filter type and provide examples from existing industry leaders to illustrate their practical implementation. Based on my findings, I will highlight the importance of these filters in SaaS products and provide recommendations for their effective utilization.
There are hundreds of types of filters classified based on the context, interface requirement, nature of the content, user preferences, and complexity of data.
There are two main categories for the filters which are as below;
1. Static (Top-Level) Filters
Static filters are pre-defined filters that are applied at the top level or the initial stage of data retrieval or content presentation. They are typically fixed and don’t change dynamically based on user interactions or contextual information.
A few types of static filters are as below;
Range Filter
Users can specify a range of values for numerical attributes, such as filtering products by price range, specific time, or rating range.
Example: Asana, a popular project management tool, offers a time range filter that allows users to view tasks and project progress within customizable time frames like “This Week,” “Next 30 Days,” or a specific date range. This feature helps users plan and prioritize their work effectively.
Category Filter
Users can filter content based on specific categories or classifications, such as product categories or content types.
Example: Amazon’s category filter allows users to narrow down their product searches by selecting specific categories of interest, such as electronics, home and kitchen, or clothing. By utilizing the category filter, users can quickly find products within their desired category, streamlining their shopping experience on the platform.
Boolean Filter
Users can apply Boolean/ binary filters to include or exclude specific attributes, such as filtering products by availability (in-stock or out-of-stock).
Example: Shopify, an e-commerce platform, allows users to apply Boolean filters to their product catalog, enabling customers to easily search and filter products based on availability.
Geolocation Filter
Users can filter content based on location or proximity, such as finding nearby stores or filtering search results by location.
Example: Google Maps, a popular mapping and navigation service, offers geolocation filters to help users locate nearby stores, restaurants, gas stations, and other points of interest.
These are some examples of static filters commonly used, but there can be variations and combinations based on specific design contexts and requirements.
2. Contextual Filters
Contextual filters, also known as dynamic filters, are filters that change based on the context or user interactions. These filters allow users to refine data or content based on specific conditions or variables.
Data Source Filter
A data source filter allows users to select and filter data based on specific sources or channels from which the data is collected.
Example: In Google Analytics, users can apply a data source filter to focus on data from different channels, such as organic search, social media, or paid campaigns. This enables users to analyze and compare the performance of various marketing channels.
Custom Field Filter
A custom field filter enables users to filter and organize data based on custom-defined attributes or fields.
Example: Trello, a widely used project management tool, allows users to create custom fields, such as priority, due date, or project type. Users can then apply filters based on these custom fields to sort and prioritize tasks according to specific criteria, improving organization and workflow management.
Tag-based Filter
A tag-based filter allows users to filter and retrieve specific data items or records based on assigned tags or labels. Status filters, Role-based filters, and many other filters come under this category of tag-based filters.
Example: Zendesk, a renowned customer support platform, utilizes tag-based filters to help support agents categorize and prioritize support tickets. By assigning relevant tags to tickets, agents can filter and focus on specific topics or issues, streamlining their workflow and improving response times.
Example: Jira, a popular project management and issue tracking tool, offers a status filter that allows users to filter tasks or issues based on categories like “To Do,” “In Progress,” or “Done.” This feature aids teams in tracking progress, identifying bottlenecks, and ensuring efficient task management.
Appearance of Filters
Filters can be visually shown in many forms based on user requirements, type of data to be filtered, type of industry, and technical feasibility.
Following are a few types of filters based on their appearance.
Dropdown Filters
These filters present a list of options in a dropdown menu, allowing users to select a single option to filter the content.
Checkbox Filters
Checkbox filters allow users to select multiple options simultaneously to filter the content. Each option is represented by a checkbox, and users can choose one or more checkboxes to apply the desired filters.
Radio Button Filters
Radio button filters are similar to checkbox filters, but users can select only one option at a time. They are often used when mutually exclusive options are available.
Slider Filters
Slider filters enable users to adjust a slider along a range to filter content based on a numerical or continuous value, such as price range or date range.
Toggle Filters
Toggle filters provide a switch or button that users can toggle on or off to apply or remove a specific filter. They are useful for binary or boolean filters.
Tag Filters
Tag filters display a set of tags or labels that represent different filter options. Users can click on tags to apply filters based on specific tags.
Recommendation
The case study findings demonstrate the importance of filters in SaaS products for enhancing the user experience. By implementing a variety of filters such as data source, range, custom field, tag-based, and geolocation. SaaS products can empower users to efficiently navigate, analyze, and organize data according to their specific needs. To further improve user experience, it is recommended that SaaS product developers prioritize the following:
- Intuitive User Interface: Design a user-friendly interface that allows users to easily apply and adjust filters, ensuring a seamless filtering experience.
- Customizability: Provide flexibility for users to create and utilize custom fields or tags, enabling them to tailor the filtering options to their unique requirements.
- Performance Optimization: Optimize the performance of filter functionalities to ensure fast and accurate filtering, especially when dealing with large datasets.
- Clear Documentation and Training: Provide comprehensive documentation and training resources to help users understand the available filters and effectively utilize them to achieve their goals.
By incorporating these recommendations, SaaS product providers can enhance the overall user experience, improve productivity, and empower users to extract actionable insights from their data.
Conclusion
Filters are integral to the success of SaaS products, enabling users to efficiently navigate, analyze, and organize data within these platforms. By implementing various filter types, SaaS products empower users to customize their data exploration, enhance productivity, and make informed decisions. The examples provided by industry leaders showcase the practical application of different filter types in real-world scenarios. SaaS providers must prioritize the development and optimization of filters to ensure a superior user experience and gain a competitive edge in the market.
