As an developer working in the iMIS space, I often hear from clients after completing a project or website implementation, “How do I know if users are interacting with this?” or “How can I tell how it’s performing and what value it is adding?”
The standard solution out of the box is Google Analytics. This works to some extent, but it has significant limitations. It does not integrate natively with iMIS, making it clunky to use with its complex tagging system. Most importantly, it cannot distinguish between signed-in users and anonymous visitors, so you have no way of knowing what members are interacting with what content.
Nonprofits need accurate information about how members and visitors use their websites. This is especially important for organizations that depend on advertising revenue or need insights into engagement with directories, profiles, resources, and promotions. Associations often collect click activity, but without connecting it back to real users, the data cannot be presented in a way that is meaningful to staff, advertisers, or members.
For that reason, we developed a product called Click Tracker. It works directly with iMIS and records any link click on an iMIS website or any site linked through SSO, even when users are not logged in. It captures standard web data such as page URL, destination, browser information, language, location, etc. When the user later authenticates, Click Tracker adds their iMIS ID to the earlier anonymous clicks, allowing you to track a member’s journey through the join process.
Any tracked link can be assigned to an advertiser or member, who can view their engagement data through a portal in iMIS. With the user’s iMIS ID, you know nearly everything about that user. All data is stored in standard iMIS panels, enabling custom reports and queries. Click Tracker also supports location tracking with user permission, slideshow formatting, dashboards for staff and advertisers, multi-domain tracking, and built-in security.
Recently we even added several major enhancements including time on page, meta title, meta description, and order ID. However, with all this data there needs to be a better way to interpret it. Dashboards and reports can help, but they often fall short when faced with the variety of data and the challenge of turning it into something meaningful.
Jean Baudrillard, a French philosopher whose work I first encountered in a college class on Social and Ethical Issues in Information Technology, wrote Simulacra and Simulation, in which he stated, “We live in a world where there is more and more information, and less and less meaning.” When staring at logs filled with pages, timestamps, user IDs, and metadata, it can feel like a mass of numbers and links. Even with graphs and tables laid out in dashboards, it can be hard to see a story. The human mind is a story processor, not a logic processor. As Baudrillard suggested, we need a way to make sense of this data.
It just so happens that one of the things AI is best at is exactly this. It can take large volumes of structured data, find patterns, summarize trends, and turn raw information into meaningful stories that staff and advertisers can understand.
For a near-future update of Click Tracker, we plan to add a chat menu directly in the dashboard. This will create a simple chatbot interface, allowing staff to ask questions about the data, identify patterns, and trace the journey of specific users. IQA queries can filter and reduce the amount of data the AI processes, keeping the system efficient.
We also need to choose an AI model capable of processing all this data. From previous experience working with large CSV and Excel datasets, Claude Haiku in GitHub Copilot proved excellent at handling large datasets. It is designed to efficiently process structured data such as JSON at a low cost. Haiku can review large blocks of records, extract patterns, summarize trends, and answer questions quickly, making it ideal for frequent analysis of click logs, user sessions, and ad engagement.
With Click Tracker, Haiku can process thousands of rows of click data, time on page values, metadata, and order IDs. It can group records, highlight unusual behavior, and describe how users moved through the site. This gives staff meaningful insights without writing SQL or interpreting raw tables.
The plan is to integrate AI into Click Tracker soon, marking our first step into AI-powered tools. We hope to bring this innovation to the iMIS community.
For more information about available Click Tracker, please visit the following link: Click Tracker.
