In our AI Explainers series, we talk about new AI functionalities in the next generation Market Insights Platform that make life easy for insights managers. Today, we explain how the system automatically tags reports with no manual effort on the user’s part, using the semantic auto-tagger.
Today, I’d like to continue our AI series by talking about a key feature of our new Market Insights Platform: the semantic auto-tagger. Let’s start with the “why”– the motivation behind it.
Market insights platforms are a necessity
Insights managers don’t want to have to comb through thousands of reports. To find answers, they only need the ones related to their questions. But those can be hard to find, especially if they’re lost in an email chain, or sitting on a dusty flash drive.
This is why insights platforms are necessary—when properly used, all data is made available to every user, and everyone is able to answer their respective questions. In the end, the best report is the report you have access to.
Keeping the platform up-to-date is critical here. If the latest research isn’t on the platform, teams will stop using it and the whole thing will fall apart. This happens with depressing regularity; once a project is finished, it’s immediately on to the next one. Uploading results to your database need to be made a key part of the workflow.
Of course, insights engines still need a structure. When projects are uploaded, they need to be tagged with related data. To do this, the platform needs a taxonomy and a filter structure. Users have to ensure they’re tagging reports properly so others can find them in the system. It gets complicated quickly, from manually setting up a structure to developing tagging guidelines.
All of this adds up to a lot of time spent on knowledge management. At Market Logic, many of our clients task agencies with tagging projects, but even then it’s extra time and cost burden on partners.
Enter the auto-tagger
So how can we overcome these tagging barriers? Enter the document uploader with semantic auto-tagging. It’s as simple as it sounds but uses some very clever AI technology. All you have to do is drop a file into the upload dialog box in your browser, and the machine does the rest.
The platform “reads” through the file to automatically tag it with your specific categories, regions, and brands. All of this is done using your organization’s corporate taxonomy, so the user doesn’t have to be an expert in organizational knowledge. No more stressing about manual tagging guidelines, and more time for other important tasks.
After your file is uploaded and tagged, the platform asks you to share your new research with colleagues. If you choose yes, AI technology creates an automatic summary that functions as a preview for whoever receives it. This allows you to easily promote new content to peers and stakeholders.
So that’s the semantic auto-tagger. After your taxonomy and filters are set up, users don’t need to worry about how to tag new content. They can just drag and drop, while the platform does all the heavy lifting in the background. The functionality is fully integrated and built for teamwork, so users can engage colleagues with new material in a matter of seconds.