With the growing volume of images and data being produced and shared online, manual tagging has become time-consuming and almost impossible. That’s why AI tagging is gaining interest. But how do you use it in your organization’s UX research?
In this article, you will:
- Understand AI tagging and explore its benefits.
- Get the 10 steps to choose an auto-tagging software.
- Learn our top tips to elevate your AI tagging.
Ready to implement AI tagging in your UX research?
HeyMarvin, the best AI research assistant software, has a simple, intuitive interface to allow you to quickly categorize and apply tags to your sales calls and survey responses without requiring technical expertise. Our tool keeps everyone in the loop, allowing them to prioritize valuable customer insights.
Start analyzing for free and get actionable insights that drive impact.
What Is AI Tagging and Why Does It Matter for User Research
AI tagging automatically categorizes and applies relevant keywords and labels to data and content, making it easily discoverable and analyzable.
It’s an essential aspect of user research since it:
- Uncovers Patterns at Scale: AI organizes large volumes of data in real-time, pinpointing the key themes, sentiments, and summaries.
- Empowers Non-Researchers: With AI tagging, you don’t have to be a researcher to categorize content by tags and discover actionable feedback. You don’t need to manually dig through raw data.
- Speeds Up Analysis of Data: With an AI tagging system, you can reduce the time and effort required to label and categorize data compared to manual tagging.
Benefits of AI Tagging in Enterprise Workflows
So, how is AI tagging a game changer in your business?
Check out some of the benefits below:
- Supports Scaling Projects Effortlessly: With AI tagging, you can efficiently organize and label large amounts of data at scale.
- Improves Discoverability: Accurate labeling makes searching and retrieving information easy, especially from extensive data.
- Improves Consistency y: Auto tagging reduces the risks of human error and ensures tags are applied consistently across large datasets.
- Allows You to Deliver Personalized Experiences: AI tagging allows you to analyze your users’ preferences and tailor products that meet their needs, delivering customized experiences.
Top Use Cases of AI Tagging in Different Industries
AI tagging is crucial in managing content across different fields and promoting a positive user experience in various applications.
Read the examples below so you can learn how you can include AI in user research:
- Ecommerce: You can use AI tagging to categorize feedback from customer reviews and chat logs. Also, segment it according to the customers’ location and behavior, helping you identify their pain points and personalize strategies based on the impact it will have.
- Healthcare: You can tag patient records for easy retrieval. You can also tag medical research papers to help researchers quickly find relevant information to create diagnosis plans.
- Entertainment: Film studios and broadcasters can assign tags and labels in vast video and audio files to identify key scenes in footage, allowing for easy editing and repurposing.
- Digital Asset Management (DAM): AI tagging enables automatic tagging of assets like photos, videos, and audio files so that you can locate specific files instantly.
How to Choose the Right AI Tagging Tool
When selecting the best tool for AI tagging, you should follow the 10 steps we’ve discussed below:
1. Understand Your Project Requirements
To narrow down your tagging tool, you should define the specific needs of your research. For instance, if you have a large team, you should pick a tool with robust features, such as role-based access controls and collaboration features.
Also, one tool can be excellent in image recognition tagging, while another can be great in sentiment analysis. Understand what your project needs.
2. Check Its Interface
Choose a tool with an easy-to-use interface to streamline your tagging process. Check if it’s easy for your research team to use it, especially if they aren’t tech-savvy or you’re planning to onboard new hires. Beyond data tagging, assess if it’s easy to retrieve data and set up workflows.
3. Consider Multiple Data Formats
Your tagging tool should support all data formats your project requires. It should handle videos, text, audio files, and documents. The chosen software should also let you switch between the formats seamlessly.
4. Check Collaboration Features
Choose an AI tagging tool with collaborative features. It will simplify labeling data and allow all your team members to work on it in real-time.
With a seamless flow of data at every step of the UX design process, you can minimize delays and boost the quality of your products.
5. Consider Its Integration Capabilities
An auto-tagging tool is one component of your research process, so it should seamlessly integrate with your existing tech stack. Your AI software should connect with your cloud provider and have smooth data export (and import) capability to provide an unrestricted flow of data.
Assess the APIs and SDK to ensure they support customization and effectively refine your processes.
6. Evaluate the Customer Support
While a platform might have great AI tagging features that streamline your research process, mistakes can happen. So, your chosen software should have a solid support team that can rapidly respond to your challenges and has in-depth knowledge of your use case.
A responsive and proactive customer support team reduces downtimes and keeps your team on track.
7. Check Its Scalability
The AI tool should handle your growing team size and increasing datasets while maintaining performance. Your AI software should also offer cloud storage options, handle complex projects, and batch tagging. This will allow you to expand and remain effective as your project evolves.
8. Consider the Privacy and Security
Your tool of choice should protect your users’ data and meet security standards such as GDPR and SOC 2. So, you can safely tag user research even in sensitive industries such as healthcare.
9. Compare the AI Tools Pricing
Explore the prices of different tagging software and check if they match your budget. However, it’s not always a good idea to go for the tagging platform with the lowest costs. Instead, you should choose a tool that gives you the maximum value and meets your needs.
10. Request a Free Demo or Free Trial
Once you’ve shortlisted a few tools, request a free demo or free trial. Take it for a test drive to check its ease of use and features. Remember to ask the customer support team specific questions about your use case.
If you’re looking for AI-driven software with all these features, look no further than HeyMarvin.
With HeyMarvin, you can:
- Tag interviews, sales calls, and customer interactions in real-time, enabling you to get immediate insights and respond swiftly.
- Focus more on analyzing data rather than on manual tagging.
- Collaborate efficiently among teams and empower teams to make more informed decisions.
- Connect with tools you already use, including Google Drive and Asana.
Book a free demo and see how you can use our AI agent to turn your raw data into insights in minutes, not days.
How Product Teams Use AI Tagging to Build Faster
For product teams, auto-tagging helps them better understand their users, personalize their experiences, and build marketing strategies.
Let’s look closely at how AI tagging is reshaping product management:
- Improved Product Designs: Your product team can use insights from AI tagging to develop product features that solve the users’ problems, boost satisfaction, and reduce customer churn.
- Faster Decision Making: AI tagging shortens the time between receiving feedback and taking action. You can get real-time insights, and your product team can make data-driven decisions without assumptions.
- Automatic Categorization of Feedback: Whether it’s 100 or 100,000 users’ responses, AI analyzes them quickly and accurately, allowing your product team to keep up with growth without sacrificing the user experience.
- Cross-Team Collaboration: Insights from AI tagging can be shared across departments, including sales, support, design, and marketing, allowing everyone to be aligned on the customer priorities.
Tips to Maximize ROI From Your AI Tagging Workflow
The real point of using AI isn’t about working less but doing more important work that matters and increasing your ROI.
Here are our top tips on how you can achieve maximum productivity with AI:
- AI Insights to Iterate Quickly: With AI tagging solutions, you can get insights faster, which you can use to quickly create prototypes. You can also test and refine different designs and make necessary adjustments to your product.
- Have Human Oversight in AI Tagging: You should validate the tags from AI to ensure they are relevant and get more reliable insights that drive better decision-making.
- Choose the Right AI Tool: Leverage AI tools tailored for accurate AI tagging and those that address your research needs.
- Invest in Training Your Team: Ensure your team is well-equipped to handle AI tagging and encourage them to solve problems as they arise.
Frequently Asked Questions (FAQs)
Got questions?
We’ve compiled quick answers to commonly asked questions about AI tagging.
What Makes AI Tagging Better Than Manual Coding?
Here’s a table to show why AI tagging is better than manual tagging:
| Feature | AI Tagging | Manual Tagging |
| Speed | Fast updates that live sync with other tools | Slow, resulting in delayed updates |
| Scalability | High since it can handle high-volume tasks easily | Limited because it relies on a team |
| Accuracy | Highly consistent since it relies on AI-driven technology | Inconsistent because it’s prone to human error |
| Time Investment | Low since the categorization is automated | High since it requires manual categorization, reviews, and brainstorming |
Can AI Tagging Handle Large Volumes of Interview Data?
Absolutely!
Whether it’s 100 or 10,000 interviews, HeyMarvin AI software can transcribe them automatically and tag them in minutes to extract the key themes, patterns, and sentiments.
That’s not all; our platform supports 40+ languages, making tagging interviews across multiple languages easy.
Is AI Tagging Suitable for Unmoderated Research?
Yes.
Unmoderated research is where the researcher isn’t present for the study, and the participants interact with the product (or service) by themselves. AI-driven tagging for such research uses machine learning to automatically analyze data from transcripts, video recordings, etc.
It helps researchers understand patterns, trends, and user needs in real-time, even if they’re not present.
How Secure Is AI Tagging for Sensitive Research Data?
AI tagging is generally secure for sensitive research data if you choose software that is compliant with data privacy laws. Your tool should also secure your data with user permissions and access controls.
HeyMarvin takes your privacy and data security seriously. Our software complies with privacy laws such as SOC 2, ISO 27001, HIPAA, and GDPR. We also allow you to use access controls based on roles, allowing only authorized personnel to access sensitive data.GDPR Compliant
Conclusion
AI tagging helps free up the time you would have used to tag your data manually. It’s also cost-effective and allows you to understand your data clearly.
Remember, some non-negotiables when choosing an AI tool include: catering to your unique use case, providing data security, enabling scalability, and creating a centralized data hub. You can also refer to our top tips to maximize your ROI from the AI tool.
If you’re ready to automate your tagging, the HeyMarvin AI research tool has got you covered.
Our software tags your customer feedback into themes and sentiments. In just minutes, you can turn your scattered customer information into insights, helping you identify and act on gaps in your products.
Try HeyMarvin for free and let our software do the heavy lifting of uncovering insights in your user feedback.
