Competitive Research & Product Strategy for AI-Powered Analytics Experiences
ROLE: DESIGN RESEARCH LEAD
CLIENT: MINUTE MEDIA
INDUSTRY: DIGITAL MEDIA & VIDEO TECHNOLOGY
YEAR: 2025
MEDIUM: UX Research, Competitive Analysis, Product Strategy
SCOPE: Competitive Analysis, UX Research, UX Pattern Analysis, Product Strategy
As AI rapidly transformed analytics and reporting tools, Minute Media explored how conversational AI and intelligent assistance could improve complex, data-heavy workflows.
I led a research initiative focused on identifying emerging AI UX patterns, competitive trends, and strategic opportunities across analytics platforms. Alongside competitive analysis, I also developed Userlytics-based research to better understand user trust, sentiment, and comfort levels around AI-generated insights and reporting workflows.
The Challenge
Traditional analytics platforms often overwhelm users with complexity and technical workflows. AI introduced both new opportunities and uncertainty.
The goal was to uncover opportunities for AI-assisted workflows while ensuring experiences remained intuitive, transparent, and trustworthy.
The challenge was understanding:
Which AI interactions genuinely improved usability
What patterns users already understood and trusted
How leading competitors were implementing AI-assisted workflows
Where conversational UX could reduce friction in analytics tasks
How users emotionally responded to AI-generated reporting and insights
What level of transparency users needed before trusting AI outputs
Research Objectives
The initiative focused on answering several key questions:
Competitive & Product Research
How are modern analytics platforms integrating AI into reporting workflows?
Which AI interaction patterns appear most effective and user-friendly?
What expectations are users developing around AI-assisted analytics?
Which UX patterns are becoming industry standards?
How might AI reduce friction for non-technical users?
User Sentiment & Trust Research
Do users trust AI-generated insights and summaries?
Which analytics tasks are users comfortable automating?
What concerns do users have around AI accuracy and reliability?
How comfortable are users presenting AI-generated reporting content?
What would increase user confidence in AI-assisted analytics workflows?
Competitive Analysis
I conducted a large-scale competitive analysis across analytics, BI, advertising, and media platforms to identify recurring AI UX patterns and emerging interaction models.
The research focused on:
Conversational AI interfaces
AI-generated summaries and insights
Prompt-driven workflows
Predictive analytics
AI-assisted visualization
Reporting automation
Guidance and onboarding systems
Explainability and trust-building patterns
AI-generated narratives and storytelling
Platforms Reviewed
Microsoft Power Bi
IBM Cognos
Meta Ads Manager
Google Looker
LinkedIn Campaign Manager
Google Ads
YouTube Studio
Hubspot
Oracle Analytics
Klaviyo
Bright
Wistia
JW Player
Vimeo
UX & Product Insights
Natural Language Became the Primary Interaction Model
Users increasingly interact with analytics tools through conversational prompts rather than manual filtering and report building.
AI Shifted Analytics Toward Storytelling
Getting started is simple. Reach out through our contact form or schedule a call—we’ll walk you through the next steps and answer any questions along the way.
Guidance Reduced Cognitive Load
Prompt suggestions, recommended filters, and contextual assistance helped users navigate complex systems with less friction.
Collaboration Became Embedded
Trust & Transparency Were Critical
The strongest implementations explained how conclusions were generated, helping users validate and trust AI outputs.
Users Wanted Assistance — Not Full Automation
AI-generated summaries and shareable insights were increasingly integrated into Slack, email, and presentation workflows.
User research revealed that participants were generally open to AI-assisted analytics, but still wanted visibility, editability, and control before relying on AI-generated outputs in professional settings.
Validating AI Trust & Adoption
I developed a Userlytics-based unmoderated survey to better understand how users perceived AI within analytics workflows. 27 users who fit the research criteria completed the survey on video.
The research helped identify both excitement and hesitation around AI-assisted reporting, reinforcing the importance of explainability, user oversight, and human-centered interaction design.
Deliverables:
User survey questionnaires
Unmoderated testing scripts
Userlytics testing strategy documentation
Using surveys and moderated research prompts, the study explored:
Whether users trusted AI-generated insights
Which workflows users wanted automated
How users interpreted AI-generated summaries
Where AI interactions created confusion or clarity
What level of transparency users needed to feel confident using AI outputs
How comfortable users felt presenting AI-generated reporting content
What would increase confidence in delegating analytics work to AI
Strategic Reccomendations
Based on the research, several opportunities emerged:
Introduce conversational querying
Use AI-generated summaries to surface insights faster
Provide onboarding guidance and prompt suggestions
Embed explainability into AI outputs
Prioritize transparency and user control
Support collaboration and reporting workflows
Outcome & Impact
The research established a foundational understanding of how AI was reshaping analytics experiences and identified emerging UX standards across enterprise platforms.Introduce conversational querying
The work helped clarify:
Which AI patterns users were becoming familiar with
Which trust factors influenced adoption
Where AI could reduce workflow friction
How future AI initiatives could be validated through user research
Reflection
This project strengthened my ability to connect UX research, product strategy, emerging technology trends, and user sentiment analysis into actionable product direction.
One of the biggest takeaways was recognizing that successful AI experiences are not defined by novelty alone — they succeed when they reduce complexity, build trust, and help users make decisions more confidently.
Conducting Userlytics-based trust and sentiment research also reinforced how important transparency, explainability, and user control are when designing AI-assisted workflows for professional environments.
The project ultimately highlighted that understanding emotional trust in AI is just as important as understanding technical capability.