Explore top LinkedIn content from expert professionals.
Summary
Data plays a vital role in UX design decisions by offering actionable insights into user behavior, helping teams create intuitive, user-friendly experiences. By analyzing data, designers can move beyond assumptions to make impactful, data-supported improvements to products.
Analyze user behavior: Use tools like clickstream and session analysis to understand how users interact with your product and identify areas where they may struggle or drop off.
Measure impact: Quantify the business value of UX changes by tracking metrics such as conversion rates, engagement, and task efficiency before and after updates.
Bridge feedback with action: Design user interfaces that not only improve usability but also encourage active feedback and enable continuous product development through user interactions and suggestions.
Here's the exact framework our clients use to tie UX research directly to revenue.
It's called the I.M.P.A.C.T method:
👉 I - Identify high-friction touchpoints
Systematically gather data on where customers struggle most in your product journey. Focus on high-traffic areas first.
👉 M - Measure the business cost
Calculate the direct cost of each friction point:
- Conversion drop-offs
- Support ticket volume
- Churn related to specific features
👉 P - Prioritize by revenue potential
Rank issues by potential revenue impact, not just severity or ease of fix.
👉 A - Act with evidence-based solutions
Design solutions based on actual user behavior, not assumptions.
👉 C - Communicate in business terms
Present findings as "This issue is costing us $X per month" rather than "Users are confused by this flow."
👉 T - Track improvements continuously
Measure the before/after impact of changes in business terms.
With this, you can move the perception of a UX team to a strategic partner
When you can say "We increased conversion by 22% through research-driven changes," executives listen differently
Does your team have a framework for tying research to revenue? I'd love to hear about it!
User behavior is more than what they say - it’s what they do. While surveys and usability tests provide valuable insights, log analysis reveals real interaction patterns, helping UX researchers make informed decisions based on data, not just assumptions. By analyzing interactions - clicks, page views, and session times - teams move beyond assumptions to data-driven decisions.
Here are five key log analysis methods every UX researcher should know:
1. Clickstream Analysis - Mapping User Journeys
Tracks how users navigate a product, highlighting where they drop off or backtrack. Helps refine navigation and improve user flows.
2. Session Analysis - Seeing UX Through the User’s Eyes
Session replays reveal hesitation, rage clicks, and abandoned tasks. Helps pinpoint where and why users struggle.
3. Funnel Analysis - Identifying Drop-Off Points
Tracks user progression through key workflows like onboarding or checkout, pinpointing exact steps causing drop-offs.
4. Anomaly Detection - Catching UX Issues Early
Flags unexpected changes in user behavior, like sudden drops in engagement or error spikes, signaling potential UX problems.
5. Time-on-Task Analysis - Measuring Efficiency
Tracks how long users take to complete actions. Longer times may indicate confusion, while shorter times can suggest disengagement.
Data Products are NOT all code, infra, and biz data. Even from a PURE technical POV, a Data Product must also have the ability to capture HUMAN Feedback. The User’s insight is technically part of the product and defines 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭’𝐬 𝐟𝐢𝐧𝐚𝐥 𝐬𝐭𝐚𝐭𝐞 & shape.
This implies Human Action is an integrated part of the Data Product, and it turns out 𝐚𝐜𝐭𝐢𝐨𝐧 𝐢𝐬 𝐭𝐡𝐞 𝐩𝐫𝐞𝐥𝐢𝐦𝐢𝐧𝐚𝐫𝐲 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐛𝐥𝐨𝐜𝐤 𝐨𝐟 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤.
How the user interacts with the product influences how the product develops.
But what is the 𝐛𝐫𝐢𝐝𝐠𝐞 𝐛/𝐰 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 𝐚𝐧𝐝 𝐇𝐮𝐦𝐚𝐧 𝐀𝐜𝐭𝐢𝐨𝐧𝐬?
It’s a 𝐆𝐎𝐎𝐃 𝐔𝐬𝐞𝐫 𝐈𝐧𝐭𝐞𝐫𝐟𝐚𝐜𝐞 that doesn’t just offer a read-only experience like dashboards (no action or way to capture action), but enables the user to interact actively.
This bridge is entirely a user-experience (UX) problem. With the goal of how to enhance the User's Experience that encourages action, the interface/bridge between Data Products and Human Action must address the following:
𝐇𝐨𝐰 𝐭𝐨 𝐟𝐢𝐧𝐝 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐭𝐡𝐚𝐭 𝐬𝐞𝐫𝐯𝐞𝐬 𝐦𝐲 𝐧𝐞𝐞𝐝?
A discovery problem addressed by UX features such as natural language search (contextual search), browsing, & product exploration features.
𝐇𝐨𝐰 𝐜𝐚𝐧 𝐈 𝐮𝐬𝐞 𝐭𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭?
An accessibility problem addressed by UX features such as native integrability- interoperability with native stacks, policy granularity (and scalable management of granules), documentation, and lineage transparency.
𝐇𝐨𝐰 𝐜𝐚𝐧 𝐈 𝐮𝐬𝐞 𝐭𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐰𝐢𝐭𝐡 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞?
A more deep-rooted accessibility problem. You can't use data you don't trust. Addressed by UX features such as quality/SLO overview & lineage (think contracts), downstream updates & request channels. Note that it's the data product that's enabling quality but the UI that's exposing trust features.
𝐇𝐨𝐰 𝐜𝐚𝐧 𝐈 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 & 𝐬𝐮𝐠𝐠𝐞𝐬𝐭 𝐧𝐞𝐰 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬?
A data evolution problem. Addressed by UX features such as logical modelling interface, easily operable by both adept and non-technical data users.
𝐇𝐨𝐰 𝐭𝐨 𝐠𝐞𝐭 𝐚𝐧 𝐨𝐯𝐞𝐫𝐯𝐢𝐞𝐰 𝐨𝐟 𝐭𝐡𝐞 𝐠𝐨𝐚𝐥𝐬 𝐈’𝐦 𝐟𝐮𝐥𝐟𝐢𝐥𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬 𝐩𝐫𝐨𝐝𝐮𝐜𝐭?
A measurement/attribution problem. Addressed by UX features such as global and local metrics trees.
...and so on. You get the picture.
Note that not only the active user suggestions but also the user’s usage patterns are recorded, acting as active feedback for data product dev and managers.
This UI is like a product hub for users to actively discover, understand, and leverage data products while passively enabling product development at the same time through consistent 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤 𝐥𝐨𝐨𝐩𝐬 𝐦𝐚𝐧𝐚𝐠𝐞𝐝 𝐚𝐧𝐝 𝐟𝐞𝐝 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐫𝐞𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬 by the UI.
How have you been solving the UX for your Data Products?