Dania Abdulhamid دانية عبد الحميد
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Google Analytics · 2020–2022

Intelligent home

Google Analytics has a steep learning curve, and users found the homepage confusing. I led the design vision through to execution of a new homepage that adapts to each user’s needs and abilities instead of treating everyone the same. It took three years, and it started with an engineering team who had built something powerful and were looking for a problem to point it at.

Role

Lead designer

Team

Design, 20+ engineers, PM, research, data science

Span

2020–2022

Status

Shipped

Experiment results

+15%

interactions across Google Analytics overall

+20%

interactions with Home, despite far less content on the page

↑

retention at both 7 and 28 days

The UX process deck

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Google Analytics — Intelligent Home
Summary
WHAT’S IT? — Intelligent Home is the new entry
[2020] Google Analytics 4 Home
User Pain Points — OVERWHELMING JUMP BACK IN BUSINESS INSIGHTS
[2022] Intelligent Home MVP — Personalized Metrics
[2022] Intelligent Home MVP Results
[2022] Intelligent Home MVP Results
The Setup: Context + Challenges
February 2020 — — I led a workshop to improve
Slide
February 2020 — — I tested the results of that
Challenges — 1. I had no dedicated UXR support, so I designed the study
Who responded? — ● 7 out of 7 “content delivery” blog or news site
What do they want home to do? — ● Google organize by relevance, layout is dynamic
What do they want home to do? — 1. MONITOR - “signs of life,” explicit alerts when
March 2020 — — I led a “Home UX” sprint
Challenges — 1. COVID — everyone was suddenly remote
Current Gold Home
Intelligent Home Coverage — Property
“I wish insights were more proactive and
User Pain Points — OVERWHELMING JUMP BACK IN BUSINESS INSIGHTS
Research findings — 6 out of 7 7 out of 7 7 out of 7
Focus user — Regular yet shallow users of GA
User Needs — MONITOR SURFACE DIRECT
1. Accelerant — I get answers faster.
March 2020 — — I created a vision storyboard
Meet Dave — Director of Digital Marketing
Getting Started — Dave lands on Home
Tailored Learning — Suggestions
Data Overview — Scorecard highlights
Personalized — Recommendations
Dave sees data is — flowing, selects his
What we’ve learned about Dave — ● He’s an advertiser
One week in — Dave returns to find a
Tailored Suggestions — Updated search
Focused Metrics — Updated scorecard
Personalized — Recommendations
Personalized — Recommendations
Dave monitors his top — metrics, dives into a
What we’ve learned about Dave — ● Additional reports of interest
Several months in — Dave’s Home
Tailored Suggestions — Updated search
Focused metrics — Realtime & scorecard
Personalized — Recommendations
Personalized — Recommendations
Over time, Home adapts to Dave’s needs.
“Wizard of Oz” UXR study — 51
Challenge — “Personalized” data is hard to prototype in a compelling way
Search and Suggestions — highlighting easy access to the
Anomaly detection and — in product education
Day 1: People love the new home design…...
Day 1: People love the new home design…...
Day 1: People love the new home design…...
Day 1: Personalization is awesome…...
Day 1: Personalization is awesome…...
Day 1: Personalization is awesome…...
Seems like this page can be customized? ……
Search and Suggestions — ● About half of participants liked the search widget & prominent placement and woul
Day 1: Priority — ● Key metrics shown first that users track regularly were liked by all. Users like…
Realtime and overview — ● Participants liked realtime and overview as they were familiar analytics outputs t
Recommended for you - Data stories
Recommended for you - feature recommendations
Day 1: Tell me about yourself — ● Being at the bottom and having visual appearance similar to a pop up, som
Anomaly detection and — in product education
Day 30 - Feedback on dynamic content
Day 30 - Feedback and Control Overall
I wish…. — Intelligent home specific ideas:
Impact — We received xfn leads buy-in & additional xfn resourcing to support
I used the study — outcomes to design
Personalized Metrics — Overview and Realtime
Experience — Zero State
Challenges — 1. The myth of: “The more data, the better”
Qualitative Feedback — Confidential Information
Methodology, Goals & Participants
What users liked — “What def was a nice surprise for me was the recently viewed
User feedback — “I think it’s a great start, and there are elements
How do users feel about their experience with IH4?
Experiment launch — results
Success Metrics — North Star Metrics
[2022] Intelligent Home MVP Results
Executive Summary — ● Retention: Users returned to GA4 more frequently
What I learned… — 1. Tight collaboration with engineering is a major key to success, resilience and fulfillm
What I would do differently… — 1. Insights / data stories section — if I had known LLMs were going to have a
Thank you!
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