Evolved Insights For Great

User Experience 16/11/18

Today’s discussion

UX evolution

Disruption &

Challenges

A sunny day is

emerging for UX

Understanding the online user journey

Low

High

Years Weeks Real-Time

Subject matter experts

Detailed specifications

Input from a few customers

Field studies & surveys

Automated testing

Paper prototyping

Focus groups

Feature-rich interfaces

Usability sessions

Experimentation

User behavior analytics

Cohorts and segmentation

Responsive UX

Biometric UXR

Remote Moderated UXR

~1990

~2000

~2015

Months Days

Now that we are here… what can we do about it?

Remove what we think we know

Research the available insights

Find the clear purpose

Be able to design for easy

Make better products

Products achieve better, quicker than ever

How can we make the transition?

Embrace evolved insights to truly understand user

expectations and influences

Go beyond basic expectations

The Kano model, named after

its creator Noriaki Kano, aims

to standardize the way

companies see the needs of

their customers, and is a great

tool in resolving one of the

most common problems that

arise in modern organizations –

misunderstood requirements.

“We are not our users, we

need to remove ourselves

from perceptions and see

the proof of what is

actually happening”

https://www.ted.com/talks/dan_ariely_asks_are_we_in_control_of_our_own_decisions?language=en

Inspiration from Dan Ariely: video clip

Real Time User Behavioral Analysis

○ Impact of the Cloud

○ Democratization of Data

○ Hybrid UX Research

○ Anomaly Detection

Evolved insights: two example methods

Experimentation

○ Scientific UX Design (hypothesis driven)

○ Micro Experimentation

○ Machine Driven Design

Comprehensive,

Actionable Findings

Task Based, Ease of

Use Benchmarking

Produces Hypothesis

and Experimentation

Enables De-risking

Assists in Persona

Enrichment

UX research: hybrid study methodology

19 Simmons University Graduate Students

Topics of Study: Culinary arts, library science, network neutrality,

pharmacology, anti-slavery, information science, urban planning, game

design, landscaping, podcasting & oral history, children’s literature, film

studies, sociology, copyright law

Conducted August 28-29, at the Simmons University

Usability Lab

~30 minute moderated usability tests/questionnaire

Pre-populated search: “climate change”

Quantitative data collected:

- Eye-tracking metrics via iMotions Biometric

Research Platform

- Time on Task

- Likert Scale survey responses

UX research: hybrid study methodology

User View Heatmap Areas of Interest Plot

UX research: study outputs & insights

Key takeaways

• Research Starter & Subjects present

great value, but right-panel content

suffers diminishing returns as the

user moves down the page.

• Filters are critical to user workflows,

but they weren’t immediately

apparent in this design.

(1st in fixations, revisits, and time

spent, but 6th in TTFF)

Search Results AOI Plot Ranked by TTFF

(Time to First Fixation)

Average Usability Score

4.33 out of 5

Combines scores for look & feel,

organization, and ease of use

• Data from 1M users over 30 days

• Nearly 10% of them clicked the

“Zoom Out” button

• Zoom In usage was less prevalent

• Hypothesis: Initial zoom level is too

large

Micro experimentation – eBook zoom level

Micro experimentation – eBook zoom level

• A/B test conducted to determine if 75% zoom level results in fewer zoom outs

• Indeed, there was a 42% decrease in zoom outs, but zoom ins increased by 33%

• Question: 75% is better than 100%, but is there some other optimal setting?

• A few more experiment variations were tried

• At 85% zoom vs. 100% zoom, there was an overall 33.98% drop in the

percentage of users changing their zoom levels

Micro experimentation – eBook zoom level

• SaaS tools + ubiquitous cloud storage and processing

facilitates rapid design iteration

• Powerful insights are easily accessible by product

management and UX teams

• Human input required at every stage to interpret+take

action on analytics data and conceive+run experiments

Micro-Experimentation

• Small, scoped, independent

changes

• Machine driven hypotheses

• Automatic deployment+analysis

• “Insights in hours”

• Human input on final action

Augmented analytics

• Smart anomaly detection

• Suggested courses of action

Current State (Near) Future State

Micro experimentation – eBook zoom level

Data-Driven Design

● This AI-Generated portrait recently

sold for $432,000 at auction

● It was produced by an algorithm

that was fed a data set of 15,000

portraits painted between the 14th

and 20th centuries

● How far away are we from

mainstream machine-generated UI?

Data-Driven Design

To conclude

• In the end, do we really know what user’s want?

•Should we question our decisions and better

understand user expectations?

•Examine the data to form hypotheses

•Test early and often to validate design solutions

•Now GO get started and design great UX!

Thank You Questions?