Content Development and UX Analysis

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Content Development and UX Analysis
senior lecturer Myrzakul Aiya Danabekkyzy

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Learning Objectives
● Explain how content decisions shape UX (and business
outcomes).
● Differentiate UX analysis types: qualitative vs quantitative;
generative vs evaluative.
● Select appropriate UX methods for a question, constraint, and
risk level.
● Run core methods: content audit, heuristic review, usability
test, and basic experiment design.
● Communicate findings with clear evidence, priorities, and next
steps.
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What is UX (and why content is part of it)?
UX = outcome of interaction
Content is the interface
• User achieves a goal
• In a context (device, time, constraints)
• With effort + emotion
• Over time (not just one screen)
Labels, instructions, error messages
Product information & trust signals
Onboarding & help content
Empty states, notifications, emails
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Content development (content design) in a nutshell
Plan: audience, goals, messaging, channels, constraints.
Design: information architecture (IA), content model, voice &
tone, patterns.
Write: draft, edit, microcopy, structured content fields.
Validate: comprehension, findability, accessibility, trust.
Govern: workflows, approvals, versioning, localization, analytics.
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How content shapes UX (common failure modes)
1. Ambiguous labels → wrong clicks and backtracking.
2. Missing context → users don’t understand what to do next.
3. Dense text → low scanability, higher cognitive load.
4. Vague errors → repeated failures and support tickets.
5. Inconsistent voice → reduced trust and brand confusion.
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UX analysis: what it is (and isn’t)
UX analysis = using evidence to understand and improve the user experience.
It’s both: (1) diagnosing problems and (2) validating improvements.
Not opinion, not “pixel pushing,” not only surveys, not only analytics.
Good UX analysis connects:
user needs ↔ product behavior ↔ content ↔ business outcomes.
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UX Analysis Process: from problem to proof
Discover
Define
Design
Validate
Qual + Quant
Understand users
& context
Synthesize
Frame problems
Prioritize
Ideate
Prototype
Content drafts
Test & measure
Iterate
Ship & learn
Loop: measure → learn → update UX + content → repeat
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Types of evidence you’ll use
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Behavioral: what people do (tasks, clicks, paths, errors).
Attitudinal: what people say/feel (interviews, surveys).
Performance: time on task, success rate, error rate.
Contextual: environment, device, constraints, motivations.
Content quality: clarity, completeness, accessibility, findability.
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Method Map: choose the right tool
GENERATIVE (explore / learn)
EVALUATIVE (judge / compare)
Generative + Qual
Evaluative + Qual
• Interviews
• Contextual inquiry
• Diary studies
• Ethnography
• JTBD discovery
• Usability testing
• Heuristic evaluation
• Cognitive walkthrough
• Accessibility review
Generative + Quant
Evaluative + Quant
• Surveys (needs & attitudes)
• Market sizing
• Search log analysis
• Content gap analysis
• Funnel analytics
• A/B tests
• Tree testing scores
• Benchmark metrics (SUS)
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How to choose a UX analysis method (fast checklist)
Question type: explore needs? evaluate a design? compare variants?
Risk: what happens if we’re wrong (money, safety, reputation)?
Stage: early concept vs prototype vs live product.
Constraints: time, budget, access to users, analytics maturity.
Signal strength: do we already have strong evidence? if not, start
generative.
Decision clarity: what decision will be made from the results?
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Voice & tone (with examples)
Voice = consistent personality. Tone = adjusts to situation.
When to use
How it works
Outputs + pitfalls
• When many writers touch the product
• When trust matters (finance, health,
education)
• When errors/support volume is high
• Define voice traits (3–5): e.g., “clear,
respectful, calm”
• Define tone rules by scenario
(success, warning, error)
• Create do/don’t examples and
reusable patterns
• Test: comprehension + emotional
response in usability sessions
• Output: Voice & tone guide
• Output: Microcopy patterns for states
• Output: Terminology glossary
• Pitfall: Over-polishing marketing tone
in functional UI
• Pitfall: Inconsistent terminology
across screens

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Microcopy: small text, big impact
Buttons, labels, hints, errors, empty states, confirmations.
When to use
How it works
Outputs + pitfalls
• When users make mistakes or
hesitate
• Any critical flow (checkout, signup,
payment)
• When trust is fragile
• Write for action: start with verbs
(“Save”, “Continue”)
• Be specific: avoid “Submit” and
“Something went wrong”
• Set expectations: time, steps,
requirements
• Prevent errors: constraints +
examples + inline validation
• Output: Reduced drop-off
• Output: Fewer support tickets
• Output: Higher task success
• Pitfall: Blaming the user (“You did it
wrong”)
• Pitfall: Hiding important constraints in
fine print

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Information Architecture (IA): make things findable
Structure, labels, navigation, and content relationships.
When to use
How it works
Outputs + pitfalls
• When users can’t find content
• When site/app has many categories
• Before redesigning navigation
• Inventory content types + user tasks
• Define taxonomy: categories, tags,
metadata
• Validate with card sorting (mental
models)
• Validate navigation with tree testing
• Output: Navigation model
• Output: Label set
• Output: Taxonomy + metadata plan
• Pitfall: Designing IA from org chart
• Pitfall: Too many levels; unclear labels

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Content audit: measure what you have
A systematic review of existing content quality + performance.
When to use
How it works
Outputs + pitfalls
• Before a redesign or migration
• When content feels inconsistent or
stale
• When SEO performance is unclear
• Inventory: URLs/screens + content
types + owners
• Assess quality: clarity, accuracy, tone,
accessibility
• Assess performance: search queries,
engagement, conversions
• Decide: keep, update, merge, remove,
rewrite
• Output: Audit spreadsheet
• Output: Rewrite backlog
• Output: Governance/ownership map
• Pitfall: Only counting pages (no
quality)
• Pitfall: Ignoring “hidden” UI content
(errors, emails)

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Accessibility for content (beyond color contrast)
Accessible content = perceivable, operable, understandable, robust.
When to use
How it works
Outputs + pitfalls
• Always (especially in education,
government, finance)
• When you have forms, instructions, or
long reading
• Use plain language + clear headings
• Write descriptive links (not “click
here”)
• Provide error identification + recovery
text
• Support screen readers with labels/alt
text (where applicable)
• Output: Accessibility findings
• Output: Rewrite recommendations
• Output: Prioritized fixes
• Pitfall: Assuming accessibility is “just
dev work”
• Pitfall: Overloading users with jargon

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Case 1 — E-commerce checkout drop-off
Case study template: turn messy signals into a focused analysis plan
Context
Plan (methods + outputs)
A retail site sees many carts created, but few purchases completed. The
team suspects checkout friction and unclear costs.
Symptoms (evidence)
• Analytics: high drop-off on Shipping and Payment steps
• Support tickets mention “surprise fees” and “promo code issues”
• Mobile conversion is much lower than desktop
Hypotheses
• Users don’t trust total cost until late (fees/taxes unclear)
• Form fields are hard on mobile; errors are unclear
• Promo-code UI steals attention and causes confusion
• Heuristic evaluation focusing on trust + error prevention
• Moderated usability test: 6–8 users on mobile & desktop
• Funnel + error analytics: field-level drop-offs
• Prototype improved cost transparency + error microcopy
• A/B test: cost summary placement + promo-code pattern

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Usability testing (core evaluative method)
Observe users attempting realistic tasks; measure success and friction.
When to use
How it works
Outputs + pitfalls
• When you have a prototype or
product
• High-risk flows
• Before major release
• Define tasks + success criteria (time,
errors, completion)
• Recruit representative participants
• Run moderated (depth) or
unmoderated (scale) sessions
• Analyze patterns: where + why users
fail
• Output: Task success rate
• Output: Friction points + root causes
• Output: Prioritized fixes
• Pitfall: Testing “opinions” not tasks
• Pitfall: Prompting too much
• Pitfall: Ignoring accessibility needs

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Usability testing types (choose based on constraints)
Rule of thumb: moderated for “why”; unmoderated for “how often”
• Moderated (remote or in-person): deeper probing, smaller samples.
• Unmoderated: larger sample, faster, less context.
• Guerrilla testing: quick directional feedback (be cautious with representativeness).
• Benchmark tests: repeatable tasks for measuring improvement over time.
• Accessibility testing: include assistive tech users when relevant.

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Surveys + questionnaires (attitudinal quant)
Measure perceptions at scale; great for trends, not root causes.
When to use
How it works
Outputs + pitfalls
• Track satisfaction over time
• Compare cohorts
• After key tasks (post-task survey)
• Ask fewer, clearer questions
• Use validated scales when possible
(e.g., SUS)
• Combine with an open-ended “why”
• Segment results (new vs returning,
device, region)
• Output: Scores + benchmarks
• Output: Trend dashboards
• Output: Top themes from open ends
• Pitfall: Surveying instead of observing
behavior
• Pitfall: Leading/ambiguous questions

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Interviews (generative qual)
Understand motivations, language, and decision-making.
When to use
How it works
Outputs + pitfalls
• Early discovery
• Complex domains (finance,
healthcare, education)
• When you need users’ words for
content
• Use open questions (tell me about the
last time…)
• Probe for specifics (examples,
constraints, workarounds)
• Capture vocabulary for labels and
help content
• Synthesize into themes, JTBD, and
opportunities
• Output: Needs + pain points
• Output: User language bank
• Output: Opportunity statements
• Pitfall: Hypothetical questions
• Pitfall: Talking to only enthusiasts

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Card sorting (IA / mental models)
Users group topics; reveals how people expect information to be organized.
When to use
How it works
Outputs + pitfalls
• Creating or improving navigation
• Large content sets
• Before writing labels
• Choose type: open / closed / hybrid
• Recruit target users
• Collect groupings + label suggestions
• Cluster patterns; propose IA
candidates
• Output: IA options
• Output: User-friendly categories
• Output: Label language insights
• Pitfall: Treating one sort as “the
answer”
• Pitfall: Too many cards; unclear items

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First-click testing & 5-second test (rapid evaluative)
Check whether people understand where to go and what a screen means.
When to use
How it works
Outputs + pitfalls
• Early UI / content drafts
• Landing pages
• Navigation or CTA clarity
• First-click: ask “Where would you click
to…?”
• 5-second: show screen briefly; ask
what it is and what to do next
• Look for confusion + mismatched
expectations
• Iterate labels, hierarchy, and visual
emphasis
• Output: Directional clarity signals
• Output: Better labels and calls-toaction
• Pitfall: Over-trusting tiny samples
• Pitfall: Ignoring task context

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Product analytics (behavioral quant)
Instrument events and funnels to see where users succeed or fail.
When to use
How it works
Outputs + pitfalls
• Live products
• High-volume flows
• Monitoring after launch
• Define key events (view, start,
complete, error)
• Build funnels by task
• Segment by device, cohort, region
• Combine with qual methods to
explain “why”
• Output: Funnel drop-offs
• Output: Cohort retention curves
• Output: Alerting + dashboards
• Pitfall: Measuring without clear goals
• Pitfall: Event spam (too many events)
• Pitfall: Privacy/consent gaps

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UX metrics frameworks: HEART
Rule of thumb: moderated for “why”; unmoderated for “how often”
If we improve something, we had better be able to measure the impact and learn from it.
If we can’t, we really need to question why we’re doing it, because it will be hard to know if we’re
right, wrong, or somewhere in the middle.
Simply put HEART stands for:
1. Happiness
2. Engagement
3. Adoption
4. Retention
5. Task success

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Adoption
Rule of thumb: moderated for “why”; unmoderated for “how often”
Adoption is quite simple and usually refers to the number of users who use a feature or product for
the first time. Signals of adoption can include downloading the app, signing up for an account, using
new features
Metrics: Download rate, registration rate, feature adoption rate.

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Adoption
Rule of thumb: moderated for “why”; unmoderated for “how often”
After onboarding, you may want to track whether the user can achieve their goals with your product
or new features. Task success refers to the efficiency, effectiveness, and error rate of a user
completing a task with your product’s workflow.
Signal ideas: incomplete or completed task, time to complete task.
Metrics: Error rate, time to task completion, task completion rate, etc.

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Engagement
Rule of thumb: moderated for “why”; unmoderated for “how often”
Engagement measures the frequency, intensity, or depth of a user’s engagement with your product.
How many users have visited the product in the last 7 days? Month and etc.
How often do they visit my website? Or what is the open rate for my email?

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Retention
Rule of thumb: moderated for “why”; unmoderated for “how often”
Retention measures returning users’ continuous, repeated engagement with the feature or product
over time. This allows you to track how often people use your product or feature, or buy your new
offerings, etc.

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Happines
Rule of thumb: moderated for “why”; unmoderated for “how often”
How do users feel about your product? Are they satisfied with the quality? Basically, you need
measures of user attitudes, which are often collected via surveys. For example satisfaction,
perceived ease of use, and net promoter score.

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https://www.youtube.com/watch?v
=ulQ3NKPBCt0

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A/B testing
A/B testing (also known as split testing or bucket testing) is a methodology
for comparing two versions of a webpage or app against each other to
determine which one performs better. It works by showing two variants of a
page to users at random and using statistical analysis to determine which
variation achieves better results for your conversion goals.
In practice, this is how A/B testing works:
1. Creating two versions of a page - the original (control or A) and a
modified version (variation or B)
2. Randomly splitting your traffic between these versions
3. Measuring user engagement through a dashboard
4. Analyzing results to determine if the changes had positive, negative, or
neutral effects
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A/B testing (Examples)
1. B2B lead generation: If you're a technology company, you can improve
your landing pages by testing changes to headlines, form fields, and
CTAs. By testing one element at a time, you can identify which changes
increase lead quality and conversion rates.
1. Campaign performance: If you're a marketer running a product
marketing campaign, you can optimize ad spend by testing both ad copy
and landing pages. For example, testing different layouts helped identify
which version converted visitors to customers most efficiently, reducing
overall customer acquisition costs.
1. Product experience: The product teams in your company can use A/B
testing to validate assumptions, prioritize features that matter, and
deliver products without risks. From onboarding flows to in-product
notifications, testing helps optimize the user experience while
maintaining clear goals and hypotheses.
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Case 2 — SaaS onboarding: high signup, low activation
Case study template: turn messy signals into a focused analysis plan
Context
Plan (methods + outputs)
A B2B product gets signups, but users do not reach the “Aha” moment
(first successful project setup).
Symptoms (evidence)
• Many users abandon during setup wizard step 2
• Users skip help content; support asks “where do I start?”
• Trial-to-paid conversion is weak
Hypotheses
• Setup requirements are unclear (content + IA)
• Wizard doesn’t match mental model; too many choices too early
• Value messaging doesn’t connect to the user’s job
• Cognitive walkthrough of wizard for first-time user
• Interviews with new trial users (JTBD + vocabulary)
• Usability test with success criteria: reach first success
• Rewrite guidance + progressive disclosure
• Measure activation rate and time-to-value; iterate

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Case 3 — News site paywall: subscriptions not growing
Case study template: turn messy signals into a focused analysis plan
Context
Plan (methods + outputs)
Traffic is healthy, but subscription conversion is low; many users
bounce when paywall appears.
Symptoms (evidence)
• High bounce on paywall view
• Users complain “why should I pay?”
• Low trust in subscription benefits
Hypotheses
• Value proposition copy is too generic
• Paywall timing is frustrating (interrupts too early)
• Pricing and cancellation terms aren’t clear
• Content audit of paywall messaging + clarity
• 5-second test: do users understand the offer?
• Usability test: can users compare plans and understand cancellation?
• Experiment: paywall copy + plan comparison layout + timing
guardrails

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Case 4 — Mobile banking transfer: errors + low trust
Case study template: turn messy signals into a focused analysis plan
Context
Plan (methods + outputs)
Users frequently fail transfers or abandon during confirmation. Trust
and clarity are critical.
Symptoms (evidence)
• High error rate on account number field
• Support: “Is my money gone?”
• Users re-check balances multiple times
Hypotheses
• Error messages don’t explain what to fix
• Confirmation screen lacks reassurance and details
• Content doesn’t set expectations about processing time
• Heuristic review (error prevention + visibility of system status)
• Accessibility + readability audit
• Moderated usability test with stressful scenarios
• Rewrite errors + confirmations; add status timeline; validate again

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CJM
A customer-journey map is an infographic visualization of the process that a
persona segment goes through in order to accomplish a goal. Journey maps
are useful in communicating the general narratives and themes uncovered by
longitudinal research done to understand how a customer works toward a
goal over time.
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JTBD
A Jobs-to-be-Done Analysis or simply a JTBD Analysis (originally in 1960s
defined as a Segmentation Analysis) is an interdisciplinary methodology
rooted in behavioral economics that is used for observational causal
inference to understand what drives economic decisions, such as purchasing
or voting behavior, in given situation.
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JTBD
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First, conduct JTBD interviews and define personal outcome metrics (Read more about that in the first story in
the series)
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Then create and send a JTBD survey, and collect as many responses, as you can (More about that in the second
story in the series)
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Optionally look into other data sources, such as product analytics, historical data to collect as much profiling data
and needs data as possible.
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Start analyzing results:
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Calculate Opportunity Score
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Prioritize needs based on Opportunity Score
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If you have more data (at least 70-90+ responses):
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Perform Cluster (and other) analysis to segment respondents into groups (clusters) based on similar rates of
Importance and Satisfaction
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Profile segments. The most important part of the JTBD analysis is to determine what is causing respondents in
one segment to have different needs or to struggle more/differently than others. For example, it could be due to
disabilities, health, lifestyle, financial situation, environment, interests, and many other unknown factors.
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Finally, put anything together and define clear market segments as a “group of people” (with the same
characteristics) + “job they try to get done” where each segment has clear JTBDs and personal metrics they need
the most; What they have in common; Combine it with basic market research and do bottom-up market sizing.
Now you have the most powerful insight in front of your eyes from operational all the way to strategic view, and
you clearly know what to do next.
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https://habr.com/ru/articles/731206/
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