Practical Examample: Theme Analysis
Example Case: Theme Analysis in a Funnel Investigation
Imagine a typical e‑commerce funnel:
Product Page → Add to Cart → Checkout → Payment → Confirmation
The quantitative data shows:
Strong traffic to product pages
Good add‑to‑cart rate
Sharp drop‑off at Checkout Step 1
Lower‑than‑expected payment completion
Numbers tell us where the problem is. Theme analysis tells us why it happens.
Below is a full example of how the analysis unfolds.
1. Raw Qualitative Inputs
Session recordings
Users scroll up and down the checkout page repeatedly
Users hover over the “Shipping Method” dropdown for several seconds
Users click the “Continue” button but nothing happens (validation error not visible)
Mobile users pinch‑zoom on the form fields
Several users abandon the page after clicking “View return policy”
Survey responses
“I wasn’t sure about the final shipping cost.”
“The form kept saying something was wrong but I didn’t see the error.”
“Too many fields on mobile.”
“I wanted to check the return policy before paying.”
Support tickets
“I can’t get past the checkout form.”
“Where do I see the total cost including shipping?”
At this stage, nothing is grouped — it’s just raw observations.
2. Initial Codes (Labeling the Data)
From the raw data, we assign short, neutral labels:
Shipping cost uncertainty
Hidden form error
Mobile form difficulty
Return policy hesitation
Information hunting
Form validation confusion
Too many fields
Trust check before payment
These codes are the building blocks of the themes.
3. Theme Formation (Grouping Codes Into Patterns)
Now we cluster related codes into broader themes.
Theme 1: Unclear cost expectations
Codes included:
Shipping cost uncertainty
Information hunting
Return policy hesitation
Theme 2: Form friction and validation issues
Codes included:
Hidden form error
Form validation confusion
Too many fields
Mobile form difficulty
Theme 3: Trust checks before committing
Codes included:
Trust check before payment
Return policy hesitation
Themes reveal the recurring patterns behind the drop‑off.
4. Thematic Insights (Explaining the Why)
This is where the themes become meaningful explanations.
Insight 1: Users hesitate because they cannot confirm the final cost before continuing.
Evidence: long hovers on shipping dropdown, survey mentions uncertainty, repeated scrolling.
Insight 2: Users get stuck due to invisible or unclear form validation errors.
Evidence: clicks on “Continue” with no visible feedback, support tickets, repeated form retries.
Insight 3: Users pause to verify trust elements (returns, guarantees) before paying.
Evidence: exits to return policy page, survey mentions wanting reassurance.
These insights explain the root causes of the funnel drop‑off.
5. Recommendations (Actionable Output)
Recommendation 1: Show total cost (including shipping) earlier in the checkout.
Expected impact: reduce hesitation and increase progression to payment.
Recommendation 2: Improve form validation visibility and reduce field count on mobile.
Expected impact: reduce form abandonment and user frustration.
Recommendation 3: Surface trust signals (returns, guarantees) directly on the checkout page.
Expected impact: reduce exits to secondary pages and increase payment completion.
6. Final Executive Summary
Users drop off at Checkout Step 1 because they cannot confirm the final cost, encounter hidden form errors, and leave the page to verify trust information. Improving cost visibility, fixing validation feedback, and surfacing trust signals directly on the page will significantly increase checkout completion.