Subscription Fatigue Audit: What Survives the Cuts
Maps how many paid subscriptions people juggle, which categories they'd protect versus cancel first, and what actually triggers a cancellation. Built for subscription businesses and researchers tracking churn risk, with an AI follow-up that reconstructs a real recent cancel-or-keep decision instead of a hypothetical one.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
Roughly how many active paid subscriptions do you currently have (streaming, software, meal kits, apps, memberships, etc.)?
- 1-2
- 3-5
- 6-9
- 10 or more
- Not sure
About how much do you spend on all subscriptions combined per month, in dollars? (Your best estimate is fine.)
For each pair shown, pick the subscription category you'd protect the most and the one you'd cut the soonest if you had to trim your budget.
- Video streaming (Netflix, Hulu, etc.)
- Music streaming (Spotify, Apple Music, etc.)
- News & media subscriptions
- Cloud storage
- Fitness & wellness apps
- Meal kits or subscription boxes
- Productivity/software tools
- Gaming subscriptions
If you had $100 to redistribute across these categories based on how much value they give you, how would you split it?
- Video streaming
- Music streaming
- Cloud storage
- News/media
- Fitness or wellness apps
- Software/productivity tools
For each type of subscription, which best describes where it stands for you right now?
- Video streaming service
- Music streaming service
- Cloud storage
- News or media subscription
- Fitness or wellness app subscription
How likely are you to cancel at least one subscription in the next 3 months?
In the last 30 days, which of these have made you seriously reconsider a subscription? Select all that apply.
- A price increase
- Realizing I barely use it
- Found a free or cheaper alternative
- Overall budget tightened
- Bad experience with customer support
- Forgot I had it until reviewing my bank statement
Rank these factors by how much they matter when you decide whether a subscription is worth keeping.
- Price relative to how much I use it
- How often I actually use it
- Quality of content or features
- Whether I can share it with family/household
- Habit or forgetting to cancel
- Whether it's bundled with something else
Reconstruct the respondent's most recent real decision about a subscription: what almost made them cancel it, what made them stay (or actually cancel), and what a competitor or the same provider could have done to change that outcome. If they haven't cancelled anything recently, probe which single subscription feels most at risk right now and why it hasn't been cut yet.
What's your age range?
- 18-24
- 25-34
- 35-44
- 45-54
- 55-64
- 65+
- Prefer not to say
What's your approximate annual household income?
- Under $30,000
- $30,000-$59,999
- $60,000-$99,999
- $100,000-$149,999
- $150,000 or more
- Prefer not to say
That's everything — thank you! Your answers feed into a study on which subscriptions earn long-term loyalty and which get cut first when budgets tighten.
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
Why this template
What this template is built to do — we found no directly comparable template from other survey tools to review.
What sets it apart
- Uses a max-diff exercise and a $100 constant-sum allocation to reveal which subscription categories people actually value versus tolerate, not just self-reported ratings
- Includes an AI follow-up interview that reconstructs a respondent's real, recent cancel-or-keep decision instead of asking a generic hypothetical question
- Combines quantitative sizing (subscription count, monthly spend, a matrix of category status, an opinion-scale cancellation likelihood, and a ranked list of cancellation triggers) with qualitative depth in one flow
- Automated per-response quality scoring and an auto-generated report make the churn-risk data usable for subscription businesses and researchers without manual coding
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.
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