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Simulated consumer research for consumer internet analysts
TL;DR
- Consumer internet models turn on whether users keep opening the product, pay, renew and switch when a rival ships a better offer.
- Neon Blue runs simulated user studies on engagement intent, churn reasons, price tolerance, switching and launch demand.
- Each study returns answers by user segment (tenure, spend, platform, cohort) as a labeled input to your model, never a stock call.
- Results come as a report. An ongoing data stream and an API are in build. The first study is free.
Neon Blue builds simulated audiences of a consumer internet product's users, AI models that answer the way a defined group of people would, grounded in Neon Blue's own paid studies and AI-moderated interviews with real people. Consumer internet analysts at hedge funds and investment firms use Neon Blue to answer the user questions behind an engagement and revenue model while the quarter is still open.
What consumer questions matter most for consumer internet stocks?
The same user questions show up across marketplaces, social, streaming, gaming and consumer apps:
- Will engagement hold next quarter? Opens, sessions and core actions.
- Is churn a pricing problem or a product problem?
- How much price can the product take before paid users leave?
- Which rival picks up switchers?
- Will a new feature or SKU earn trial and repeat?
- Do promotions create lasting users, or only temporary activity?
Each of these maps to a variable page with the full method, linked below.
Which Neon Blue studies answer them?
| Analyst question | Study to run | Audience | What comes back |
|---|---|---|---|
| Will engagement hold? | Open and action intent vs last 30 days | Recent users by tenure and spend | Open more, same or less, and which actions change |
| Why are users leaving? | Churn reason and win-back | Lapsed and at-risk users | Price, product, rival or life-change drivers |
| How much price can it take? | Price ladder on the current plan | Paid users by tenure | Keep, downgrade, pause, switch or cancel |
| Who gains switchers? | Next-product choice | Users who cut or left | Share of next use by rival, with reasons |
| Will a launch stick? | Trial and repeat intent | Current users and category non-users | Trial timing, repeat and what it replaces |
| Do promos create lasting users? | Promo response and post-promo intent | New and existing users | Stay after promo vs churn when the deal ends |
How do you read engagement before the company reports?
Ask recent users how often they opened the product in the last four weeks, what they mainly did, and whether they expect to open more, the same or less in the next four to twelve weeks. Weight by spend or by core-action share. App engagement intent covers the method.
How do you separate pricing churn from product churn?
Ask lapsed and at-risk users why they left or cut back, then what would bring them back: a lower price, a missing feature, fewer ads, or nothing. Subscriber churn covers the method, and is churn a pricing problem or a features problem walks through the framing.
How do you test a price increase on a paid plan?
Show paid users the current plan at today's price and at the new price, and ask keep, downgrade, pause, switch or cancel. Weight by revenue. The ladder matches price increase tolerance. For membership-style renewals, see membership fee tolerance.
What does a consumer internet study look like?
Hypothetical: a subscription product plans a mid-teens price increase and says engagement among heavy users is stable. The analyst wants to know whether paid users renew at the new price, and whether lighter users churn first.
- Study question: "Your current plan will cost about 15 percent more at the next renewal. Would you keep the same plan, move to a cheaper plan, pause, switch to another product, or cancel?"
- Audience: paid users in the last ninety days, split by tenure, monthly spend and weekly open frequency.
- Follow-ups: the main reason to leave, and which rival they would try first.
Example output layout (illustrative, not a real result)
The shape of a Neon Blue user read. Cells are blank on purpose; a real study fills them.
| Segment | Keep plan | Downgrade | Pause | Switch or cancel | Main reason if leaving |
|---|---|---|---|---|---|
| Heavy users, tenure 2+ years | __% | __% | __% | __% | Coded themes |
| Light users, tenure under 1 year | __% | __% | __% | __% | Coded themes |
| Promo-acquired, still on deal | __% | __% | __% | __% | Coded themes |
How the analyst reads it: if heavy long-tenure users keep the plan, the ARPU assumption holds on the core base. If promo-acquired users leave, net adds need a harder look. Both lines go into the model labeled as simulated.
Want this run on your own name? Neon Blue's first study is free: start a free first study.
When should consumer internet analysts run these studies?
After a pricing change, a feed or feature redesign, a rival launch, or ahead of a renewals-heavy month, and before the quarter closes. A repeat read with the same questions each quarter gives a trend to line up against reported engagement and churn. Pre-earnings consumer checks lays out the calendar.
What can't a user study see?
- Platform ranking, crash rates and latency.
- Ads load and advertiser demand, which move revenue without changing user intent alone.
- Supply-side constraints on marketplaces (seller inventory, creator supply).
- Treat each read as one directional input, not a settled number.
Questions about consumer research for consumer internet analysts
What consumer data do consumer internet analysts use?
Mostly product analytics, card panels and other observed-behavior sources for what already happened. A Neon Blue study adds why users will stay, pay or switch next quarter. See how simulated consumer data differs from card panel data.
Can a study cover a specific product's users?
Yes. The audience is defined as that product's recent users, cut by tenure, spend, platform and cohort. You can name the product in the study.
Can it compare two apps or marketplaces?
Yes. Ask users of both, or ask one product's users which rival they would try next. Brand switching covers the method.
Does Neon Blue predict consumer internet earnings or stock moves?
No. Neon Blue answers the user variables in your model, such as engagement intent or churn reason. It doesn't make stock calls or give price targets.
How do I start a free first study?
Bring the product, the quarter and the user assumption you trust least. Neon Blue's first study is free. Start a free first study