What your dashboard can't tell you

Know why users
didn't act

We call users who didn't act and tell you the top reasons + what to fix. In 48 hours.

Input: A cohort of users who didn't complete an action
Output: Top reasons why + what to fix next

You know what happened.
You don't know why.

What you know

  • Users didn't pay
  • Users didn't convert
  • Users dropped off at step 3
  • Usage fell 20% this month
  • Onboarding completion is at 40%

What you don't

  • Why did they decide not to act?
  • What broke their trust?
  • Where did expectation diverge from reality?
  • What would have changed their mind?

So teams guess, run experiments blindly, or delay decisions waiting for clarity that never comes.

Three steps. 15 minutes of your time.

1

Define the problem

Tell us which users didn't complete which action. Upload a cohort CSV.

Your effort: ~15 minutes
2

We run structured conversations

Our AI + human team runs structured conversations with your users. Not surveys. Not free-flow chats. Structured probing for intent, expectations, friction, and decisions.

12-15 completed conversations
3

You get reasons + fixes

Top reasons users didn't act, with percentage splits, real quotes, and concrete next steps for your team.

Delivered in 48 hours

Not transcripts. Not summaries.
Decision-ready output.

Top reasons

Clear patterns across users with percentage breakdown

Why users made the decision they made

Real language

Actual user quotes explaining their decisions

Not your interpretation — their words

What broke

Expectation mismatches, trust gaps, friction points, value gaps

Where the mental model diverged from reality

What to fix

Concrete product or messaging changes to act on immediately

Hypotheses ranked by frequency + impact

Sample report

whylayer diagnosis — order drop-off — quick commerce app
Input

Users who opened the app 3+ times in the last week but placed zero orders

Do this now
  • Show minimum-order progress bar on cart (not just a blocker at checkout)
  • Surface "frequently bought together" when cart is Rs. 50-80 below minimum
  • Test removing minimum order for first 3 orders from new users
Why they didn't order
Minimum order forced unwanted items 41% of users

Users needed 1-2 items but were Rs. 50-150 short of the minimum. Adding random items to meet the threshold felt wasteful. They closed the app and went to the nearby store instead.

"I just needed milk and eggs. But then it says add Rs. 89 more. I'm not buying chips I don't want."

Key item out of stock, killed the trip 28% of users

The one item that triggered the app open was unavailable. Without it, the rest of the cart lost its reason to exist. Users didn't substitute — they abandoned entirely.

"I wanted the Amul Gold 500ml. It wasn't there. So I just thought I'll go downstairs."

Delivery time broke the urgency 19% of users

Users opened the app for an immediate need (cooking, guests arriving). When ETA showed 25-35 min instead of expected 10-15, the time gap made the kirana store faster. The "quick" promise was broken.

"It said 30 minutes. My dukaan is 2 minutes away. What's the point then?"

Who's dropping off
Small-basket daily buyers highest frequency, lowest AOV

Need 1-3 items daily. Minimum order is a daily friction. Will churn to kirana if not resolved.

Trigger-item shoppers highest recovery potential

Open the app for one specific item. If it's there, they'll build a cart around it. If not, entire session dies.

What to fix
1. Smart top-up suggestions when cart is near minimum "Add Rs. 62 more — here are items you usually buy" instead of a dead-end blocker
2. "Notify when back" for out-of-stock trigger items Retains the session intent. User gets a push when their item is restocked — converts abandoned session into delayed order.
3. Show real-time ETA before cart (not after) Users who see "12 min" on the home screen build a cart. Users who see "32 min" at checkout feel betrayed.
15 conversations High confidence Delivered in 40 hours
View full sample report →

We don't automate calls.
We automate figuring out why.

Voice AI platform
Voice is just delivery
Survey tool
Decisions, not opinions
Research agency
No coordination overhead
Transcript summarizer
Patterns + what to fix

Common questions

How is this different from Vapi / Retell / voice tools?

+

Those tools help you run calls. We tell you why users didn't act and what to fix. They give infrastructure. We give structure + insights.

Why not just do user interviews manually?

+

You can — but it's slow, inconsistent, and rarely happens at the right moment. We give you patterns across users, not anecdotes from a few calls.

Will users actually pick up?

+

We only need 12-15 conversations to see clear patterns. That's achievable with a cohort of 50-80 users, with proper framing and retry logic.

What if we already know why users didn't act?

+

You likely have hypotheses. We tell you which ones are true across users — and what you're missing.

Is this just a survey in voice form?

+

No. Surveys ask predefined questions and capture surface-level answers. We run structured behavioral flows that probe for depth and capture decision-making context.

How long does it take?

+

Setup: ~10-15 minutes of your time. Execution: automated. Output: delivered within 48 hours.

Can we build this ourselves?

+

You can run calls. The hard part is getting consistent reasons across users with a structured behavioral lens. That's what we do.

Give us a drop-off.
We'll tell you why.

48 hours. 15 minutes of your time.

Start a diagnosis