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Find where users are struggling

Ask Fullstory MCP what's frustrating your users and it will surface the biggest friction points, ranked by impact, backed by real session evidence, and ready to act on.

Example questions​

What are the biggest frustrations on our checkout flow right now?
What's causing the most friction for mobile users on our product pages?
What problems have gotten worse in the last week compared to the week before?
What frustrations are users experiencing after they add something to their cart?

What you get back​

A ranked analysis of the most significant friction points: what's happening, which pages and devices are affected, how many users are impacted, and whether it's getting better or worse. For example:

Top frustrations on checkout (last 30 days):

1. Rage clicks on "Place Order" button: 4.2% of checkout visitors, up 18% vs. prior period
   Concentrated on mobile (73%). Occurs at the final payment step.

2. Network errors on /api/cart/apply-promo: 2.8% of users
   Spike started 6 days ago. Affects all platforms equally.

3. Abandoned form on the shipping address step: 1.9% of users
   Higher rate on first-time visitors vs. returning customers.

Follow-up questions​

Once you have the list, you can go deeper on anything:

Tell me more about those rage clicks on "Place Order." 
What does a typical session look like?
Is the promo code error affecting a specific device or browser?
Show me sessions where users abandoned the shipping form. 
What were they trying to do?

The AI will read real sessions to explain the behavior: what users were doing, whether they recovered, and what the likely cause is, without you needing to watch recordings.

Example follow-up​

You:  Tell me more about the "Place Order" rage clicks.
      What's actually happening in those sessions?

AI:   Reviewed 5 sessions. Consistent pattern across all of them:
      users tap "Place Order" multiple times in quick succession,
      and the session logs a 500 error on /api/orders. Two users
      navigated away immediately after the error.
      
      The repeated taps followed by a 500 are consistent enough
      to suggest something is going wrong at submission. Worth
      investigating whether the endpoint is handling concurrent
      requests correctly, or whether the error is masking a
      partial success.