14. Lift AI

Lift AI is FullSession's automated optimization layer. You point it at a funnel and a target metric, and it works continuously in the background — analyzing sessions, finding the issues most likely to be costing you conversions, recommending fixes, and then measuring whether your fix actually worked. Where the rest of FullSession helps you investigate, Lift AI helps you prioritize and prove impact.

Lift AI is available from the Pro plan ([Chapter 17, section 17.1]). On Free and Growth the Lift AI item is locked, with an Upgrade plan link to Settings → Subscription.

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14.1 What Lift AI Is

Lift AI is built around three connected objects:

Object	What it is
Goal	A monitoring target you create — a specific funnel and a KPI to improve (e.g. "improve checkout conversion")
Run	A scheduled analysis of a goal. Lift AI runs automatically every 8 hours for each active goal
Issue	A conversion-blocking problem a run detects — with a severity, a recommendation, and an estimated impact

The flow is: you create a goal → Lift AI runs on it automatically → each run surfaces issues (recommendations) → you act on an issue → Lift AI measures the real lift over a validation window.

How it differs from the Funnels AI explanation

FullSession has two AI capabilities that are easy to confuse:

	Funnels AI ([Chapter 12, section 12.5])	Lift AI (this chapter)
Trigger	You click "analyze" on a funnel step	Runs automatically on a schedule
Scope	One step, one drop-off, right now	A whole goal, tracked continuously
Persistence	Ephemeral explanation	Persistent goals, runs, and issues
Measures fixes?	No	Yes — predicted and actual lift

In short: Funnels AI is an on-demand explanation; Lift AI is an always-on optimization program.

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14.2 Creating a Goal

You create a goal in a short setup wizard. The goal ties together a funnel, an audience, and a KPI, plus some context that helps the AI tailor its analysis.

Step 1 — Context

Field	What you provide
Name	A name for the goal (e.g. "Checkout Conversion")
Description	Optional notes
Industry	Your industry, from a list (e.g. Software / SaaS, E-commerce / DTC, Financial Services)
Role	Your team's role(s) — Support, UX & Design, Engineering, Marketing, Customer Experience, Product Management, Data & Analysis, Account Management

The industry and role help Lift AI frame its recommendations for your context.

Step 2 — Funnel selection

Choose the funnel the goal will monitor — pick an existing funnel ([Chapter 12 — Funnels]) or define a new one on the spot.

A goal's funnel needs more than three steps. Lift AI analyzes transitions between steps, so a meaningful funnel is required.

Step 3 — Verify (data readiness & KPI)

The final step checks that Lift AI has what it needs and sets your KPI:

• Installation & data-readiness check — confirms the tracker is installed and there's enough data for reliable analysis.

• Baseline segment — the audience to measure against (defaults to Everyone, or pick a segment).

• KPI — Conversion Rate or Revenue Per Visitor (section 14.3).

What you set vs. what Lift AI computes

Set by you	Computed automatically
Name, description, industry, role	Baseline value (your current KPI)
Funnel and baseline segment	Prediction confidence (low / medium / high)
Validation window (days)
KPI (and RPV config, if applicable)

The baseline is read from your funnel's current performance, and the prediction confidence reflects how much data is available — neither is hand-entered. If there isn't enough data or the required signals are missing, Lift AI won't let you create the goal until that's resolved.

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14.3 KPIs: Conversion Rate & Revenue Per Visitor

A goal targets one of two KPIs:

Conversion Rate

The share of visitors who complete the funnel. This is the default and needs no extra configuration — Lift AI reads the baseline from your funnel.

Revenue Per Visitor (RPV)

Revenue divided by visitors — useful when you care about money, not just completion. RPV combines your funnel's conversion rate with an Average Order Value (AOV), which you supply one of two ways:

AOV source	How it works
Enter directly	Type your average order value
From a custom event	Point Lift AI at a numeric value on one of your FUS.event(...) events ([Chapter 7, section 7.5]) and it averages it for you

The KPI is fixed once the goal is created. You can edit most of a goal later, but the KPI and baseline can't be changed — to switch KPI, create a new goal.

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14.4 How Analysis Runs

Once a goal is active, you don't trigger anything — Lift AI does the work on a schedule.

Scheduled, automatic runs

Lift AI runs every 8 hours for each activated goal. Each run:

• Analyzes recent sessions against the goal's funnel.

• Produces a predicted conversion rate (with a confidence range) representing where the KPI could land if the detected issues were addressed.

• Calculates a predicted improvement — the lift on the table.

• Detects and updates the goal's issues (section 14.5).

Paused or archived goals are skipped.

There's no "run now" button. Analysis is driven entirely by the schedule — you can't force an immediate run. New goals will show their first results after the next scheduled run.

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14.5 Reading Recommendations (Issues)

Each goal has a recommendations view listing the issues Lift AI has found, prioritized so you can work top-down.

Summary stat cards

At the top, counters summarize issues by severity — Severe, High, Medium, Low — with trends, so you can see your overall problem load at a glance.

The recommendations table

Column	Shows
Recommendation	The issue title (e.g. "Rage clicks at the shipping step")
Status	Where the issue is in its lifecycle (section 14.6)
Page	The URL where it occurs
Issue type	Rage click, dead click, error click, page-load error, uncaught exception, or network error
Severity	Severe / High / Medium / Low
Funnel steps	The affected transition (e.g. 2 → 3)
Baseline	Current conversion at those steps
Predicted lift	The expected improvement if fixed (a range, e.g. 15.2% → 16.9%)
Actual lift	The measured improvement after a fix (appears once validated)
Last seen	The most recent run that detected it

Issue detail

Open an issue to see its full detail:

• Rationale — the AI's explanation of the root cause.

• Recommendation — a suggested fix.

• Impact — the estimated conversion loss and the affected-vs-unaffected session breakdown.

• Sample sessions — example recordings, so you can watch the issue happen ([Chapter 6 — The Session Player]).

Tip — work the Severe and High issues with the largest predicted lift first. The sample sessions let you confirm the AI's read before you invest in a fix.

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14.6 The Issue Lifecycle & Measuring Real Lift

Lift AI's distinctive value is closing the loop: not just finding issues, but proving whether your fix worked. That happens through an issue's status lifecycle.

The status flow

Status	Meaning
New	Just detected
Undecided	Acknowledged, not yet triaged
Dismissed	You've rejected it as not worth acting on
Planned	Scheduled for a fix
Change is live	You've deployed a fix
Validating	Lift AI is monitoring the impact
Validation complete	The validation period has ended and the result is in

You move an issue through these states as you work it.

Predicted lift vs. actual lift

• Predicted lift is Lift AI's estimate — what the KPI could gain if the issue were resolved. You see it as soon as the issue is detected.

• Actual lift is the measured result. When you mark an issue Change is live and then Validating, Lift AI captures the baseline and monitors performance for the goal's validation window (a number of days you set). When that window closes, the issue reaches Validation complete and the Actual lift column shows what really happened.

This before/after measurement is what turns Lift AI from a recommendation engine into an accountable optimization program — every fix gets a verdict.

Tip — set the validation window long enough to gather representative traffic for the affected steps. Too short and the "actual lift" reading will be noisy.

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14.7 Managing Goals & Issues

Managing goals

Action	Notes
Create	Via the setup wizard (section 14.2)
Edit	Change name, description, funnel, baseline segment, industry, role, and validation window — but not the KPI or baseline
Pause	Stop scheduled runs without deleting the goal
Archive	Hide a goal from the active list
Delete	Remove the goal entirely

Goals are scoped to the funnels you own.

Managing issues

• Change status — move issues through the lifecycle (section 14.6); status changes are tracked over time.

• Filter — by status, severity, issue type, funnel steps, and date.

• Sort — by impact, conversion loss, predicted lift, or detection date.

What Lift AI doesn't do

To set expectations clearly, these don't exist:

• No alerts/notifications when a new issue is found (no email or Slack on detection) — check the recommendations view, or use Alerts ([Chapter 15]) for metric-based notifications.

• No industry benchmark comparisons.

• No auto-generated fix code — recommendations are written guidance, not patches.

• No issue clustering, A/B-test integration, custom alert thresholds, leaderboards, export/reporting, or webhooks.

• No manual run trigger — runs are scheduled only.

The big picture — Lift AI turns a funnel and a KPI (Conversion Rate or RPV) into a continuously analyzed goal. It runs every 8 hours, surfaces ranked issues with a root-cause rationale, a recommendation, an estimated impact, and sample sessions to watch, then measures actual lift over a validation window as you move each issue from new to validation complete. It's automated (no prompts, no manual runs), accountable (predicted vs. actual lift), and distinct from the on-demand Funnels AI explanation.

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Next up: [Chapter 15 — Alerts & Notifications] covers proactive, metric-based alerting — being told when a conversion rate drops or errors spike, complementing Lift AI's continuous issue detection.