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Most event stacks treat feedback as a closing ritual. They ship a post-event email, collect a thin response rate, export a spreadsheet, and forget it. That mindset wastes the most valuable asset an event produces: a quantified record of expectation, emotion, and trust.
InEvent treats feedback as a first-class dataset. Event Survey Software becomes an instrumentation layer that measures experience across time, not a form builder. InEvent captures feedback pre-event, in-session, and post-event, then pushes it into CRM so sales, customer success, and product teams can compute Return on Emotion (ROE) as a durable signal, not an anecdote.
This page explains the architecture: touchpoint capture, data validation, conditional logic, real-time scoring, webhook automation, AI clustering, and CRM synchronization. It also covers native and third-party integrations including SurveyMonkey Integration, Typeform, and Slido, plus how to separate visual embeds from real data sync.A mature event program behaves like a product. Products run telemetry. Events can run telemetry too, but only if the platform designs feedback as a distributed system:
Many capture points
Multiple question schemas
Identity resolution across devices
Near-real-time scoring for operations
Durable storage for longitudinal analysis
Controlled exports and CRM writes
Automated escalation when sentiment dips
InEvent builds that system with three core components:
InEvent Logic-Branching: conditional survey flows and dynamic question graphs
InEvent Sentiment Engine: qualitative comment interpretation at scale using the ChatGPT integration
Event feedback differs from “website feedback” or “product feedback” because events amplify:
social proof (people anchor on the crowd and speakers)
temporal compression (many stimuli in a short window)
expectation gaps (the event promise sets a mental contract)
memory peaks (keynotes and moments dominate perception)
This creates a rule for data design:
If you only ask after the event, you measure memory, not experience.
InEvent instruments three phases because each phase answers a different question:
Pre-event: What did attendees expect and why did they register?
In-session: What did they feel in the moment and what content performed?
Post-event: Did the event deliver enough value to change loyalty and advocacy?
When you connect these phases by identity and timestamp, you can compute ROE as “expectation-to-satisfaction delta” rather than a single score.
Most survey tools store answers as loosely typed blobs. That blocks reliable automation. InEvent stores feedback as structured entities.
Respondent: resolved attendee identity (or anonymous if required)
Instrument: the survey/poll/rating template definition
Response: a completed (or partial) run of the instrument
Answer: atomic question response with typed value and metadata
Context: the event/session/booth/campaign context that produced the answer
Score: computed metrics derived from answers (NPS, CSAT, speaker score, sentiment)
Action: triggered outcomes (webhooks, tickets, CRM updates)
InEvent stores answers with types to enable automation without fragile parsing:
number (NPS 0–10, CSAT 1–5)
boolean
single_select / multi_select
text (open response)
scale_likert
timestamp
duration
url (optional)
file (optional, controlled)
Each answer carries:
question_id
instrument_id
respondent_id
context_id
value
confidence (for AI-derived fields)
created_at
This becomes the substrate for real-time analytics and CRM writes.
The registration flow is the earliest stable moment where you have:
authenticated identity
explicit consent capture
high completion probability
This is why the registration context matters and why you referenced “Event Leaders Spotlight Smarter Registration Strategies.” Pre-event questions at registration establish the baseline.
InEvent uses pre-event instruments to capture:
primary goal (networking, learning, procurement, partner search)
urgency and timeline (buying stage)
topic priorities
role and industry attributes not present in CRM
accessibility and language needs
satisfaction risks (“What would make this event a failure for you?”)
InEvent writes those answers into the attendee profile and the CRM timeline so post-event satisfaction can compare against the baseline rather than guess.
In-session feedback has two unique properties:
it reflects actual experience with minimal memory distortion
it can trigger operations in real time (fix issues, escalate, adjust programming)
InEvent uses:
live polls for engagement signals
“rate this session” for content performance scoring
structured issue reporting (“audio issues,” “slides unreadable”)
Post-event feedback answers loyalty and advocacy:
NPS: would they recommend?
CSAT: did it satisfy?
qualitative: why or why not?
InEvent treats post-event as a continuation, not a separate dataset. It attaches post-event responses to the same respondent identity and context graph.
Integrations fail when teams confuse “embedded form” with “integrated data.”
InEvent supports two categories:
Use this when the goal is presentation speed, not unified analytics:
render SurveyMonkey/Typeform inside InEvent UI
keep vendor logic and UI intact
accept that vendor stores the source-of-truth response
Engineering behavior:
InEvent wraps the iFrame with:
safe sizing and responsive behavior
accessibility considerations for focus and keyboard navigation
optional context injection via query parameters (non-sensitive IDs)
InEvent tracks high-level events:
iFrame shown
completion redirect fired (if supported)
basic engagement time
Limitations:
You do not get reliable per-question answers in InEvent unless you also sync via API.
You cannot trigger fine-grained webhooks on single question values without vendor callbacks.
Use this when the goal is ROE, CRM writes, and automation.
InEvent connects via API to:
SurveyMonkey
Typeform
Slido (poll results, Q&A signals depending on configuration)
Data sync behavior:
InEvent pulls schema definitions (questions, types, option lists)
InEvent ingests responses via webhooks or scheduled sync
InEvent normalizes into typed answers
InEvent maps identity to attendees using stable keys:
external respondent IDs
signed tokens passed at launch (preferred)
This distinction matters: the sponsor or CX leader wants outcomes, not embedded widgets.
Branching drives completion and improves signal quality because it reduces irrelevant questions.
InEvent Logic-Branching implements conditional logic as a directed graph:
nodes: questions
edges: conditions that select the next node
terminal nodes: completion outcomes
side effects: scoring and triggers
equality: q1 == "Yes"
numeric thresholds: nps <= 6
membership: "Audio" in issues_selected
regex match: free text contains keywords (with caution)
contextual: session type, attendee segment, ticket type
When a respondent answers a question:
InEvent validates the answer against question type and constraints.
InEvent stores the answer.
InEvent evaluates outgoing edges from the current node in deterministic order.
InEvent selects the next node.
InEvent emits an event for analytics and triggers.
Branching becomes critical for negative feedback flows:
If an attendee selects “Audio problems,” InEvent routes them to:
severity rating
device/environment check
“do you want a staff follow-up now?”
If NPS ≤ 6, InEvent routes them to:
root cause categories
free text
opt-in for follow-up
This yields actionable negatives, not just a low score.
A Session Rating App must capture ratings while the attendee still holds context, but not so early that they miss the conclusion.
InEvent schedules a “rate this session” prompt based on stream state.
InEvent calculates rating_prompt_time as:
session_end_time - 120 seconds for fixed schedules, or
stream_end_signal - 120 seconds when the stream sends end cues, or
a dynamic estimator based on:
planned duration
playback state
session chapter markers
Then InEvent executes:
show non-blocking prompt with keyboard-accessible focus management
allow “rate later” without penalizing completion
store rating immediately upon selection
Client posts rating:
POST /v1/sessions/{session_id}/ratings
Backend stores:
rating value
respondent identity
playback context (live vs replay)
timestamp
InEvent ROI-Calculator updates:
session score
speaker scorecard
program track score
InEvent avoids naïve averages.
Speaker scoring requires:
volume weighting (confidence increases with sample size)
recency weighting (live vs replay, and day-of-event vs post-event)
bias control (some sessions attract harsher audiences)
A practical scoring model:
Let r be rating 1–5.
Compute a Bayesian-adjusted mean:
score = (m * C + sum(r)) / (C + n)
Where:
m = global mean rating across sessions
C = confidence constant (e.g., 20)
n = number of ratings for this speaker/session
This prevents a speaker with 3 ratings from outranking a speaker with 300 ratings.
InEvent can also incorporate “watch time” as a reliability signal:
a rating after ≥ 60% watch time carries more weight than a rating after 10%.
Most post-event surveys get single-digit response rates because the incentive structure is broken.
InEvent improves completion by tying feedback into the same reward mechanics that drive engagement.
Attendees respond when they feel:
their input matters
the system acknowledges effort
they get closure (completion state)
InEvent applies a gamified exchange:
complete survey → earn points
points → unlock:
badges
leaderboard visibility
raffle entry
exclusive content access
priority networking slots (if configured)
InEvent awards points based on:
completion state (only complete responses get full points)
quality gates (optional)
minimum time spent
required questions answered
anti-abuse logic
rate limiting
duplicate suppression by identity
anomaly detection (mass low-effort submissions)
InEvent then writes:
gamification.points_awarded
survey.completed
This creates a measurable lift. The exact “5% to 65%” depends on audience and incentive design, but the mechanism is stable: rewards convert dead surveys into an engagement loop.
Open-text feedback contains the highest value signal, but it does not scale.
This is where ChatGPT and InEvent’s New Integration becomes the analysis brain behind InEvent Sentiment Engine.
Ingest
Accept free text answers tied to context: session, speaker, booth, day, track
Normalize
language detect
strip PII where policy requires
de-duplicate obvious repeats
Classify
sentiment label: Positive / Neutral / Negative
confidence score
Cluster
group comments into topics:
audio quality
content depth
pacing
networking value
registration friction
accessibility issues
Summarize
generate cluster summaries and representative quotes (short, compliant)
Route
attach clusters to dashboards
trigger webhooks for critical negatives
AI produces strong generalization. Rules provide guardrails.
InEvent combines:
keyword/rule detectors for safety-critical categories:
harassment
discrimination
safety threats
accessibility barriers
AI classification for nuance and mixed sentiment
This reduces false negatives for high-risk categories.
Generic sentiment analysis fails because it ignores context.
“Incredible content but audio was terrible” is mixed sentiment. InEvent Sentiment Engine treats that as:
positive: content
negative: production quality
InEvent uses aspect-based classification:
label sentiment by dimension (content, logistics, tech, speaker, accessibility)
That is actionable. A simple Positive/Neutral/Negative label is not.
Feedback becomes valuable when it triggers intervention.
InEvent supports webhook triggers for:
low NPS
low CSAT
negative sentiment clusters
accessibility complaints
VIP dissatisfaction
session-level collapses (audio failures)
This is not a dashboard feature. It is an event-driven workflow.
condition:
NPS answer exists
NPS value ≤ 6
filters (typical):
attendee segment: VIP, sponsor, speaker, government delegate
ticket type: premium
session context: keynote vs breakout
action:
create ticket in Zendesk
or create case/task in Salesforce
and optionally notify a Slack channel (if configured)
{
"event": "feedback.crisis_triggered",
"trigger": "nps_below_threshold",
"threshold": 6,
"value": 4,
"respondent": {
"attendee_id": "att_18fe...",
"email": "user@example.com",
"name": "Amina K.",
"segment": "VIP"
},
"context": {
"event_id": "evt_9c3b...",
"survey_id": "srv_post_nps_01",
"session_id": "sess_keynote_02",
"timestamp": "2026-02-10T17:22:09Z"
},
"evidence": {
"csat": 2,
"sentiment": "negative",
"clusters": ["audio_quality", "registration_wait"],
"comment": "Could not hear half the keynote. Support never responded."
},
"idempotency_key": "crisis_att_18fe_srv_post_nps_01"
}
This payload includes enough evidence for a support agent to act immediately.
Tickets explode when the same trigger fires repeatedly.
InEvent uses idempotency keys per respondent per instrument so:
a retry updates the same ticket
a second low score on the same survey does not create duplicates
operators can set cooling windows (e.g., one crisis ticket per attendee per 24 hours)
NPS question uses 0–10 scale.
Promoters: 9–10
Passives: 7–8
Detractors: 0–6
NPS = (%Promoters − %Detractors) × 100
InEvent ROI-Calculator computes NPS at multiple levels:
overall event NPS
per segment (VIP vs general)
per track
per session day
per sponsor cohort (when applicable)
CSAT typically uses 1–5 or 1–7.
CSAT = (number of satisfied responses / total responses) × 100
Where “satisfied” often means 4–5 on a 5-point scale.
InEvent stores the definition used for that event to preserve comparability.
Session rating often uses 1–5.
InEvent avoids raw means and supports:
Bayesian adjustment
watch-time weighting
outlier suppression policies (configurable)
ROE becomes meaningful when tied to business outcomes.
A practical ROE model:
Define Expectation Score (E) from pre-event survey:
goals clarity, anticipated value, confidence in agenda, intent to attend sessions
Define Experience Score (X) from in-session ratings, engagement, and CSAT
Define Advocacy Score (A) from NPS and qualitative sentiment
Then compute:
ROE = w1*(X − E) + w2A + w3SentimentIndex
Where weights reflect your organization’s priorities.
InEvent ROI-Calculator can compute these rollups and push ROE to CRM objects for customer success and account management.
You required an explicit CRM context: InEvent Launches Integration with HubSpot CRM. The value is persistence and operationalization.
InEvent pushes feedback into CRM so it becomes:
part of the contact timeline
part of account health scoring
a trigger for customer success workflows
an attribution signal for renewal and expansion
InEvent maps:
contact properties:
inevent_last_nps
inevent_last_csat
inevent_last_sentiment
inevent_roe_score
timeline events:
“Completed Post-Event NPS”
“Rated Session: Keynote 2”
“Submitted Accessibility Issue”
HubSpot workflows can then:
route detractors to CS
create tasks for account managers
add contacts to recovery sequences
Salesforce buyers want structured objects.
InEvent can write:
Campaign Member status updates for attendance and engagement
Tasks for follow-up
Cases for crisis triggers
Custom object records for survey responses when deep reporting matters
The CRM becomes the system of action, not the graveyard.
Feedback becomes useless if you cannot trust who said what, or if you violate consent boundaries.
InEvent enforces:
stable respondent IDs tied to authentication
secure links for email-delivered surveys with signed tokens
dedupe logic:
one response per respondent per instrument unless configured otherwise
versioning when repeat responses are allowed
privacy controls:
consent flags attached to each response
retention policies
export controls by role
InEvent exposes feedback insights at multiple resolutions:
executive: ROE, NPS, sentiment index by segment
operations: session rating heatmaps, issue clusters by time
content: speaker scorecards, topic satisfaction deltas
CS: detractor lists with context and recommended actions
data teams: raw exports and API access
A speaker scorecard includes:
adjusted rating score
rating distribution
sample size and confidence
sentiment breakdown of comments tied to that speaker/session
top positive and negative clusters
correlation with watch time and drop-off
This turns subjective speaker evaluation into a repeatable process.
Feedback engineering needs explicit payloads and event flows.
POST /v1/feedback/responses
Payload includes:
respondent identity token
instrument ID and version
answers as typed fields
context object (event/session)
InEvent emits events such as:
feedback.response.created
feedback.score.updated
feedback.sentiment.classified
feedback.crisis_triggered
crm.sync.completed
Each event includes an idempotency key so downstream systems can process reliably.
Yes. A “good” B2B event NPS usually sits above 30, strong programs often exceed 50, and elite experiences can reach 60+. Compare scores only within similar audiences and formats, and track trend lines by segment rather than a single overall number.
Yes. InEvent ROI-Calculator computes NPS by classifying responses into promoters, passives, and detractors, then calculating %promoters minus %detractors. InEvent updates dashboards in real time and can push NPS values to HubSpot or Salesforce as contact-level fields.
No. An embedded iFrame only displays the form. A real integration syncs schemas and responses through APIs, normalizes typed answers, resolves identity, and writes results into analytics and CRM. InEvent supports both, but only API sync enables automation and ROE.
Yes. InEvent Sentiment Engine uses the ChatGPT integration to classify comments into sentiment and cluster themes like audio, content depth, logistics, and accessibility. InEvent attaches confidence scores, generates summaries, and triggers alerts for critical negative clusters automatically.
Yes. InEvent triggers webhooks when conditions are met, such as NPS ≤ 6 or negative sentiment toward critical issues. The webhook can create a high-priority Zendesk ticket or Salesforce Case with respondent identity, context, and evidence to support rapid intervention.
