Behavioral cues

Behavioral cues

Behavioral cues are the "telemetry" of a user's interaction with a product: the events, context, and time series from which we infer intent, interest, traffic quality, risk, and value. Reliable circuit of work with signals: instrumentation → collection → cleaning → normalization → sign formation → use in solutions → monitoring and ethics.

1) What to consider behavioral signals

Sessions: start/stop, duration, number of screens, depth, repetitions per day, "quiet" sessions.
Clicks/touch/scroll: density of clicks, scrolling speed, depth, stops (scroll-stops).
Dwell-time: time on the screen/element, active time (idle filter).
Navigation/interrelation of screens: sequences, loops, rage-navigation.
Input/forms: filling speed, corrections, tab navigation, paste rate.
Micro-interactions: hovers, reveals, switches, sorts/filters.

Content/search: queries, CTR, CTCVR, saves, "postpone for later."

Technique: device/browser, FPS/battery status, errors, latency, networks (IP/ASN), offline/online.
Time/context: hour/day/local calendar, geo-patterns (no precise geolocation unless required).
Negative feedback: hide, complain, unsubscribe, refuse cookies/personalization.

2) Instrumentation and event diagram

event_id, user_id, session_id, ts_utc, type, screen/page, element, value, duration_ms,
device_id, platform, app_version, locale, referrer, ip_hash, asn, experiment_id, schema_version

Principles: idempotency (dedup by '(source_id, checksum)'), UTC time, schema version, stable identity keys, PII minimization (hashes/tokens).

3) Cleaning and anti-bots

Headless/automation flags: WebDriver/puppeteer signatures, missing custom gestures.
Abnormal speed: superhuman clicks/scrolling, "perfect" intervals.
Network: data hosting centers, known proxy/VPN ASN.
Pattern repeatability: same trajectories and sequences.
QA/internal: lists of test accounts/devices.
Fraud: device/IP-graph (one device → many accounts, geo-velocity).

4) Normalization and Point-in-Time (PIT)

Time windows: 5 minutes/1 hour/24 hours/7 days; expon. smoothing.
Seasonality: day-of-week, hour-of-day, holiday flags.
PIT slices: all features are built up to the evaluation time; no information from the future.
Online/offline parity: identical recipes in the feature store.

5) Signal quality and validity

Coverage: share of sessions/screens with full events.
Freshness: admission lag.
Consistency: proportions of events per user/session in "corridors" (emission control).
Attention: active time/idl filter, scroll depth, stops.
Intent: transitions to deep action (filtr→detal→tselevoye).
Reliability: anti-bot-speed, trust in the device/IP.

6) Feature engineering

R/F: recency of the last interaction, frequencies over windows 7/30/90.
Dwell/scroll: medians/quantiles, proportion of screens with dwell ≥ X, depth ≥ p%.
Sequences: n-grams, Markov transitions, patterns of "remorse" (back-forth), run-length.
Device stability: device/browser changes, entropy user-agents.
Click quality: ratio of clicks to clickable elements, rage-clicks.
Search/intent: length/refinement of queries, dwell after search, success rate.
Aggregations by identity: user_id, device_id, ip_hash, asn.
Hybrids: Session embeddings (Doc2Vec/Transformer) → clustering/ranking.

7) Signal → Action: Decision Table

Hysteresis and cooldowns are mandatory so as not to "blink" clues.

8) Pseudo-SQL/Recipes

A. Active scrolling time and depth

sql
WITH ev AS (
SELECT user_id, session_id, page, ts,
SUM(CASE WHEN event='user_active' THEN duration_ms ELSE 0 END) AS active_ms,
MAX(CASE WHEN event='scroll' THEN depth_pct ELSE 0 END)     AS max_depth
FROM raw_events
WHERE ts BETWEEN:from AND:to
GROUP BY 1,2,3,4
)
SELECT user_id, session_id,
AVG(active_ms) AS avg_dwell_ms,
PERCENTILE_CONT(0. 5) WITHIN GROUP (ORDER BY max_depth) AS scroll_median
FROM ev
GROUP BY 1,2;

B. Rage-clicks / back-forth

sql
WITH clicks AS (
SELECT user_id, session_id, ts,
LAG(ts) OVER (PARTITION BY user_id, session_id ORDER BY ts) AS prev_ts,
element
FROM ui_events WHERE event='click'
),
rage AS (
SELECT user_id, session_id,
COUNT() FILTER (WHERE EXTRACT(EPOCH FROM (ts - prev_ts)) <= 0. 3) AS rage_clicks
FROM clicks GROUP BY 1,2
),
backforth AS (
SELECT user_id, session_id,
SUM(CASE WHEN action IN ('back','forward') THEN 1 ELSE 0 END) AS nav_bf
FROM nav_events GROUP BY 1,2
)
SELECT r. user_id, r. session_id, r. rage_clicks, b. nav_bf
FROM rage r JOIN backforth b USING (user_id, session_id);

C. Antibot speed (sketch)

sql
SELECT user_id, session_id,
(CASE WHEN headless OR webdriver THEN 1 ELSE 0 END)0. 4 +
(CASE WHEN asn_cat='hosting' THEN 1 ELSE 0 END)0. 2 +
(CASE WHEN click_interval_std < 50 THEN 1 ELSE 0 END)0. 2 +
(CASE WHEN scroll_speed_avg > 5000 THEN 1 ELSE 0 END)0. 2 AS bot_score
FROM telemetry_features;

D. n-gram sequences

sql
-- Collect screen sequences and transition frequencies
SELECT screen_seq, COUNT() AS freq
FROM (
SELECT user_id, session_id,
STRING_AGG(screen, '→' ORDER BY ts) AS screen_seq
FROM nav_events
GROUP BY 1,2
) t
GROUP BY screen_seq
ORDER BY freq DESC
LIMIT 1000;

9) Behavioral cues in ML/Analytics

Inclinations/personalization: CTR/CTCVR models, session embeddings, next-best-action.
Outflow/retention: hazard models, signs of recency/frequency/sequences.

Antifraud: speed of forms, geo-velo, device/IP-graph, templates of "farms."

Traffic quality: "valid views," engaged sessions, negative feedback.
A/B and causality: attention metrics as mediators, but conclusions by increment (ROMI/LTV, retention).

10) Visualization

Sankey/step-bars: paths and drop-off.
Heatmaps: scrolling depth, click cards (impersonal).
Cohort × age: how signals change by cohort age.
Bridge graphs: the contribution of factors (speed, scrolling, errors) to the change in conversion.

11) Privacy, Ethics, RG/Compliance

PII minimization: identifier hashes, RLS/CLS, masking during export.
Consent/transparency: tracking setting, refusal - respected; the logic is explainable.
RG: do not use signals to encourage harmful behavior; soft reminders/limits.
Fairness: checking error/intervention differences by group; exclude invalid characteristics.
Storage: TTL timing for "raw" events, aggregation preferred.

12) Observability and drift

Data quality: coverage, duplicates, lags, percentage of empty fields.
Signal drift: PSI/KL by dwell/scroll/frequencies; "new" patterns.
Operating: latency collection, p95 calculation of signs, share of folbacks.
Guardrails: bot-scor surge, complaints, unsubscribes; "stop-crane" on aggressive interventions.

13) Anti-patterns

Raw clicks without context/idl filter → false "attention."

Mixing of units (sessii↔polzovateli), TZ, windows → disparity.
Faces from the future (no PIT) → reassessment of models.

Non-tolerance to noise: hard thresholds without hysteresis → "blinking."

Ignore anti-bots/QA filters → overestimated metrics.
Recording extra PII for no reason → risks and fines.

14) Behavioral Signal Loop Trigger Checklist

• Event schema (versions, UTC, idempotency), PII minimization
• Anti-bots/QA filters, ASN/device black/white lists
• PIT recipes, 5m/1h/24h/7d windows, online/offline parity
• Quality metrics: coverage, freshness, engagement validators
• R/F/dwell/scroll/sequence/search, session embeddings
• Decision tables: actions, hysteresis, cooldowns, guardrails
• Drift dashboards and alerts (PSI/KL), complaints/unsubscribes, RG indicators
• Documentation: data dictionary, signal/metric passports, owners and runibooks

Total

Behavioral signals provide value only in a disciplined circuit: correct instrumentation and PIT, cleaning and anti-bots, stable signs and clear action policies, privacy and RG, observability and drift response. This approach translates clicks and scrolls into solutions that increase conversion, retention, and LTV - safely, transparently, and reproducibly.