What is the Snapshot live KPI banner?

The Snapshot banner is a compact live dashboard showing current KPIs used for bidding defense such as openings flagged in the last 48 hours, average response latency, month over month win rate delta, and Margin At Risk so teams see urgency at a glance.

Where do signals come from and what is the ingest cadence?

Signals are sourced from quote feeds, bid calendars, and marketplace crawls. Typical cadence is hourly quote aggregation, daily bid calendar sync, and automated quality checks including null checks, outlier detection, and schema versioning.

What are the proximity rules for candidate events?

A candidate is an event that falls inside a geo radius, has time to close less than 30 days, and has Margin At Risk above a configured threshold. These simple rules focus attention on nearby and imminent threats.

How are events scored and ranked?

Scoring uses a rubric of urgency (time to close and proximity), Margin At Risk (expected value loss), and win likelihood (historical win rate by region and product). Scores rank events for analyst review and action.

How do we prioritize rivals and regions?

Prioritization scores each region by competitor density multiplied by recent price drop magnitude multiplied by Margin At Risk. Focus first on areas with high density and high Margin At Risk and sort by margin pressure or last price drop.

What trigger rules and first order actions are typical?

Example triggers include price drop alerts when percent drop is greater than or equal to ten percent and delta EV is less than minus two thousand dollars, high MAR regions when MAR is greater than or equal to fifty thousand dollars, new nearby openings within geo radius and time to close under 30 days, and elasticity breaches that require approval. Actions range from escalating to a pricing analyst to monthly playbook reviews and targeted outreach.

What audit and compliance practices should be followed?

Record every signal, model version, decision, and outreach. Retain raw signals, normalized events, scoring snapshots, thresholds, playbook versions, replayable snippets for failed detections, and regression tests. Maintain audit trails for approvals and consult legal counsel to avoid competitor coordination or unlawful price signaling.

Snapshot — live KPI banner

TLDR

Turn live bidding signals into a monthly, decision-ready playbook for a 500+ employee HVAC business serving B2B2C partners.
See live KPIs (Openings, latency, win-rate delta, margin at risk) to prioritize high-impact bids in real time.
Use simple proximity and urgency rules to decide when to defend prices or deploy targeted discounts, with auditable decisions.
Follow a repeatable monthly cadence: weekly scans, battlefield reviews, and A/B tests to optimize outreach and pricing.
Keep logs, replays, and regression tests to ensure governance, traceability, and safe rollbacks.

Snapshot — live KPI banner

2025-09-12T00:00Z — quick, current signals and health for bidding defense.

Openings flagged (48h): 12
Avg response latency: 28h
Win-rate delta (mo): +3.2%
Margin at Risk (current): $92k

How much value is at risk when a nearby supplier cuts price? The snapshot ties that question to live KPIs so teams see urgency at a glance.

A minimalist dashboard banner showing four KPI cards with numbers and microcharts; each card labeled: Openings, Latency, Win-rate delta, Margin at Risk. Snapped by Василь Вовк

36% High

Detection — ingest, proximity rules, sample queries

Signals come from quote feeds, bid calendars, and marketplace crawls. Rules turn raw feeds into candidate events for action.

Ingest & cadence

Real-time quote feed: hourly minimum for market aggregation.
Bid calendar sync: daily update, with alerts for events closing within 30 days.
Automated QC: null checks, outlier detection, schema versioning for traceable feeds.

Proximity rules (simple)

Candidate = within geo radius AND time-to-close < 30 days AND MarginAtRisk > threshold.

Scoring rubric

Urgency: Time to close and proximity of opening.
Margin At Risk: Estimated expected-value loss if no action is taken.
Win likelihood: Historical win-rate by region and product class.

Sample SQL (expand to copy)

SELECT supplier, region, MAX(price) AS baseline, latest_price, (baseline - latest_price) / NULLIF(baseline,0) AS pct_drop FROM supplier_quotes WHERE ts > now() - interval '90 days' GROUP BY supplier, region;

Use NULLIF to avoid division by zero and keep baseline windows consistent.

Sample ES-like predicate (expand)

{ "query": { "range": { "price_change": { "gte": 0.05 } } } }

Thresholds on percent-change filter the noisy long tail.

Impact — delta matrix and statistical flagging

Show live deltas on the battlecard so the team knows what changed and whether it matters.

Delta matrix: before → after outcomes with statistical confidence
Metric	Baseline	Post-drop	Delta	p-value
Win-rate	22%	17%	-5 ppt	0.02
Avg margin	18%	15%	-3 ppt	0.04
Expected Value (EV)	$12.4k	$9.2k	-$3.2k	0.01
Quote volume	320	410	+90	0.03
Notes: p-value column flags where change is unlikely to be noise. Use elasticity mapping (delta_price → delta_win_prob) to compute delta_EV. Search keywords: living metrics, delta matrix, price elasticity, competing on analytics.

Practical tip: map price drop to win probability with a simple slope, then recalc EV = win_prob × average_contract_value. Reference living-metrics discipline from analytics literature (see cited works below for frameworks).

Recommended reads: Davenport & Harris; Rao et al. — consult library or journal access to locate these works for implementation frameworks.

Rival Map — geo heat and sortable list

Prioritize efforts by density and MAR (Margin At Risk).

A geo-heatmap showing competitor density with recent price-drop dots and a legend for density and MAR intensity.. Photo taken by HANUMAN PHOTO STUDIO🏕️📸

Top rival incidents (sortable fields: rival, last price drop %, win-rate change, margin pressure)
Rival	Last price drop %	Win-rate change	Margin pressure
Rival A	8%	-2 ppt	Medium
Rival B	12%	-4 ppt	High
Rival C	5%	-1 ppt	Low
Rival D	15%	-6 ppt	Very High
Use filters to focus on high density + high MAR areas first. Sort by margin pressure or last price drop to surface urgent targets.

How map prioritization works

Score each region by: competitor density × recent price drop magnitude × Margin At Risk. Regions above the set threshold get a monthly playbook review.

Responses — codified rules, monthly cadence, rollout notes

Rules convert signal into action. Keep rules simple, auditable, and tied to EV impact.

Trigger rules & first-order actions
Trigger	Condition	Action
Price drop alert	pct_price_drop >= 10% AND delta_EV < -$2k	Escalate to pricing analyst; recommend defend or selective discount.
High MAR region	MAR >= $50k in region	Monthly battlefield review and targeted outreach test.
New nearby opening	New listing within geo radius AND time-to-close < 30d	Assign monitoring owner; prepare localized playbook.
Elasticity breach	Estimated win_prob change > threshold	Trigger approval checkpoint for outreach if EV loss > approval threshold.
Keep thresholds staged: detection → analyst review → sales outreach. A/B test scripts and measure response lift to refine playbooks.

Monthly cadence

Weekly automated scans for new signals.
Monthly battlefield review: update playbooks, thresholds, and scripts.
Monthly A/B tests: outreach variants and messaging.

Trigger-to-action flow

Detect signal — monitoring owner (0–2h).
Compute EV impact & recommend action — pricing analyst (2–6h).
Trigger outreach or defend — sales owner (first outreach within 48h) with approval if EV loss > approval threshold.

Rollout notes and training

Train owners on playbook steps, keep thresholds conservative at launch, and run staged rollouts. Log every decision and approval for audit and continuous improvement.

Audit — logs, replay snippets, regression tests

Every signal, model version, decision, and outreach must be recorded for replay and review.

Logs retained: Raw signals, normalized events, scoring snapshots, chosen thresholds, and the playbook version used.
Replays: Store replayable snippets for failed detections to debug rules and data issues.
Regression tests: Automated tests that verify trigger logic, elasticity mapping, and p-value thresholds after model or code changes.

Example log entry (format)

timestamp: 2025-09-12T00:03Z; event: price_drop; supplier: X; region: Y; pct_drop: 0.12; MAR: 92000; score: 78; playbook_version: v1.4; action: escalate_to_pricing;

Legal guardrails: avoid competitor coordination or price-signaling. Consult counsel when complex outreach touches market prices. Referenced statutes and guidance include consumer-protection and antitrust frameworks (statutes cited where applicable).

Retention guidance: align system logs and approval trails with internal retention policies and material-disclosure practices. Keep auditable approvals for material pricing risk.

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