B2B Campaign Analytics & Decision Support

See the structure inside
campaign performance.

Resonant Lens transforms campaign data into segment-level patterns, anomalies, comparative metrics, visualizations, and executive-ready analysis.

See How It Works Explore the Analysis
The Problem

Campaign performance is fragmented by design.

Modern campaigns produce a dozen independent measurements. Viewed separately, they rarely answer the questions that matter most: where did behavior differ, which segments responded differently, and what is worth investigating further?

Open Rate Click-Through Rate Click-to-Open Rate Reply Rate Forward Rate Conversion Rate Bounce Rate Audience Segments Campaign Variants
Resonant Lens organizes these measurements into a consistent analytical workflow so that differences between campaigns and audience segments become easier to inspect — without implying that the numbers reveal anything beyond observed behavior.
Workflow

Six analytical stages. One coherent review.

Campaign data enters as structured input and passes through a defined sequence of validation, metric computation, comparison, pattern detection, and reporting stages.

01
Ingest
Import structured campaign data from CSV sources.
02
Validate
Check and normalize analytical inputs for consistency.
03
Measure
Calculate campaign metrics and derived analytical indicators.
04
Compare
Examine differences across campaigns, segments, and behavioral dimensions.
05
Detect
Surface notable patterns, deviations, and anomalies worth examining.
06
Report
Convert analysis into visualizations and executive-readable outputs.

Resonant Lens supports human interpretation at each stage. It does not make autonomous campaign, targeting, or budget decisions — a human analyst reviews the outputs and determines next steps.

Interface Showcase

What the analytical surface looks like.

Static demonstrations of the application's major views, using synthetic fictional data. No real campaign data, customer information, or private metrics are shown.

Resonance
74.2
+6.1 vs prior
Open Rate
28.4%
+2.3%
CTR
6.1%
+0.8%
CTOR
21.5%
+1.2%
Reply
3.2%
steady
Conversion
4.7%
+0.4%
Resonance Score by Campaign — Q3 2025 (synthetic)
Q3-Alpha · Urban Planning
82.4
Q3-Beta · Smart Utilities
74.2
Q2-Gamma · Coastal Infra
68.7
Q2-Delta · Digital Trans.
61.3
Q1-Epsilon · Environ. Policy
54.8
Comparative segment performance — Campaign Q3-Alpha (synthetic)
Segment Open Rate CTR CTOR Reply Conversion Score
Urban Planning 31.2% 7.4% 23.7% 3.8% 5.2% 82
Smart Utilities 28.4% 6.1% 21.5% 3.2% 4.7% 74
Coastal Infrastructure 26.1% 5.6% 21.5% 2.9% 4.1% 68
Digital Transformation 22.7% 4.8% 21.1% 2.1% 3.4% 61
Environmental Policy 24.3% 5.1% 21.0% 2.6% 3.8% 55
Detected observations — Campaign Q3-Alpha (synthetic)
HIGH
Urban Planning segment shows CTR 3.4× the expected rate relative to prior campaign. Flagged for deeper review before attribution.
Metric: CTR · Segment: Urban Planning · Deviation: +240%
MED
Reply rate for Digital Transformation segment declined 38% quarter-over-quarter while open rate held steady — CTOR divergence observed.
Metric: Reply Rate · Segment: Digital Transformation · Deviation: −38%
MED
Forward rate for Coastal Infrastructure is 2.1× campaign average. Indicates potential peer-sharing behavior — pattern worth monitoring in subsequent campaigns.
Metric: Forward Rate · Segment: Coastal Infra · Deviation: +110%
NOTE
Bounce rate for Environmental Policy is below 0.3% — the cleanest list segment. No anomalies detected; consistent with prior quarterly behavior.
Metric: Bounce Rate · Segment: Environmental Policy · Status: Normal
Custom composite analytical metric — not a validated predictive model (synthetic data)
74 /100
Q3-Alpha · Smart Utilities
Dimension weights
Open Rate
28.4%
Click-to-Open Rate
21.5%
Reply Rate
3.2%
Forward Rate
1.8%
Conversion Rate
4.7%
Q3 2025 Campaign Analysis
5 segments · 4 campaigns · Synthetic demonstration data
Working Draft
Peak Segment
Urban Planning
Composite Score
82 / 100
Anomalies
3
Key findings
Urban Planning open rate exceeds benchmark by 18% — strongest engagement signal across segments.
Digital Transformation reply rate declined despite stable open rate — CTOR divergence flagged for review.
Coastal Infrastructure forward rate anomaly (2.1× average) is an observation, not a confirmed causal pattern.
Environmental Policy maintains list hygiene; bounce rate consistently below 0.3%.
Analytical Dimensions

Six structured views of campaign behavior.

Resonant Lens organizes campaign data across six analytical surfaces. Each represents a distinct type of question, not a distinct conclusion.

Performance
Standard campaign measurements and comparative KPIs — open rate, CTR, CTOR, reply, forward, conversion, and bounce rate across campaigns.
Segments
Differences in response across defined audience groups. Where did behavior diverge? Which segments moved differently from the aggregate?
Patterns
Relationships and repeated structures across observed metrics — correlated movements, consistent timing, recurring engagement shapes. No causal claims.
Anomalies
Observations that depart meaningfully from surrounding campaign behavior. Flagged for review, not automatically interpreted.
Composite Signals
Application-specific derived indicators — including the Resonance Score — designed to summarize multiple observable measurements into a single comparison surface.
Reporting
Conversion of analytical results into readable visual outputs and executive-readable summaries, including PDF export and structured findings.
Resonance Score

A composite view of campaign response.

The Resonance Score is an application-specific composite metric that combines five observed campaign-response dimensions into a single indicator for comparative analysis across campaigns and segments.

Open Rateweighted
Click-to-Open Rateweighted
Reply Rateweighted
Forward Rateweighted
Conversion Rateweighted
Custom analytical construct. The Resonance Score is an application-specific heuristic designed to compress multiple dimensions into an additional comparison surface. It has not been independently validated as a predictive model, causal measure, psychological construct, or industry-standard scoring system.
Resonance Score — Synthetic Example
74/100
Open Rate CTOR Reply Rate Forward Rate Conversion
From Raw Data to Decision Surface

Decision support, not automated decisions.

Resonant Lens is a structured analytical layer between raw campaign exports and the decisions a human analyst makes.

Before
CSV campaign exports
Individual KPI columns
Disconnected measurements
No cross-segment view
Manual aggregation required
Resonant Lens
Analytical Layer
Validation & normalization
Derived metric computation
Segment comparison
Pattern & anomaly detection
Visualization & reporting
After
Consolidated analytical surface
Segment-level comparison
Flagged anomalies for review
Resonance Score per campaign
Executive-readable report
The analyst reviews outputs and determines what actions to take. Resonant Lens provides decision support, not automated decision-making.
Technical Architecture

A full-stack analytical application.

Built as a working browser-based analytical environment. The stack reflects the data processing, computation, visualization, and reporting pipeline.

Backend
Python FastAPI Uvicorn
Data Processing
Pandas NumPy CSV ingestion
Visualization & Output
Matplotlib ReportLab Jinja2
Analytical Layer
Metric computation Anomaly detection Pattern analysis
Reporting
PDF generation Executive summary CSV export
Interface
Browser-based Responsive No install required
Product Status

A working analytical application.

Working Analytical Application
Current stage

Resonant Lens is a functional campaign-analysis and reporting environment built to explore structured B2B campaign data. Public materials demonstrate the workflow and interface; access to the operational application is not publicly exposed.

The application is a working full-stack analytical product, not a prototype or concept deck. It ingests real data, computes derived metrics, performs segment comparison, detects anomalies, and generates executive outputs.

Analytical Scope

What Resonant Lens does not claim.

Precision about the limits of analytical evidence is part of using it responsibly.

Correlation does not establish causation. Detected relationships are observations, not causal findings.
Detected patterns should be treated as analytical leads for human review, not confirmed conclusions.
Composite metrics — including the Resonance Score — are application-specific analytical constructs, not industry-standard validated scores.
Behavioral observations do not establish psychological states or customer intent.
Resonant Lens supports decision-making. It does not make autonomous campaign, targeting, or budget decisions.

Interested in the system?

Resonant Lens is part of Symbolic Suite's applied AI and analytical systems portfolio.

Contact Symbolic Suite