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Attribution Modelling → B2B Marketing Analytics

Marketing reports are everywhere.
Marketing insight that changes
commercial decisions is rare.

B2B Marketing Analytics is a practice in B2B marketing. Most B2B marketing analytics reports tell you what happened. The programmes that drive better commercial decisions go further — they tell you what it means, what to do about it, and what you can expect if you do.

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78%of B2B marketing teams report having analytics capability but only 31% can demonstrate that analytics is influencing budget allocation decisions
4×higher marketing efficiency gains for teams with measurement frameworks connected to pipeline vs those measuring marketing activity in isolation
60%of marketing analytics spend is on tools and dashboards that nobody acts on — the bottleneck is insight and decision-making frameworks, not data collection
What is B2B marketing analytics

The gap between data
and decisions is where most
analytics programmes fail

B2B marketing analytics is not a reporting function — it is a decision-support function. The purpose of collecting, organising, and presenting marketing data is to improve commercial decisions: which channels to invest more in, which content to produce next, which audience segments to prioritise, and which campaigns to scale or cut. Data that doesn’t improve decisions is an operational cost, not an investment.

The most common failure in B2B marketing analytics is building measurement infrastructure around what’s easy to measure rather than what’s important to know. Website traffic is easy to measure; the connection between a specific content piece and a closed deal six months later is hard to measure but commercially valuable. Building analytics capability around the decisions that matter — and accepting the data infrastructure investment required to measure the right things rather than the convenient things — is what separates marketing analytics programmes that change decisions from those that produce regular reports that nobody acts on.

The right analytics framework for B2B varies by the maturity of the marketing programme and the commercial questions leadership is asking. A company in the first year of a content programme needs different analytics than a mature programme managing a £2m annual marketing budget across nine channels. We design measurement frameworks calibrated to where the programme is and where it needs to go.

Signs your marketing analytics needs work
01You have dashboards but no documented framework connecting the metrics on them to specific commercial decisions
02Marketing reports are reviewed in monthly meetings but don’t lead to specific allocation changes, creative decisions, or channel shifts
03The metrics you report on are those that are easy to pull from your tools rather than those that best reflect commercial performance
04Marketing and sales are reviewing different data in the same meeting and drawing different conclusions about what’s working
05You can’t tell a CFO how much revenue last quarter’s marketing investment generated, and you don’t have a credible path to being able to
Our approach

How we build B2B marketing analytics
that drives commercial decisions

Marketing analytics infrastructure is built from the commercial decisions it needs to support, not from the data that happens to be available.

01

Decision-First Measurement Framework

We start by identifying the specific commercial decisions that better marketing analytics would improve — channel budget allocation, content investment prioritisation, audience targeting, and campaign scaling decisions — and design the measurement framework that provides the data needed to make those decisions with evidence rather than intuition.

02

Metric Architecture & KPI Design

We design a three-tier metric architecture for B2B marketing: leading indicators (marketing activity metrics), pipeline metrics (MQL volume, cost per MQL, MQL-to-SQL conversion rate), and commercial outcomes (pipeline influenced, revenue contribution, marketing ROI). Each tier serves a different audience and decision purpose. We align stakeholders on which metrics belong in which tier and why.

03

Analytics Infrastructure Build

We build the data connections, attribution models, and reporting infrastructure that populate the measurement framework with accurate data: UTM standards, MAP-to-CRM attribution, revenue tracking, and the reporting dashboards that present the right metrics to the right audiences in the right format.

Why Harmonic

What makes our analytics approach
produce commercial insight

Marketing analytics that produces commercial insight is designed from decisions, built on clean data, and presented in the language of the decision-maker.

Decisions before dashboards

We identify the commercial decisions the analytics needs to support before designing any dashboard. A dashboard built without a clear decision it’s designed to inform becomes a data display rather than a decision support tool.

Metric tiers that serve different audiences

The CMO, the CFO, and the channel manager are all looking at marketing performance but asking different questions. We build distinct reporting layers that answer the specific questions each audience is asking rather than presenting the same aggregated metrics to everyone.

Attribution that reflects the full buying journey

We implement multi-touch attribution as standard — not last-click — so that the analytics reflects how B2B deals actually close rather than crediting only the final touchpoint and making awareness and consideration channels appear to contribute nothing.

Insight cadence, not just reporting cadence

We structure analytics reviews around insights and recommendations rather than around data summaries. A marketing analytics review that ends with ‘traffic was up 12%’ has produced reporting. One that ends with ‘we should reallocate 20% of the LinkedIn budget to content syndication based on the pipeline data’ has produced insight.

What you get

What the marketing analytics programme
delivers

Deliverables span the measurement framework, the data infrastructure, and the reporting cadence that keeps analytics decision-relevant.

Decision-First Measurement Framework

A documented measurement framework mapping specific commercial decisions to the specific metrics needed to make them, with data source requirements and reporting cadence.

Metric Architecture & KPI Document

A three-tier metric architecture covering leading indicators, pipeline metrics, and commercial outcomes — with definitions, calculation methods, and audience alignment.

Attribution Infrastructure

Multi-touch attribution model implementation connecting marketing touchpoints to pipeline and revenue outcomes.

Marketing Analytics Dashboard

A reporting dashboard designed around the specific decisions marketing leadership needs to make, with appropriate metric tiers for each audience.

Channel Performance Reporting

Channel-level performance reporting connecting spend to pipeline outcomes for each major marketing channel, with comparative efficiency metrics.

Monthly Analytics Reviews

Monthly analytics review cadence structured around insights and recommendations rather than data summaries.

How we work

How we build your
B2B marketing analytics programme

Decision mapping before data infrastructure. We won’t build dashboards until we understand what commercial decisions they need to support.

1
Week 1–2

Decision & Metric Mapping

Commercial decisions mapped. Metric architecture designed. Data source requirements identified.

Decision MapMetric Architecture
2
Weeks 2–4

Infrastructure Build

Attribution model implemented. Data connections built. UTM standards documented.

Attribution SetupData Connections
3
Weeks 4–6

Dashboard Build & Stakeholder Alignment

Reporting dashboards built for each audience tier. Reviewed and approved by stakeholders before live use.

Dashboard BuildStakeholder Review
4
Monthly

Analytics Reviews

Monthly insights-led analytics reviews. Attribution model calibrated quarterly.

Monthly ReviewsQuarterly Calibration
Common questions

B2B marketing analytics — answered

The questions we hear from B2B marketing teams trying to improve the quality and commercial impact of their analytics programmes.

What marketing metrics should we report to the board?+

Three commercial metrics tell the complete story: marketing-influenced pipeline (what proportion of total pipeline had a marketing touchpoint in the buying journey), marketing-sourced pipeline (what proportion was first-touch marketing), and marketing ROI (revenue from marketing-influenced deals divided by total marketing investment). Supporting these with conversion rate trends and pipeline coverage ratio gives the board everything they need to evaluate marketing’s commercial contribution.

How do we improve marketing reporting without hiring a data team?

Prioritise tool integration over data complexity. Most B2B companies can produce commercially useful marketing analytics with a well-connected HubSpot or Salesforce instance, consistent UTM parameters, and a MAP with a properly implemented lead scoring model. The bottleneck is usually data cleanliness and attribution model implementation, not the absence of advanced analytics tools.+

What is the difference between marketing analytics and business intelligence?

Marketing analytics focuses specifically on the performance of marketing activities and their commercial outcomes — channel performance, campaign effectiveness, content attribution, and pipeline contribution. Business intelligence is broader — it encompasses all business data including operations, finance, sales, and customer data, typically in a unified data warehouse.

For most B2B companies, marketing analytics in a well-configured MAP and CRM is sufficient. Business intelligence investment is justified when marketing data needs to be combined with operational or financial data for the commercial decisions being made.+

How do we know if our analytics is actually influencing decisions?

Track which recommendations from analytics reviews were acted on and what happened as a result. If analytics reviews produce reports that are acknowledged but don’t change behaviour, the analytics isn’t influencing decisions regardless of its technical sophistication. The test is whether marketing investment allocation, content production priorities, or channel targeting changed as a direct result of something the analytics revealed.

How do we build a marketing analytics function without dedicated resource?+

Start with the highest-decision-value metrics and build from there. A CMO who can answer ‘what did marketing’s investment generate in pipeline last quarter?’ with reliable data is more commercially useful than one with a comprehensive dashboard that nobody acts on. Implement multi-touch attribution for pipeline visibility first, then add channel performance reporting, then add content attribution. Build depth where it drives decisions.

Ready to build marketing analytics
that actually changes commercial decisions?

We’ll map the decisions your analytics needs to support, design the measurement framework, and build the reporting infrastructure that connects marketing activity to commercial outcomes.+

Start with the highest-decision-value metrics and build from there. A CMO who can answer ‘what did marketing’s investment generate in pipeline last quarter?’ with reliable data is more commercially useful than one with a comprehensive dashboard that nobody acts on.

Implement multi-touch attribution for pipeline visibility first, then add channel performance reporting, then add content attribution. Build depth where it drives decisions, not where it creates impressive-looking data displays.

Ready to build marketing analytics
that actually changes commercial decisions?

We’ll map the decisions your analytics needs to support, design the measurement framework, and build the reporting infrastructure that connects marketing activity to commercial outcomes.

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