What is Marketing Analytics
Marketing Analytics encompasses the technologies, processes and skills that collect and analyse data across the entire funnel – from the first click on a banner to the signing of a contract in the CRM. Key components:
- Data integration
– linking web analytics (GA4), advertising platforms (Google Ads, LinkedIn), marketing automation, CRM and ERP. - Attribution models
– first-touch, last-touch, linear or data-driven models assessing each channel’s contribution to conversion. - Dashboards and BI
– visualisation of metrics (CPL, CAC, ROAS, pipeline contribution) in tools such as Looker, Power BI, Tableau. - Predictive analytics
– machine learning estimates the probability of closing a deal, churn risk or optimal budget allocation. - Experimental framework
– A/B and multivariate tests validate hypotheses before a company invests millions in scaling.
How Marketing Analytics fits into the tech stack
| Layer | Typical tools | Key data |
|---|---|---|
| Data sources | Google Ads, LinkedIn, email, website, CRM, ERP | Clicks, impressions, leads, revenue, margins |
| ETL / ELT | Fivetran, Stitch, Airbyte, Matillion | Extraction and storage in a data warehouse |
| Data warehouse | BigQuery, Snowflake, Redshift | Normalised tabular data, time series |
| BI & dashboards | Looker, Power BI, Tableau, Metabase | Interactive reports for marketing, sales and finance |
| Prediction & AI | Dataiku, Vertex AI, AWS Sagemaker | Lead scoring, budgeting, product recommendations |
Why Marketing Analytics is important for B2B
- Measures the actual impact of campaigns
Over a long cycle, a lead often touches five channels. Multi-touch attribution shows that a webinar contributed to 25% of revenue, even though it is not usually the last click. - Optimises the budget based on ROI
Data reveals that even more expensive LinkedIn Ads with a CPL of CZK 1,800 have a higher conversion rate for clients with a CLV of CZK 2 million, whilst cheaper display ads generate low-value leads. - Shortens the sales cycle Funnel velocity
analysis reveals a ‘bottleneck’ in the demo > proposal phase. Marketing adds case studies and sales enablement materials, thereby shortening the phase by 12 days. - Aligns marketing, sales and finance
Everyone works with a single source of truth – how much revenue a specific channel, campaign or blog post has generated. - Supports scaling
As the budget grows, so does the volume of data. Robust analytics maintain efficiency and prevent waste when expanding into new markets.
Practical applications and examples
- Data-driven attribution in GA4
An industrial pump manufacturer discovers that 40% of closed deals began with organic search but ended with a direct visit. It reallocates 15% of its budget from banners to the ‘pumps for the food industry’ SEO cluster and increases qualified leads by 28% over the quarter. - Lead scoring model in HubSpot
It combines firmographics (company size, turnover) and behavioural data (visits to the price list page). Once 80 points are reached, the lead is automatically assigned to a sales representative; conversion to Opportunities rises from 18% to 27%. - Revenue attribution dashboard
In Looker, the “Campaign → Pipeline → Closed Deals” view is updated daily. C-level managers see that the July ABM campaign generated CZK 7.3 million in the pipeline and CZK 2.1 million in closed revenue – the ABM budget is doubled. - Pricing page experiment
An A/B test of the “Get a quote within 24 hours” vs. “Find out the price” buttons delivers 34% more forms (95% statistical significance). By switching to variant B, the company gains 120 additional enquiries per month. - Churn prediction model: The
SaaS platform trains a machine learning model using usage data. The model predicts customer churn 30 days before the contract expires with 87% accuracy. Customer Success manages to intervene and reduces churn by 9 percentage points.
5 tips for getting started with Marketing Analytics in B2B
- Start with the goal, not the tool
Define KPIs (pipeline contribution, CAC : CLV) and only then select the technology. - Build a unified data model
Standardise identifiers (lead ID, account ID) across systems so that data can be matched seamlessly. - Implement multi-touch attribution
Last-click isn’t enough in B2B. Evaluate the contribution of all channels based on contract duration and value. - Democratise access to data
Share dashboards with marketing, sales and management; transparency improves alignment and speeds up decision-making. - Iterate – test, learn, repeat
Each quarter, select a hypothesis (e.g. “a chatbot will increase demo request conversions by 10%”) and test it. Scale up what works, scrap what doesn’t.
Related terms
- Business Intelligence (BI) – a broader discipline of data visualisation and analysis across the entire organisation.
- Attribution Model – a methodology for attributing credit to channels for conversions.
- KPIs & OKRs – metrics and objectives for which Marketing Analytics provides data.
Further resources
- Google – Marketing Analytics Guide (https://analytics.google.com/analytics/academy)
- Gartner – Marketing Data and Analytics Primer (https://www.gartner.com/)
- HubSpot – Ultimate Guide to Marketing Reporting (https://www.hubspot.com/marketing-reporting)
Summary
Marketing Analytics transforms marketing from an “art and gut feeling” into a precisely measurable, predictable investment. By linking data from your website, campaigns and CRM, you know exactly where every penny is actually contributing to revenue, and you can redirect your budget towards the most profitable activities. If you’re looking for a way to set up analytics or take them to the next level, please don’t hesitate to contact us.