PA Data Analytics
Marketing Analytics

Marketing analytics that shows where your budget is really working

Every ad platform reports its own results, and each one claims credit for the same sale. PA Data Analytics connects your campaign, web analytics and revenue data so you can see which channels, campaigns and customer journeys really drive revenue, and where next month's budget should go.

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Data → Insights → Decisions

The questions marketing analytics answers

  1. What happened?

    How did each channel and campaign perform against spend, revenue and targets, measured on the same basis?

  2. Why did it happen?

    Which touchpoints influenced the sale, which channels are over-credited, and where does the funnel leak?

  3. What should happen next?

    How should budget be reallocated, what should be tested first, and what should be stopped?

What we deliver

Marketing Analytics services

Campaign performance analytics

ROAS, CPA, CPL and conversion rate by channel, campaign and creative, benchmarked across platforms so paid search, paid social and email are compared on the same terms.

  • Google Ads
  • Meta Ads
  • GA4

Marketing ROI & channel performance

Spend-to-revenue analysis by channel, including customer acquisition cost and, where customer data allows, LTV-to-CAC by acquisition channel.

  • SQL
  • Excel
  • Python

Marketing attribution modelling

Last click, first click, linear, time decay and Shapley models built side by side on the same data, so you can see how much a channel's credit depends on the model, and reconcile platform-reported conversions with GA4.

  • Python
  • SQL
  • GA4

Marketing mix modelling (MMM)

Regression models with adstock and saturation effects that estimate each channel's contribution to revenue from weekly spend data. Useful when user-level tracking is incomplete.

  • Python
  • Statsmodels
  • SciPy

Budget allocation scenarios

Constrained optimisation that shows how a fixed budget could be redistributed across channels, with assumptions stated and a plan to test changes step by step.

  • Python
  • Excel

Funnel analysis & conversion

Drop-off analysis from product view to purchase by device, channel and segment, plus A/B test design and statistical analysis for proposed fixes.

  • GA4
  • GTM
  • SciPy
Why it matters

What it helps your business do

  • A consistent view of marketing ROI by channel and campaign
  • Budget decisions based on evidence rather than platform-reported credit
  • Specific funnel stages and channels to prioritise
  • Tests to run before making large budget changes
Typical deliverables

What you receive

  • Channel and campaign performance analysis
  • Attribution model comparison
  • Marketing mix model with channel contributions (where the data supports it)
  • Budget reallocation scenarios
  • Power BI or Looker Studio dashboard
  • Written recommendations and a walkthrough session

Scope depends on your data and question. It is agreed after the discovery call and data audit.

Proof of work

Related case studies

Portfolio projects built on demonstration datasets. They are not client engagements, and their figures describe the datasets and models, not real business results.

SHARE OF REVENUE CREDITShapleyLast ClickPaid SearchPaid Search: 35.6% of credit under Last ClickPaid Search: 41.9% of credit under Shapley35.6%41.9%DirectDirect: 8.4% of credit under Last ClickDirect: 51.9% of credit under Shapley8.4%51.9%EmailEmail: 18.9% of credit under Last ClickEmail: 0.0% of credit under Shapley18.9%0%Paid SocialPaid Social: 15.0% of credit under Last ClickPaid Social: 6.2% of credit under Shapley15%6.2%InfluencerInfluencer: 7.6% of credit under Last ClickInfluencer: 0.0% of credit under Shapley7.6%0%Portfolio project · Synthetic dataset
  • Portfolio project
  • Synthetic dataset
  • Marketing Analytics

Comparing five attribution models to understand channel contribution

A portfolio analysis that builds five attribution models on the same customer journeys, to show how much a channel's credit depends on the model chosen.

Synthetic dataset: 11,292 touchpoints · 3,500 customers · 5 models

Read the attribution case study
MONTHLY REVENUEModel-fittedActual$0.6M$1.0M2023-01: actual $780,659, model-fitted $777,9402023-02: actual $756,909, model-fitted $756,3382023-03: actual $724,723, model-fitted $728,0512023-04: actual $705,401, model-fitted $705,1562023-05: actual $945,021, model-fitted $952,0832023-06: actual $752,169, model-fitted $730,9202023-07: actual $873,979, model-fitted $891,5472023-08: actual $791,576, model-fitted $790,1582023-09: actual $729,243, model-fitted $724,5182023-10: actual $894,399, model-fitted $913,6452023-11: actual $933,780, model-fitted $924,6182023-12: actual $948,259, model-fitted $952,0402024-01: actual $846,935, model-fitted $858,9172024-02: actual $794,305, model-fitted $805,7142024-03: actual $766,408, model-fitted $763,6472024-04: actual $948,479, model-fitted $936,6722024-05: actual $789,934, model-fitted $797,6692024-06: actual $787,235, model-fitted $784,7892024-07: actual $928,345, model-fitted $942,9262024-08: actual $826,781, model-fitted $803,0612024-09: actual $963,461, model-fitted $963,2772024-10: actual $752,190, model-fitted $763,5462024-11: actual $978,515, model-fitted $975,4352024-12: actual $1,004,106, model-fitted $980,147Jan 2023Jan 2024Dec 2024Portfolio project · Synthetic dataset · R² 0.957 in-sample
  • Portfolio project
  • Synthetic dataset
  • Marketing Analytics

Marketing mix modelling and budget allocation analysis

A portfolio marketing mix model that estimates each channel's contribution to revenue from two years of weekly data and tests how a fixed budget could be reallocated.

Synthetic dataset: 104 weeks · 4 paid channels · R² 0.957 (in-sample)

Read the marketing mix modelling case study
USERS AT EACH FUNNEL STEPProduct viewProduct view: 61,252 users61,252Add to cartAdd to cart: 12,545 users12,545Begin checkoutBegin checkout: 9,715 users9,715Shipping infoShipping info: 9,714 users9,714Payment infoPayment info: 5,751 users5,751PurchasePurchase: 4,419 users4,41920.5% of viewersadded to cart40.8% drop-offat payment stepPortfolio project · Public GA4 sample dataset
  • Portfolio project
  • Public dataset
  • Marketing Analytics

E-commerce funnel and commercial performance analysis (GA4)

A portfolio analysis of Google's public GA4 sample e-commerce data that locates funnel drop-offs and compares performance by device and traffic source.

Public dataset: 270,154 users · 354,970 sessions · $362,165 revenue

Read the e-commerce funnel case study
VARIANT A VS VARIANT BVariant BVariant AOpen rate, Variant A: 23.99%23.99%AOpen rate, Variant B: 36.66%36.66%BOpen rateClick rate, Variant A: 4.24%4.24%AClick rate, Variant B: 10.15%10.15%BClick rateConversion rate, Variant A: 0.59%0.59%AConversion rate, Variant B: 1.76%1.76%BConversion ratePortfolio project · Synthetic dataset · each panel has its own scale
  • Portfolio project
  • Synthetic dataset
  • Marketing Analytics

Email A/B test analysis: evaluating two campaign variants

A portfolio A/B test evaluation comparing two email variants across the full funnel, from opens to purchases, with statistical significance testing.

Synthetic dataset: 25,000 records · 2 variants · 4 funnel metrics tested

Read the email A/B test case study
Questions

Common questions about marketing analytics

What data do you need for marketing analytics?

At minimum: spend and results by channel or campaign (from ad platforms or a spreadsheet), and revenue or conversion data (from GA4, Shopify, a CRM or your order system). Attribution modelling also needs journey-level data. Marketing mix modelling needs a long history of weekly spend and revenue, usually around two years, to give stable results. We confirm what is feasible during the data audit.

Do I need attribution or marketing mix modelling?

Attribution works from individual customer journeys and is strongest for digital channels you can track. Marketing mix modelling works from aggregated weekly data and can include channels that are hard to track, such as offline or brand activity. Many businesses benefit from comparing both.

Will this improve my ROAS?

No analysis can guarantee a result. It can show where spend is likely over- or under-credited, so you can test changes with evidence and measure what happens.

InsightWhy Last-Click Attribution is Killing Your Ad EfficiencyRelated serviceCustomer IntelligenceRelated servicePredictive Analytics

Discuss a marketing analytics project

Share the question you are trying to answer and the data you have. We will reply with practical next steps.

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