Your data is already
telling a story.
Read it.
Advanced marketing analytics, customer intelligence and predictive modelling for e-commerce brands, growing businesses and nonprofits across Africa and beyond. Real numbers. Real decisions.
- Marketing Mix Modelling (MMM)Advanced
- Attribution Model ComparisonMulti-model
- Customer Churn PredictionML
- Cohort Retention AnalysisSQL
- Customer Segmentation (RFM + ML)Clustering
- A/B Testing & ExperimentationStats
- Power BI Executive DashboardsBI
- NGO Impact ReportingM&E
Specialized intelligence for
modern businesses.
We partner with teams that have outgrown basic reporting and need rigorous, actionable data models to drive strategy.
Full-stack data solutions.
From raw database extraction to executive dashboards and predictive models, we handle the entire pipeline. No hand-offs. No lost context.
Campaign intelligence and channel performance
Most marketing reporting tells you what happened. This pillar tells you why — and which channels deserve next month's budget. We build attribution models, campaign analysis and funnel diagnostics that connect your ad spend to actual revenue outcomes rather than platform-reported clicks.
Marketing Attribution Modelling
- Last Click, Linear, Time Decay & Shapley models side-by-side
- Revenue share comparison across all model types
- Budget reallocation with dollar-impact quantification
- Platform attribution reconciliation (Google Ads vs actual)
Marketing Mix Modelling (MMM)
- Adstock transformation per channel
- OLS + Ridge regression with full diagnostics
- Channel contribution decomposition + ROAS per channel
- Constrained budget optimisation with revenue uplift projection
Web Analytics & Traffic Intelligence
- Full GA4 audit and reporting setup
- Traffic source analysis by revenue contribution
- Session quality and funnel drop-off by channel
- Conversion rate analysis end-to-end
Campaign Performance Analysis
- ROAS, CPL and CPA by channel, campaign and creative
- Cross-channel performance benchmarking
- Ad fatigue detection and creative refresh scheduling
- Automated weekly performance reporting
Funnel Analytics & CRO
- Funnel drop-off analysis by device, channel and segment
- A/B test design, execution and statistical analysis
- Checkout abandonment root cause diagnosis
- Conversion rate optimisation opportunity identification
Content & Social Media Analytics
- Engagement rate analysis and content performance benchmarking
- Organic vs paid performance comparison
- Audience growth and reach trend analysis
- Content calendar optimisation based on performance data
Who your customers are, what they do, and when they leave
Aggregate revenue numbers hide the customer dynamics underneath them. This pillar builds the systems that let you see individual and group-level behaviour — segmentation that drives personalisation, retention analysis that predicts churn before it happens, and lifetime value modelling that tells you which customers are worth acquiring.
Customer Segmentation (RFM + ML)
- Recency, Frequency and Monetary segmentation
- K-Means clustering for behavioural segments beyond RFM
- Segment-level revenue contribution and reactivation opportunity sizing
- Actionable segment profiles with campaign recommendations
Cohort Retention Analysis
- 5-view SQL pipeline — BigQuery and Snowflake ready
- Retention heatmaps tracking M0 through M12 per cohort
- Revenue-weighted cohort value analysis
- Identification of high-value acquisition months and channels
Customer Lifetime Value Modelling
- Historical LTV calculation by segment and acquisition channel
- LTV-to-CAC ratio by channel for budget allocation decisions
- Payback period analysis for acquisition spend
- Predicted LTV for new customer cohorts
Retention Strategy Analysis
- Churn rate by segment, channel and product category
- Win-back campaign opportunity sizing
- Repeat purchase interval analysis
- Post-purchase journey mapping to identify drop-off points
What is going to happen, and what should you do about it
Predictive models move analytics from explaining the past to influencing the future. Whether that is a machine learning churn model that flags at-risk customers before they cancel or a demand forecast that tells finance what to expect next quarter — this is where analytics becomes directly measurable in revenue saved or earned.
Churn Prediction Modelling
- XGBoost and Logistic Regression churn models
- SHAP feature importance — understand why customers leave
- Customer risk scoring for CRM integration
- Intervention ROI calculation — cost of saving vs losing a customer
A/B Testing & Experimentation
- Test design — power analysis and sample size calculation
- Statistical significance testing (t-test, chi-square, Mann-Whitney)
- Bayesian A/B analysis for early decision making
- Full stakeholder report with business impact and recommendations
Demand Forecasting
- Time-series forecasting for revenue, orders and inventory
- Seasonality decomposition and calendar effect modelling
- Scenario-based projections: conservative, base and growth
- Confidence interval outputs for planning and finance teams
Predictive Lifetime Value
- Predicted LTV for new customer cohorts
- Segment-level LTV distribution analysis
- Budget allocation recommendations by LTV band
- LTV-to-CAC tracking over time
The right number, in the right hands, at the right time
Analysis that lives in a notebook is analysis that does not get acted on. This pillar turns model outputs and raw data into live dashboards, automated reports and self-service tools that your team uses every week — not only when an analyst is in the room.
Power BI Executive Dashboards
- KPI overview with revenue, orders, ROAS and customer metrics
- Channel attribution and campaign performance pages
- Customer segmentation and retention visualisations
- Drill-through to product, campaign and cohort level
Looker Studio & Google Dashboards
- GA4-connected live dashboards updated daily
- Cross-platform data blending (GA4 + Sheets + Ads)
- Shareable, embeddable reports for stakeholders
- Automated email delivery schedules
Excel & Sheets Intelligence Systems
- KPI trackers with formula validation and automated alerts
- Sales pipeline and CRM trackers
- Financial performance dashboards with scenario modelling
- Templates built to be maintained by non-technical teams
Automated Reporting Pipelines
- Weekly and monthly report generation from raw data sources
- Data cleaning and transformation pipelines
- Scheduled exports and stakeholder distribution
- Alert systems for KPI threshold breaches
Real problems.
Actual results.
A look at how we've turned raw data into strategic advantage for our clients.
Optimizing Multi-Channel Attribution for a Global D2C Brand
The client was spending over $100k/mo across Facebook, Google, and TikTok, but their blended CAC was rising. They were relying …
We identified that TikTok was undervalued by 40% in standard last-click models. Reallocating budget reduced overall CAC by 15% and increased monthly revenue by 22%.
Read full case studyPredictive Churn Modelling for a B2B SaaS
The company was experiencing a 6% monthly churn rate but had no way to identify at-risk accounts until they had …
The model predicted churn with 85% accuracy 30 days out. Armed with this list, the Customer Success team reduced actual churn by 25% in the first quarter of deployment.
Read full case studyWhat clients say.
Don't just take our word for it — hear directly from the teams we've worked with.
"PA Data Analytics helped us understand that our social ad spend was completely cannibalizing our organic traffic. We shifted budget and immediately saw a 32% lift in overall ROAS."
"The churn prediction model was a game-changer. We can now proactively reach out to accounts before they cancel. It paid for itself in the first month."
Thinking out loud.
Articles on marketing mix modelling, attribution, and how to stop making bad decisions with good data.
Why Last-Click Attribution is Killing Your Ad Efficiency
Here is a scenario I see constantly with ecommerce businesses that are spending serious money on ads.
Read ArticleA Beginner's Guide to RFM Segmentation in SQL
Most businesses have more customer data than they know what to do with. They can tell you total revenue, average order value, monthovermonth growth. What they struggle to answer is a simpler and more useful question: which customers actually matter, and which ones are slipping away?
Read ArticleTemplates & Resources.
Accelerate your analytics journey with our plug-and-play templates.
Data transparency for
a better world.
We partner with nonprofits and NGOs to build scalable M&E reporting frameworks, ensuring stakeholders and donors see exactly where their impact is felt.
The team behind
the numbers.
We started PA Data Analytics because we noticed a gap: businesses were buying expensive tools but still couldn't answer basic questions about their marketing ROI or customer retention.
We don't just build dashboards. We build the pipelines that feed them, the statistical models that power them, and the strategies that make them useful.
- Based in Nairobi, Kenya. Working with clients globally.
- Certified in Advanced Google Analytics & Data Engineering.
Let the data tell you
something useful.
Most businesses are sitting on enough data to make significantly better decisions. The question is whether anyone has looked at it properly. Let's find out.
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