PILLARDATA & ANALYTICS

Numbers that move the needle.

MMM, MTA, predictive models, and BI — shipped with assumptions, validation, and refresh cadence.

Book a consultSee sub-services
Models
MMM + MTA
Bayesian MMM in R/Python plus user-level MTA. Triangulated for strategic and tactical decisions.
Discipline
Pre-registered
Model family, features, and validation method agreed before outcomes are seen.
Explainability
SHAP
Every predictive model ships with feature-importance and plain-English interpretation.
Practice proof

Numbers the practice will defend in writing.

Triangulated measurement · strategic + tactical
MMM + MTA
Model assumptions documented before build
Pre-registered
Feature-importance on every predictive model
SHAP
Measurement aligned to MASB standards
MASB

Quick answer
NUUN Digital's Data & Analytics practice builds marketing analytics and attribution (including Marketing Mix Modeling and Multi-Touch Attribution), business intelligence dashboards, predictive analytics, and reporting infrastructure. Every model ships with its assumptions, validation results, and a review rhythm. Dashboards that answer the CFO — not dashboards that decorate the office TV.
Sub-services

4 discrete engagements inside the Data & Analytics pillar.

001
Data & Analytics

Marketing Analytics & Attribution

MMM, MTA, and triangulated measurement you can defend in the boardroom.

002
Data & Analytics

Business Intelligence & Dashboarding

BI stacks that replace the weekly reporting tax.

003
Data & Analytics

Predictive Analytics

Forecasts, propensity models, and decision automation.

004
Data & Analytics

Reporting Infrastructure

Single-source reporting with lineage, not tab spaghetti.

How we work.

A six-step process from discovery to measured outcome.

  1. 01

    Discover

    Interviews, audits, and a written problem statement.

  2. 02

    Design

    Approach options with trade-offs and pricing.

  3. 03

    Plan

    Phase-by-phase plan with a single accountable owner.

  4. 04

    Build

    Execution in weekly sprints, stakeholder demos every 2 weeks.

  5. 05

    Measure

    Against the KPI we set in week one. No vanity metrics.

  6. 06

    Compound

    Quarterly review, roadmap refresh, next bet.

Measurement the CFO will defend

Most marketing dashboards look professional and say nothing. Ours start with the decision the executive needs to make — then reverse-engineer the model, the data, and the pipeline that justify it.

We publish the assumptions, the validation, and the known limits of every model. If the number isn't defensible, we don't ship it.

Comparison — MMM vs MTA vs hybrid

| You need to | Best approach | Data requirements | Typical timeline | Starting cost | |---|---|---|---|---| | Understand total-marketing ROI across paid + organic + offline | Marketing Mix Modeling (MMM) | 2–3 yrs aggregated media + sales data | 10–14 weeks | $120k+ | | Optimize digital channel mix week-to-week | Multi-Touch Attribution (MTA) | User-level event data with identity resolution | 8–12 weeks | $90k+ | | Both — strategic + tactical decisions | Hybrid (MMM + MTA triangulation) | Both data stacks | 14–20 weeks | $180k+ | | Predict which customers will churn | Predictive churn model | Customer event + subscription data | 6–10 weeks | $60k+ | | See campaign performance daily | BI dashboards | Connected ad platforms + CRM | 4–8 weeks | $35k+ |

Industries we know

Analytics patterns matched to the industry's real data across CPG, Financial Services, Health & Wellness, Healthcare & Pharma, Lottery & Gaming, Retail & E-commerce, Travel & Hospitality, Public Affairs, Energy, Real Estate, Education, and Tech & SaaS.

Browse industry pages →

Related reading

Sources & further reading

NUUN Digital Analytics Practice — Head of Analytics. Marketing mix modelling, multi-touch attribution, Bayesian modelling, predictive analytics with SHAP explainability, MASB-aligned measurement.

Proof.

Recent wins across NUUN Digital engagements.

Retail & E-commerce

A loyalty app worth keeping on the home screen.

NativeiOS + Android rebuild · Swift + Kotlin
Financial Services & Fintech

A bank AI assistant with zero material findings.

ZeroMaterial audit findings at rollout

FAQ.

What's the difference between MMM and MTA?
MMM (Marketing Mix Modeling) uses aggregated, time-series data to measure the incremental contribution of every channel — including offline media — over months and years. MTA (Multi-Touch Attribution) uses user-level event data to assign credit to digital touchpoints over days and weeks. They answer different questions. Mature brands run both and triangulate.
Do we need a data warehouse before you can help?
Not necessarily. For MTA and most dashboarding work, we can start with connected platforms and a lightweight warehouse. For MMM and predictive modeling, a stable warehouse makes life easier but isn't strictly required — we've built models on flat-file exports when needed. We scope the foundation work honestly before the modeling work.
Which attribution tools or platforms do you use?
Model-agnostic. We build custom MMM in R and Python (Meridian, Robyn, bespoke Bayesian models), deploy MTA on platforms like Rockerbox, Northbeam, or custom stacks, and run BI on Looker, Power BI, or Tableau based on the client's existing investment. Tool choice is a consequence of the decision architecture, not the other way around.
How often should models be refreshed?
MMM — quarterly refresh, annual rebuild. MTA — weekly refresh, quarterly rebuild. Predictive models — weekly scoring, monthly validation, quarterly rebuild. Every model ships with an explicit refresh and retirement policy, documented in the method statement.
Can you work with our existing analytics team?
Yes. Most of our Data & Analytics engagements embed one or two NUUN analysts alongside the client's team. We transfer every model and dashboard at the end of the build — with documentation the internal team can maintain — unless the client wants us to continue operating them.
Do your predictive models ship with explainability?
Yes. Every client-deployed model includes feature-importance output (SHAP or equivalent), validation results, and a plain-English interpretation document. If a regulator, auditor, or executive asks "why did the model say that?", the answer is in the package.
What happens when the data is messy?
Data messiness is the starting condition, not an obstacle. Our Data Management practice partners on the clean-up — architecture, ETL/ELT, governance — and we scope it transparently up front rather than pretending the model can survive bad inputs.

Ready to talk Data & Analytics?

Bring the target and the deadline — we'll scope an approach in 5 business days.

Book a call