Corporate Training · The Adoption Engine · New for 2026

Advanced Sales & Marketing Analytics: Customer Analytics, MMM, and Decision Systems

Your agencies grade their own homework, attribution arguments eat Mondays, and churn gets noticed after the customer leaves. This program trains your team to run the customer analytics, marketing mix models, and next-best-action systems in-house, on open-source tools, with the calibration discipline that separates a media argument from a budget decision.

What Your Team Leaves With

A churn and propensity suite

Scored on your CRM data with uplift discipline: act on persuadables, not sure-things

A working MMM v1

Robyn, Meridian, or PyMC-Marketing, with a calibration plan attached

Customer economics views

Segmentation, RFM, and CLV tied to cost-to-serve and profitability

A 90-day activation roadmap

From model scores to next-best-action inside your CRM

Hands-on program hours

Open-source MMM stacks compared

52–104

Weeks of data a credible MMM needs

5–20%

Typical marketing ROI improvement range

Why This Program Exists

Mid-market demand-side teams sit on CRM, POS, and media data everywhere while renewal risk hides in a spreadsheet and the media budget gets set at last year plus inflation. The tooling changed underneath them: open-source MMM frameworks and AI-assisted modeling now put in reach what only enterprise vendors sold five years ago.

The catch is discipline. An uncalibrated MMM is an expensive opinion: media endogeneity credits advertising with demand that was already rising, promotion confounding hands media the volume that discounts drove, and churn models without uplift framing chase customers who would have stayed anyway. This program teaches the methods together with their failure modes, from practitioners who build these systems inside mid-market revenue stacks.

Who This Is For

Teams that own the demand side

Marketing, sales operations, RevOps, and commercial finance professionals from analyst to director, plus the business analysts and data scientists aligned with sales and marketing domains.

Sales Finance & RevOps

Teams that own pipeline, quota math, and rep productivity, and want models that tell the team what to do next instead of describing last quarter.

Marketing & Consumer Insights

Teams accountable for media ROI, campaign readouts, and budget defense in the annual plan, ready to stop outsourcing the scoreboard.

Data & BI Professionals

The people who support demand-side teams and want production score pipelines and calibrated models, not one-off notebooks.

Learning Outcomes

What your team will master

Churn, cross-sell, and upsell propensity

Predictive modeling with uplift discipline: separating persuadables from sure-things and lost causes, survival analysis for churn timing, and calibration so scores mean what they say

Customer economics

CLV modeling, RFM and behavioral segmentation, customer profitability with cost-to-serve, and lookalike modeling for audience expansion you own instead of rent.

Marketing mix modeling, in-house

Robyn vs Meridian vs PyMC-Marketing honestly compared, adstock and saturation specification, marginal (not average) ROAS, and budget optimization your CFO can interrogate

Calibration and incrementality

Geo-lift and holdout experiment design, triangulating MMM with experiments and attribution, and the endogeneity traps that quietly inflate media ROI.

Activation and decision systems

Next-best-action delivery into CRM workflows, sales and marketing knowledge graphs where they earn their complexity, and the human approval gates that keep automated recommendations honest.

Program Modules

Three days, from customer economics to funded reallocation

Cohorts run in person or online; hands-on sessions use your data under mutual NDA where feasible, or realistic synthetic datasets where not.

Day 1

Customer Analytics Foundations

Day 2

Marketing Mix Modeling In-House

Day 3

Attribution, Decision Systems & Activation

What We Cover in Depth

The full method inventory

Each topic is taught with the practical pitfall attached: the mistake that invalidates it in production and how to catch it

Customer and revenue analytics

Uplift modeling and persuadable targeting
CLV with discounted margin flows
Segment migration analytics
Rep next-best-action design
Survival analysis for churn timing
Cost-to-serve and customer-level P&L
Pipeline analytics and forecasting
Territory and incentive analytics

Marketing measurement

Geometric and Weibull adstock transformations
Marginal vs average ROAS (the oversaturation tell)
Bayesian priors and convergence diagnostics (R-hat)
Promotion-media confounding controls
Hill saturation and diminishing returns
Hierarchical geo-level MMM
Geo-lift and holdout experiment design
Media endogeneity and baseline discipline

Production and governance

Score pipelines and refresh cadence
Leakage prevention and point-in-time features
Agency-to-in-house transition planning
Calibration monitoring and model drift
Privacy and consent constraints in modeling
Triangulation scorecards: MMM x experiments x attribution

Taught the Way We Work

Built by the people who deploy these systems

Led by Armin Kakas (Founder; former head of advanced analytics commercialization at a leading North American distributor) and Rudy Agovic, PhD (Partner, AI for Sales & Customer Growth; co-founder of a leading AI/ML solutions firm, published extensively in top-tier AI conferences). The curriculum mirrors systems the instructors have deployed across retail, industrial, CPG, healthcare, and SaaS companies.

Partner-led instruction

Taught by the senior practitioners who architect and deploy these systems for clients

Built on your stack

Labs adapt to your CRM, media data, and BI environment; everything runs on open-source tools

Working assets, not notes

Teams leave with scored models, an MMM v1, and a 90-day activation roadmap

Frequently Asked Questions

Program details, answered

Do participants need programming experience?

No. Labs are AI-assisted: participants who have never written Python leave with working models because the AI writes the first draft of the code and they learn to direct, verify, and improve it. Experienced analysts go deeper, faster.

Which tools does the program cover?

R and Python throughout: Robyn, Meridian, and PyMC-Marketing for MMM; scikit-learn, lifetimes, and standard causal libraries for customer analytics; Neo4j for the knowledge-graph demonstration. All open source; we adapt to the tools your company has approved.

Can we audit our agency's MMM in the program?

Yes, and it is one of the most valuable exercises available: rebuilding an incumbent agency model on an open-source stack, comparing assumptions, and pressure-testing the calibration. Bring the model documentation and we will design the lab around it.

How is company data handled during training?

Hands-on sessions run on your data under mutual NDA where feasible, or on realistic synthetic datasets where not. A governance module covers confidentiality, consent constraints, and review policies your team takes home.

How does this program relate to Revology's advisory work?

It is the Adoption & Execution layer of our end-to-end practice, available standalone. Teams that have been through a Revology engagement use it to run and extend what was built; teams that have not use it to bring demand-side analytics in-house.

Ready to own the demand-side models?

Tell us about your stack and the questions your team keeps outsourcing; we will shape the cohort around both.