Experimentation · Causal Measurement · Customer Analytics

Move from opinions
to evidence.

Outcome Effect helps teams understand what drives customer behavior. We use experimentation, causal measurement, and customer analytics to uncover what works, what doesn’t, and where to focus next across product, UX, ecommerce, and marketing.

Talk about your program Explore the capabilities

Available for fractional leadership and embedded engagements

An experimentation learning system
01
QuestionProduct + UX
02
QuestionMarketing + growth
03
Learn throughExperiment + analysis
04
BuildCustomer knowledge
Randomize when possible · infer carefully
01

Executive perspective
Grounded in business decisions

02

Hands-on analysis
Close to the data and methods

03

Capability building
Stronger teams after the engagement

What we help you solve

Experimentation is bigger than a product A/B test.

The business question should determine the design. That may be a randomized controlled test, UX research experiment, usability test, marketing holdout, quasi-experiment, or deeper analysis of customer data.

01

Randomized Controlled Tests

Experiment Strategy, Design & Analysis

Turn product, ecommerce, UX, and marketing questions into trustworthy experiments and decision-ready evidence.

  • A/B, multivariate, and factorial designs
  • UX research and behavioral experiments
  • Power, sample size, guardrails, sequential and non-inferiority methods
  • Interpretation, recommendation, learnings
What changed—and what did we learn?
02

Causal Impact

Quasi-Experiments & Causal Analysis

Estimate impact when individual randomization is unavailable, impractical, or interferes with decision making.

  • Marketing incrementality, holdouts, and geo experiments
  • Difference-in-differences and synthetic controls
  • Phased rollouts and observational causal analysis
  • Assumption checks, sensitivity analysis, and triangulation
What can we credibly say caused the outcome?
03

Customer understanding

Customer Analytics & Data Science

Explore customer data to reveal meaningful differences in behavior, needs, journeys, adoption, and response.

  • Behavioral segmentation and customer profiling
  • Targeting, propensity, and response analysis
  • Journey, funnel, adoption, and retention analysis
  • Treatment heterogeneity and personalization opportunities
Who behaves differently—and what should we do about it?
Randomize when you can. Otherwise use quasi-experimental designs and alternative research methods.Customer segmentation helps us to understand for whom the effect occurred, why it may have happened, and what to test next.

Ways to work together

Senior expertise, shaped around what your team needs now.

Start with a focused assessment, add ongoing leadership, or bring specialized analytical depth directly into product, customer, and marketing decisions.

01

Focused engagement

Experimentation Program Assessment

Understand whether your organization is producing trustworthy evidence and meaningful customer learning across product, UX, ecommerce, and marketing decisions.

  • Strategy, intake, and operating model
  • Hypotheses, KPIs, guardrails, and decision rules
  • Instrumentation, data quality, and methods
  • Learning capture and organizational adoption
Best whenYou run tests, but question whether the program is creating enough value.
02

Ongoing partnership

Fractional Experimentation & Analytics Leadership

Add experienced leadership without another full-time executive. Build experimentation into a cross-functional learning capability while keeping senior expertise close to the work.

  • Experimentation roadmap and portfolio strategy
  • Program establishment and governance
  • Analyst and data scientist coaching
  • Executive recommendations and decision support
Best whenYou need someone to establish or lead the capability rather than deliver an analysis.
03

Embedded expertise

Experimentation & Customer Data Science

Bring analytical depth into a product, UX, ecommerce, or marketing decision—from the initial question through data extraction, analysis, and recommendation.

  • Randomized and quasi-experimental analysis
  • SQL, R, Python, GA4, CRM, and warehouse data
  • Segmentation, targeting, and behavioral analysis
  • Causal inference and heterogeneous effects
Best whenYour team has the platform and data, but needs specialized analytical horsepower.

The outcome

Not just tests.
A learning system.

A healthy experimentation capability does more than declare winners. It uses the right design for the question, adds customer context to the result, and turns each study into knowledge the organization can reuse.

01

Growth decisions grounded in evidence

Clear links between the business question, customer behavior, marketing investment, and action.

02

Experimentation

A shared approach to learning across Product, UX, Ecommerce, Marketing, Analytics, and Data Science.

03

A stronger view of the customer

Each experiment contributes to a growing body of knowledge, even when it does not produce a lift.

04

Capability that compounds

Better methods, sharper questions, and accumulated customer knowledge remain after the engagement.

How the work moves

From business question to defensible action.

Outcome Effect can own the analytical path while your engineering, data engineering, martech, and development teams own production implementation.

01

Frame

Clarify the decision, expected customer behavior, and what evidence would change the course of action.

02

Design

Define the hypothesis, metrics, guardrails, population, sample requirements, and decision rules.

03

Measure

Specify instrumentation, validate the data, extract the right population, and apply the appropriate method.

04

Learn

Translate the result into a recommendation, capture the customer insight, and improve the next decision.

A practical principle

Rigor should match the risk of the decision.

Low risk
Moderate
High stakes

Build the evidence the decision requires. The greater the uncertainty or consequence of getting a decision wrong, the stronger the evidence should be. We choose methods that provide the right level of evidence for the decision at hand.

Why Outcome Effect

One partner across the whole decision.

Outcome Effect is led by Mark Jodlowski, an experimentation, analytics, and data science leader who has worked from both sides of the table: as an ecommerce operator accountable for customer and business outcomes and as a hands-on data scientist responsible for the evidence.

That range matters when a question crosses functional boundaries. Mark can work with an executive on the decision, partner with Product, Marketing, and UX on the research question, define measurement requirements with technology teams, analyze the data, and turn the findings into a clear recommendation.

BusinessEcommerce, product, UX, marketing, customer experience
TechnicalExperiment design, causal inference, segmentation, SQL, R, Python
OrganizationalCapability building, coaching, executive decision support
“Senior enough to advise the decision. Technical enough to do the analysis.”

Start a conversation

What do you need to learn about your customers?

Share the decision your team is facing and how confident the evidence needs to be. We can determine whether the answer calls for an experiment, a quasi-experiment, or deeper customer analysis.

info@outcomeeffect.com