What is PeopleAnalyst?

PeopleAnalyst is the front door for people-analytics research: 205+ works indexed and profiled, 40+ citation-grade findings extracted, and peer-reviewed behavioral science translated from academic to actionable — the missing manual for the people analytics you always meant to do.

What is people analytics?

People analytics is not a dashboard. It is behavioral science and statistical inference applied to workforce decisions — a discipline with its own methodology, spanning measurement, organizational design, talent, leadership, and analytics craft.

Why does AI in HR need measurement science?

AI is being deployed in high-stakes people decisions — hiring, performance, attrition — without the measurement science to evaluate whether it works or whom it harms. Construct validity, effect sizes, and criterion validity are the vocabulary for asking an AI vendor the right questions.

How is the research made accessible?

The evidence is indexed and searchable: 205+ works, 40+ citation-grade insight cards, and 8 research arcs, so the right finding reaches the right decision at the right time.

What separates good people measurement from assertion?

Good measurement has a method: construct validity, reliability, and effect-size interpretation are not optional — they are what separates evidence from assertion.

library / lib347aed5fe69ed927

Lean Analytics: Use Data to Build a Better Startup Faster

Alistair Croll, Benjamin Yoskovitz · 2013

In a sentence

A practical guide to using the right metrics at the right stage of a startup to validate problems, build products people want, and grow a sustainable business faster.

Lean Analytics extends the Lean Startup movement by giving entrepreneurs, intrapreneurs, and business leaders a rigorous, data-informed approach to building businesses. It argues that entrepreneurs are 'all liars' whose reality distortion fields must be checked against hard data, and it provides a framework for finding the One Metric That Matters at any given moment. By mapping six common business models (e-commerce, SaaS, free mobile app, media site, user-generated content, and two-sided marketplace) against five stages of growth (Empathy, Stickiness, Virality, Revenue, and Scale), the book helps readers know exactly which metric to track and optimize right now. With more than 30 case studies, dozens of benchmarks ('lines in the sand'), and chapters on selling to enterprises and innovating from within large organizations, Lean Analytics is a hands-on manual for turning gut instincts into testable experiments and building a better startup faster.

The four lenses

Science
Statistics
Systems
Strategy

The model

A causal framework in which design levers (focus discipline, experimentation, problem/solution validation) and contextual conditions (business model, growth stage) drive psychological and behavioral states (engagement, virality, retention) that in turn produce outcome metrics (revenue, scalable growth). The model asserts that the right metric to optimize depends on the intersection of business model and growth stage, and that progress through stages is gated by achieving target levels on key states.

One Metric That Matters Focus Disciplinedesign lever

The degree to which an organization concentrates on a single, stage-appropriate key metric (the OMTM) rather than tracking many metrics at once, including drawing a clear line in the sand (target) for success before acting.

Experimentation and Iteration Intensitydesign lever

The frequency and rigor with which an organization runs disciplined experiments through the build-measure-learn cycle, forming testable hypotheses with predefined success criteria and acting on the results to learn quickly.

Problem and Solution Validation Strengthdesign lever

The extent to which an organization has confirmed, through qualitative interviews and at-scale quantitative methods, that it has identified a real and painful problem worth solving and a solution that customers will adopt and pay for.

Business Model Typecontextual condition

The category of business the organization operates as—e-commerce, SaaS, free mobile app, media site, user-generated content, or two-sided marketplace—which determines how money is made and therefore which metrics are most relevant to track and optimize.

Growth Stage (Empathy/Stickiness/Virality/Revenue/Scale)contextual condition

The current developmental stage of the startup along the Lean Analytics progression, from validating problems (Empathy) through proving engagement (Stickiness), growing through word of mouth (Virality), monetizing (Revenue), and expanding into markets (Scale).

User Engagement and Stickinesspsychological state

The degree to which users actively and repeatedly interact with the product in a meaningful, valuable way, measured through return frequency, daily/monthly active use, feature utilization, and time spent, indicating the product has become integral to users' lives.

Customer Retention (Inverse of Churn)behavioral pattern

The rate at which users and paying customers continue using the product over time rather than abandoning it, the inverse of churn, which serves as a leading indicator of product-market fit and the foundation for sustainable growth.

Virality (Coefficient and Cycle Time)behavioral pattern

The extent to which existing users bring in new users through inherent, artificial, or word-of-mouth sharing, captured by the viral coefficient (new users per existing user) and viral cycle time (how quickly invitations propagate).

Customer Acquisition Costoutcome metric

The total money spent to acquire a paying customer, including advertising, marketing, and onboarding, which must be substantially less than the customer's lifetime value (ideally under one third) for the business to be sustainable.

Customer Lifetime Valueoutcome metric

The total revenue a customer generates over the entirety of their relationship with the business, derived from average revenue per period and customer lifespan (driven by churn), which anchors decisions about acquisition spending and cash flow.

Revenue Per Customeroutcome metric

The amount of money generated per customer in a given period, a ratio that reveals business health better than raw revenue, encompassing conversion, transaction size, repeat purchases, and upselling.

Pricing Optimizationdesign lever

The degree to which an organization has tested and set prices to balance revenue maximization, unit-sales growth, and value perception, accounting for price elasticity of demand and tiered/bundled offerings.

Scalable, Sustainable Growthoutcome metric

The ultimate outcome state in which the business has a repeatable, sustainable model that generates a return, grows into new markets and channels, and participates in a broader ecosystem—proving not just a product but a market.

Organizational Data Culturecontextual condition

The extent to which an organization makes decisions based on data and evidence rather than opinion or gut alone, with transparency, clear goals, executive buy-in, and accountability propagated throughout the company.

How they connect

metric focus discipline → influences experimentation intensity
problem solution validation → predicts user engagement
experimentation intensity → influences user engagement
user engagement → predicts customer retention
user engagement → predicts virality
virality − influences customer acquisition cost
customer retention → predicts customer lifetime value
pricing optimization → influences revenue per customer
revenue per customer → predicts customer lifetime value
customer lifetime value → predicts scalable sustainable growth
customer acquisition cost − influences scalable sustainable growth
business model type → moderates metric focus discipline
growth stage → moderates metric focus discipline
data culture → moderates experimentation intensity
metric focus discipline → predicts scalable sustainable growth

The story

The reader An entrepreneur, intrapreneur, or business leader who wants to build a successful, sustainable business without wasting time and money building something nobody wants.

External problem

They don't know which metrics to track or whether their business is actually making progress toward a viable model.

Internal problem

They are delusional and prone to lying to themselves, feeling uncertain whether they're crushing it or being crushed.

Philosophical problem

Building on gut instinct alone, in the absence of data, is wrong because it leads to wasted lives and businesses that fail despite being 'on time and on budget.'

The plan

Determine your business model and your stage of growth.
Find the One Metric That Matters to you right now.
Draw a line in the sand—set a target—so you know when you've succeeded or failed.
Run quick, iterative experiments to improve that metric.
Compare your results to industry benchmarks to know when to move on to the next stage.

Success

You build a product people actually want and pay for.
You avoid premature scaling and wasted spending.
You move confidently and faster through each stage of growth toward a sustainable, scalable business.
You create a data-informed culture that makes smarter, faster decisions.

At stake

You build something nobody wants and run out of money.
You stay trapped in your reality distortion field until you hit the wall at full speed.
You waste your life and capital optimizing the wrong things.
You scale prematurely and watch your users churn away.

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