Understanding Basic Analytics Terminology

Before diving into tools or dashboards, you need to speak the language. This section breaks down essential analytics terms so that your team can align on definitions and avoid confusion when interpreting results.

Why it's Important

Misunderstood terms can lead to misinformed decisions.
Alignment across teams reduces reporting errors.
It builds confidence in interpreting data from day one.
Investors expect clear, consistent reporting—this sets you up early.
Knowing the difference between terms like “metric” and “KPI” helps focus energy on what really matters.

How to Implement

Define key terms: metric, KPI, conversion rate, churn, CAC, LTV, MAU, etc.
Create a shared glossary document for your team.
Host a data 101 session with your core team.
Use real startup examples to make it stick.
Align on what success means for your stage (e.g., retention over revenue).
Ensure consistent metric naming across tools (CRM, dashboards, etc.).
Post your “top 5 metrics” visibly on your Slack or Notion homepage.
Review these definitions quarterly as your business evolves.

Available Workshops

Data Language Game: Match terms to definitions.
KPI Prioritization Matrix: Identify what’s most important now vs. later.
Metric Mapping: Align 10 key metrics with business goals.
Scenario Review: “What does this metric tell us?” practice.
Analytics Glossary Sprint: Build a collaborative glossary in 30 minutes.
Data Storytime: Have team members explain one metric in plain English.

Deliverables

A team-approved analytics glossary.
A visual cheat sheet of top startup metrics.
Documentation for how you define each tracked metric.
Internal agreement on 3-5 startup-specific KPIs.
A channel or dashboard for regular metric updates.

How to Measure

Number of team members who can define top 10 metrics accurately.
Internal surveys on data confidence pre/post workshop.
Review consistency in reporting across tools.
Audit recent pitch decks or reports for correct use of terms.
Spot check dashboards for clarity and alignment.
Evaluate decision-making meetings: are terms used consistently?
Track frequency of metrics shared in team updates.

Real-World Examples

Buffer

Created a public metrics dashboard, forcing them to define every metric transparently.

Superhuman

Focused early on activation rate and NPS, but first aligned the team around exactly how they defined those.

Stripe

Prioritized internal metric consistency so that any team could understand a KPI with no translation needed.

Get It Right

Make metric definitions part of onboarding.
Choose a few key terms and go deep on them.
Update definitions as your product evolves.
Regularly revisit metric usage in retros.
Encourage cross-functional participation in learning.

Don't Make These Mistakes

Using “retention” or “conversion” without clear definitions.
Reporting vanity metrics without context.
Letting every team define the same metric differently.
Skipping a glossary because it feels too “corporate.”
Assuming everyone knows the jargon already.