# Datalyze: full reference

> Datalyze is an analytics and growth consultancy for startups. We rebuild the data foundation - events, pipelines, definitions, models - connect product, marketing, revenue and warehouse tools into one source of truth, and run the experiments that turn it into revenue. Founded 2019 by Ansh Agrawal.

Short index: https://www.joindatalyze.com/llms.txt

## Frequently asked questions (https://www.joindatalyze.com/faqs)

### How much does Datalyze cost?

The cost depends on the complexity of your product, and scope of the project. We do one-time projects, and monthly retainers.

### Do I have to commit to a retainer?

No. The audit and infra setup tiers are both one-time projects. Some clients start with an audit and scale into a retainer; others take a project and run it themselves afterwards.

### Why not just hire a full-time analyst?

A full-time analyst costs $100,000 to $150,000 loaded, takes 3 to 6 months to ramp, and brings experience from one or two companies. Datalyze brings senior operators who've seen the patterns across 150+ startups, starts within a day, and costs a fraction of a full-time salary. When you're ready for that hire, they'll inherit a clean, documented foundation instead of the mess they'd normally spend their first six months untangling.

### How fast can you actually start?

Within a day of kickoff. We don't need a month to learn your stack or a week of onboarding meetings. Share access and we're auditing your data by tomorrow.

### How long until we see results?

Most teams see their first actionable insights within 2 to 3 weeks of kickoff.

### What does a typical engagement look like?

Week one: full data audit. We identify what's broken, what's missing, and what's being tracked but never used. From there, we prioritize, fix critical tracking issues, unify data sources, build the reporting your team actually needs, and start identifying growth levers. Weekly syncs keep everything aligned.

### What happens when an engagement ends?

Everything we built - tracking plans, models, dashboards, documentation - is in your tools and owned by your team.

### Who will I work with?

You'll work with a small, focused team based on your needs. This may include a product analyst, analytics engineer, growth strategist, and developer. No unnecessary layers - you talk directly to the people doing the work.

### What do you need from our team?

We may need light developer support to implement tracking, add events, or help with data foundations for modeling.

### We already have analytics tools. Why do we need you?

The tool is rarely the problem. The implementation is. Whether you're running Mixpanel, Amplitude, PostHog, BigQuery, Snowflake, or some combination of all of them, most setups we audit have 20 to 40% of events, models, or pipelines that are misconfigured, missing, or quietly broken. Your tools work fine. The data flowing through them doesn't. We fix the foundation across your whole stack, then make it actually useful for decisions.

### What if we already have some analytics setup?

Most companies do. The question is whether anyone trusts it. We audit what you have, fix what's broken, fill what's missing, and build on what's working. We don't rip and replace, we make your current investment reliable.

### Which tools do you work with?

Whatever you're already running: Mixpanel (Certified Partner), PostHog (Implementation Specialist), Amplitude, Heap, GA4, Google Tag Manager, Google Ads, Meta Ads, HubSpot, Stripe, Chargebee, Recurly, RevenueCat, BigQuery, Snowflake, Postgres, Looker Studio, Metabase, Databricks, Segment, dbt, Fivetran, Hightouch and Airbyte.

### How do you measure the 14% revenue lift?

We measure the before-and-after revenue impact of the specific changes made during each engagement: conversion improvements from tracking fixes, retention gains from product changes, revenue from experiments we design and run. The 14% is a weighted average across 150+ engagements, not a cherry-picked best case.

### What if the work does not move the numbers?

When an experiment doesn't move the needle, we tell you and run a better one. We measure revenue impact on every change we ship.

## Case studies (https://www.joindatalyze.com/case-studies)

### Copyfy

- Sector: AI website builder
- Focus: Conversion, data quality
- Result: Sign-up rate 30% → 61%, data unified
- Summary: Improving sign up rate (30% → 61%) & unifying data.
- Full case study: https://www.joindatalyze.com/blog/case-study-copyfy/

### Foriio

- Sector: Portfolio platform
- Focus: Activation, lifecycle
- Result: Activation +8%, retention +6%
- Summary: Improving Activation (+8%) & Retention (6%).
- Full case study: https://www.joindatalyze.com/blog/case-study-foriio/

### CRED

- Sector: Fintech
- Focus: Monetization, reliability
- Result: Payment success rate +7%, cost −12%, provider outages −93%
- Summary: Improving PSR (+7%), reducing Cost (12%) and outage (93%).
- Full case study: https://www.joindatalyze.com/blog/case-study-cred/

### Sama.io

- Sector: Coaching SaaS
- Focus: Retention, UX
- Result: Retention +4%
- Summary: Improving retention (+4%).
- Full case study: https://www.joindatalyze.com/blog/case-study-sama/

### GameTree

- Sector: Social
- Focus: Localization, activation
- Result: Day 0 activation +5%
- Summary: Improving Day 0 Activation (+5%).
- Full case study: https://www.joindatalyze.com/blog/case-study-gametree/

### Petcademy

- Sector: Pet tech
- Focus: Messaging, personalization
- Result: SMS cost −12% with no drop in engagement
- Summary: Reduced SMS cost (-12%) with no decline in engagement.
- Full case study: https://www.joindatalyze.com/blog/case-study-petcademy/

### Confidential

- Sector: Stealth
- Focus: Activation, pricing
- Result: Activation +22%, time in app +2 min
- Summary: Increased Activation (+22%), and time spent on app (+2 mins).
- Full case study: https://www.joindatalyze.com/blog/case-study-stealth/

### VideoTap

- Sector: AI video
- Focus: Onboarding, activation
- Result: Onboarding completion 28% → 80%, activation 7% → 19%
- Summary: Increased activation (7% → 19%) and onboarding completion (28% → 80%).
- Full case study: https://www.joindatalyze.com/blog/case-study-videotap/

### zeroone

- Sector: Web3 social
- Focus: Engagement, habit loops
- Result: Week 2 retention +15%, engagement +30%
- Summary: Improved Week 2 retention (+15%), engagement (+30%), and implementation of new tracking.
- Full case study: https://www.joindatalyze.com/blog/case-study-zeroone/

### Speedyloans

- Sector: Lending
- Focus: Segmentation, cost
- Result: Email cost −30%, engagement +1%
- Summary: Reduced email cost (-30%) and increased engagement (+1%).
- Full case study: https://www.joindatalyze.com/blog/case-study-speedyloans/

### Wellness Coach

- Sector: Wellbeing
- Focus: Taxonomy, data quality
- Result: Trust in data restored
- Summary: Helped regain trust in data.
- Full case study: https://www.joindatalyze.com/blog/case-study-wellnesscoach/

### Sol

- Sector: Productivity
- Focus: Governance, scalability
- Result: Complex data structure simplified into an accurate setup
- Summary: Helped clean up complex data structure into a simple accurate setup.
- Full case study: https://www.joindatalyze.com/blog/case-study-sol/

### Answering Agent

- Sector: AI voice
- Focus: Measurement, quality
- Result: AI-generated call scoring built
- Summary: Helped create a call score using AI.
- Full case study: https://www.joindatalyze.com/blog/case-study-answering-agent/

### TermPlus

- Sector: Finserv
- Focus: Infrastructure
- Result: Full analytics infra built, user journey unified
- Summary: Setup the entire Data Analytics Infra, & unified user journey.
- Full case study: https://www.joindatalyze.com/blog/case-study-termplus/

### FRAI

- Sector: Interview prep
- Focus: Conversion, experiments
- Result: Paid conversion rate 2×
- Summary: Improved paid conversion rate (2x).
- Full case study: https://www.joindatalyze.com/blog/case-study-frai/

### AnyIP

- Sector: Infrastructure
- Focus: Marketing analytics
- Result: Marketing visibility, improved CAC and spend efficiency
- Summary: Helped get more visibility into marketing, and improving CAC, spend efficiency.
- Full case study: https://www.joindatalyze.com/blog/case-study-anyip/

### Buildern

- Sector: Construction SaaS
- Focus: Attribution, warehouse modelling
- Result: Attribution fixed, warehouse modelled in dbt, account health scored
- Summary: Fixed attribution, modelled the warehouse in dbt, and scored account health.
- Full case study: https://www.joindatalyze.com/blog/case-study-buildern/