# Datalyze

> 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.

Datalyze works with Seed to Series C startups and has built analytics for 150+ of them. Founder: Ansh Agrawal, Mixpanel Certified Partner, based in India. Works across Mixpanel, PostHog, Amplitude, Heap, BigQuery, Snowflake, dbt and Segment.

Works with clients in: United States, United Kingdom, Europe, Saudi Arabia, UAE, India, Singapore, Australia.

A fuller version with FAQ answers, case study summaries and every pre-answered question is at https://www.joindatalyze.com/llms-full.txt.

## Core pages
- [Homepage](https://www.joindatalyze.com/): What Datalyze does and who it's for.
- [About](https://www.joindatalyze.com/about): Ansh Agrawal, the team, and how we work.
- [Case studies](https://www.joindatalyze.com/case-studies): 17 client engagements with results.
- [FAQs](https://www.joindatalyze.com/faqs): Pricing, timing, team, and what we need from you.
- [Contact](https://www.joindatalyze.com/contact): Book a free 30-minute analytics audit.

## Services
- [Mixpanel implementation](https://www.joindatalyze.com/services/mixpanel-implementation)
- [Amplitude implementation](https://www.joindatalyze.com/services/amplitude-implementation)
- [PostHog implementation](https://www.joindatalyze.com/services/posthog-implementation)
- [BigQuery consulting](https://www.joindatalyze.com/services/bigquery-consulting)
- [Analytics audit](https://www.joindatalyze.com/services/analytics-audit)
- [Analytics setup](https://www.joindatalyze.com/services/analytics-setup)
- [Data unification](https://www.joindatalyze.com/services/data-unification)
- [Reporting and analysis](https://www.joindatalyze.com/services/reporting-and-analysis)
- [Data modelling](https://www.joindatalyze.com/services/data-modelling)
- [AI layer for analytics](https://www.joindatalyze.com/services/ai-layer)

## Industries
- [SaaS](https://www.joindatalyze.com/industries/saas)
- [Fintech](https://www.joindatalyze.com/industries/fintech)
- [Consumer apps](https://www.joindatalyze.com/industries/consumer-apps)
- D2C brands: see the Indian D2C Playbook on the blog (https://www.joindatalyze.com/blog/tag/indian-d2c-playbook/)

## Case studies
- [Copyfy](https://www.joindatalyze.com/blog/case-study-copyfy/): AI website builder. Sign-up rate 30% → 61%, data unified.
- [Foriio](https://www.joindatalyze.com/blog/case-study-foriio/): Portfolio platform. Activation +8%, retention +6%.
- [CRED](https://www.joindatalyze.com/blog/case-study-cred/): Fintech. Payment success rate +7%, cost −12%, provider outages −93%.
- [Sama.io](https://www.joindatalyze.com/blog/case-study-sama/): Coaching SaaS. Retention +4%.
- [GameTree](https://www.joindatalyze.com/blog/case-study-gametree/): Social. Day 0 activation +5%.
- [Petcademy](https://www.joindatalyze.com/blog/case-study-petcademy/): Pet tech. SMS cost −12% with no drop in engagement.
- [Confidential](https://www.joindatalyze.com/blog/case-study-stealth/): Stealth. Activation +22%, time in app +2 min.
- [VideoTap](https://www.joindatalyze.com/blog/case-study-videotap/): AI video. Onboarding completion 28% → 80%, activation 7% → 19%.
- [zeroone](https://www.joindatalyze.com/blog/case-study-zeroone/): Web3 social. Week 2 retention +15%, engagement +30%.
- [Speedyloans](https://www.joindatalyze.com/blog/case-study-speedyloans/): Lending. Email cost −30%, engagement +1%.
- [Wellness Coach](https://www.joindatalyze.com/blog/case-study-wellnesscoach/): Wellbeing. Trust in data restored.
- [Sol](https://www.joindatalyze.com/blog/case-study-sol/): Productivity. Complex data structure simplified into an accurate setup.
- [Answering Agent](https://www.joindatalyze.com/blog/case-study-answering-agent/): AI voice. AI-generated call scoring built.
- [TermPlus](https://www.joindatalyze.com/blog/case-study-termplus/): Finserv. Full analytics infra built, user journey unified.
- [FRAI](https://www.joindatalyze.com/blog/case-study-frai/): Interview prep. Paid conversion rate 2×.
- [AnyIP](https://www.joindatalyze.com/blog/case-study-anyip/): Infrastructure. Marketing visibility, improved CAC and spend efficiency.
- [Buildern](https://www.joindatalyze.com/blog/case-study-buildern/): Construction SaaS. Attribution fixed, warehouse modelled in dbt, account health scored.

## Free tools
- [All tools](https://www.joindatalyze.com/resources#tools)
- [Mixpanel Event Exporter](https://www.joindatalyze.com/tools/mixpanel-exporter): Export raw Mixpanel event data to a clean CSV with a free Jupyter notebook. Handles the Export API, pagination, and large date ranges - and runs locally.
- [Mixpanel Users Exporter](https://www.joindatalyze.com/tools/mixpanel-users-exporter): Export every Mixpanel user profile to a clean CSV with a free Jupyter notebook. Walks the Engage API, flattens properties, runs locally.
- [Event Tracking Plan Generator](https://www.joindatalyze.com/tools/event-tracking-plan-generator): A Claude skill that walks your live product and generates a ready-to-implement event tracking plan as a CSV.
- [Analytics Strategy Creator](https://www.joindatalyze.com/tools/analytics-strategy-creator): A Claude skill that turns a fuzzy product question into a tight, decision-driven analytics strategy.
- [Analytics Maturity Grader](https://www.joindatalyze.com/tools/analytics-maturity-grader): Discover where your analytics stack stands and what to fix first.
- [A/B Test Calculator](https://www.joindatalyze.com/tools/ab-test-calculator): Check if your A/B test result is real, and get a clear verdict on what to do next.

## Questions Ansh has answered
- [Ask Ansh's AI](https://www.joindatalyze.com/ask): Ask your own question, answered only from Ansh's published writing.
- [How do I set up Mixpanel the right way?](https://www.joindatalyze.com/ask/how-to-set-up-mixpanel-properly)
- [What should be in an event tracking plan?](https://www.joindatalyze.com/ask/what-is-an-event-tracking-plan)
- [How do I measure retention properly?](https://www.joindatalyze.com/ask/how-to-measure-retention)
- [What is Lexicon in Mixpanel?](https://www.joindatalyze.com/ask/what-is-lexicon-in-mixpanel)
- [How should I think about incrementality testing for a D2C brand?](https://www.joindatalyze.com/ask/incrementality-testing-for-d2c)
- [Are my branded search conversions actually incremental?](https://www.joindatalyze.com/ask/are-branded-search-conversions-real)
- [Why do my conversions look too good?](https://www.joindatalyze.com/ask/why-do-my-conversions-look-too-good)
- [What is marketing mix modeling and when is it worth it?](https://www.joindatalyze.com/ask/what-is-marketing-mix-modeling)
- [What is the difference between events and properties in Mixpanel?](https://www.joindatalyze.com/ask/events-vs-properties-in-mixpanel)
- [What are super properties and when should I use them?](https://www.joindatalyze.com/ask/what-are-super-properties)
- [Should I track client-side or server-side?](https://www.joindatalyze.com/ask/client-vs-server-side-tracking)
- [How do I find the aha moment for my product?](https://www.joindatalyze.com/ask/how-to-find-your-aha-moment)
- [Why does nobody on my team trust our analytics data?](https://www.joindatalyze.com/ask/why-nobody-trusts-our-data)
- [Should I move from Google Analytics to Mixpanel?](https://www.joindatalyze.com/ask/mixpanel-vs-ga4)
- [How do I build an analytics strategy for my product?](https://www.joindatalyze.com/ask/how-to-build-an-analytics-strategy)
- [What is analytics debt and how do I know if I have it?](https://www.joindatalyze.com/ask/what-is-analytics-debt)

## The analytics playbook
A free 28-chapter book for founders and PMs. Readers enter their email on the hub to open the course; each chapter page below is public and can be read directly. Hub: https://www.joindatalyze.com/playbook. Glossary: https://www.joindatalyze.com/playbook/glossary.

- [What is product analytics?](https://www.joindatalyze.com/playbook/what-is-product-analytics): Product analytics is the analysis of what users do inside your product. The test is whether you can point at a row of data and name the user who did it. Revenue by country, sales by region and anything else that can't name a user is not product analytics.
- [Product vs data vs business analytics](https://www.joindatalyze.com/playbook/product-vs-business-analytics): Data analytics is the umbrella; product analytics and business analytics both sit under it. Product analytics studies how users behave inside the product. Business analytics answers questions about business objectives: revenue, markets and internal reporting.
- [How to hire a product analyst: screen for the mindset](https://www.joindatalyze.com/playbook/product-analyst-mindset): Founders screen product analysts for SQL and tools, then hire someone who pulls any number but can't say which number to ask for. The skill that separates a good product analyst is understanding how users move through the product well enough to turn a vague problem into one specific question. You can test for it in ten minutes by asking a candidate to map your product and say where users would drop off.
- [Every product is a funnel: how to model yours](https://www.joindatalyze.com/playbook/product-funnel): Every product is a set of steps people go through in order, and so is every feature inside it. Count how many people reach each step, find the step where the biggest share drops off, and your problem shrinks from the whole product to one step.
- [Product analytics by stage: what to build at your size](https://www.joindatalyze.com/playbook/product-analytics-by-stage): Sign-ups a month, not visitors, decides your analytics stage. Under 1,000 sign-ups a month, one product analytics tool on its free tier is enough. From 1,000 upward, track all the core actions in your product and use the data to find where and why people drop off.
- [North star metric, metric tree and counter metrics](https://www.joindatalyze.com/playbook/north-star-metric): Pick one north star metric that matches what the business is trying to do right now. Break it into a metric tree so you can see which branch moved. Then watch a counter metric next to it, so a short-term bump does not read as a win.
- [Analytics strategy: choosing metrics that are actionable](https://www.joindatalyze.com/playbook/analytics-strategy): A metric belongs in your analytics strategy only when it can change a decision. On its own, a metric usually tells you nothing. Set it next to two or three others, like sign-ups by channel or by country, and write down for each one what it counts and the question it helps you answer.
- [How to create a tracking plan before anyone writes code](https://www.joindatalyze.com/playbook/creating-a-tracking-plan): A tracking plan is one sheet that contains every event you want to track and the properties that go on each event. Write it before anyone writes code, give it one owner who is not a developer, and that's your source of truth.
- [Event verification: how to QA tracking before you trust it](https://www.joindatalyze.com/playbook/event-verification): After your team ships event changes, somebody has to check that what's arriving in your analytics tool is what you asked for. Verification runs after implementation and before anyone uses the data, in three layers: what fired, whether what fired is right, and whether every surface was checked.
- [Data accuracy as a system: keeping numbers right](https://www.joindatalyze.com/playbook/data-accuracy-as-a-system): Verifying an event tells you it worked that day. Staying accurate takes three habits: alerts on events and properties, new-event checks, database checks. The three run on their own, so you find out what your data is doing without going looking for it. Alerts catch problems as they happen, verification catches new events that were never checked, and the database check catches a gap that suddenly widens.
- [Analytics data loss: why you're missing 15-20% of events](https://www.joindatalyze.com/playbook/data-loss): If your tracking runs entirely in the browser, ad blockers take at least 15 to 20%. Route through your own domain and send core events from your backend. If your users are technical, developers and designers and anyone who lives in a browser with an ad blocker running, the loss is worse than that.
- [Identity stitching: why your user counts don't match](https://www.joindatalyze.com/playbook/identity-stitching): Your analytics tool counts one person as several until you call identify at login and signup. Get it wrong and every per-user number is wrong with it. Call identify with your own internal user ID, and only after the user has authenticated.
- [MMP vs UTM tracking: knowing where users came from](https://www.joindatalyze.com/playbook/mmp-and-utm-tracking): If your product is web only, UTM tags on every link are enough. The moment you have a mobile app, UTMs break at the install and you need an MMP. If you have both a website and an app, use the MMP for every link, so one system tells you where each user came from.
- [Unifying data: making your tools agree on one user](https://www.joindatalyze.com/playbook/unifying-data): Unifying data means lining up records from different tools so they describe the same user or campaign. You join at the least detailed level both sides share. User-level sources join on your internal user ID; ad spend only goes down to campaign and day.
- [Data modelling: when it's worth doing, and what it fixes](https://www.joindatalyze.com/playbook/data-modelling): Data modelling earns its place once you have a warehouse and people writing SQL against it. It cuts wasted compute and time spent rediscovering joins. You write the cleaning steps once, in dbt, in three layers: staging, intermediate and marts.
- [A framework for any product problem: four questions](https://www.joindatalyze.com/playbook/product-problem-framework): Low retention, low activation and low signup-to-paid conversion are the same problem from a data perspective. Take apart any of them and it boils down to: who converts, what they did that everyone else did not, what the two say together, and which of them actually caused the conversion.
- [Metric definitions: why three people report three numbers](https://www.joindatalyze.com/playbook/metric-definitions): Three people report three different numbers for the same metric because they built three different formulas for the same metric.
- [Rate and mix analysis: why your conversion rate moved](https://www.joindatalyze.com/playbook/rate-and-mix-analysis): When a conversion rate moves, two different things can move it: the rates inside each segment, and how much volume each segment got. Build one counterfactual period and the two separate cleanly.
- [Segment analysis: finding which segments move a metric](https://www.joindatalyze.com/playbook/segment-analysis): Fugly is a segment analysis that finds which segments move with a metric you care about. You build one table with every factor you suspect, run a classification model over it, and read off which segments convert well and which do not.
- [The art of reading data: definition plus context](https://www.joindatalyze.com/playbook/reading-data): Reading data well means defining a metric so a movement points at something concrete, then reading it with the business context to know what it means.
- [Qual plus quant analysis: why half the picture misleads](https://www.joindatalyze.com/playbook/qual-plus-quant-analysis): Quantitative analysis tells you what is happening. Qualitative analysis tells you why. Most people do one half and stop, thinking they have the whole picture.
- [Correlation is not causation: how to check before acting](https://www.joindatalyze.com/playbook/correlation-is-not-causation): A finding that users who do X are more likely to convert may be pure correlation. Those users might simply be your most motivated ones, and would have converted anyway. Your existing data cannot tell you which one you are looking at, so check: look at the users who converted without doing X, run a small experiment on the people who have not converted, and talk to users.
- [Sense checking: testing a number before you report it](https://www.joindatalyze.com/playbook/sense-checking-numbers): A sense check is holding a number against what you already know about the business, to see whether it is in the right ballpark before anyone acts on it. Compare it with numbers you have heard, with your own experience, and with a rough rebuild from visits and sign-ups a day. If it fails, verify it before you decide anything on it.
- [How to run experiments at different stages?](https://www.joindatalyze.com/playbook/experimentation): Most companies collect insights and never test them. You might not have the traffic for a textbook A/B test, and that is fine. Run a smaller version instead, such as a before-and-after comparison or a group of users held back, and keep running it.
- [Incremental testing: are your ads creating demand?](https://www.joindatalyze.com/playbook/incremental-testing): Incremental testing tells you whether your ad spend brings signups that would not have arrived anyway. You switch ads off in a holdout group of markets, leave them running in a matched group, and compare signups. The gap between the two groups is your incremental signups.
- [Media mix modelling: measuring untrackable channels](https://www.joindatalyze.com/playbook/media-mix-modelling): Media mix modelling measures the channels you cannot track with a link, such as influencers, offline campaigns and brand spend. You give it one row per week with spend per channel, total signups and what else was going on. It works backwards to split your signups into base and the part each channel caused.
- [Data storytelling: building a dashboard people understand](https://www.joindatalyze.com/playbook/data-storytelling): A dashboard people understand opens on a question, orders its reports so each one follows from the last, and lands on an answer.
- [Analytics in the age of AI: making agents trustworthy](https://www.joindatalyze.com/playbook/analytics-and-ai): AI can answer your analytics questions, but only with clean data, a written explanation of what it means, and the exact calculation for each core metric. Skip those and it still answers but a good chunk of the time the answer is wrong.

## Guides
- [All resources](https://www.joindatalyze.com/resources)
- [How to improve app retention](https://www.joindatalyze.com/resources/improve-app-retention)
- [Client vs proxy vs server-side tracking](https://www.joindatalyze.com/resources/client-vs-proxy-vs-server-tracking)

## Blog: start here
- [Blog](https://www.joindatalyze.com/blog/): Product analytics, Mixpanel, retention, attribution and the Indian D2C playbook.
- [How to set up product analytics the right way](https://www.joindatalyze.com/blog/how-to-set-up-product-analytics-the-right-way/)
- [How to set up Mixpanel the right way](https://www.joindatalyze.com/blog/mixpanel-setup-the-right-way/)
- [How to create a tracking plan](https://www.joindatalyze.com/blog/how-to-create-a-tracking-plan-and/)
- [Why nobody trusts your data, and how to fix it](https://www.joindatalyze.com/blog/you-have-data-flowing-everywhere-and-trust-none-of-it/)
- [Analytics debt](https://www.joindatalyze.com/blog/analytics-debt-the-silent-killer-nobody-talks-about-until-series-b/)
- [Solving retention problems](https://www.joindatalyze.com/blog/solving-retention-problems-a-strategic/)
- [Finding the aha moment for your product](https://www.joindatalyze.com/blog/finding-the-aha-moment-for-your-product/)
- [Indian D2C playbook 1: the data foundation](https://www.joindatalyze.com/blog/d2c-playbook-1-the-data-foundation/)
- [Indian D2C playbook 6: incrementality testing](https://www.joindatalyze.com/blog/d2c-playbook-6-incrementality-testing/)
- [The click is the only thing your ad platform can see](https://www.joindatalyze.com/blog/the-click-is-the-only-thing-your-ad-platform-can-see/)

## Contact
- Email: ansh@joindatalyze.com
- Book a call: https://www.joindatalyze.com/contact
- LinkedIn (company): https://www.linkedin.com/company/joindatalyze
- LinkedIn (founder): https://www.linkedin.com/in/anshagrawal/
- YouTube: https://www.youtube.com/channel/UCKdowFIRu4Z5lFxNM_JNxSQ
- Mixpanel partner directory: https://mixpanel.com/partners/experts/datalyze