DataPowered

In today’s analytics-driven world, companies need more than surface—level analytics— deep behavioral insights to optimize user engagement, conversion, and overall product performance. That’s why I’ve seen many teams making the switch to Amplitude – a powerful event-based analytics platform that, among many things, provides detailed cohort analysis, funnel tracking, and real-time insights to drive smarter decisions.

However, migrating to a new analytics platform isn’t just about flipping a switch. From restructuring event tracking to ensuring data integrity, the process comes with challenges that require careful planning and execution. Without a solid migration strategy, businesses risk losing historical data, breaking key reports, or missing out on critical insights.

This guide will walk you through a structured, step-by-step approach to successfully migrate to Amplitude—ensuring a smooth transition, minimal disruptions, and a strong foundation for advanced analytics. Let’s get started!

Plan Your Migration Strategy

Prepare for the migration and allow between 2 – 8 weeks per product/business unit, depending on the complexity of your migration.

• Ensure you have overlap between your old platform and Amplitude as you will need to QA your implementation and recreate charts. Ideally, this overlap is your estimated implementation time + another 4 weeks.
• Start assembling your migration team. Ensure you have at least one end-user of your previous platform and Amplitude. Request engineering support depending on your data collection method (more on that below) and cookie consent policy. If this is your first time doing this, try to get an Amplitude expert (shameless plug) to guide you.
• Prepare your use cases. This includes charts you had on the previous analytics platform and any new ones you’d like to build.
• Assess your Amplitude subscription needs: are you going for an event volume model or an MTU volume? What is the level of support you need during the migration? What are your use cases (to ensure Amplitude is the right fit)? What are your training needs and how many people will need to use Amplitude in your organization?
• Request a proof of concept from Amplitude (or do one yourself on a free account) if you’re unsure whether Amplitude will meet your use cases.
• Choose the most appropriate data collection method(s) for Amplitude.
  • Consider if all your use cases can be tracked using one method or whether you need to combine multiple data sources to achieve the desired result.
  • Check if you can reuse existing data tracking methods. For example, if you’ve used Google Tag Manager in the past, you can integrate it with Amplitude. Be aware that using third-party methods, while easier to set up, may obfuscate troubleshooting and other aspects later.

Begin The Migration

At this stage, we’re assuming you completed most of the previous steps and aligned migration support: both internal team members and an Amplitude migration expert.

This is what I recommend doing next:

• Define your tracking plan: the building blocks of data in Amplitude are events (user actions) and properties (attributes of user actions or users themselves). You can do this directly in Amplitude in Data > Events. Keep in mind your tracking plan needs to reflect your use cases.
• Start sending data to a test project in your Amplitude account.
  • QA the data quality to ensure the data reflects reality.
    • Check your user journeys and ensure all planned events and properties are there, in the correct order.
    • Check event totals to avoid losing data. At low volume, at least, if you’re sending 10 “page view” events from GTM, for example, you should see 10 “page view” events in Amplitude.
    • Discrepancies will be present, however this is expected to some extent regardless of where you decide to migrate to. Event totals discrepancies should be the smallest, usually under 10%. Session totals and unique user totals will be higher but hopefully not above 15%. The easiest way to troubleshoot is to try and simulate user journeys and use browser extensions (or dev tools) to check that events are triggered correctly. Common reasons include:
      • Incorrect SDK/ingestion method setup.
      • Check any block filters you might’ve set in Amplitude accidentally.
      • Different definitions between platforms. Some platforms define users differently than others and have different user merging mechanics. Same with sessions.
      • Adblockers and bot traffic: some tools defend better against this than others. Leverage proxy domains or server-side methods.
      • Cookie consent layer, especially if you didn’t have this before.
      • Your site/app’s performance.
      • If you’re using a third party (GTM) that can slow down event collection slightly. Check how the tracking is prioritized within that tool and against other code on your site/app.
      • It’s possible you weren’t tracking the data correctly in your previous tool to begin with.
  • Lastly, QA the use cases by building example charts.
• This is an optional step, but now is the time to backfill historical data if your use cases depend on it.
  • Ensure the backfilled data has the same structure as the live data. That means having the same event and property names.
  • Don’t rush to backfill everything – use your use cases as a lens to decide what’s important vs. what’s not or what can be backfilled later.