5 Best Product Analytics Tools for SaaS Product Managers

Product analytics is the foundation of a product manager’s decision-making process. It is the task of collecting, analyzing, and interpreting data about how users interact with a product.

In early-stage or hybrid teams, the PM handles product analytics. This is done by looking at event-level data - evidence of actions taken in the product’s environment. It is a record of who did what, when, and in what context. Naturally, access to this data and the ability to interpret it give the PM enormous leverage. He understands user behaviour and can thus adjust the strategy of the product accordingly. 

In this article, we delve deeper into the role and briefly discuss the best product analytics tools for PMs. Being familiar with these tools helps you build product analytics frameworks that simplify your daily tasks while simultaneously improving your skills and market value. 

Don’t listen to what they say, watch what they do!

Product analytics rests on the fact that humans don’t know what they want. Words don’t align with actions, and, thus, asking users for feedback directly paints, at best, an incomplete picture. In that way, modern analytics product managers are more similar to human behaviour analysts than they’d admit. The role requires one to look closely at human actions and investigate their motives. But how exactly does this investigation take place?

A few years back, PMs relied primarily on data engineer product analytics teams for their reports. This was not only painfully slow but also limited to retrospective analysis. Everyone simply investigated what happened. 

Over time, PMs started to become more familiar with SQL, claiming back their independence. Tools were still used, but not exclusively relied on. Predictive thinking started to become more important, especially in the mobile app market. Today, we have the analytical, forward-looking thinker who makes hypotheses based on interpreted (event-level) data. Instead of asking what happened, today’s PM looks at historical data and asks, “What is likely to happen if we do X”? They embrace tools to the extent that these can help them make assumptions for the future. And this comes with its own set of challenges, as the PM needs to be very accustomed to the industry (and audience) they are trying to serve.

Launching analytics - An example

Take the launch of a new dev tool, for example. In the first few months, the tool lands on the market and marketing activities are going strong, the PM will be tracking product launch analytics - adoption rate, onboarding completion, feature usage, upgrade to paid version, and more. Tools can simplify this process, and they can even integrate data from multiple sources. This is done using APIs for product managers. We discuss some of these tools below. However, this is only one part of the job.

What tools cannot simplify is the ability to relate the data to contextual information. This means the current state of the industry, trends among the userbase, potential limitations, and even the response of the market to the tool itself (e.g. what a competitor will do in response). This, in essence, is what leads one to adopt a predictive mindset that leads to more calculated decisions.

The Best Product Analytics Tools in 2026

The best tools for product analytics should focus on one thing - reducing the PM's uncertainty when making decisions. The following tools can help to observe real user behaviour and validate assumptions. At the end of the chapter, we showcase all features in a comparison table.

1. Clickhouse

Clickhouse is your boots on the ground when it comes to product analytics. It reveals user behaviour in a column-oriented database built for large volumes of event-level data. It removes abstraction layers and unnecessary information, allowing PMs to explore historical data and real-time behavioural data simultaneously. For many, it is the tool with the most adaptable UI, and thus easy to customize depending on your needs.

Clickhouse pros and cons for product analytics

ClickHouse is perhaps best for scaling companies that are dealing with millions of events, complex joins, and custom metrics that are hard to find in off-the-shelf tooling. The tools is perhaps not ideal for beginners, or small(er) projects that have a simple product or a few hundred customers. A PM uses ClickHouse to avoid UI and metric limitations, as it offers a very adaptable framework. It does, however, require SQL fluency, which for some can be a limiting factor. It also does not support native A/B testing experimentation.

2. Amplitude

Amplitude is built specifically for analysing user behaviour over time. It transforms event-level data into behavioural narratives in the form of funnels, cohorts, retention curves, and more. For those with a predictive mindset, Amplitude is your hypothesis-testing weapon - its wide range of metrics helps you form assumptions about user behaviour and validate them fast against usage patterns.

Amplitude pros and cons for product analytics
Amplitude is a great tool for behavioural analysis in a lifecycle-thinking context. This is very important for SaaS tools and mobile apps that have complex user journeys. It is also less SQL-heavy than other tools on this list, but still primarily aimed towards technical users. Speaking of drawbacks, Amplitude has a high cost, which can act as a barrier for many. It also has a steep learning curve, which can take time if undergone unguided.

3. Mixpanel

Mixpanel is a particularly useful tool during the early stages of product development, where PMs are tasked with validating assumptions quickly and pivoting often. It is considered one of the best product analytics tools of 2026 because it supports the “watch what they do” philosophy. It does this by offering a dashboard that observes micro behaviour signals that enable you to validate whether a feature delivers as intended or is slowly becoming insignificant.

Mixpanel pros and cons for product analysis
Mixpanel is ideal for fast iterations and analysis at the feature level, especially in early-stage teams. It is thus a great tool for beginners who are looking to enhance the functions of their product and are looking to understand what is needed and what is not. It provides fast validation of customer onboarding flows, feature adoption analysis, and short-term experimentation. Ideal, therefore, in the discovery stage, but perhaps limited when it comes to scaling teams and growing a product.

4. PostHog

Another great tool to consider is PostHog, which bridges product analytics with experimentation, session recordings, and feature flags into a single platform. The tool aligns with the values of predictive PMs: hypothesize, ship a change behind a flag, track behaviour changes, and adapt where needed. PostHog is open-source and self-hosted, which makes it more desirable than several competitors in this list, and also gives teams more control over data ownership.

PostHog pros and cons for SaaS product analytics
PostHog is ideal for experiment-driven teams that want to shorten the distance between insights and action. It allows PMs to analyse data and ship changes from one platform, which is valuable for PMs who want to keep things minimal and agile.

5. Segment

Our final contender in this list is not a typical analytics tool. Segment provides the infrastructure that makes analytics usable. It standardizes and routes event-level data from the product to multiple tools to ensure a consistent, ordered environment. It can be considered a supplementary tool for many, as it gives the data foundation that leads to more accurate insights. Without that in place, even the best product analytics tools can lead to misleading assumptions.

Segment for the data analytics product manager

The tool is best for data governance, systemization, and scalability of your product analytics stack. It also reduces dependency on engineering for tracking changes, meaning SQL becomes optional, and gives more flexibility to the professional PM.

Feature comparison

Enhancing Your Analytics Workflow

Among the best product analytics tools, several require SQL for day-to-day tasks, while others need it for advanced customization. SQL has been one of the main barriers to accessing more complex analytics until today. That is easy to see when we consider that PostgreSQL is the main source of the pulled data. With TalkBI, however, everything becomes simpler. The tool enables professionals to automate SQL generation with its Natural Language into SQL functionality. Type a question in plain English, and TalkBI will automatically generate the query that gives you access to the data. 

And that’s not all. TalkBI also improves the workflow of your existing tools. Raw data of ClickHouse, an otherwise complex tool, can be analyzed and visualized without any technical knowledge. Mixpanel event data can be segmented and analysed in more depth. Overall, integrating with TalkBI reduces friction and leads to better decisions.

How Tools Enable Framework Implementation

Once the right tool stack has been found, PMs can set up a product analytics framework to organize their data and decision-making. Having a system in place leads to more consistent and repeatable insights.

The framework that is usually implemented is known as AARRR. This stands for Acquisition, Activation, Retention, Revenue, Referral. Here’s how some of the above tools can work together:

Acquisition: Use API analytics for product managers to extract data from marketing and advertising campaigns into TalkBI or Mixpanel dashboards.

Activation: Investigate onboarding funnels with Mixpanel or Amplitude. Discover where users drop off and adjust features accordingly.

Retention: Use ClickHouse to analyze long-term user behavior, then visualize findings through a TalkBI dashboard to present them to your team.

Revenue: Pair transactional data in Amplitude with user events from Mixpanel to track monetization.

Referral: See your referral campaigns and loyalty structures via Amplitude cohorts.

Pairing tools in this fashion makes it easier for PMs to build a structured product analytics framework. They can then monitor a variety of metrics, run A/B tests, and make improvements without relying on SQL.

Impact of Emerging Trends in Product Analytics

In the previous chapters, we discussed the shift from retrospective to predictive analytics. As a result, small-scale and SaaS product analytics are slowly adjusting to meet this, providing more predictive insights and AI-assisted analysis. Features like anomaly detection, auto targeting, and personalized recommendations are now essential for PMs who want to be the best at their game. At the same time, SQL is becoming less important than it was just 5 years ago, and we expect it to become unnecessary to know SQL as an analyst, at least at entry-level positions. 

All these trends and the tools that abide by them serve the purpose of allowing independent product manager data analytics. By combining the best product analytics tools with TalkBI, SQL becomes optional, while work output increases. The latter can be tested with demo datasets in secure environments, enabling anyone to learn and practise before applying their knowledge to real product data. 

The future of project management analytics is self-service, action-driven, and integrated. It is therefore not the tool that makes the professional, but the professional who builds the right stack and thus the success of his strategy. 

Wrapping up

The list of top product analytics tools 2026 allows PMs to increase their perceived value while making fast, data-driven decisions. The combination of primary and supplementary tools to create a tailored stack is perhaps the best approach when paired with structured product analytics frameworks.

Those in early-stage SaaS can perhaps embrace tools that are less popular but much better when it comes to work output. TalkBI is one such tool, allowing PMs to turn raw data into decisions without relying on SQL. So head over to the dashboard and start chatting with our demo databases to discover more.