What are the 4 best analytics tools for product managers?

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Product management is intense and tricky, with every business and startup in the race for product market fit, agility and faster time to market with well informed data driven decisions really matter.

At its core, the Product Manager ensures the decisions that are made to prioritise what to build are backed up by research, insights, and deep customer understanding, rather than on intuition and guesswork. They’re responsible for defining the vision, strategy and development process from idea to launch and further which will ultimately determine the success of a product in the market.

This necessitates a balance of prioritising the right features, managing stakeholders, and making sure the product delivers value to the end user. To do this with the most confidence in ROI, Product Managers need rich data.

The Importance of Data for Product Managers

As a Product Manager you have to know what’s happening inside your product as much as you need to understand what’s happening inside your users’ minds. To do this we must combine feedback with data and knowing how to interpret the two will determine how to refine the product. Looking at data and using the right tools to understand user behaviour and identify trends can provide evidence based insights of where the pain points are for users and where they’re experiencing bottlenecks. This highlights the most pressing areas that should be prioritised to address first and feeds into a cycle of continuous improvement.

However, it’s not enough to just build the right thing at the right time. Continuous improvement and optimisation also requires product managers to quantify the impact of their decisions more holistically which requires more diverse datasets. But its important to remember that data is not a replacement for strategic thinking. Product managers must combine this with their knowledge of the product, market, and users in order to develop and iterate products with confidence in its ability to deliver real value.

An important component to effective product management is also to rally a team on a common goal to turn any insights for further iteration into organised development. However, it’s unrealistic to expect a product manager to make every decision all the time, therefore, one of the keys to great product management is empowering your team to make their own decisions by creating a shared brain. When someone asks a product manager a question about a decision they could have made themselves, nine times out of ten it’s because that person doesn’t have enough context to make the decision themselves. Great product managers build that context with data using analytics tools.

Overview of the 4 Best Analytics Tools for Product Managers

1) Jira

Jira is a project management tool has been around for so long in Product Managers’ arsenals that it’s become somewhat of a classic. It primarily allows team members to communicate and collaborate efficiently by allocating, moving, and changing tasks within a project to track progress.

However, it’s also great for features such as customisable dashboards to track, analyse and share information about projects and team progress. For a lot of Product Managers it's no secret that dashboards are a great way to respond quickly and track various processes in the here and now.

Interestingly, Jira can also be used to log user feedback as ‘issues’ which allows teams to track and prioritise user concerns systematically. Teams can also create specific workflows in Jira for handling these logged issues to ensure what the team consider to be proper review and action.

Jira also offers a range of customisable reports that enable you to visualise trends about your project, versions, epics, sprints, and issues to provide insights into overall performance. By analysing trends in user feedback product managers can guide informed product improvements.

Jira is part of the Atlassian family and unsurprisingly offers a large variety of both internal and external integrations that facilitate data visualisation and report creation.

On a side note, Jira regularly reviews and optimises its interface, which makes it very intuitive and easy-to-use which sidesteps a common pain of learning curves from having to use multiple different tools.

2) Google Analytics

Google Analytics is the best known analytics platform and as a free product, it is implemented widely. However, it’s safe to say that it aims to satisfy an extensive range of functions, which means that it isn’t necessarily ideal for Product Managers specifically.

Nevertheless, Google Analytics collects real-time data from your website and mobile apps to help you understand your customers across multiple touch points in their journey as well as providing immediate insights.

Google Analytics particularly thrives at understanding website traffic with comprehensive oversight into which sources are the most effective. It can also provide insights into the path users are taking on your site which highlights potential friction points to rectify. You can also see behaviour metrics visitors exhibit such as bounce rate, average session duration, and pages per session to get an overall idea of the level of engagement from them.

You can also visualise your traffic and audience based on different factors such as demographics, geography, behaviour and you can even create your own for more personalised segmentation. This can help with tailoring product offerings to the groups of people they will resonate with most.

Google Analytics is also very good for tracking conversions to measure the effectiveness of marketing campaigns by looking at the steps users take toward conversion with funnel visualisation, helping to identify where users drop off.

Google’s machine learning enhances your data with predictive insights so you can save time, and customisable reports make it easy to visualise and share your data with the wider team.

Obviously when it comes to integrations you can connect seamlessly with other google tools in their suite, but with its universal popularity, Google Analytics can also integrate easily with most other go-to tools for teams.

3) Mixpanel

Mixpanel is a product analytics platform designed for mobile and web applications to track how users engage with your product, analyse trends over time, and measure the effectiveness of specific features or campaigns.

What makes the tool stand out is it combines the use of user analytics, marketing, and a CRM to assist you in analysing how and why customers engage, convert, and retain in real-time to enhance their user experience.

Mixpanel is a really good tool for user event tracking and conversion funnel analysis. Events are specific user interactions that can illustrate larger trends and insights in user engagement and behaviour. With Mixpanel’s funnel analysis features, product managers and marketing teams can track users’ progression and spot where users drop off in the conversion process.

Mixpanel also highlights your power users and reveals behaviours linked to long term customer retention. These cohort analysis insights can help you reduce churn and encourage more users to take a desired action.

Mixpanel’s A/B testing tool helps you test the impact of new features by testing different variants. The tool will help you identify which variant performs better and contributes more highly to an increase in the KPIs you’re tracking.

Mixpanel also allows for customisable reports that let you enrich them with data from different charts, long form text, pictures or video so that product managers can convey these insights in a more personalised and intentional way.

Mixpanel offers a wide array of integration options to reinforce insights gathering with more tools. Some key integrations include Google Cloud, Zoho, Slack, Hotjar, Amazon S3, and Stripe as well the option to build your own custom integrations. You can test these in a sandbox environment and connect other tools via an API.

To read a more in depth analysis of Mixpanel, read our previous article comparing it to another product analytics tool Amplitude here.

4) Hotjar

Hotjar is an analytics platform that provides insights on behaviour analytics and feedback data. It helps identify usability issues and optimises the user experience by providing granular data on how users interact with and navigate a page or app screen.

Hotjar can give teams comprehensive insights into how users behave and what matters to them. By visualising how users navigate and engage with your product using heatmaps and session recordings, you can see which parts are causing friction and quickly identify bugs.

Hotjar also enables qualitative feedback collection with in-context, real-time suggestion boxes that let users tell you how they feel about specific parts of your product.

Optimising the path to conversion is also simple with conversion funnel analysis where you can visualise the user journey to spot drop offs. Being able to clearly see the points where this happen can give insights into what influences decisions and address these areas in the funnel for higher conversion rates.

With this knowledge, your team is well equipped to make optimisations that improve the customer experience, strategically prioritise your product roadmap, and use customer data to get buy-in from stakeholders.

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You can easily integrate Hotjar with thousands of popular apps including Slack, Microsoft Teams, Google Analytics, Amplitude, Hubspot, and Zapier to create an automated and connected product analytics tech stack.

As we can see from our overview of the 4 most popular tools right now, very few of them offer a one size fits all solution for the diverse and specific needs of different product managers, but more alarmingly none of them offer robust data analytics for AI integrated products, and this is quickly becoming a big problem with the growing market of LLM apps.

The Need for New Tools in the Evolving Landscape

The field of product management is facing a big challenge with AI products because there’s a lack of tools made specifically to acquire data and real-time insights from them. Traditional tools like the ones we’ve looked at in this article are incapable of providing it and we risk missing critical insights, trends and opportunities for improving AI apps.

Reports even suggest 70% of new applications in 2024 integrate AI LLMs which makes this more pressing. With more and more products integrating LLMs into their apps for AI powered features, product managers need more capable and specific tools like Phospho which we have built specifically to tackle this very problem.

For a deeper dive into the importance of these new tools, read our previous article on how to do AI product management here.

Phospho: The Go-To Solution for Modern Product Managers

Phospho is an open-source text analytics platform specifically designed for LLM (Large Language Model) applications. Phospho enables companies to log and extract real-time insights from user inputs, evaluate models continuously, and streamline the iteration cycle of their GenAI products with better informed product decisions.

Phospho's features for managing products more effectively include:

1. Real-Time Monitoring: This lets you track and log user inputs to identify issues or trends and continuously fine-tune the performance of your LLM app.
2. Custom KPIs Extraction: Create your own KPIs and custom criteria to ‘flag’ for, and you can label whether it was a successful or unsuccessful interaction.
3. Continuous Evaluation: use our automatic evaluation pipeline that runs continuously to keep improving your model’s performance.
4. Easy Integration: simply add Phospho to your tech stack with any popular tools and languages like JavaScript, Python, CSV, OpenAI, LangChain, and Mistral.
5. User Feedback Linking: collect, attach, and analyze user feedback in context to make targeted improvements toward overall app performance.

Our goal with Phospho is to streamline iteration cycles with rich product usage data previously unavailable to Product Managers, especially those building AI integrated products. We envision this capability without the need for big budgets or stitching together multiple tools and apps with painful learning curves. So if you want to integrate Phospho into your LLM app, sign up here. It’s as easy as importing a CSV or Excel file and we have plenty of documentation to help you get started as well.

AI Analytics: Untapped Insights For Product Managers

Whether AI takes over or not, the core responsibility of Product Managers is still to create a product that effectively meets users’ needs with streamlined development. Staying on top of relevant data ensures you’re putting your users at the centre of your product’s iteration cycles.

For that we ned the right tools, we’ve looked at the 4 most popular analytics tools in this article and why Product managers need more advanced AI analytics like phospho to manage the evolving landscape. It’s by embracing the power and further potential of modern AI analytics tools that we really stay competitive and meet their users’ needs more effectively.

To start leveraging AI for insights into your LLM app users, sign up here to try phospho on your own data!