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Alberto IncisaProduct and Growth at June

18 Nov 22

Combining product analytics and BI tools for a complete data stack

Data is becoming increasingly important not only in the product development process but also in the go-to-market one.

Product managers need to be able to access and analyze data in order to make informed decisions about product direction and strategy.

Go-to-market leaders instead, need meaningful data points to support their initiatives to activate, convert and retain more accounts.

Product analytics can provide valuable insights into customer behavior and usage patterns, whereas BI tools can help the team answer more uncommon and complex questions. Combining product analytics and BI tools can give you a complete picture of your data, which can then be used for better decision-making. Data-driven product development is essential for success in the modern B2B SaaS space.

The benefits of combining product analytics and BI tools

A complete data stack provides a more complete understanding of your users and can help you identify trends and correlations that you may not be able to see with separate tools. This can give you a competitive edge by helping you make better informed decisions about your product roadmap and go-to-market strategy.

Having both a BI tool on top of your database and product analytics on top of your event data stream, increases transparency across your organization. Specifically, this can empower all members of your team to make better use of data, from customer success to sales to data analysts.

A complete data stack can also help you optimize your product portfolio for maximum efficiency and profitability. By understanding which products are performing best, you can improve customer retention and reduce churn rates while increasing market share.

As technology evolves, so too does the need for an ever-more comprehensive data stack in order to stay ahead of the curve and maximize performance within your organization

How to set up a complete data stack for your team

A complete data stack can help your team make better decisions by providing insights about customer behavior and product performance. There are a few key components to setting up a complete data stack: data sources, analytics tools, and BI tools.

First, you'll need to select which data sources you want to use. Specifically this includes things like your company's database, product usage data from an SDK, customer data from a CRM and billing informations. Then, you'll need to choose which analytics and BI tools you want to use. There are many different options available on the market, so it's important to do your research and select the ones that will best fit your needs. We've heard from our users that the best BI tools are Metabase and Power BI whereas for product analytics the simplest option is June.so followed by the more complex Mixpanel and Amplitude.

Finally, you'll need to set up the actual infrastructure for your stack. This includes things like properly adopting the software, setting up user accounts and connecting all of the different components together. Lastly, make sure to integrate them into your workflows in order to have all the information you need for better decision making. For example pushing your product analytics data into your CRM (like Hubspot, Salesforce) will help your sales team to be more effective.

Empowering non-technical people with simple yet trustworthy product analytics

Product analytics is a valuable tool that can help businesses understand how their products are being used and what needs to be improved. In this way companies can empower non-data savvy people with simple yet trustworthy product analytics.

The key features of product analytics that make it an essential tool for business use cases include the ability to track customer behaviour and performance over time, identify problems early on, and find possible solutions quickly. A tool like June will help you to quickly answer questions like:

  • who are my top 5 users last week?
  • how many accounts did properly activate in the last month?
  • which features are used the most? which ones need an iteration?
  • what's the retention rate for my paying customers?

Product analytics can help businesses improve their understanding of customer behavior and ensure they are meeting users' needs in a timely manner. But more importantly, modern product analytics tools like June can make these informations available to everyone within a company. This will result in more awareness which, in turn, will definitely increase team's motivation and performance.

Helping the technical team to answer the most complex questions with a BI tool

To make sense of the data that is generated by a B2B SaaS company, it is necessary to have a data stack in place which can also answer more complex question. For this kind of use cases what you need is a BI tool on top of your database.

A business intelligence (BI) tool can help the technical team to answer complex questions quickly and effectively, without having to hack their way around in a non-flexible product analytics tool that was not designed for that. For example if you are Slack and wish to find the the median length of huddles for your paying customers, you should definitely use your BI tool instead of trying to find the answer in June or Mixpanel.

Tips for getting the most out of your data stack

Maintaining a data stack can be a complex and time-consuming task. But it's important to keep on top of it, in order to ensure that your product analytics and BI tools are working effectively.

There are a few key things you can do to help manage your data stack:

1. Keep an eye on your data sources. Make sure they're reliable and up-to-date, so that your analytics are based on accurate information

2. Stay organized. Keep track of which tool is used for what purpose, so that you can quickly find the right one when you need it

3. Automate where possible. Use automation tools to help with tasks like data collection and processing, so that you can focus on more complex analysis tasks

4. Don't track too many things. You should only have a couple of focus metrics. If not you'll start looking into every detail loosing sight on what really matters. This product cycle could be retention, the next one activation and so on

5. Name your track events in a meaningful way so that everyone will be able to use them for their own tasks

When it comes to setting up your data stack, always try to keep things as simple as possible. Slowly scale your setup and keep in mind that it will evolve a lot over time. By doing this, you'll be able to set up your stack in a way that's future-proof. Keep in mind that data migrations and tracking plan revamps are very painful and delicate.

How this is different from the previous generation of data analytics tools like Amplitude or Mixpanel

The key benefits of using a complete data stack are that it can provide a more holistic view of your product performance and that it allows to better answer both the most complex and most simple questions your team might have, in an independent, self-served way.

In the past, most teams adopted only a product analytics tool like Amplitude or Mixpanel to handle all the possible questions. However this approach turned out to be pretty limiting mainly because of the following reasons:

  • non-data savvy people in the org were are not able to self serve data and are always relying on someone else to retrieve the data. This causes a lot of bottlenecks that slow down the whole company
  • most complex and articulated questions cannot be answered in those tools but only through SQL queries

These situations typically occur when the startup is starting to become a real company with more than 20 employees, which in most cases happens to be after raising a Series A.

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