Product & Growth Tips

The difference between product analytics and business intelligence tools — and why you need both

Ellie Kulick Product Marketing @ Mixpanel

In 2019, industry-leading Business Intelligence tools (BI tools), Looker and Tableau, were acquired by Google and Salesforce for over $18 billion combined. These massive deals show that BI tools and data warehouses are a powerful combo that companies across the globe are incorporating into their tech stacks. However, powerful isn’t always useful — particularly for product teams.

Looker, and other tools like it, are often deployed with good intent. But it’s a solution that requires out-sized technical expertise, time, and resources. And for on-the-fly, deep analysis of user behavior metrics that PMs care about, such as engagement, retention, and conversion, these tools often fall short.

According to one Mixpanel customer, deploying a data warehouse and Looker still wasn’t enough to meet the product team’s desire to answer complex questions about its product, reducing its ability to make data-driven decisions. And it’s not just anecdotal evidence that lets us know product teams are struggling to get data-driven insights from current BI tools. In a recent survey Mixpanel conducted with over 450+ product managers and leaders, 52% of PMs feel they don’t have the right tools to get the answers they need.

So what do product teams need when BI tools aren’t enough? A product analytics solution that is self-serve and built to answer product questions related to user behavior.

In this post, we’ll walk through some of the key differences between a BI tool and a product analytics solution through the lens of product teams, and how they’re complementary to one another.

What’s the difference between a BI tool and a product analytics solution?

Businesses generate and process a ton of data every day from financial and revenue data to Salesforce, to account data, product event logs, accounting, and others. This data, in its isolated format, is difficult to use for decision making and assessing overall business health.

BI tools offer a solution by ingesting and collating all this data and putting it in a format that allows businesses to create powerful dashboards, reports and visualizations that are used to inform business strategy and tactics — hence the name “business intelligence.” Due to their complexity, BI tools often require a data expert for deployment and active use.

A product analytics solution like Mixpanel, on the other hand, is a much more focused solution that tracks and analyzes user behavior within a digital product to help answer questions around customer engagement, conversion, and retention. Its main purpose is to help product teams self-serve answers to their questions through deep, behavioral product analytics without the help of a data expert.

Here’s a diagram of how various tools can sit together within a tech stack, mapped to the team that most frequently uses the tool. Where BI tools, product analytics, and marketing solutions may all share data sources, each tool serves a different team within the organization and meets different needs.

Now, let’s unpack how these differences play out practically for product teams. Operating on the safe assumption product teams have two goals in common — speed of innovation and depth of understanding of user behavior — we know that BI tools pose two major challenges for product teams:

  1. Outsized reliance on technical skills and heavy lifting from data experts to get answers.
  2. Exploratory analytics of user behavior lack intuitiveness and support. 

Ease of use: relying on experts for answers

BI tools require some pretty advanced technical skills in SQL and coding chops to query the right information and make subsequent changes. 

Here’s a snippet of what a sample query would look like to build a standard conversion funnel in a BI tool:

Even if you have the technical skills to do it, it’s time-consuming at best, making it a difficult tool to use for getting product answers in an expeditious manner. And that leads us to the most common challenge facing product teams relying on convoluted BI tools: the data breadline problem.

The typical org structure looks a little something like this: various teams like Executives, Marketers, and Product teams have lots of questions and need answers. They are supported by a disproportionately small number of data analysts and data experts who, by curse of their own skills, become the gatekeepers of data.

Product teams struggle to get answers to their questions quickly (or sometimes at all), and data experts, who are hired to solve larger and more complex data challenges, are inundated with requests from various teams and can’t focus their energy on bigger, more impactful initiatives.

It’s a perpetual problem that does a disservice to the entire organization, bottlenecking product innovation and growth.

With a product analytics solution like Mixpanel, it eliminates the need of a data queue, empowering product teams to self-serve answers to burning questions about the product and its users. Take the same conversion funnel from before. Where it took lines of code to produce one conversion funnel, in a product analytics tool, product managers can produce that and more in just a few clicks through an intuitive UI built for non-technical teams.

Not only can you build a funnel in far less steps, it gives PMs the ability to build as many funnels with as many granular segments and steps as they want to examine without having to get back in the data queue. 

This brings us to our second major challenge of BI tools as a product management tool.

Depth of analysis: understanding user behavior 

Unlike product analytics solutions, BI tools don’t support deep, exploratory analysis of user behavior within a product.

The reason behind this is two-fold:

  1. It’s hard to do exploratory analysis with a series of undefined questions when you have to go back and forth with a technical resource to write a script for each question.
  2. BI tools, by trade, aren’t necessarily designed to track or examine user behavior.

BI tools are great at processing large amounts of data to give you an answer at a point in time, like conversion rate, or total number of users, or daily signups. However, the questions you have about your users behavior through the product is very difficult, if not impossible, to answer with a BI tool.

For example, as a product team, you may want to answer the following: 

  • How do my MAU/WAU/DAU change over time?
  • How are my power users growing or shrinking over time?
  • What is the conversion rate of sign up to first download split out by demographics?
  • What is a user’s likelihood to upgrade their plan based on the number of downloads? 
  • How do users interact with the mobile app and with the web app across sessions?

With a product analytics tool like Mixpanel, the answers to these questions are a few clicks away. With a BI tool, it’s much more labor-intensive, time-consuming, and likely a one-time analysis rather than an on-going tool to track progress.

Here’s a side-by-side comparison of BI tools and product analytics as a summary of their key differences:

It should be pretty clear by now that BI tools alone aren’t enough for product teams. BI tools, while incredibly powerful and useful across the broader organizations, are not an effective solution for product teams looking to answer product analytics questions with depth and clarity.

So what does a merged solution look like?

Incorporating a Product Analytics Solution into your tech stack

At Mixpanel, we work with several companies who are leveraging both BI tools and Mixpanel to satisfy a complex and growing need for data insights across the organization. Here’s how they’ve most commonly implemented Mixpanel into their tech stack. 

Yellow = internal data pipeline, Black = BI tool, Blue = Mixpanel

Many of our customers have successfully incorporated Mixpanel into their tech stacks alongside popular BI tools like Tableau and Looker to empower their teams to leverage the tools that’s most aligned with their goals.

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