Transforming analytics from a support function to a growth function at your organization

The short version: Analytics as a support function answers other people's questions and waits for the next request. As a growth function it owns questions of its own, brings hypotheses to the product team, and is measured on outcomes rather than tickets closed. The shift is mostly about who sets the agenda.

In the majority of organizations, analytics functions primarily as a support, reacting to the needs of other teams such as product development or customer support.

But, to truly leverage analytics for organizational growth, it needs to evolve into a proactive, strategic driver.

Before learning how to transform support → growth function, let’s understand what these functions even mean.

What does analytics look like as a support function?

The primary work of the analytics team is to respond to requests for specific data points or help in solving issues identified by other teams by providing data points.

This is very limiting in nature - confining analytics to a troubleshooting or confirmatory role, rather than a source of actionable insights & decision-making.

What does analytics look like as a growth function?

As a growth function - the analytics team acts as a key driver in proactive decision-making and strategic planning.

Instead of just providing data, the analytics team actively participates in formulating the problem, designing the approach to tackle it, and generating insights that guide actions.

This helps with:

• Enhancing product features for better user engagement.
• Understanding what’s working & what’s not, along with “WHY”
• Coming up with experiments to test - to improve product experience

Why make the shift?

Shifting analytics from a support to a growth function can unlock significant value for the organization.

Instead of just responding to requests, analytics teams now analyze & provide insights into user behavior.

These insights can then be used by the product, customer, and sales team to improve the product and its processes, & eventually offer a better user experience.

And, with decisions made on data, the likelihood of success is much higher - because data does not lie.

How do you make the shift?

This transformation requires a fundamental shift in mindset from reactive data reporting to proactive data exploration and strategic influence - being able to integrate analytics into all stages of product development.

But before I talk about how to do that, you need to ensure:

• Invest in hiring and developing skilled analysts who can do more than crunch numbers—they should be able to translate data into actionable.
• Access to quality data - Time spent on data cleaning and preparation should be minimal, thereby allowing the analytics team to focus more on analysis & generating insights.

This is how you can utilize analytics in different stages of product development:

• Feature Viability: Involve analytics to assess potential impact and viability, based on existing data.
• Performance Monitoring: After launching new features, analyze their performance and iterate based on data.
• Problem Solving: Engage analytics to deep dive into issues as they arise, understanding the root causes and potential solutions.
• Metric Improvement: Work with analytics to dissect why certain metrics perform as they do and how they can be optimized.
• Product development: Include specific data tracking requirements in PRDs to ensure that every feature is measurable and insights are actionable.

By embedding analytics into every phase of product development and decision-making, you can lead to more successful outcomes for your business.

Hope this was helpful. If you’re looking for any help with Mixpanel, feel free to reach out using any of the below methods.

LinkedIn | Email - anshdoesanalytics@gmail.com | Book a slot on my calendar

Frequently asked questions

What's the difference between analytics as support and analytics as growth?

A support function answers the questions it's handed and waits for the next one. A growth function sets its own agenda, brings hypotheses to the product team, and is judged on whether the business moved.

Why does it matter which one you have?

Because a support function's ceiling is the quality of the questions other people think to ask. Most of the valuable questions never get asked, so they never get answered.

Who has to change for the shift to happen?

Not only the analyst. It needs the product and growth teams to treat analytics as a source of hypotheses rather than a reporting queue — which is a leadership decision more than a tooling one.