The use of data analytics at SaaS startups have accelerated their growth

There comes a turning point in a startup's growth when data analytics – for marketing, for product development, for sales – necessarily shifts from being relatively ad hoc to being a business-critical department. Before I joined Mux in 2021, the developer video platform was focused on product-led growth with a small, scrappy team.

As we neared 100 employees and raised our Series D, it was time to build out an analytics function that could facilitate decision making and optimization for the next stage of growth—which is when I came in. To build a successful analytics program for the long term, you need a skilled team, the right stack, and a thriving culture. Here's how we approached the three.

Building an analytics team

There are 2 general analytical ecosystems that every company needs to be aware of: marketing analytics and core product/onboarding analytics. While these domains are obviously learnable, they're rife with their own increasingly specialized toolkits, jargon, and challenges. The right team needs experimentation chops and hard skills built through on-the-ground experience. For Mux, the right team at this stage had to be built with specialized expertise.

When our leadership asked for a cost-of-acquisition analysis on different ad campaigns with a last-touch attribution model, the request helped me prioritize the first roles to hire on my team:

• Marketing analytics manager
• Product analytics manager
• Product analytics specialist

Building an analytics stack

In parallel with building out the business analytics team, our first task was to set up an analytics infrastructure that could scale with the business and its ever-evolving needs. While we already had a robust Airflow infrastructure to load internal product data into a database of our choice (and had previously been using Metabase on Amazon RDS), we had a lot of optionality in building out the rest of the ecosystem.

In retrospect, I'm lucky that the team took a chance on a BigTech alum like myself; after nearly 8 years working in the analytics space at Google, I knew little about the increasingly specialized and diverse set of players who contribute to the modern analytics stack for the rest of the world. Fortunately, we had a lot going for us:

• The fragmented constellation of providers has an increasingly clear set of winners (e.g., dbt) that serve as the nucleus of a mature stack.
• Beyond that nucleus, there is a whole opt-in solar system of purpose-built tools that offer plug-and-play functionality with the core providers.
• The cross-tool/service set of integrations (e.g., one-click Salesforce to Snowflake integrations) made our big-data dreams attainable even with a small team.
• The broader analytics community is incredibly welcoming and full of key opinion formers who live and breathe this world, actively sharing opinions on Medium, over Slack, at industry conferences, and beyond.

While we are continually evaluating other services (data ops, data catalogs, metrics layers, etc.), we've settled into a modern stack with an ecosystem built around dbt, Snowflake, and Looker. This nucleus lets us manage our data model, permissions, and documentation and ultimately allows us to get data into end-users ' hands, with an emphasis on self-service exploration. Orbiting around the nucleus, we also have Fivetran and Hightouch, which help us seamlessly move data into and out of third-party systems.

Finally, we are very excited about our work with Pecan and Rupert. Pecan is helping us accelerate advanced analytics at Mux, so we can go from raw data to actionable insights and workflows around specific use cases. Rupert, meanwhile, will help us get the most out of all this investment by making business intelligence, analytics, and data more approachable for our business stakeholders.

Building an analytics culture

Even after building the team and the stack, there was still more to do. Building a culture around data is the real job of an analytics leader, and that means getting decision-makers to look at the dashboard you built for them; it means injecting data into operational workflows for go-to-market teams, and it means understanding what success and failure look like.

Like our work on the data warehouse and business intelligence layers, our efforts to build a data-driven culture will never be finished—but we have a few principles and projects we are tackling in 2022 that will go a long way to help us scale:

• Make data a habit: We are standing up a recurring metrics review forum that dives deep into our leading indicators across the business. While this is critical in order for company leaders to have a pulse on the business, it also means that leaders from across the company engage with our analytics infrastructure on a recurring basis and start building that data muscle—an added bonus for our team.
• Inject data into processes and playbooks: Most importantly, our teams are adopting metrics-based OKRs that will serve as a forcing function to measure and manage our team-level priorities and goals; we're also building data-driven playbooks for go-to-market teams that live in their tools (e.g., Salesforce). Everyone becomes an analyst without even realizing it!
• Push, don't pull: We're not waiting for anybody to ask us for data; instead, we're using Rupert and Looker to make source-of-truth business intelligence assets easily searchable and proactively delivering relevant information to interested parties where they work, communicate, and collaborate (e.g., sales portfolios sent every Monday morning to account executives and sales development reps, top-line metrics pushed to leaders on a weekly basis, configuring alerts, and Pecan predictions in Salesforce).
• (Over)communicate, (over)train, (over)communicate (again): To bring internal users into our world, we lead regular training sessions on our Looker instance, hold office hours weekly, and send weekly Snowflake and Looker feature updates to our users on Slack and by email. We also review our quarterly roadmap (for data infrastructure and analytics) with the leadership team on a quarterly basis to align priorities (which can then be communicated more broadly).

Just a few weeks into 2022, we are already seeing the benefits of this three-pronged approach to building an analytics team, stack, and culture. We are nearing our business intelligence engagement targets – doubling user engagement in the last two months. With the widespread adoption of that table stakes reporting, we are increasingly engaging on higher value and more interesting problems (e.g., what's the ROI of our promotional credit programs? how much should we spend on ad campaigns this quarter?), ultimately helping Mux make the most important business and product decisions in 2022 and beyond.

Author:

James Isbell, Director of Analytics & Data Science at Mux