Data analytics is too big to fail, even though most analytics projects do. This leaves businesses stuck in an endless loop: invest in data to answer basic and critical business questions, and then be frustrated at the lack of answers, only to actually invest more and hope for the best.
This seems like a problem technology should be able to solve, and there are many vendors that do promise various silver bullets: marketing attribution that highlights the top converting channels, sales intelligence to predict the next best touch point, and even early-warning customer churn systems. These vendors are a dime a dozen, but they rarely work, and in the end, they actually add more maintenance to a company’s existing suite of now-siloed data tools. I know. In my 15+ years in the business intelligence industry, I have built some of these tools while at SAP and Tableau. Adding more department-specific tech isn’t the answer.
So what is a modern revenue team supposed to do?
The truth is that using analytics to grow revenue is hard, but we make it even harder by reinventing the wheel and trying to do it each on our own. This is despite the fact that most revenue teams are trying to answer very similar, fundamental business questions. In the past year, we’ve talked with more than 100 revenue leaders, and we’ve seen and heard the same frustrations and roadblocks from every business, no matter the stage or the industry. We think we can learn from each other.
In this new on-going series, I want to combine that collective knowledge with our team’s decades of experience in building data analytics tools to create a modern, practical, best-practice guide for using data analytics to drive revenue growth in 2021 and beyond. This guide will discuss the common analytics journey of businesses from raw data to spreadsheets, from department-specific tools to full-fledged business intelligence solutions.
We’ll also discuss when and if you need any tools at all, starting with:
- The almighty spreadsheet. They are the first and often most-relied on revenue analytics tool. Spreadsheets are flexible, powerful, and have their place. Every company must use them in some capacity, but many companies actually use them almost exclusively in their data reporting toolkit, much to their detriment. We’ll discuss the strengths and weaknesses of spreadsheets, when they are the perfect tool for the job, and the telltale signs that solely relying on spreadsheets is starting to hold your company back.
- The fork in the road. When maintaining spreadsheets becomes unsustainable, what are the alternatives? We’ll discuss 3 specific choices and their pros and cons:
- Invest more resources into spreadsheets and force them to work for you.
- Buy point solutions to solve specific revenue questions, such as marketing attribution, sales insights, or customer churn signals.
- Invest in full-fledged analytics solutions. We believe this is the ideal path, but there are many ways to “do analytics,” and analytics is not without its many potential pitfalls too.
- So, how should modern SaaS businesses do analytics? Analytics is like an iceberg: most people only see the beautiful dashboards, not realizing the complexities and expensive infrastructure that lurks beneath the surface. We’ll spend the bulk of this series outlining the journey of equipping your teams with the tools and best practices they need to use data to grow revenue predictably. We’ll demystify the technologies in the revenue analytics stack (i.e. data pipelines, data transformation, visualization, predictive analytics, and more), but also discuss when you need them, why, and how to choose each one without losing focus on the business questions themselves.
In each of these areas, we will also specifically focus on a critical but often overlooked point-of-view: the perspective of revenue operations. Revenue operators are uniquely positioned to lead the revenue analytics journey. They tend to combine data-driven practices with sharp business instincts. They have a holistic view of the entire customer life cycle, from lead to customer to champion. Practically, they also own the go-to-market tech stack. This usually means others treat them as order takers, but we believe that revenue operators should actually be strategic partners, providing insights and action plans for growth by equipping their teams with the right tools to meet the growing challenges of the business.
And the challenges are growing. Although it is an exciting time to run a company, it is also a stressful and uncertain time. With or without a global pandemic, growing revenue is harder than ever, with more challenges to face, more tools to choose from, and more data to make sense of.
We think the time is ripe for a new perspective on data-driven revenue growth. One that keeps the focus on business questions, shares the best-practices from the top revenue organizations, and helps you understand your business’ full revenue funnel: from marketing to sales, customer success, and even product.
Let’s finally use data analytics to do what it was always supposed to help us do: grow.