All spreadsheets-as-revenue-data-systems eventually fail. There is no way around it.
Many businesses see this coming and respond in one of two ways: They invest in point solutions — department-specific tools that help answer specific revenue questions, such as marketing attribution, sales forecasting, or potential customer churn. The other alternative is to build your own revenue analytics solution by investing in a full-fledged analytics stack.
Most companies go with point solutions because they seem fast to deploy, they provide immediate help answering tough questions, and the alternative of an analytics stack seems like a herculean task.
Despite the apparent benefits though, I believe that at the end of the day, all companies need an analytics stack that takes into account all departments across the full revenue funnel. Point solutions are simply not enough.
Point solutions are good, up to a certain point
Vendors selling department-specific solutions are a dime a dozen, each one typically focused on one particular stage of your revenue process or answering a particular revenue-related question.
How can you better run your bottoms-up forecasting process? How should you attribute pipe across different marketing channels or campaigns? Which sales activities have the most impact in the deal process and which reps are the best at them?
Most point solutions answer one of these questions, sometimes two or three, but the answers are department-specific, and only allow you to optimize one portion of the funnel independently of the others. The hope is that when you combine all these solutions together, you’re able to optimize your full revenue funnel across Marketing, Sales, and Customer Success.
There is a fundamental problem with trying to understand your revenue funnel this way.
Fundamentally, the questions we really want to answer go beyond our own departments. Marketing’s goal is not to be able to determine which campaigns drive the most MQLs; their goal, and the goal of every revenue-impacting department is to know how their activities impact pipeline and revenue.
I am accountable to a pipeline number.
That’s the number to optimize.– CMO, SaaS customer services company
In the end, revenue leaders want to know how everything they do connects to revenue generation—how did our actions change our bookings? No point solution can truly give you an answer to this question.
An additional issue is that point solutions can reinforce siloes between revenue teams, which leads to more strategic and tactical alignment problems. Different point solutions tend to have their own datastores as well as definitions for critical revenue metrics, which makes our answers quickly lose value if we can’t agree on what they mean.
Take a simple metric like “Pipeline Value”, for example, whose value is often assigned and reassessed as a lead progresses through the revenue funnel. Moreover, this definition not only varies from company to company, its definition tends to change and evolve within a single company as it grows and scales. Some teams may choose to only include opportunities that are “upside” and “commit,” where others may be more broad and include “stage 0” opportunities that are still being qualified. How then, can you know if two different teams are evaluating the same pipeline number if they’re using two different tools?
The reality is that point solutions, while useful, often require us to bring back our favorite duct tape tool—spreadsheets—to bring the full funnel together. This means we’re back to square one: Spreadsheets as our shared source of truth, even if it’s an unsustainable one.
The promise of a unified revenue analytics stack
I believe that the ideal solution for most organizations that have begun to streamline their GTM motions, is to have a analytics stack that unifies data from across their entire revenue funnel into a single platform. n all-in-one, full funnel analytics solution.
Imagine a single pane of glass, a single source of truth for open opportunity and historical data that everyone across all revenue-impacting departments uses. When Sales looks at a metric such as open pipeline, it’s the exact same number as Marketing. Each department drills into the data to the level of detail they need depending on their questions—Sales breaks pipeline by roles, individual reps, regions, while Marketing breaks pipeline down by campaigns and channels. Everyone is on the same page, able to analyze different aspects of the funnel but knowing that they are in fact, looking at the same metrics. The data and metric definitions are in one place, with the flexibility to be used and then reused to answer new questions that emerge.
We all want this. It seems obvious. So why don’t more organizations build such a data stack?
The simple answer is because it’s hard. Very hard. On the surface, it seems like it should be as simple as standing up a data warehouse, connecting it to a dashboarding BI tool, and visualizing some tables. The reality is that there’s so much more work underneath that needs to be done. There’s data transformation, cataloging of definitions, and many more layers to an analytics solution purpose-built for answering revenue-specific questions.
In the next post, I’ll detail what a modern revenue data stack looks like and how you can start building one. We will look at each part of the system, what purpose they serve, why they’re important, and even what pitfalls to watch for.