Besides starting the business during a pandemic, the story of how we started Datajoy is actually quite common. We began the same way many founders begin companies—by connecting over a shared frustration:
Why do businesses continue to rely on intuition, rather than data, to grow the go-to-market funnel?
We came at the problem from opposite ends. As a builder of analytical tools, I saw that even when customers had the latest in technology, they still struggled to answer the most basic questions about their business’ performance metrics.
In Jon’s case as a business leader, trying to use data to drive revenue growth was constantly an expensive and drawn-out problem with little results to show for it.
Together, we ran a survey, interviewing a diverse group of more than 100 of today’s revenue leaders who run departments from Sales to Sales Ops to Marketing to Customer Success, from CFOs to CROs, from startup SaaS companies to enterprises with more than $2B in annual revenue.
So what did we find
I have an ocean of data and a desert of insights.– VP of Revenue Operations
Today’s revenue leaders are not satisfied with their data and the tools they have to make sense of it. Some of them shared stories similar to Jon’s experience as well as those of my past customers—spending millions of dollars only to wait months, sometimes years, for answers to seemingly simple questions. A significant portion rely on a set of mismatched and manually maintained spreadsheets. Some leaders are exhausted by the whole data process and would rather just go with their gut.
Their answers reaffirmed the frustrations that led us to start Datajoy, that there must be a better way to use data to drive revenue growth. But their answers also surprised us. They challenged and influenced how we ought to think about our own SaaS business in three key areas. In the next three posts, we’d like to share those three takeaways.
The three lessons are:
The Go-to-Market (GTM) funnel has become so fragmented that Revenue Leaders don’t truly understand how all the pieces fit together anymore. Each department knows that it contributes to pipeline generation and ultimately revenue growth but doesn’t know how these contributions impact the end result, and so everybody just crosses their fingers and optimizes locally for their own numbers. Revenue Leaders know this is a problem and that to fix it, they need to unify the GTM funnel from end to end. They just don’t know how.
Revenue Leaders are feeling metric overload. They’re inundated with endless KPIs of questionable value by their teams and vendors. They’re asked by their board to report “best practice” metrics like LTV/CAC at the snap of a finger. What revenue leaders want is to know how to define, measure and improve the numbers that both actually matter and can be operationalized. We believe that most SaaS businesses are asking the same basic business questions and can learn from existing funnel-analytics best practices rather than reinvent the wheel.
Revenue Leaders want to build a true data-driven culture within their teams (beyond using spreadsheets). Today’s SaaS business is always going to market, which means every department can adopt a culture of continuous improvement as they look for creative new ways to influence and grow revenue. In the context of a B2B SaaS business, Sales Ops is uniquely positioned to lead here, as they are often tasked with the technology to empower others to make smarter business decisions.
Let’s discuss the first lesson in the rest of this post.
How to unify the Go-to-Market funnel in 2021
Traditionally, every business funnel looks like this.
This funnel begins with Marketing and lead generation. The leads are then passed to the Sales team. When those opportunities are closed and won, they’re passed to Customer Success who focuses on customer renewal and satisfaction.
As businesses have grown, this segmented view of the GTM funnel has become a fragmented view, with revenue leaders only able to understand one segment at a time and no chance at understanding the funnel as a whole from end to end. This causes two problems:
- We now optimize for each segment of the funnel in isolation, without any idea how those local optimizations influence the funnel as a whole.
- We turn to advanced, but heavyweight tools to help bridge the gaps in understanding the funnel, which often has the reverse effect of slowing down our ability to make decisions at all.
Let’s dive deeper into both issues.
Local optimization does not equal global optimization
In many businesses today, every department owns a discrete start and end of the funnel. Although this makes it easy to optimize for specific numbers like leads or churn rate, it can actually make it harder to understand your business as a whole and across time. In some cases, these kinds of optimizations can be downright misleading.
Take Marketing for example, who is tasked with lead generation at the top of the funnel, ferrying leads down through a series of “touches” (emails, ads, webinars, etc.) before qualifying them and passing them to the Sales Team.
Despite this focus on leads, Marketing knows that ultimately its goal is to increase revenue pipeline, and the best marketing teams attempt to optimize their contribution to it by measuring dollars spent on leads and correlating it with dollars expected in revenue. This level of attribution helps them choose which marketing channels and campaigns to focus on, knowing that every dollar they spend on leads will increase revenue pipeline accordingly.
Except for one problem: Revenue pipeline is in the Sales Team’s part of the funnel, segmented away from Marketing with the two often only connected in aggregate. This means Sales can provide expected revenue numbers, but Marketing has no way to compare the channels and campaigns that contribute most to those numbers (other than to simply divide total revenue by total number of leads which most leaders agree is meaningless, or use various substitute theories, e.g. time-delay attribution).
In our conversations with marketing leaders, the reality is that determining which marketing programs truly influenced customer purchases is done by “expert knowledge,” and not actual data. In the end, everybody uses their gut.
Most marketing leaders recognize this shortcoming, as do revenue leaders in other departments which have their own data gaps (e.g. Sales over-targeting accounts with a high Annual Contract Value (ACV) without considering Lifetime Value (LTV) or Customer Acquisition Cost (CAC).
To compensate, they turn to tools and technology to help them unify the funnel into a single view that explains how every action in the business affects revenue. This, of course, almost never works.
Many existing tools can’t help us view the full funnel
To optimize each funnel stage, every department invests in tools to track exactly how customers move through the funnel and where they drop off. This creates a lot of data, and because every department has its own tools generating siloed data sets, companies must also invest in multiple layers of technology to bring all the data together to understand the full funnel from end to end.
Building one of these data analytics stacks is possible, but is an enormous investment of time and money. Today, there are as many tools as there are stages in the GTM funnel and they’re equally as fragmented: data capturing tools, data cleaning tools, ETL tools, a data warehouse, data visualization and dashboarding tools, predictive modeling tools—the list goes on, and there are more every year.
Most companies can never hope to find the time, money or even staff to build such a stack. Even the ones who do often run into other problems: The data is wrong, there are too many dashboards, decision makers are stuck waiting for answers. Building one of these stacks is also only the tip of the iceberg—every single layer in the stack must be maintained, changed and evolved just as your business does, and for a fast-growing SaaS business, keeping up with that pace is a tall order.
So how do revenue leaders understand basic questions about their business today? Through our interviews with our customers, we’ve learned that most take a bifurcated approach.
To report where they stand operationally, they look to the system of record for their respective functions (e.g. CRM for Sales).
However, to address strategic decisions that require a fuller understand than the “snapshot in time” view that most transactional systems offer, leaders they return to the most basic, but trustworthy form of data technology: spreadsheets, lots and lots of spreadsheets, diligently updated by a team of “analysts” whose labor hides the messiness of the data underneath. Quietly, a few revenue leaders whisper that it’s all just a guessing game and most of the answers are wrong anyway.
Every month there is something wrong [with the data]– SVP of Finance
How can we unify the GTM funnel?
The truth is that today, the combination of a fragmented funnel and a constant focus on technology has created more data silos, more manual (and therefore error-prone) data entry, slower decision making, rifts between teams and ultimately, missed business goals. Answering basic business questions such as “Will we hit our targets?” has become so difficult that some revenue leaders don’t even bother with asking anymore.
We rely more on judgement…there is a lot of gut and judgement.– SVP of Finance
To fix this systemic problem, we need to rethink how we use technology to answer our questions, which means we also need to rethink our approach towards the funnel in the first place.
In speaking with revenue leaders, we’ve come up with three ways to do just that:
- Go back to the basics. Start with the core business questions, and then find the right metrics and tools to answer them and not the other way around.
- Technology comes second to business questions. It’s easy to get caught up with the shiniest technology, but no tool is going to help you if you don’t know what questions you want to answer and more importantly, who needs those answers. Once you have that figured out, then look for the tools that will actually help.
- Follow the customer’s journey through the entire funnel. Although aggregating a customer’s activities in the funnel into discrete stages is tempting and helpful for optimizing locally, the true value of a funnel is seeing how cohorts of customers move through the whole funnel over time.
Start with core business questions
Nobody judges a business’ success by how many leads its Marketing team generates. We judge businesses with much more basic questions. To rethink the way we go-to-market, we should do the same.
At Datajoy, we focus on three questions:
- Will we beat our target? Every business has a goal and tries to beat it, plain and simple. The most common number that represents this goal is net new annual recurring revenue (Net New ARR). Some revenue leaders call it a north star metric, and the most successful SaaS businesses understand that every action in the GTM funnel can influence it.
- What are the surprises that could cause us to miss? Even in the most well-oiled GTM funnels, no one can predict everything. Go beyond just passively watching metrics upstream of revenue (e.g. MQLs) and proactively analyze how they affect revenue pipeline. This prepares the business to better anticipate changes to the funnel in future quarters.
- Where can we find new areas of growth? Many revenue leaders still think finding new areas of growth must mean net new outbound lead generation or New Logo ARR. There are many more ways to grow new revenue if we eliminate these types of assumptions. We will discuss this more in our final post on how revenue leaders can build teams with a real data-driven culture.
Tools and technology come second
We can’t overemphasize that revenue leaders need to focus on business questions first, and critically who needs to know the answers. But, when it is time to look at technology and tools to help find those answers, we think there are four things to keep in mind:
- Look at the full funnel and at the full data stack. Many tools will either help you analyze Marketing metrics, or will help you do one specific thing in the analytics stack such as clean the data or predict where the numbers will go. Look instead for the tools that evaluate the full GTM funnel from end to end (and will show you how MQLs affect revenue pipeline).
- Have built-in best practices. Believe it or not, SaaS businesses have been asking very similar questions about their GTM funnel for some time. Look for analytics tools that take this wealth of expertise and build it directly into their solutions. This includes built-in answers to the most important business questions (including our top three above) and analyses that uncover root causes across the full funnel.
- A single source of truth. A “single source of truth” sounds like a perennial myth, with dozens of spreadsheets and dashboards being the actual reality. Deliver on this promise with modern data tools that take advantage of known data schemas to bring all your GTM funnel data into a single data store. Yes, that includes both Marketing and Sales, Customer Success and Product Telemetry.
- Recognize the realities of Data Gravity. In the context of B2B SaaS businesses, we have found that the center of the revenue funnel tends to be CRM; many systems exist for the sole purpose of servicing it. As a result, the stewards of that system (i.e. Sales Ops) are likely to be the choke point for the majority of your analytics initiatives. As a consequence, when it comes time to select a new data technology or tool, be absolutely certain that the Sales Ops teams’ needs and concerns are fully met, ideally by having them lead the selection process in the first place.
Follow the customer’s journey through the entire funnel
Every revenue organization we have spoken to is constantly trying new approaches and initiatives to drive greater output from their funnel. Yet, the ability to measure the impact of these efforts is sorely lacking, because most organizations are unable to follow the data generated by their prospects as they progress through the funnel from top to bottom. When you are only able to track the performance of your funnel through aggregated KPIs, the impact of each initiative is diluted to near zero. Instead, what you should be looking to do is:
- Track cohorts through the funnel. Instead of aggregating numbers and comparing them (such as MQLs and Opportunities), follow cohorts of prospects throughout the whole funnel so you understand the impact of upstream behavior on downstream outcomes. For example, to understand the most effective campaigns, Marketing should follow a cohort of leads from one campaign all the way through to closed won or lost, and compare that cohort with others from other campaigns.
- Create KPIs that recognize velocity. A common trap we see in many organizations is an over-focus on stage-to-stage conversion metrics without considering the time it takes to move between those stages. A change that slows the progression of prospects through a particular stage in the funnel can often temporarily inflate the conversion rates of stages downstream of it, while harming it in the long run. The throughput of a funnel is a function of both its velocity and conversion efficiency—you need metrics to capture both. Better yet, define composite metrics, like Conversion % at day N, that help you incorporate both elements into a single number.
- Apply the same analytical rigor to internal teams. To optimize the GTM funnel, we also need to optimize our own teams and measure our internal performance. In the same way then, we shouldn’t track sales reps simply by aggregating their number of opportunities won and lost. Follow and show their performance over time and across similar cohorts of marketing activities to find out what really works and help the whole business improve.
These are the insights from the first of three lessons that we’ve taken to heart as we build Datajoy and our own GTM funnel. We’re lucky to share in the expertise of so many other revenue leaders who spoke candidly with us and shared their challenges and hopes of how we can all drive more revenue growth and value for both our businesses and our customers.
In our next post, we’ll talk about the second lesson we learned: What are the metrics that really matter and how do we measure them? We discussed a few metrics in passing in this post (Net New ARR, LTV, pipeline value) and we’ll do a deep dive into the best practices for understanding, defining and measuring them.
Do you want to share your experience using data with us? Help us build the service to help you drive revenue growth. Get in touch.