Point Solutions for Revenue Analytics: Short Term Relief for Chronic Pain
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.
I was at SAP working on SAP Analytics Cloud for almost 5 years, where I designed and rewrote a new application component to migrate from SAPUI5. After completing that project, I ended up leading a team to apply that component into SAP Data Warehouse Cloud.
It was during my time at SAP where I first worked with our teammates André and Ray. I may’ve met Patrick for an interview, and Jacky on a cross-team project!
What attracted you to Datajoy?
The people! During my time at SAP, I worked with a lot of the early Datajoy engineers, and having the opportunity to work with them again was very intriguing. Then when I met with our co-founder Ken, the work-to-be-done he outlined was exciting — some of it was familiar and I knew I could contribute right away, but it was also new at the same time.
Another great thing about the company is being able to work remotely as a new Dad. The team here really does promote a healthy work life balance. I’m really grateful to be able to enjoy the special moments of my daughter during the day. I still remember the first time she started giggling intentionally – my partner called me upstairs, and when she would play with a stuffie in front of our daughter, she had this amazing smile and giggle.
What does your typical day-to-day look like?
I’ve got a 3 hour head start on most of the team, so I usually start my day by prioritizing the work I want to accomplish. From there I’ll jump right in before our standup at noon eastern.
Lately I’ve been working on feature enhancements and bug fixes for our customer HackerRank — adding support to compare data year over year, making more parts of our app configurable and flexible to support different customer use cases. It’s all about making our customers as happy as possible with Datajoy!
What makes Datajoy special compared to other companies you worked at?
As a small team, it’s very rewarding to be able to see the impact of your work. The trust that’s given to our team is amazing. When we talk about “innovation”, we live and breathe it everyday. Each one of us checks our egos at the door (albeit nobody’s got a crazy ego) and we’re all working on the same goal: to build the best software possible for customers.
Favorite thing to do outside of work?
So I used to play in a ska/punk band back in high school, and if I could find a few bandmates and the time, would 100% love to revive my career 😂. I do also enjoy playing rock and indie, bands like Elder Brother, Brand New, and Death Cab for Cutie.
Aside from music, I also love to play and watch basketball. Don’t tell my Canadian friends, but I’m a big Detroit Pistons fan. After all, I’m just across the river from Little Caesars arena vs 3.5 hours to Scotiabank arena in Toronto. And I’m starting to get into American Football because my partner Alyssa and her family are huge Lions fans.
What is your superpower?
I’ve got really good hearing, spookily good. After listening to a song a few times, I’m able to pick it up and play it decently well. And after a few more listens, pick up the small little “extras” in a song that make a song feel full.
Spreadsheets for Revenue Analytics: A Love & Hate Relationship
Spreadsheets are the duct tape of the data world. Love them or hate them, they have been a dependable tool for more than 40 years, built on a fundamental concept that hasn’t really changed. Spreadsheets are a blank canvas and a mathematical workhorse. For how simple they are, they’re versatile and surprisingly robust, seemingly capable of patching together any number of datasets and creating a fully functioning analytics system.
That also happens to be a weakness.
For us in revenue analytics, we use spreadsheets to do everything: record historical revenue numbers, follow critical business metrics, and even predict the outcomes of various strategic choices. Spreadsheets are so versatile that every business, whether startup or enterprise, will use them on a regular basis. The problem is that some businesses, most businesses in fact, rely on spreadsheets almost exclusively in their data reporting toolkit, and they do this to their detriment.
The fast, but brittle solution
Let’s pose some questions:
Does your revenue organization take weekly or even daily snapshots of your CRM so you can compare current numbers to historical ones? Is it somebody’s dedicated job to maintain these snapshots?
Do you have an explosion of reports, such as bookings numbers by region, by sales rep, by industry—even if the underlying bookings data is the same?
Do you ignore numbers that seem like anomalies, outliers, or might be actual errors because it would take too long to triage and investigate?
If this sounds like your business, you’re not alone. Spreadsheets are powerful tools, but they are not always the perfect tool for the job.
Take the need to capture historical data, for example. A big flaw of many CRM tools is they typically provide very little ability to analyze current numbers against historical performance. How bookings, MQLs, or opportunity pipeline are trending this month or quarter isn’t very useful if you can’t compare them against last month, last quarter, or even last year.
Spreadsheets can be a fast band-aid solution to this flaw. Take a snapshot of the CRM, combine it with the previous week’s snapshot, and now you have a historical record to use for further analysis. Essentially, you’ve created a de facto database without any of the upfront investment.
The problem is that although spreadsheets can act as databases, they are very flimsy ones. Manually aligning 52 spreadsheet tabs for 52 weeks of data for one year is troublesome, though not impossible. But try to maintain that process for two or three years, and suddenly you end up with a critical but brittle operational system whose update and servicing is a major concern of your analyst staff.
I’ve been surprised by how many tech-savvy SaaS companies rely on their spreadsheets as the only source of historical CRM data for analysis, and continue to live with labor-intense updates and the inherent brittleness of such a patchwork solution even as they scale. The problem with building your data foundation with duct tape is that once your solution bursts at the seams, repairing it is a herculean effort.
Customer Case Study:
I recently engaged with a successful SaaS unicorn that utilized a spreadsheet for all their
inside sales reporting. Much love and care was invested into this spreadsheet, and the organization
developed a set of smart processes and workarounds to minimize the effort required to update and
maintain it. Unfortunately, as the file has grown to be 500+ MB in size, they are now seeing timeouts and errors from their cloud-based spreadsheet, leaving the organization scrambling to replace this critical operational reporting system.
More spreadsheets, more problems
Spreadsheets have similar pros and cons when we use them as reporting and analysis tools. They’re fast to spin up but cumbersome to scale. They’re rigid and static, and over time, spreadsheet reports need to be updated and increasingly customized as different people ask different questions—or even permutations of the same question—creating an enormous, unscalable, and ineffective analytics system.
If the data is incorrect, it can take weeks to triage the source of the error. If a surprising phenomenon (e.g. conversion rate is lower than historical norms) is reported, net new spreadsheets are needed to diagnose the issue. If metrics definitions need to be altered (because of a change in forecasting process, or new definitions of opportunity stages, etc.) all impacted reports become dysfunctional. If a report is updated, it needs to be redistributed, creating multiple sources of “truth.”
In larger organizations, these problems become particularly acute when revenue operators have to combine data from multiple sources, while also creating hundreds of reports for different stakeholders. With different revenue leaders asking different questions, it doesn’t take long to have 500 reports—often 500 cuts of the same data floating around week after week. This simply doesn’t work for very long.
Are spreadsheets ever the right tool?
So far we’ve mostly discussed using spreadsheets as tools for recording historical data and for reports and analytics. In the early stages of a company, these are great use cases for spreadsheets. They’re not perfect, but for startups where velocity is paramount and change is rapid, they’ll get the job done until the company scales to a point where GTM motions start to stabilize. The key is being able to recognize the need to transition away from spreadsheets before they become a significant operational challenge.
What we have not discussed is that data is useful for many other things besides historical records and analytics. We also use data to reason through different scenarios, and build and test numeric models based on human expertise.
This is exactly where spreadsheets are still the perfect tool for the job.
Spreadsheets—with their blank cells, formulas, and even the ability to add text—make it easy for revenue leaders to pair historical data with their own human judgement. Take budget planning, which executives often do in spreadsheets to show how different levels of spending could impact metrics across the revenue funnel. Importantly, this makes all the what-if scenarios appear concrete, allowing leaders to argue for, or against real numbers instead of theoretical predictions and “expert” knowledge.
Spreadsheets may not allow for full analytical simulations, showing the entire distribution of outcomes given certain assumptions, but the reality is that such simulations are often excessive and unnecessary. For most use cases, the spreadsheet is more than good enough.
This is why even the largest enterprises, with an entire analytics stack in hand, will still use spreadsheets for many non-operational situations. They simply use them for what they do best: the quick one-off problem, and also in strategic planning.
Planning Process Example:
Spreadsheets’ combination of a blank canvas and human judgement is perfectly reasonable for annual planning. Take your revenue targets and inject your assumptions about the outcome of various efforts (e.g. a promising new account-based marketing approach, a streamlined rep onboarding system) and work backwards to determine desired pipeline coverage, number of AEs needed, quotas for those AEs, number of SQLs and MQLs Marketing needs to generate, etc.
You can definitely systematize this type of planning, but in most organizations, this will be done at most once a quarter, and human judgement on new initiatives will likely affect the results as much as historical data would. In this case, the flexibility of a spreadsheet likely outweighs its shortcomings, particularly for most mid-sized organizations.
So if not spreadsheets, then what?
In our next post, we’ll talk about the fork in the road. How do you know exactly when to begin moving away from spreadsheets for revenue analytics? What are your choices? Investing in technology and tools to move beyond spreadsheets is itself a problem full of questions.
We will discuss each of the alternatives, their strengths and weaknesses, and how to select which path is right for you.
A Modern Guide to Using Data and Analytics to Grow Revenue
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.
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.
Most data analytics projects fail. Only 1 in 5 projects will deliver any meaningful business outcomes. Data Science, a newer darling of the analytics space, fares even worse; Per MIT Sloan, only 11% of businesses will report any financial benefits of AI, and Gartner predicts that 85% of such projects will actually deliver erroneous outcomes.
Despite a ton of failure and cynicism, Business Intelligence investment is as high as ever. In fact, even in 2020, with the uncertainty of the COVID-19 pandemic and an ongoing distrust over current data projects, most businesses report that they will actually continue to invest morein data analytics and machine learning.
So, why this seemingly irrational behavior? Why do business leaders consistently make what appears to be one of the least data-driven decisions possible—the decision to keep investing in data and analytics itself?
The truth is that there simply isn’t an alternative: To answer the most basic questions about your business—will we hit our numbers, what types of demand gen efforts are most valuable / effective, who are our top performing teams / channels / segments etc — you must have and must make use of data.
Organizations also know analytics investments provide non-linear returns, an exponential progression often drawn as a maturity curve. When businesses can find a way to create a competitive advantage using data, ROI is through the roof. In the world of customer analytics, McKinsey came to the conclusion that businesses that are effective in using data are 23 times more likely to acquire customers and nine times more likely to retain them.
But the reality is that most businesses never make it to this level of maturity. Despite a ton of investment and innovation, businesses of all sizes continue to find their data engineers and analysts forever stuck on the most basic of reporting requests—financials, sales pipeline, opportunity win rate etc.
We all think it, so we might as well say it: analytics is hard, expensive, and we will never really be “done” implementing it.
There are more data sources now than ever, each of which continuously evolves and requires ongoing maintenance. Data itself is more complicated and often has to be shaped for analysis. Translating business questions into data analytics requires a combination of technical capabilities and domain expertise. And then there is the semantics of business metrics itself, and it is an all-too-common scenario for business leaders to be loose with their definitions of what the numbers mean.
Today, each of these tasks represent additional tools, an additional layer in an analytics stack that you must implement and integrate to deliver value. Given this, it really shouldn’t be a surprise that failure rates in data analytics are so astronomically high.
This is the real problem: the focus on analytics tooling itself. We are measuring the progress of data analytics by the deployment of technology rather than by the business outcomes we want to achieve.
I believe there are three keys to tackling this problem:
Work backwards. Don’t start your analytics journey looking for a Data Warehouse, a BI platform or an AutoML tool. Start with the business questions you need to answer, and who needs to answer them (e.g. Will we hit our numbers, which customers are likely to churn, what can we do about it).
Recognize that many of your top questions are not unique to your business. There is no competitive advantage to be gained from you reinventing the wheel. Believe it or not, every sales organization needs to figure out whether they have enough pipeline and forecast whether they’ll hit their numbers with a combination of top and bottom-down analysis. Every marketing organization needs to figure out what the most effective campaigns are and track the efficiencies of their campaign. Look for an analytics solution that embeds the best practices regarding how to answer these universal questions, so that your analytics investment can go towards truly value-creating efforts. However, make sure that the solution you select is customizable, open and extensible so that it can scale and evolve with the needs of your business.
Identify the data questions that are truly unique to you. These are the questions that will net your business a true advantage over the competition. Task your analysts and data scientists with solving those questions, and only then, focus on the analytics tooling they need to be successful.
For all its frustrations and failures, I believe we’re still at an exciting time in data analytics. In my 15+ year career building BI tools, I’ve learned lots from my conversations with hundreds of thoughtful and data-driven leaders out there. I’m inspired by the increasing value they place on data literacy through their organizations and the creative ways they use data to gain a competitive edge.
Sales leaders want to moneyball their sales team. Marketing leaders want to drive opportunities. Revenue leaders want to optimize their funnel. And that desire continues to motivate a growing market of analytics tools to help leaders solve those problems.
More than ever, every single one of us wants to make better data-driven decisions and challenge our colleagues to do the same. Let’s start by focusing less on the analytical tools, and focus more on questions those tools should help us answer.
Data and analytics initiatives are failing. Companies are spending less on BI. Technology is rapidly evolving, but becoming more out of reach for all but the largest companies.
And yet here in 2021, we’re announcing Datajoy, a cutting-edge data stack for SaaS revenue leaders that helps them use data to grow new and existing ARR.
We believe every SaaS company needs a cutting-edge data stack. A customizable but query-ready data warehouse. Purpose-built ML models to answer the key questions of every SaaS business leader. Ready to use within days and weeks.
The reality is that most companies today can’t afford such a stack, can’t staff it, and can’t wait the months and quarters needed to build it. Even those that can are likely to see their BI implementation fail.
The problem is not that companies aren’t data-driven. They are, but every department traditionally optimizes only for itself. Marketing focuses on hitting a certain number of leads. Customer Success focuses on a renewal number. This method is slow, creates data silos and erodes trust and culture, and the only cross-departmental solutions are expensive horizontal tools or bespoke implementations.
The problem is not capability. Analytics tools are packed more and more with the latest technologies. But building and running them is challenging and expensive and requires talent that is increasingly scarce.
The problem is not a lack of data. In fact, companies are collecting more data than ever. But data is still often entered manually. Making sense of it takes months, even quarters. Some of the biggest businesses struggle to get any meaningful insights from data they already have, even just to make a revenue projection going beyond the sales pipeline.
So what is the problem? We believe the problem is the focus on tooling and solutions itself. Most companies are simply ill-equipped to tackle this problem on their own. We know. We’ve built SaaS businesses ourselves. We had this exact problem, over and over.
In fact, that’s what led us to start Datajoy.
Datajoy helps SaaS revenue leaders find answers in their data by just giving them those answers and skipping the tooling altogether. We do this by focusing specifically on revenue intelligence. Although no two companies are identical, most SaaS businesses have revenue metrics, data sources, and integration problems that are strikingly similar.
Rather than have every company build a bespoke solution from the ground up, Datajoy is a bespoke solution for all SaaS companies: an engineering, data operations and data science team in a box, built for the whole revenue funnel.
This means a unified data stack, a clean single source of truth for all revenue-affecting departments.
This means the latest in ML technology for SaaS businesses of any size.
This means answers from data for revenue leaders, automated and updated in real time.
Datajoy is the cutting edge data stack that simply gives SaaS leaders the answers they need so they can stop focusing on building data infrastructure, and focus instead on what they do best: growing the business.