Point Solutions for Revenue Analytics: Short Term Relief for Chronic Pain Banner image

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.

October Employee Highlight

Eric Alas

Role: Staff Software Engineer
Joined: Jan. 4th, 2021
Home: Windsor, Ontario, Canada

What were you doing before joining Datajoy?

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.

Businessman using iPad

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:

  1. 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?
  2. 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?
  3. 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.