Our vision and mission aren’t just words on a page. They guide everything we do. From the content we produce to the product updates we ship, it’s all about making progress against our ambition. And in 2024, we made a lot. As we approach the start of another year, let's reflect on the year that’s been. Starting with our vision.

Making every decision a better decision

Having access to data, along with the right tool to make sense of it (ahem, a spreadsheet), is essential to enabling better decision-making. And “making sense of it” requires an understanding of what you want to do with it. If you’re a SaaS business, that’s often calculating and doing analysis on metrics specific to operating a subscription-based business.

That’s why we published The Guide to SaaS Metrics. It covers everything from CAC to LTC, to Payback Period to Burn Multiple, to ARR, and everything in between. It’s all there–completely free and ungated. If the word on the street is anything to go by, it’s worth a look.

We wrote this guide after publishing our first book, The Ultimate Guide to ARR. It’s been downloaded by over 10k+ founders and finance professionals at world-class companies, including Uber, Atlassian, Plaid, Retool, Intercom, and more. Also free, it was so popular we even produced a hard copy.

We’ll keep investing in producing and sharing quality content in 2025. Just like we’ve been doing on the product front.

Creating the spreadsheet for modern data analysis

As Bobby recently shared, we learned that building a horizontal product is hard, but fortunately, by building Equals backwards, we’ve created an incredible foundation enabling us to solve our customers’ problems in entirely new and unmatched ways.

Here’s a quick recap of some of the more notable things we’ve shipped in our goal to be the only tool you need to report on your SaaS business.

Tapping into AI

At the start of the year, we shipped a new AI Assist(ant) to help you write, edit, and fix SQL queries, formulas, charts, and more. Clippy would be proud.

Syncing, storing, and transforming your data

Decisions are only as good as the data they’re informed by. And when your data is spread across multiple sources and raw… well, you get it. Bringing it all together and cleaning it so it’s ready for analysis takes a lot of time and a lot of money. Unless, of course, you’re using Equals.

We launched Equals Warehouse so you can sync, store, and transform data from Stripe, HubSpot, Salesforce, and more without the cost or complexity of implementing your own data warehouse. Now, Equals is the only tool that lets you go from raw data to beautiful dashboard–all in one place.

With a redesigned query experience, support for ARRAY formulas, and up to 500k rows of data per sheet (soon a lot more), choosing where to explore and work with your data is a no-brainer.

Vizualizing your data

After taking out Product Hunt's Product of the Day and Golden Kitty for Data & Security products in 2023, we shipped Dashboards V2, a release that addressed top-voted customer requests. A fresh new look and feel, cell comparisons, filters, custom branding, improved resizing, better table formatting, and Notion embed are just some of the improvements we made. Add new chart styles, and your dashboards have never looked so good.

As we’ve kept up our ongoing effort to improve the foundations of Equals, this year, we doubled down on helping customers turn their raw data into insights faster.

Accelerating time to insight

After onboarding hundreds of customers, including Descript, Attio, and LaunchDarkly, we’ve designed, built, and shipped a new ARR reporting solution for Stripe.

For all the world-class ARR reports we set up at Intercom, Stripe, Notion, and Atlassian, now all you have to do is click… sync. It’s 100% self-serve. No call required. There’s even a free trial.

This is just the start. We’re already working on a new solution for RevOps reporting with HubSpot and Salesforce data. Something Launchdarkly got $50k of value from Equals in two weeks.

As we focus on delivering tailor-made reporting solutions for every facet of your SaaS business, we promise to continue to improve upon the core fundamentals of Equals. From fixing “papercuts” to improving performance and solving your problems in new and better ways, making Equals the spreadsheet for modern data analysis is our number one priority.

Thanks for reading Wrap Text this year. We’ll see you in the new one.

Startups are a winding road. We turn one direction with a certain degree of conviction, and we adjust based on what we learn. That’s the game we play, over and over, until we figure it out. Whatever that really means. I mean, do we ever actually figure it out? At every level of scale, the challenges just seem to get different.

Today, we make our latest adjustment to Equals. One we’ve made before, but this time, the situation is different.

After successfully onboarding hundreds of customers, including Descript, Attio, Oso, and LaunchDarkly, we’ve designed, built, and shipped a new ARR reporting solution for Stripe.

For all the world-class ARR reports we set up at Intercom, Stripe, Notion, Atlassian, and many other world-class companies, now all you have to do is click… sync.

It’s 100% self-serve. No call required. There’s even a free trial.

Here’s the story of how we got there.

Our journey (back) to self-serve

If you’ve followed along for the few years we’ve been going to market, you’ve heard us share our story pretty openly. From sales-only to free, to self-serve, back to sales-only. 😅 And now self-serve again.

Every step along the way has been a necessary step in our learning. You can read our stories on why free didn’t work, why more friction in onboarding seems to outperform, why the challenge was really in the breadth of the product, which hurt us more than free.

Sales-first worked, but frankly, we’re just not growing fast enough with that alone. We needed a wider aperture for the number of people that can use Equals. Not a problem with the category of product we’re building—our TAM is infinite—but certainly a problem that’s a byproduct of our go-to-market strategy.

As Ben puts it, we freed ourselves to explore our Idea Map. And it’s paid off.

Things are different this time

The biggest benefit of turning off self-serve was being forced to get closer to prospects. We spoke directly to hundreds. Hearing first-hand the challenges they were facing. The pain points they had. The problems they wanted to solve. Ultimately, and unsurprisingly, it boiled down to two things:

1. Time

Founders and operators know they need to report on their revenue—not just for their internal team but also for their board and investors—but it was hard to prioritize among all the other things they also had to do in building their company.

2. Difficulty

ARR is an intricate beast. It’s why we wrote a 72-page book on it. While there are plenty of “out-of-the-box” solutions out there, none of them get it quite right. One-size-fits-all solutions simply don’t work when every company is uniquely different.

Ultimately, it was obvious that ARR reporting is a necessary evil. Prospects wanted to report on their ARR. And do so accurately. They needed reports that they, their board, and their (future) investors would trust. And here’s where things started to click.

We didn’t set out to solve reporting on ARR. We set out to build a spreadsheet. However, it turns out a spreadsheet is an excellent tool for solving ARR reporting problems for SaaS companies. It enables folks to do the customizations they need from a reporting perspective, that no other reporting tool on the market can handle. As long as they’re working with the right data.

We had the spreadsheet. And it was connected to the data (Stripe). But that data wasn’t “clean”. It wasn’t in an analysis-ready format. That’s where most of the work and complexity lies in accurately reporting on ARR.

So, we “cleaned” our customers’ data for them. For each customer, we created a set of views of their Stripe data. These simple, analysis-ready tables are the secret sauce to powering all their ARR reports and any analysis they want to build based on ARR.

With this, we’d solved the problem of reducing the time and effort involved on their part to stand up ARR reporting. However, since every business is different, we had to find a way to tailor each customer’s reports to reflect how they calculated ARR. And do so at scale.

Enter the blueprint. A dead easy way to configure ARR reports to map to specific revenue logic. Things like when new and churned ARR is recognized.

And that brings us to where we are today. For the world’s most accurate and only tailor-made ARR reports, all you need to do is click.

Ready to self-serve?

Twelve months after we decided to turn off self-serve, we learned that our “backwards” approach to building Equals has been a massive advantage for ARR reporting. It actually works and is nearly impossible to replicate.

If you’d like to see for yourself, head to our website and click Get started. It’s risk-free:

1. You connect to Stripe

2. We sync (and clean) your Stripe data

3. You get world-class ARR dashboards

It sounds too good to be true, but I promise you it isn't.

At first, it caught me off guard. Surely, with countless deal reviews, an army of analysts crunching numbers, and 100+ CRM dashboards at their fingertips, the Sales team knew exactly how the quarter was shaping up.

But they weren’t even close to knowing. And it wasn’t their fault.

Reporting on your CRM metrics (i.e., pipeline) is so challenging because it’s one-dimensional. You can see a perfect snapshot of where things stand today but have no context on how you got there or what the future might look like.

But I have good news. Your sales pipeline reporting doesn't have to live down to the limitations of what's available in your CRM. The key to unlocking its power is transitioning from static pipeline snapshots to a deal-based lifecycle view of the data.

Here’s why and how we transitioned at Equals and are now helping our customers do the same.

The power of deal-based lifecycle reporting

What do we mean by deal-based lifecycle reporting, and why does it matter?

What is it?
Deal-based lifecycle reporting shows the entire journey from when a deal is created to when it is closed. It’s more than just tracking the various stages of the sales funnel; it also includes changes in other attributes like deal value or expected close date.

A typical example looks something like this:

• The opportunity was created on Oct 3rd, 2023 for $5K with a Nov 30th close date

• Since the prospect moved slowly, real negotiations didn’t initiate until Feb 2024

• The deal finally moved to Closed Won on Mar 31st

Why it matters
Traditional CRM reporting would lose all of the historical context. However, with deal-based lifecycle reporting, we can reconstruct the complete journey of how every deal evolved to draw insights about the future.

Four analyses unlocked by pipeline history

I eventually realized deal-based lifecycle reporting was the missing piece to answer my CRO’s question.

“Am I happy?”
Do we have the coverage needed to hit our number? Are there some promising deals that are undervalued right now?

“Am I sad?” 
Are we hanging on to stale deals that will never close? Are we creating enough pipeline today to help position us for success next quarter? 

With a complete historical view of your pipeline, you can answer all these questions, making entirely new categories of analysis possible.

1. Change tracking

Question
How does this week’s pipeline compare to last week, and what happened to the deals we talked about last week? 

Answer
With deal-based lifecycle reporting, it’s easy to turn the clock back in time and compare your pipeline from the prior week to this week at the individual deal level. From there, you can easily categorize the variance between the two time periods based on distinct sources of change. This gives you a clear sense of what changed and where to focus the attention of your follow-up investigations or actions.

Here’s an example of what that might look like comparing the open pipeline set to close this month from 1 week ago vs. the current week. Yes, we love a good bridge at Equals.

For companies with longer sales cycles, it’s helpful to supplement the current month snapshot above with the table view below that shows the open pipeline changes in the next six months. 

2. Bookings forecast

Question
How does our pacing compare to this same time last month or the month before that? 

Answer
Instead of relying on straight-line pacing as a comparison, it’s helpful to create what the actual bookings build looks like in a given month or quarter. This can give you a more realistic picture of pacing because we all know the last week or even day of the period brings a flurry of closed-won deals.

The chart below looks at the historical average of bookings progression, based on the last 12 months, using the company’s actual performance data and plots that against the period-to-date actuals.

Question
How much of the pipeline we started this period with should I expect to convert? 

Answer
Instead of relying on a generic coverage ratio (e.g., 3.0x), deal-based lifecycle reporting allows you to run a more accurate analysis using your own data. The chart below compares how much pipeline you had on the first day of the month that was set to close that same month vs. what actually converted to closed-won.

Understanding this ahead of time gives you the confidence to know where you stand and enables you to shift into action mode sooner when you’re behind.

3. Conversion rates

Question
Has there been a performance shift in recent months?

Answer
The easiest way to spot longer-tail changes is cohorting conversion rates by when the deal was created. Doing so makes it easier to identify the source of the change and whether you expect that trend to continue, a critical component of any planning process.

In the table below, we show conversion rates of deals across each stage of the sales funnel. Note that it’s important to clearly show the number of Open deals to avoid drawing false conclusions from cohorts that haven’t fully aged yet.

In general, we look at stage-to-stage level conversion rates to quickly identify drop-offs in the funnel (e.g., S1-S2 CVR is the only point where CVR is <50%), as well as overall conversion rates to track overall funnel efficiency and benchmark performance vs. peers.

Side note
Identifying the source of the change can sometimes be more art than science, but I recommend grouping these into internal or external factors. 

Internal changes include things like tweaks to your sales motion, product offerings, or pricing models, while external factors relate to the competitive landscape (e.g., win rates in head-to-head deals) or macro-environment (e.g., prospects citing budget constraints on lost deals). 

4. Deal velocity

Questions
How long is it taking us to close deals? Are we hanging on to stale deals longer than we should?

Answer
The length of your sales cycle is one of the biggest determinants of your team’s maximum sales capacity, but looking at deal length from start to finish only tells part of the story. 

In the charts below, we show stage-to-stage level conversion (charts on the left) plotted against the ACV and median Days-in-Stage of deals at the stage (charts on the right). Showing these side-by-side helps you see if a change in deal velocity may be coming from a spike in volume (signaling capacity constraints) or a jump in ACVs (higher deal value = more complexity/approval chains).

By tracking the movements between each stage, it’s easy to identify bottlenecks in the sales funnel and take action.

Side note
Many times, the best resolution is moving deals to closed-lost faster. At Equals, we love it when buyers say “yes”, but a close second is a quick “no”. Many teams spend too much time trying to convert the wrong prospect to a yes that 1) will be a churn risk down the line and 2) takes time away from the strong yes you could’ve converted or upsold instead. 

How to unlock your pipeline history

The good news is that all this data already exists in your CRM – it's just waiting to be unlocked. 

Salesforce offers out-of-the-box reporting through Opportunity history data, which tracks changes in fields like Amount, Close Date, Stage, and Probability. You can enhance this by manually enabling change tracking on additional fields and custom objects through the Field history object. 

HubSpot also supports change tracking automatically through their deal Property history objects.

Having historical data available is a helpful starting point. However, there’s still a good amount of effort required to transform that raw data into a usable format. You'll need to use SQL to complete the following steps, which mirror the process outlined in our post on How to build Annual Recurring Revenue:

1. Pull the raw history data from your CRM

2. Transform it into a daily record for each opportunity

3. Join it with other opportunity attributes

4. Clean up any inconsistencies in the data

Getting through the set-up phase is time-consuming, and the work is never done. You’ll constantly iterate on the data as the business’ go-to-market motion evolves, product offerings expand, and org structure changes.

But the payoff is worth it. Once you have this foundation, you can answer virtually any question about pipeline performance and evolution.

We can help

We've helped dozens of companies unlock these insights from their CRM data implementations, ranging from early-stage startups to those with $100M+ ARR. 

Everything outlined above, including all the charts and tables, can be built with Equals. Here’s how: 

1. We connect directly to Salesforce or HubSpot

2. We write the queries to clean and transform the data you need

3. We build all the analyses (that update automatically as fresh deal data rolls in)

4. We build a live, shareable dashboard that auto-sends to Slack and email (daily, weekly, or monthly)

If you're intrigued by the potential of this reporting but unsure where to start, our team of Equals Experts (including me) are ready and waiting to help.

Here's the thing - I still believe most of what I wrote in that post. But there was a blind spot in our thinking… that we've only recently begun to understand.

Killing freemium didn't just mean removing our free plan; we had to evolve further. 

Going all in on things that don’t scale

After killing our free tier, we kept a paid self-serve path to using Equals. It was working (to a certain extent), but we felt we weren’t learning fast enough. We were also too far from our prospects and customers. 

Folks were signing up and making it through our funnel, but we still couldn’t quite articulate what great customers looked like and what made them stick around. So we went back to basics.

We started with our GTM motion. We required, and still do, every prospect to jump on an onboarding call with us. Because not all friction is bad.

This enabled us to learn so much faster. We could tangibly feel the many problems that led to freemium not working and more deeply understood how to solve them. Ultimately, we simply didn’t know what use case(s) were most valuable with Equals. 

In this onboarding-first, higher-friction world, we got to speak to everyone with enough intent to get on a call with us. They had a problem dire enough that they were willing to dedicate time to talk to someone about it. From there, we started to hone in on which use cases made the most sense for Equals to solve. We saw patterns emerge from those who didn’t progress and those who effectively ripped the product out of our hands. 

More importantly, as we went back to manually onboarding every customer, we got in the trenches with them, digging into the details of their needs and building things for them. It was through this “unscalable” work that we identified patterns. Those patterns - e.g., a lot of SaaS companies need help with their ARR reporting, SaaS metrics, and pipeline reporting - turned into repeatable onboarding motions. Modules that we now offer and are actively developing templates for, like our ARR Build Blueprint for Stripe.

Free wasn’t the (entire) problem 

Turning off self-serve accelerated our realization that we needed to verticalize because building and taking a horizontal product to market is hard.

It’s now clear that a big part of our problem with freemium was that we were simply putting too much of the burden on users to figure out how to get value from Equals. We dropped them into an empty workbook with a datasource connected, with a lot of work to get from that state to a ready-built analysis. Go figure. 

And while we don’t yet have enough conviction to actually go back to freemium, we do now have enough conviction to go back to self-serve. So, this time around, as we get ready to (re)open a self-serve path to trying Equals, we’re excited about the modules we’ve built to clean up people’s data (from Stripe to begin with). They actually deliver on the promise of immediate value—with plug-and-play templates and pre-built dashboards that teams like Descript, Attio, and Oso rely on day in and day out.

It was only in going through the past year of manually onboarding every prospect that we’ve learned how even to be able to do this. The real victory wasn't in killing freemium but in finally understanding how to make our customers successful.

As we continue building Equals, we're taking this lesson to heart. Right now, we’re laser-focused on ARR reporting and SaaS metrics, but in the future, we’ll expand use case by use case, ensuring we nail each one before moving to the next.

It might not be as flashy as "open the floodgates and watch them come," but who cares? It's working.

Sound familiar?

The frustrating reality is both teams are probably right—they're just looking at different metrics.

Sales are focused on bookings (when deals are signed), while Finance tracks billings (when customers are invoiced).

This disconnect might seem academic, especially when juggling a million other priorities, but ignoring it creates real problems that compound over time.

When two teams aren't seeing eye to eye on performance, how do you bridge the ARR gap?

Here’s what I've learned from helping dozens of companies bridge this gap so you don't have to learn it the hard way. 

Why you should act now

The consequences of not addressing this disconnect between Sales and Finance early are threefold:

1. Missed opportunities for intervention
If you're halfway through the quarter and Sales feels confident while Finance is nervous, there are things you can do to course correct. But nothing will actually change if half your organization thinks everything's fine. This misalignment could stem from underperforming self-serve revenue, timing differences in recognition, or varying subscription start dates.

2. Last-minute reconciliation chaos
Many teams save all their reconciliation work for quarter-end. This means doing manual checks in an already high-pressure environment. It's bad enough not knowing if you're on track halfway through the quarter—it's even worse being a week past quarter-end and still uncertain if you hit your number.

3. Strained team relationships:
These disconnects create unnecessary tension between teams. I've seen too many meetings devolve into "my opinion versus yours" debates, which do nothing but erode trust and collaboration.

Understanding the disconnect

The fundamental challenge is that bookings and billings track different moments in time, and a lot can happen between them. Here's how to think about each:

Bookings
This is typically when a contract is signed. Sales tracks this out of the CRM, their canonical source of truth, and is typically how they are compensated.

Billings
This is tracked via subscription events or invoices from systems like Stripe. Similar to CRM data for Sales, Finance is typically tracking against billings because this corresponds to Revenue and cash collections.

The gap between these events can be thought of in three main categories:

1. Timing differences

Sales deals are like gravity: no matter how hard you try to fight it, there’s a universal pull to close on the last week or day of the quarter.

While contracts often manage to get signed on the very last day of the quarter, the corresponding subscription typically starts a few days into the next quarter.

This results in Sales counting it this quarter but Finance counting it next quarter.

This problem stacks on itself when you consider that the same issue from prior quarters is also present: deals that Sales closed last quarter are being billed this quarter in Finance’s numbers. Said another way, this customer will create a similar discrepancy when aligning on Q2 performance, but this time, it will appear in Finance’s new customer list but not Sales. Some enterprise businesses may even have deals that are closed several quarters ahead of the time they’re invoiced, which creates an even longer gap between billings and bookings recognition.

As a side note, we were able to avoid some of the confusion related to these timing discrepancies by introducing the concept of a grace period —allowing deals signed within a certain window of quarter-end to count in the current period. It’s more work to track and reconcile, but it went a long way in helping the various teams get aligned.

A separate but related issue can also present itself when considering cancellations or lost renewals. The date the Sales team recognizes the churn might be when the cancellation was requested. In contrast, Finance typically counts this at the end of the billing period to align with Rev Rec (revenue recognition).

2. Incomplete CRM data

The next set of discrepancies come from events typically executed out of the billing system and don’t make their way to the CRM where Sales track bookings.

Here are a few examples I’ve seen from my time at Intercom and Equals:

Business unit measurement
Finance typically measures performance at a global level, but if your business has a Self-Serve or PLG motion, it is typically not tracked by Sales. Surprises around how this business unit is performing can lead to different perspectives on overall performance.

Missing deals
Even though Sales reps are compensated based on data in the CRM, deals can still slip through the cracks. 

This can happen because an opportunity is stuck in deal desk review or the deal owner left the company or transferred to a new role. Some deals also include auto-escalations in pricing from Year 1 to Year 2, which isn’t always tracked in a new opportunity.

Billing-initiated changes
This last set of discrepancies is a catch-all of operational changes that Sales is often not aware of, including:

• Moving a customer to a different pricing model to unlock specific product functionality 

• Pricing migrations or graduations from a starter to a more permanent plan

• Coupons expiring, especially those for introductory pricing “[x%] off your first 3 months”

• Payment relief for companies experiencing cash flow issues

3. Mismatching values

Here, Finance and Sales agree on the deals to evaluate, but the amounts vary. 

The variance here typically stems from billings initiated outside of contracted spend, such as fluctuations in spend from monthly (M2M) customers or if overages, usage over contracted maximums, are permitted without speaking to Sales.

Here are a few examples:

• Measuring uplift for converting M2M customers onto annual contracts → do you count the full contracted amount or just the uplift from the annualized M2M spend vs. contracted Annual Recurring Revenue (ARR)? 

• Overages → Some companies allow customers to self-serve (i.e., buy without engaging with sales) additional seats or product SKUs without needing to sign an amended contract, which may not be sent back to the CRM.

• CRM deal details do not match what’s provisioned in the billing system → if this process is not automated, this could be as simple as fat-fingering a value incorrectly in the CRM or Stripe.

To be clear, not all of these things need to be tracked in the CRM, but knowing the potential points of discrepancy is essential.

Building your first bridge

The solution to ARR discrepancies is creating a systematic way to compare your entire customer base across both systems. Here's how to get started:

1. Establish your ARR methodology
Before you can bridge anything, you need a clear definition of how you calculate ARR down to the customer level. This provides the foundation for all other reconciliations.

2. Pull comprehensive lists from both sources
Generate the complete list of customers contributing to both billings and bookings in the period. At a minimum, you’ll need to include the customer names, IDs, amounts, and effective dates between both lists. It may also make sense to include other dimensions your business forecasts against, such as segment, territory, or business unit.

3. Create unique identifiers to join between lists
This simple step is often overlooked. To reliably compare customers between your billing system (e.g., Stripe) and CRM (e.g., Salesforce), you’ll need a matching ID key in both systems. 

4. Look for variances
Compare the lists, categorize variances into distinct buckets, and identify the root cause of each discrepancy bucket.

Below is an example of categorizing the discrepancies at an aggregate level. I like having a visualization like this to make it clear where the biggest variances are coming from. This also helps prioritize where to spend your time when digging deeper and proposing fixes to systematic issues

5. Review regularly
Don't wait until quarter-end. Make this a weekly exercise to catch issues early and help leaders bridge between the different signals in their forecasts. Equals makes this easy by automatically distributing your Sales and RevOps dashboards to your team in Slack or email weekly. 

Build your bridge to predictability

While this might feel like extra work, especially early on, it's an investment that pays dividends in reduced stress and better forecasting. Finance teams often get stuck in reactive mode, reporting what happened in the past. This bridge helps you become a proactive partner in steering the business.

Remember: Preparing for your board meeting or investor update shouldn't be the first time you're reconciling these numbers. Build this muscle early, and you'll thank yourself later.

Need help building your ARR bridge? We've built bridges at Intercom, Stripe, and Atlassian, to name a few, and we would love to help you. Let's chat.

I’ve participated in board meetings for 10+ years. I ran board meetings as Head of Finance at Intercom and now as CEO of Equals, and I've seen the good, the bad, and the utterly chaotic.

Here's what I've learned about how to run effective board meetings to drive your business forward.

Start with Mission and Vision. Every. Single. Meeting.

No matter how dedicated or committed a board member is - even if they’ve invested millions into your company or are the perfect investors - they’re only dropping into your company every few months. I can’t emphasize this enough. They don’t have the same context that you have. They don’t live and breathe your company in the same way that you do. And so, it’s really important to, at the start of every meeting, remind everyone of the context for why the company exists and what you’re setting out to accomplish. This gives color and context to everything that’s about to be discussed.

So, I start every board meeting by recapping our Mission and Vision. Sometimes, it's a dedicated slide; sometimes, it's just the first 5-10 minutes reminding everyone where we're trying to take the company over the next decade. This sets the stage for the strategic, high-level conversations you want to have.

The State of the Union is your moment

This is the CEO’s time to address the board. I use it to give an open and vulnerable assessment of where we are as a company. Some folks structure this as “what’s working and what’s not.” Which I’ve used in the past and have found to work. Lately, I have tried to just talk through the 3-5 most important things happening at the company. That naturally leads me to talk through some things going well (where we want to double down) and a couple of areas where I’m concerned and see an opportunity to improve/fix.

I treat this section as if there were no other discussion on the remaining slides; I would have covered my best assessment of what’s happening at the company right now. That’s what you want the board to know. We’ll often spend 20-30 minutes on this slide, discussing each point as a group. 

Product first, not finance

This was a hard lesson for me. My background is in finance. I like to run through the numbers. I like to get into the minutiae of the business. And yet, we found that whenever we started the board meeting with metrics, that’s where we’d get stuck. Board members oftentimes also tend to be numbers people :) They like metrics. They like to ask questions and dig in. While that can be helpful, it’s a surefire way to spend board meetings stuck in the details.

The biggest shift we made that’s helped has been in pulling the product section to the beginning. After all, product is the most critical long-term bet the company is making. It is the company. And so leading with a discussion about product, treating it as an opportunity to share what customers are saying, how customers are using the product, and what you’re going to do to continue solving customer problems, puts the emphasis of the early stages of the board meeting on precisely that topic, which is critical for long-term success. 

Focus go-to-market on strategy over tactics

There’s always a pull to show all the fun things you’ve done in marketing. Your new website, that first billboard, your freshly published book–the list goes on. You should share these efforts, but only within the context of your GTM strategy. 

What are you trying to achieve with your sales and marketing efforts? And more importantly, are they working? Start here. Aim to dive into questions like: Are your channels aligned with your stage and ACVs? Is your approach right for your market? This is where you want the board's pattern recognition. Then, show how you're executing against that strategy in the market.

The business update tells the metrics story

In this section, I like to focus on telling the entirety of the business story through metrics. Starting from the top, how do we acquire customers? Down to the bottom line, how much cash are we burning? It’s a comprehensive overview of the company through numbers. 

Don't let admin derail you

Every board meeting has an admin section. This is the part everyone else (should) find boring, but your lawyers love it. Review your board minutes, approve changes to 409a, approve option grants–things like those are discussed in this section. 

This section should be boring. To ensure this, any contentious items should be discussed beforehand, outside of the board meeting. You don’t want to surprise anyone here, say, with an outsized option grant or a meaningfully different 409a valuation. The admin section should be a rubber stamp, not a debate.

Some tactical tips

Here are a few more narrow things I’ve learned over time. 

Length
Ninety minutes is tight but doable in the early stages. As you scale, meetings get longer but less frequent. Over 3 hours, unless you’re a pre-IPO or public company, you’ll lose people. 

Attendees
Keep it focused. Bring in department heads for their sections only. Post Series-A, include your head of finance as secretary. Later stage, add corporate counsel.

Pre-reads
Send the deck 2 days in advance. I've tried fancy memos, but honestly, a clear deck works just as well.

The real value is in the prep

There’s a lot of prep that goes into a board meeting. Sometimes, it can feel like you spend a lot of time preparing for the meeting, and then afterward, you leave and think, well, nothing about that conversation really changed my thinking or our trajectory. That’s not a bad thing! That’s often indicative that you’ve prepared well for the meeting and that there’s strong alignment across the board. 

And that’s what I’ve found to be most of the value of board meetings. It’s been in the preparation. Much like sending monthly investor updates in the early startup days, so much of the value of board meetings comes from having to sit down every couple of months and put pencil to paper in articulating the State of the Union, your product roadmap, your go-to-market strategy and every metric that matters across your business. It’s ok if that’s the value. 

Focus on the preparation, and the rest will take care of itself. 

When we started building Equals, our mission was as big as it gets in the business world: to build the next-generation spreadsheet. First, there was Excel, then Sheets, and now Equals. Simple. I remember showing David Sacks the product before we’d launched to anyone. “Holy shit, if you pull this off, it’s a $100 billion company” was his reaction. 

Here's where it gets interesting. For the past six months, we've been laser-focused on ARR reporting. From the outside it might appear that we’ve narrowed our vision. But we haven't. The vision for Equals is still as big as it was when we first started. How we get there is clearer than ever. We’ve just done things backwards – and that’s turned out to be our secret weapon.

The "standard" way vs. our way

The most influential class I took in college was one taught by Steve Blank, serial founder and author of The Four Steps to the Epiphany (which launched the lean startup movement). He’s a legend in the startup world, and I was incredibly lucky to learn from him. This class showed me that I ultimately wanted to be a founder – it only took me 10 years to work up the courage!

In his class, Engineering Entrepreneurship, you have to start a company in 10 weeks. Steve hammered home the importance of starting small. Identify a problem and build the smallest MVP you can imagine that solves that problem. From there you’ll validate if the problems are real, if people are willing to pay for it, and you can continue to flesh it out. After all, you don’t want to spend a tremendous amount of time, energy, and money going down the wrong path. 

This is the common Silicon Valley advice. Y-combinator pitch a similar concept – they say “you should be embarrassed by the first version of what you ship.” And it’s good advice, generally speaking. But it can lead you down the wrong path. 

If from the get-go, Equals had been built with the sole purpose of solving ARR reporting for SaaS companies, we would have built something completely different. We definitely would not have said, “hey let’s build a spreadsheet”. We’d have been nuts to do that. 

So we would have done the same as everyone else who’s ever tried to solve ARR reporting. We’d have built connectors to various billing platforms, and a set of UI/charts on top of it with all of your metrics neatly calculated (i.e., not so neatly  😓). We’d have built basically the same set of reporting modules that exist in every SaaS app… and every single one of them sucks. You know the ones I’m talking about. 

Reporting tools all fail for the same reason. They’re too limited. They don’t let you touch and feel your data. They’re too rigid and pre-defined. They don’t let you manipulate your data and metrics to match your way of measuring your business. So the most used feature in all of these tools is “download to csv” 😂. And this is why out-of-the-box reporting tools don’t work and just aren’t that exciting as businesses. 

Creating an accidental advantage

So here then is how we built Equals “backwards.” 

We didn’t set out to solve ARR reporting. We set out to build a spreadsheet. But in order to build the business of the spreadsheet, it turns out we had to find and identify use cases for which we could sell it. 

It turns out a spreadsheet is an excellent tool for solving ARR reporting problems for SaaS companies. The primitive of the spreadsheet enables folks to do the customizations they need from a reporting perspective, that no other reporting tool on the market can handle. 

So our “backwards” approach has turned out to be a massive advantage for ARR reporting. It actually works and is nearly impossible to replicate. 

Yet we’d never have stumbled on that advantage had we gone down the path of building an MVP. We’d never have built the spreadsheet, which was and still is overkill, to just solve ARR reporting. 😀  

Building Equals forwards 

I recently learned the story of how ServiceNow got started. It’s pretty similar to where we’ve been with Equals. The founder, Fred Luddy, wanted to build an all-purpose tool to handle internal workflows at companies. He spent years driving up and down Silicon Valley, facing rejections until he found the killer use case: internal ticketing. But they’d already built the primitives and foundations of the product that could solve a much broader set of problems. From this, they’ve built a behemoth of a business, internal ticketing being just one module of now perhaps 40-50. What made them a $180 billion company wasn't that first use case, it was the horizontal technology that enabled all the use cases that followed. 

I see Equals playing out similarly. We’re not just an ARR reporting company. That’s where this starts. We’ll next (and already are) move to CRM reporting. Perhaps into the product engagement world. Maybe support ops. And that’s just for SaaS companies. 

From there, the spreadsheet as the primitive is still the best tool on the planet for reporting on any business. E-commerce companies. Professional services. Every Shopify business on the planet. Financial services. You name it. It then becomes a game of executing each module well, while the underlying technology remains the same. 

We might have started backwards, but that's put us in an insanely exciting position. We've got the foundation, and like our friends at ServiceNow, that’s how you can pursue a $100B opportunity. 

From that experience, we've learned that while many solutions can get you most of the way there, the key to successfully adopting off-the-shelf ARR reporting is solving for that last mile of accuracy, which inevitably comes down to the idiosyncratic nature of how you run your business.

Some people may want their Annual Recurring Revenue (ARR) to adhere to specific rules such as - “Only count ARR for a customer after they have had a successful first payment.” 

Others may know that their Stripe instance is not the cleanest and want to adjust the logic accordingly - “These invoices are marked as non-recurring in Stripe but they should be tied to a subscription”. 

That's why we built the ARR Build Blueprint for Stripe. It gives you the tools to solve for every edge case, down to every individual customer and invoice.

No more need to to manually track corrections in another place.

You'll never need to question if your ARR is "right." Again.

Just answer a few simple questions, and Equals will give you the most flexible and the most accurate view of your ARR—guaranteed.

Let’s run through the questions it uses to tailor your ARR reports, including writing all the complex SQL for you.

We’ll start with six specific rules.

1. New ARR Event

When you sign a new customer, when do you want to start tracking their ARR? 

You could either:

1. Count them immediately;

2. Delay until they’ve paid

If you’re a self-serve B2C company, perhaps you want to delay counting ARR until the customer has had a successful payment. That will lower your overall ARR but also prevent your churn number from being inflated with customers who never activate. 

If you’re more B2B, like Equals, we recommend counting those customers immediately. We also recommend building analytics to monitor customers who have yet to pay on an active subscription or invoice using our Stripe connector.

2. Churned ARR Event

When a customer cancels (or requests to), when do you want to stop tracking their ARR?

Consider the scenario where a customer requests to cancel their subscription on October 10th but will still be active on the platform through the end of their billing period on Dec 31st.

• If you stopped counting ARR on October 10th, that might enable a more accurate view of customer retention and a better reflection of future ARR. 

• If you stopped counting ARR on December 31st, your numbers would better align with your revenue recognition.

We typically see a ~50/50 split on how companies prefer to view their ARR. Internally, we default to the latter scenario to align with revenue and compensation plans for go-to-market teams.

3. Restarted ARR cut-off window

How do you treat a previously active customer who churned and returns to use your product?

This will always be a smaller bucket as a percentage of your overall ARR, but it’s a request that we’ve heard from several companies of varying sizes. 

You could: 

1. Always count them as Restart ARR. This is what we do internally today.

2. Count them as Restart ARR unless it’s been more than x months.

4. Paid trials

Should you include paid trials in ARR?

Most companies we work with don’t have paid trial options. But, if you do, you may want to decide whether or not to include those in your ARR totals. We exclude free trials from ARR by default because they are $0.

Including paid trials could impact the start date for counting that ARR and the respective amounts. Depending on your trial conversion rates, it might also impact churn and retention metrics. 

5. Discounts

If you provide discounts, do you want those to factor into ARR? 

Although there are many different types of discounts, we’ve seen these generally treated in one of two ways: 

1. You want to ignore discounts in your ARR; 

2. You want to subtract discounts from your ARR.

We recommend subtracting discounts from your ARR. This will give you a more accurate representation of the revenue you collect. 

6. Timezone

How do you want to display your timezone? 

Your Stripe dashboard is localized to a set timezone. We ask you this one so that your invoice dates in Equals match what you see in Stripe. 

We’ve only worked with a handful of customers who say their Stripe instance is error-free. For the majority of instances where errors exist, correcting them within Stripe can be nearly impossible. 

That’s why we designed the ARR Build Blueprint to make it easy to review and override key ARR inputs: Start Date, End Date, and Total ARR. 

7. Start Date override

When may you want to override an invoice’s Start Date? 

There are a few reasons why:

1. Pull forward scenario
   You want to recognize revenue for a customer based on when they signed a contract vs. based on their first invoice Start Date. You could change the invoice Start Date to a different month. 
   

2. Push back scenarios
   Maybe you issued a credit note for an invoice, but you don’t want to count the ARR immediately. You could shift the Start Date back two weeks.

8. End Date override

When may you want to override an invoice’s End Date? 

We most often see this happen for invoices that were marked as non-recurring but were, in fact, recurring. You can adjust the End Date for a specific invoice to help your ARR smoothly transition to the next billing cycle.

Here’s an example scenario:

• Invoice #1 (one-time)
  2024-01-01 → 2024-01-01 2024-01-31 to show ARR for each day that month.
  

• Invoice #2 (recurring)
  2024-02-01 → 2024-02-28.
  

• Invoice #3 (recurring)
  2024-03-01 → 2024-03-31.

9. ARR Amount override

When may you want to override Amount? 

There are many reasons why you may want to override the amount, and we’ve tried to account for that flexibility. Depending on your use case, you could change an amount ($100 → $150), zero out an amount ($100 → $0), or start counting ARR for a non-recurring line item ($0 → $125).

10. Do you need more customization?

Of course, there are many more considerations you may want to factor into reporting on Annual Recurring Revenue (ARR). How should you think about Credit Notes? Refunds? Have you transitioned billing systems at some point and want to union historicals? Do you want to treat different types of discounts differently?

Easy done. Chat with us, and we’ll figure it out together. 

The ARR Build Blueprint for Stripe is available today and is included with all Equals subscriptions with a Stripe connection.

From making sure the company doesn’t run out of money to managing the board, investors, and founders to measuring every metric you can imagine to help drive your understanding of the business. 

Yet when I was in this role at Intercom, those weren’t the parts of the job that most daunted me. 

It was setting up all the infrastructure to support measuring our business. And I’m decently technical. I die on the hill that every finance person should know SQL. I’ve been known to dabble in the ever-so-egregiously hideous Python script. 

Most finance folks aren’t this technical. 

When somebody told me that the best way to continue reporting on things at Intercom would require me to spin up a database (what’s Redshift?), hire a data engineer who would implement Airflow, Yoshi, and dbt, and build fact and dimension tables or Snowflake and star schemas… I was wholly and completely lost and overwhelmed. 

That sounded like a complete clusterf*ck. 

And it sounded like it would take forever. 

But down that path, I went. 

And it took years.

And teams of people. 

But I figured it was just me. I just didn’t know how to do this. I must be the idiot. 

Every other startup on the planet must have figured this out by now, I thought. I’m just playing catch-up. 

So, in many ways, we built Equals assuming that if you give folks access to their data in a spreadsheet, they’ll stick it on top of their beautifully designed database, and voila, we’ve done it! 

Turns out that was wrong. Very wrong. 

(Ok, I oversimplified our actual thinking here; we weren’t that naive.)

Yet, selling Equals now to young startups, I’m surprised at the number of “startups” – big and small – that haven’t figured this out either.

The messes I’ve seen. Holy moly! 🙈

And yet they’re all faced with the same daunting challenge I was at Intercom:

1. Go hire a data engineer → they’ll buy various tools to build pipelines to bring your data into a warehouse.

2. Go hire an analytics engineer → they’ll buy other tools to transform the data you’ve imported to your warehouse.

3. Go hire analysts → they’ll buy yet another set of tools to build analyses and dashboards. 

All in, you’re looking at spending hundreds of thousands of dollars, if not millions. You’ll make countless mistakes. And it will take years.

There’s (now) a better way

I’m so excited about what we’ve launched today. 

We’ve gone full stack with the release of Equals Warehouse.

It provides an alternative path—a better, more affordable, pain-free one.

Equals Warehouse solves the most daunting problem I felt at Intercom—the one I had no clue how to solve. 

How? It’s as simple as 1-2-3:

1. We connect to your data for you–Stripe, Salesforce, HubSpot, and more.

2. We clean and sync the data for you–in your Equals Warehouse powered by Snowflake. 

3. We run the pipeline for you—no need to hire a data engineer. 

And there’s one more thing…

Starting next week, we’ll release the first of many Equals transformation modules. We call them ‘Blueprints.’

Blueprints are out-of-the-box transformations designed specifically for SaaS companies and tailored to the specifics of your business. 

You don’t have to manage the transformations or think about what the right schema should look like. We take care of that for you.

So now, with Equals, you suddenly have one tool that can do it all. 

Finally, you can get clean data and start making sense of your business… at a fraction of the cost and the time it normally takes. 

Of course, on top of it all is the most familiar and flexible data tool on the planet—a spreadsheet—that makes it dead easy to build and distribute beautiful BI-grade dashboards. 

Equals is the only data tool that lets you go from raw data to beautiful dashboard– all in one place.

Getting to this point has been one of the most fascinating parts of building Equals. 

It’s why great companies like Notion, Intercom, LaunchDarkly, Attio, Descript, and many others are making the switch and trusting Equals. 

Come join them. You’ll be in great company.

To the uninitiated, counting Annual Recurring Revenue probably looks something like this:

Rule #1
Is it recurring? If yes, count it.

Rule #2
Refer back to Rule #1.

However, those of us who have dealt with the edge cases surrounding different pricing models and products in the market, and know exactly when to start and stop counting ARR, understand the situation is more nuanced.

Below are some guiding principles for what we think counts as ARR, and what doesn’t so you can develop the right methodology for your business.

If it walks like ARR, talks like ARR

Of all the variations to consider, you can boil things down into four categories:

Frequency
How often the customer will be billed (e.g. recurring vs. non-recurring).

Source
What the customer is being billed for (e.g. product or service).

Timing
When to start and stop the clock (e.g. trial periods, cancellation requested vs. end of the billing period).

Adjustments
Exceptions and concessions (e.g. discounts, coupons).

Let’s walk through each with some of the most common examples below.

Frequency

The basic question to answer here: is the charge recurring or non-recurring?

For traditional SaaS companies that charge a fixed fee per license, this is an easy yes but things are less clear for usage-based pricing.

The latter has become more popular with the rise of AI and as traditional SaaS companies shift to hybrid pricing models that charge both a flat fee per seat, along with variable pricing based on a product usage metric (e.g. tokens used, API calls, tickets resolved, contacts synced to CRM).

I think the short answer to the usage-based pricing question is yes, this revenue is recurring. Take the example of an AI company that charges its customers based on tokens consumed. In any given month, a customer’s usage on the platform might increase (more or growing workloads) or decrease (deprecating workloads) but the customer must continue to consume tokens each month to get utility.

The harder, but different, question is knowing how much a customer’s usage varies from period to period. I think some conflate the challenge of predictability with the question of whether the revenue is recurring or not. The predictability question feels similar to the challenges PLG businesses face where the majority of customers are on month-to-month plans. However, few folks debate the validity of counting revenue from month-to-month customers as ARR.

This distinction is worth reflecting on. It’s less about should this count as ARR vs. increasing the predictability of the inputs that drive customer usage and, ultimately, revenue. 

For more resources on improving predictability, have a look at our posts on building a better ARR reporting system and Snowflake’s forecast transformation.

Source

Revenue source can either be a product or service offering. 

Based on what we discussed regarding frequency above, we know that one-time charges of any kind would NOT count as ARR. This applies to both product and service-based charges.

While one-time service charges like onboarding and implementation are more common, some companies offer continuous services for things like premium support.

You can certainly make a case for including these in ARR but we would advise against doing so. The reason is these revenue streams will have very different margin structures which translate to different valuation premiums.

If you do include recurring service-based charges in ARR, just make sure you have a method for breaking out product vs. services ARR and watch out for mix-shift changes. Doing so is important because when it comes time to fundraise, investors will value these revenue streams differently.

Timing

When to count a customer

Knowing when to start counting a customer in your ARR calculation varies based on the purchase experience and go-to-market motion.

On the purchase experience, the biggest question comes down to whether trials are offered. In PLG businesses, two-week free trials are common while enterprise motions may offer a paid trial or “proof of concept.”

In these evaluation periods, we recommend erring on the side of conservatism and not counting the customer until they’re successfully through the trial phase. 

For enterprise businesses, this means waiting until the longer-term deal is complete which typically aligns with comp plans design and quota retirement for reps.

As an aside, many companies have a “grace period” for enterprise deals that starts the clock on ARR based on the contract signature date, rather than the contract start date. This is typically limited to deals where the contract start is within ~two weeks of the signature date. Doing so better aligns Sales bookings, or contracted ARR, with Finance’s view of ARR since most deals sign on the last week or day of the month.

For PLG companies, we often recommend adding one more requirement which is not counting the customer in ARR until they’ve made their first payment. This will help reduce the noise from customers who sign up using fake credit cards that never intended to pay.

When to stop counting a customer

The second component of timing is aligning on when to stop counting a customer: when they request to cancel, or at the end of their contract/billing period.

Normally, we recommend erring on the side of conservatism but in this case, either approach will work. Our advice is to align the methodology here to how compensation is designed for your sales or growth team. 

If those plans don’t yet exist, we’d recommend waiting until the end of the billing cycle to better align with GAAP revenue.

Adjustments

Because nothing’s ever as simple as setting a price and charging customers for it, it’s important to have a policy for handling adjustments like coupons, discounts, and credits.

This may sound straightforward but drawing the line on items that are one-time or have a finite life is more complicated. Support could issue an invoice credit for a poor customer experience, marketing incentivizes new signups with a three-month coupon code, or sales offers a 12-month discount for a multi-year contract.

Our advice is to keep things simple and adjust ARR for everything in the beginning. This is a great way to see the frequency and impact of concessions to list pricing. At a minimum, we advise adjusting for anything that’s applied for greater than one month.

Bringing it all together

Once you’ve aligned on your methodology, share it with your go-to-market partners. 

This will help clarify how their actions will influence ARR recognition and any potential discrepancies to metrics they’re goaled against. We’ll cover how to bridge between those in a future post. 

Until then, we hope you found this helpful. Please reach out with any questions!

P.S. For a comprehensive guide on ARR, including how to think about counting customers and making adjustments, see Chapter 4: Defining Your ARR in our book, The Ultimate Guide to ARR. It’s free.

If you take on any outside capital, you’ll want to send consistent updates to your investors. Need convincing? Here are a couple of obvious benefits.

Staying top-of-mind

By updating investors regularly, you stay top-of-mind. And you want to stay top-of-mind with as many investors as possible. When one inevitably ends up on a call with a potential employee, prospect, or future investor, you want them to remember you. 

Getting help when you need it

You can’t build a startup on your own. You need help. You’ve chosen investors for a reason, often because of their previous experiences, skillsets, or networks. The more you keep them informed on what’s happening in your journey—what’s going well and what’s not—the more they’ll be able to help proactively and reactively. 

You’re going to have asks of your investors. Help recruiting. Help selling. Help on strategy. Help in any way. And what’s the best way to get anything from someone? To give in return.

Giving investors your latest thinking on where you’re at is the best way to get something in return. Said another way, you’re in a much better position to ask for an intro to that next great lead when you’ve been sending them a regular update on how their investment is performing.  

Still not convinced? Here are a few not-so-obvious benefits.

Really know your shit

Sending regular updates is a forcing function to clarify your understanding of the market and your company. Writing is a clarifying process. Having to write a summary of where you’re at means you have to sit down and really think about it.

Give investors a reason to (re)invest

Investor updates are also a critical track record for future financings. Prospective investors LOVE to see how your thinking about the business has evolved and how your delivery compares to what you originally promised. Since so few founders send updates regularly, it’s an easy way to stand out. 

When we raised our Series A from a16z (here’s the deck we used), they asked us to forward them every investor update we’d sent up to that point. Thankfully, we’d been thoughtful about what we’d sent and done it without fail. Now, more than three years in, we still send an investor update every month.

What does a good investor update look like? I don’t have a huge sample set since I’m not an investor, but here’s the anatomy of ours and how it’s evolved as we’ve grown. 

Start simple

Our initial updates before launching Equals were simple. There were no metrics to share, so instead, we’d lead with “where we need your help” and then remind investors what we’ve set out to do. We'd then talk about the three most important things about our business at the time:

1. Our team

2. Our product

3. The content we were producing

Layer in key metrics

Once we’d launched, we started including key metrics, but our updates still aimed to cover the fundamentals of our business:

1. How the business was performing

2. Key updates on our team

3. What product we were shipping

4. How we were bringing the product to market

5. Where we needed help

If you're unsure how to communicate your metrics effectively, here’s our take on the do's and don'ts of data visualization.

Pare back as you grow

Now that we’re further along, we just share key metrics and commentary. We don’t often include requests for help (unless they’re broad, like an ask to support a launch) as they’re sent to too large a group. Instead, we send specific requests to relevant investors directly.

We occasionally send more thorough updates for big moments. For example, when we (in)famously tried free, we sent a much longer contextual update to investors.

Despite their evolution, the overall spirit of our updates remains to keep investors aligned and excited about what we’re working on and where we’re going.

Whether we have a good or bad month, Ben and I still update our investors every month.

I encourage you to do so, too.

P.S. Investors care about your ARR, which can be a real PITA to report on. If you need help getting your ARR reporting into shape, we can help, just like we did for Descript.

ARR reporting is hard.

I’ve found that the challenges come in two forms: defining the logic and building the reporting that sits on top of that logic.

Every company has a unique combination of product offerings, pricing models and go-to-market strategy, which is why there are so many edge cases to consider when defining ARR logic. We’ve detailed those considerations at length in our book, The Ultimate Guide to ARR, so I won’t get into all of that detail here.

But the challenge doesn’t end with defining ARR. It continues through to building reporting and surfacing insights on ARR performance. Talk to any finance leader and they’ll tell you the 100 ways they’ve attempted to standardize the process and how they’re still on a quest to perfect it.

There’s no doubt that ARR reporting is a never-ending journey. But in this post, I’ll share how we’ve templatized the process for our customers at Equals and hopefully provide some inspiration for how to do this at your company.

Flat file nirvana

At Equals, we like to say every analysis starts and ends in a spreadsheet — there’s just a messy middle step to get the data in a format where you can run your analysis.

ARR reporting is no different. The challenge is transforming invoice or subscription data into a clean dataset for calculating growth, retention, and pacing.

I refer to this output as the “ARR flat file.” This flat file should return a record for each customer, along with their ARR for every day they were active on your platform.

Once you have that, it’s easy to layer on definitions for the ARR component parts (new, expansion, contraction, churn) or firmographic data (segment, industry, acquisition channel). 

This sounds easy. So what makes ARR reporting so hard?

Challenges with the ARR flat file

Here are some of the most common macro-level challenges I’ve seen to get this done.

No GAAP definition

There is no GAAP definition for ARR — and because every company has their own idiosyncrasies in go-to-market strategy and pricing, there’s an infinite number of ways to calculate ARR. 

Most Finance folks don't know SQL

Most finance and ops folks are great at extracting insights and telling stories with data but they’re usually not proficient in SQL. This forces a partnership with a technical counterpart which usually leads to longer feedback cycles or details being lost in translation.

Bobby makes a great case for why every analyst should learn SQL in this post :) 

The last mile problem

Getting things to 90% or 95% accurate is usually pretty straightforward. The really hard work starts from there as you get deeper into edge cases and workarounds in the logic. Unfortunately, there’s no margin for error — and no one will trust the data until it’s 100% accurate.

How we solve for these challenges

Our team has accumulated a lot of experience building these both in-house and externally for dozens of customers. These experiences have given us insight into the challenges faced by all types of companies, from startups to those with over $100M in ARR.

With each buildout, we discover a new consideration or edge case that can be applied to the next customer. What this looks like in more tangible terms is a canonical SQL transformation that incorporates all of the learnings from prior builds and is fully customizable to each business’ needs. 

This approach speeds up the feedback cycle of defining ARR from several months to a few days — and solves the last mile problem. 

Core ARR reporting units

Once you’ve solved the underlying logic, the next step is figuring out how to visualize the data.

This part of the exercise is equally challenging, but for a different reason. Few people will take the time to educate themselves on the intricacies of your ARR logic, but EVERYONE has an opinion on how to format a chart or table. 

Getting this right means solving for different levels of granularity. How can you package the data in a clear and concise way that satisfies the high-level needs of the executive team while still providing enough detail to inform ad-hoc analysis at the analyst level?

Dashboard design principles

Our approach to ARR dashboard design revolves around two core principles. 

First, the organization of the data within a dashboard should start with the highest-level insights and transition to more granular detail as users scroll down.

Second, the data across dashboards should be mutually exclusive and collectively exhaustive. This makes it very clear for users to know what dashboard to pull up to get specific questions answered.

This is a process of continuous improvement, but we’re really excited about what we’ve built.

Let’s walk through the three reporting assets we’ve templatized for our customers, all of which sit on top of the ARR flat file we covered in the prior section.

ARR Build

The ARR Build shows how total and net new ARR and customer volume is trending over time. 

We start with a few hero metrics to make it clear where the total business stands today and then quickly move to a table breaking out the component parts of net new ARR (new, expansion, contraction and churn) that contribute to changes in the top-line number.

From there, we break out both the total business and net new component parts with a side-by-side view of ARR and total customers + ACV. This makes it really simple to see if changes in ARR came from fluctuations in customer volume or the ACV of those underlying customers, and helps with calculating key ratios like expansion rate, churn rate etc. 

Total business example:

Net new component part example:

If more digging is needed, users can navigate to the underlying workbook to look at different time intervals (daily, weekly, monthly), time periods (last 12, 24 or 48 months) or filter for the specific customers contributing to the performance trends.

View our ARR Build

ARR Pacing

The inspiration behind the pacing dashboard comes from the panic we experienced working at more enterprise-led companies. 

Somehow the last month, week, or even day of the quarter always contributed a huge amount to the period’s overall ARR. This made it really difficult to understand if we were ahead or behind where we should be at any given time.

To better understand this, we calculate the median daily performance build for a specific unit of time (month, quarter or year) across the entire history of the business (stripping out any outliers as needed). 

The end result is a clear representation of where performance should be at any point in time, plotted against the period-to-date actuals.

Here’s an example of this view by quarter:

We can display these forecasts at whatever level of granularity is needed and can seasonally adjust the historical comparisons where necessary (only compare Q4 performance against prior Q4s).

It’s also easy to create views comparing different forecast scenarios or document forecast accuracy for prior periods.

View our Pacing Summary

Cohort Summary

No ARR reporting suite is complete without a cohort dashboard.

Here we have views for net and gross revenue retention on an ARR and logo basis. Similar to the ARR Build, users are able to change the inputs for the duration of each calculation (e.g 3-month NRR vs. 12-month). This makes it easier to spot performance trends ahead of time.

Here’s an example of the uncohorted views: 

And another of the cohorted view: 

Finally, we end with one of my favorite visualizations: ARR and customer builds cut by customer acquisition year. The inspiration came from page 68 of Slack’s S-1 filing back in 2019 and is a great way to show the power of compounding NRR.

View our Cohort Summary

I hope this helps you in your ARR reporting journey, preparing you to better run your company, forecast your business, and impress your investors. 

If you have any questions on what we’ve built or need someone to brainstorm with, please reach out to me at chris@equals.com!

Telling investors your company's story is arguably a founder/CEO’s most important job. 

It’s the difference between running a competitive funding process and falling flat. 

It’s the difference between raising nothing and raising what you want. 

But how do you tell it?

There’s a subtle framing I’ve found that’s helped me. 

I’ve shared it with a few founders, and they’ve also found it helpful. 

So, I’ll share it today since it’s fundraising season. 

This post isn’t about the process of raising money. In other words, it’s not about how to reach out to investors, or how to run a process, or how to make the deck(s). We’ll cover those in upcoming posts. 

This post is about how to craft the story you tell to investors.

It’s the most important first step in any fundraising strategy. 

And it should start well before you even think about raising.

Let’s start with the wrong way. The way many founders often approach investors. 

Starting from a place of, “What do investors want to see and hear?”

Or, in other words, “What in an investor's eyes will make my business fundable?” 

The question isn’t completely off-base. It makes complete sense. 

But it creates a few behaviors that don’t work for you as a founder. 

First, it puts the investor in the driver’s seat. It makes them right about your business.

It puts you on the back foot as you search for answers and ways to think about your business outside your business. 

You’re letting the investor, in your mind, dictate whether or not your business is fundable. And so you’re giving away your power and your confidence. 

Here’s how this starts to manifest: 

1. You spend a lot of time looking for templates when putting your pitch deck together. For example, start with the problem slide, then the TAM slide, then the team slide, etc…

2. You spend a lot of time talking to other founders, trying to get intel on what investors think and how they think your business.

3. You conform to using a set of metrics you don’t even use to run your company,

Sound familiar? You’re probably operating extrinsically, thinking about how a prospective investor will see the business and letting that dictate your actions.

Here’s a different strategy and what I believe works in your favor when fundraising. 

Start with what you most believe about your business. 

Ask, “Why do I believe my business is massively fundable?” 

Do the hard work to answer those questions for yourself. Truthfully. 

Internalize that story for yourself. 

And run the company that way.

That’s the hard part. 

I think of this strategy as the “intrinsic method.” 

It puts you and the company first. It puts you in the driver’s seat. 

You’re simply telling the story of your company to investors. Because you’re the expert on your company, you know it better than anyone else. 

Here’s how you’ll know you’re on this path; 

1. Putting your pitch deck together is easy. It all flows naturally from the story you know about your company.

2. Problem statements, customer anecdotes, and other sections of the deck are story-based, not exercises in regurgitating numbers or facts. The story builds like a crescendo.

3. You can assemble a first draft of materials for investors based on existing dashboards you use to run your company. 

Finally, preparing for fundraising is a lot about mindset. And for that, I’ll leave you one final comment:

Remember that this is your company. You’re the founder. You’re the one putting your life into building the company. 

You’re selling a piece of your hard work! 

Even if it doesn’t feel this way throughout the process (it can be hard amidst rejection), keep reminding yourself that you’re not trying to convince someone else that you’re fundable. You’re seeing who’s going to be lucky enough to invest.

Given the perfect pricing model doesn’t exist, it should be no surprise that optimizing how you price your SaaS product requires constant iteration. It’s a massive growth lever. But there’s a rub. The longer you wait to pull it, the harder (and more painful) it gets. Don’t sit back, hoping things will improve on their own. Be proactive.

That’s why we changed pricing three times in one year. After sharing what we learned along the way, many people asked how we arrived at our current pricing model, specifically, “Did you A/B test it?” 

When you want to change your marketing site, product, or pricing, most people instinctively want to run a split test. It feels safer. But in reality, for most early-stage companies, split tests take far too long to run to reach any level of statistical significance. They also add complexity.

Split testing the wrong change can cost more than making the change itself. Simply put, A/B testing is not for startups, especially not for pricing.

“So, if you didn’t test it, did you just change it?” 

Yes, that’s exactly what we did. 

But that doesn’t mean you should never consider running a split test. In our experience, the right approach will depend on whether you’re early or later-stage.

Testing as an early-stage company

When you have a limited runway, you must move and decide fast. You probably don't have the luxury of waiting for an A/B test to reach statistical significance. This is especially true when understanding the impact of a pricing change. You need to let cohorts on new pricing age to understand the impact on expansion, contraction, and churn over time. You need the full picture to make an informed decision.

For example, in the early days of Intercom, as the product evolved, we changed pricing to better monetize the additional value of the new product we’d shipped. In doing so, pricing became more complex, which led to customers being confused about what they needed to buy. This resulted in people buying more than they needed to start, artificially increasing the initial Average Revenue per Account (ARPA). While the change looked great early on, the true impact of the changes eventually showed up in increased contraction and churn months later, detrimentally impacting Net Revenue Retention (NRR). 

“But, do you really need to wait that long?”

Well, unless people flat-out stop buying and the impact of the change is obvious, yes, you should wait. And here’s the thing: what would normally take at least three months to learn if you just made the change could take twice as long if you ran an A/B test. You’re better off learning that a change is bad sooner rather than later. And if it turns out to be a good change, happy days. 

“But, if you don’t run an A/B test, how will you actually know it’s better?”

At an early stage, you should be looking for big wins, not small optimizations. To know if a pricing change is 5% better, you probably need an A/B test. To know if it's 2x better, you probably don't.

Additionally, qualitative feedback from leads and customers is a strong signal in helping understand the impact of pricing that’s often hard, if not impossible, to see in the data. As we’ve shared in the past, we constantly heard people being unhappy about being forced to buy seats they didn't need. We decided to remove this requirement with our latest pricing model. We didn’t need to run an A/B test to know we achieved one of our goals there.

This is easy when you're adding tens of customers a month. It's very hard if you're adding hundreds or thousands. There'll be too many different clusters of things people say. It’s often too hard to see the forest through the trees.

Testing as a later-stage company

But what if you have the volume necessary for a split test to reach statistical significance quickly? What if you have much more revenue at risk with any pricing change? Surely, then, you should A/B test it? Maybe. It depends on your risk tolerance and whether you have the resources to set up a test. If you go down the testing route, there’s one thing to consider that will impact how you set up your test.

There are typically multiple people involved in a B2B software purchasing decision. As a result, testing pricing can be problematic because you risk stakeholders from the same company seeing different versions of your pricing. Your champion may see the A variant, and the decision maker may see the B variant. Not only does this have the potential to create confusion, but it can also erode trust, something you want to avoid at all costs when establishing a relationship with a potential customer. You can still run a split test; you just need to set things up differently—cue example.

Toward the end of my time at Intercom, I worked on a project to overhaul pricing. At that time, we were north of $100M in Annual Recurring Revenue (ARR). There was a lot of revenue at risk with any pricing change. To mitigate the risk of lost revenue, instead of rolling out new pricing to all new customers and to avoid the problem mentioned above, we tested new pricing in select countries. The US, our biggest market, was our control group, and a handful of other “lookalike” countries made up our test group. It was an imperfect test, but it allowed us to mitigate what could have been a costly change for the business.

None of this is a perfect science. But relax, it doesn't have to be. 😅 You know a lot about your business. You're consuming a million data points daily from sales calls, your customer-facing teams, and all the data you look at. It’s OK to make a gut call. You might surprised that it’s right more often than not.

The flip side is that if you feel like you broke something, you have to act on that, too. This doesn’t just apply to pricing. It applies to your product and your funnel as well. That’s why we recommend looking at your key metrics every single day.

So, just as there’s no perfect pricing model, there’s no perfect way to test pricing. If you can, our advice is to make the change, measure the impact by comparing cohorts on new pricing to cohorts on old pricing, and decide whether to keep iterating based on that. It’s imperfect, but it will allow you to learn faster.

No matter what approach you take, here are a few things we’d advise for any pricing project:

1. Know what your goal is.

As with any change you’d make to your website or product, be clear on what success looks like. It might not be increasing revenue. As we mentioned above, it might be to eradicate customer complaints and increase satisfaction. Clearly define and align on your goal upfront so you can correctly assess the impact of the change.

2. Isolate the change as best you can.

To the best of your ability, don't change pricing alongside, or in quick succession with, other big things. While A/B testing is often unnecessary, you're not helping yourself if you simultaneously launch a new version of your product with new pricing. It’ll be hard to tease apart which change impacted what.

3. Asses impact on the full funnel over time.

Pricing changes often have a domino effect. They can impact things you don’t intend them to. As with the Intercom example above, where new pricing increased ARPA but negatively impacted NRR, what might look like a win might not be when you consider churn later.

4. Make changes only for new customers first.

Most people hate change. This is particularly true when it comes to pricing. It’s also work for you to migrate customers on older pricing models to the current model. So, before you put existing customers (and your team) through that, ensure your changes have the desired result with new customers.

Like this post? You might enjoy this one, too; Your pricing page has one job.

Earlier this year we faced a problem – people loved our product but our go-to-market was all over the place. In one week we’d talk to a pet hotel that wanted to report on bookings, a radio station that wanted an automated spreadsheet tallying song requests, and a swag manufacturing company that wanted to report on its paid ad spend. Because our marketing spoke to everyone, we were speaking to everyone. And so our sales processes were all over the place, conversion was all over the place. It wasn’t clear where we should focus.

Our instinct as founders in situations like this is to put more in. If only we could be a bit smarter, get the messaging just right. If only we could perfectly describe our value proposition. If only we could get rid of every imperfect word on the homepage. If only we could do all that, and do it perfectly, we’d work it out.

This whole experience felt like walking a tightrope. That it all came down to getting everything just right. But that’s not how companies actually win. One of the things I took from the early years at Intercom is just how many things can be broken, while still winning. This isn’t a knock on the company. Talk to anyone in a successful company and they’ll tell you what a mess it feels like on the inside. Winning is more about getting a few important things directionally right than it is about avoiding mistakes.

When I gave myself some space to reflect on the situation I thought it shouldn’t be this hard. We’re answering the wrong question. The thing about starting a company is you can literally do anything. You’re not part of a corporate hierarchy that dictates the bounds of the problems you can solve. You get to make every decision. No one is coming to save you. It’s scary. It’s empowering.

If things are too hard and if you can do anything, that means you can change the question you’re answering. The question we were trying to answer was how do we reach and onboard anyone who has use for a next-generation data-connected spreadsheet. In retrospect it’s an absurd question to spend time on. Recognizing this let us refine the question. Who gets the most value from Equals today? Who’s in a position to be an early-adopter? Who are we best suited to reach?

Following this set of questions led us to verticalize our go-to-market. Instead of pitching a next-generation spreadsheet for anyone that wanted to query, analyze and report on data, we started pitching Equals as an ARR reporting solution for startups. This is as specific as it gets. We were well-positioned to reach and be an authority to this audience, starting with our ARR book. And early-stage startups are well positioned to be early adopters. We finally found a repeatable and scalable go-to-market motion.

And so if you feel like you’re walking a tightrope, trying to get it just right and then it’ll work. If you’re grinding without seeing results. Ask yourself whether you can change the question instead.

Equals, the next-generation spreadsheet, is about as horizontal as it gets. Consider who uses a spreadsheet and for what. The combinations are infinite. 

From the moment I put together the first Equals pitch deck, nearly everyone told us how hard it would be to build and take to a horizontal product to market.

We were warned. 

“Makes sense. But we’re different.” So I believed.

Turns out this was a lesson I’d have to live. 

Thus began a multi-year journey to internalize what it means to be horizontal vs. vertical, understand the implicit tradeoffs, and grapple with the challenges of bringing a horizontal product (and our massive vision) to market.

In the hopes of helping other founders grappling with the same question, here’s our journey. 

The allure of being horizontal is real

We set out to build Equals in early 2021. Our vision was (and remains) to build the next spreadsheet. Born from a frustration with BI tools, data notebooks, and the wide range of out-of-the-box reporting solutions, our first launch struck a chord. 

Our pitch and initial wedge into customers: We’re a spreadsheet natively connected to data—the only spreadsheet connected to SQL and the most commonly used SaaS tools for automating analysis. 

We started to onboard customers and tried various go-to-market motions. As we talked to folks, it became clear that people loved the premise of Equals. They just wanted more of it. 

But they became the confusing part. Who were they? 

When you tell people you’ve got the next-generation spreadsheet connected to data, that means many different things to many different people.

On any given day… 

…we’d talk to a head of product who loved the idea for Equals… if only our Mixpanel and Amplitude connector were a little deeper. Oh, and maybe make the spreadsheet less spreadsheet-y. 

…we’d talk to a head of finance at a startup who sometimes knew SQL and loved the spreadsheet. “But for this to really work, I’ll need a Netsuite connector.” And they’d also ask for more parity with Excel.

…we’d talk to a Shopify business owner running their entire business out of spreadsheets. Could our Shopify integration put their reporting on auto-pilot? How about templates?

…we’d talk to a sales ops person complaining about HubSpot or Salesforce reporting. “I could definitely see myself building sales funnels, or attainment reports, or customer health dashboards.” 

…we’d talk to an owner at a demand generation agency. Our Google Analytics plus Facebook Ads plus Google Ads connector would seemingly automate their client reporting.

…we’d talk to a founder who viewed all of our connectors as an alternative to buying a data warehouse, an ETL tool, and hiring a data engineer. We could save them hundreds of thousands of dollars! 

…we’d talk to a head of engineering. Their customers would love Equals. “Could we white-label it and embed it into our product?”

…we’d talk to a BI leader at a public company. Surely, we can help with their self-service analytics initiative. 

And then there was a very long tail of others: managers of cleaning companies, principals of school districts, managers at pet resorts, DJs at radio stations. They all use spreadsheets. Can we automate them?? 

Dizzying.

At the same time, we were eager and hungry to grow, show traction, and get our foot in the door with as many businesses as possible. “Do things that don’t scale!” right? 😅 

So we dove deep into all sorts of connectors—we built some wild ones that I’d never tell you about :). We also presented all sorts of analyses for folks, all in the spirit of getting traction, growth, and learning. 

But it became wholly confusing and wholly unscalable. 

How we sell Equals came to a head. Every deal had a slightly different buyer, at a slightly different stage company, with a slightly different use case. The bespoke nature of every deal meant they went closed-won seemingly randomly. Growth was unpredictable. Our pipeline reviews were an array of discussions—each deal ran completely differently, and each path to purchasing was different.

In this dizziness, we finally realized we had to do something different.

Everything became clear once we verticalized

As I tell you where we are now, it’s tempting to think we should have started here. But that’s not how startups work. Everything we went through above, we had to go through. We had to talk to a dizzying array of leads. We had to onboard wholly unscalable customers. We had to feel the pain of what wouldn’t work in order to find what seems like it just might. 

So, as it dawned on us that we could no longer sell to and onboard such a wide variety of customers - not to mention acquire them all at scale - we started to look through our customer base and find the use cases where Equals worked best.

One stood out. 

We had onboarded the finance team at Descript. 

They came to us looking for help with some of their core ARR and SaaS metrics reporting. Chris and I knew this problem deeply. We’d lived it at Intercom. We’d made all the mistakes (sounds like a pattern, right?) to land a powerful reporting solution there. So, we built Descript exactly what built done at Intercom—from data transformation to templates—and they loved it. 

As we evaluated all the different use cases we’d covered so far with Equals, this felt the most compelling for several reasons. Equals the product was uniquely positioned to be a 10x better solution relative to anything in the market - ARR and SaaS metrics reporting requires flexibility. Plus, Chris and I could leverage our reputations and deep subject matter expertise to help market the solution.

We turned around and offered the same but tailored ARR reporting solution to a few of our other customers. It turned out they wanted the same thing, and once they got it, they also loved it. And so, from here, we decided to make “ARR Reporting” a focus. Below is a slide from our Board deck in Q1 of this year, sharing our shift in focus from a horizontal go-to-market to a vertical one.

Yet, we didn’t feel confident enough to bet the homepage (our most important piece of real estate) on this solution. So, we put up a landing page to showcase why you should use Equals for ARR reporting. 

Then, we wrote a book, The Ultimate Guide to ARR, to build awareness, cement our position as subject matter experts, and generate leads (of course). 

More than a thousand downloads later, we made the use case way more prominent. We rearchitected the website, making use cases the first item in the primary navigation and the first section on the homepage below the fold. It’s the main message you see when you visit our site. You can’t miss it.

Focus on doing one thing really well

I suppose this is what it means to verticalize. We’ve reduced and honed in on the use cases we know we can sell, actively turning away folks we no longer want to sell to. Now, we only talk to those we know can serve excellently.

That might sound crazy, but it’s not. I can now, with complete confidence, tell you that Equals is (at least) 10x better than anything in the market for venture-backed SaaS companies with $0.5M-$50M ARR who use Stripe, Hubspot, or Salesforce. If you’re a Head of Finance or Founder at one, we should talk.

As we’ve (re)focused, we’ve seen growth accelerate and become more predictable. When a lead comes in looking to do ARR reporting, the sales team knows exactly how to run the process, I know we’re going to win it, and I know the customer will have an excellent onboarding. 

We’re no longer dizzy. What to do next is crystal clear.

Land first, then expand

On reflection, going vertical feels a bit counter to the nature of a founder. We’re commended for having as big and grand a vision as possible. Verticalizing, in some ways, feels like giving up a part of that vision. Yet most things in life are paradoxical. The bigger the vision, perhaps the smaller the first step needs to be. 

The strategy now is to build the business use case by use case. We’ll continue to nail and land customers for our ARR reporting and SaaS metrics use case. We’re already seeing those customers pull us into new use cases. Sales funnel reporting with Hubspot and Salesforce is the next obvious one. As we continue to build and land with the best reporting solution for SaaS companies, we can expand beyond SaaS.

The beauty of Equals is that we still have a horizontal product as the company's foundation. It’s a tool, a spreadsheet, with all the primitives necessary to build world-class reporting for any business. The breadth of folks coming to us in the early days is a strong signal of the opportunity ahead of us; we’ll be taking it one step at a time.

This is an adaptation of the Visualizing Metrics topic in The Guide to SaaS Metrics – a free, ungated resource for anyone looking to master their understanding of the metrics that define and drive SaaS businesses.

So, you've crafted an analysis that you believe will be a game-changer for the business, and you can’t wait to share your insights. 

Before you jump in, remember this: Just because you’ve been neck-deep in the data doesn’t mean that everyone else has. 

You’ve already internalized its most important beats: The crucial insights, how you got there, what’s important, and what’s a dead end. 

Your audience, on the other hand, is coming in cold. What looks like a goldmine to you will read like hieroglyphics to them—unless you take the time to thoughtfully organize and visualize your insights in a way that will lead them to the right conclusions. 

Here are some simple principles to ensure your audience reaches the “aha!” moments.

DO: Start with your story 

Your “story” is the unifying mental structure that will help your audience to internalize your insights and understand how they relate to each other. Without it, all you’re left with is a scattered series of data points that are difficult to interpret and easy to forget.

Here’s a litmus test: When assembling a deck you should be able to read just the headlines of each slide to grok the “so-what” and its supporting narrative. In fact, it’s good practice to start by storyboarding with headlines and empty slides before you add a single chart. Here’s an example of “action titles” in practice in our Series A data deck.

When creating a dashboard, on the other hand, you’ll want to follow a logical flow that helps your audience keep track of the relationship between different sections. 

Taking a sales pipeline dashboard as an example, you can move from north to south across the pipeline, starting with acquisition (leads), then conversion (closed opportunities), and finally, the bottom of the pipeline (revenue). 

You can also use an executive summary table to “preview” the structure of your story up front, allowing your audience to better absorb the insights as they consume the rest of your analysis. 

After you’ve established the flow of your story, you’re ready for the fun part: creating visualizations. Here, you’ll want to be very intentional about selecting and styling your visualizations to best highlight the insights you want your audience to walk away with. 

DO: Tailor charts for different time scales

As the name suggests, time series analyses track changes to a given metric over multiple time periods. They’re the bread and butter of business analysis and are typically represented using either a line or column chart. 

As a rule of thumb, the more time periods you have, the more useful it is to represent the data using a line chart. 

This makes line charts ideal for plotting metrics like daily engagement. Below, you’ll see how the line chart (left) provides a clearer view of the directionality of the data over time, while the column chart is visually overwhelming.

Use column charts only when you have relatively few time periods (like 12 months in a year). Here, the individual columns are visible enough to provide a useful piece of context – the relative magnitude of data at each period. 

DON’T: Use pie charts

Pie charts have a bad name for a good reason. While they may seem appealingly simple, in practice, it’s extremely unintuitive to gauge the relative size of each “slice” of a pie (which might explain why we’re always overindulging during the holidays). 

On the other hand, comparing the relative height of bars or columns is trivially easy. 

Take a look at the charts below. How quickly can you identify the pipeline stage with the greatest deal value on the left compared to the right?

DO: Use stacked columns to show part-to-whole relationships

Like column charts, stacked columns are useful for comparing relative magnitude. However, stacked columns add another piece of potentially useful context  —  part-to-whole relationships within categories. 

This is useful for breaking down business drivers into their constituent parts, such as marketing spend by channel, deal volume by pipeline stage, or the contribution of each component of Annual Recurring Revenue (ARR).

If you want to compare trends across categories, use a 100% stacked column chart. This approach normalizes the height of each column so that you’re left with just the relative size of the stacks, making it easier to visualize the contribution of each individual series. 

DON’T: Include unnecessary detail

Once you’ve defined your visualizations, you’re ready for a final, crucial step: Stripping out everything that is not strictly necessary to tell your story. 

Why? Stakeholders who open a busy and overly dense page will all too often “check out” of the conversation before it’s even begun. It’s human nature — we have limited time and attention, so things that take a lot of energy to process for an uncertain payoff will tend to be put on the back burner. 

To avoid this, remove everything that isn’t absolutely essential to expressing your visualization's key message. 

Take a look at the chart below. By stripping away axis lines, tick marks, and superfluous data labels, we can immediately grok the most important takeaways — the delta in ARR over the depicted period and whether we’re above or below target.

DO: Use color to your advantage

A powerful way to add clarity to your charts is through the strategic use of color. Especially when dealing with multiple series that could become overwhelming to interpret, color allows you to visually “group” series in meaningful ways that support your story. 

For example, on the left below, we’ve used shades of purple and gray to distinguish organic vs. paid channels, making it clear that the contribution of organic channels to total MQL volume has increased over time. On the right, the use of multiple contrasting colors buries this insight.

We can apply this same principle to selectively draw attention to things like trendlines and moving averages. Here, we’re using contrasting colors to push the raw data's “spikiness” to the background and pull the trendline to the foreground. 

Ultimately, creating effective visualizations is equal parts art and science. As with many things, the best way to learn is to keep iterating and refining until you find what resonates.

Just keep in mind this one guiding principle: Always start with your story, then tailor each visualization to support that story as simply and clearly as possible. 

For all the best practices on visualizing your data—from bar charts to tables and beyond—check out The Guide to SaaS Metrics. It’s 100% free and ungated.

P.S. Every chart in this post was created using Equals' next-generation spreadsheet.

There are many definitions of startups. One of them is this — startups are a truth finding exercise. Peter Thiel's spin on this is that all successful startups are based on a secret about the world. The theory goes, if your company’s truth had already been discovered, someone would have built it already.

Unfortunately startups are not as simple as uncovering a single important truth. The whole journey is a truth finding exercise. Balaji calls this The Idea Maze. I think of it as The Idea Map. Once you have a kernel of a thing that’s working, you have to find more people who want what you’ve built. You have to make them want it more. Make them use it more. Eventually you need more people to want it. It’s truth finding missions all the way down.

When you start on your first truth finding missions you don't have any data to guide you. You have a hypothesis about what’s true. From there you have to take a shot in the dark, an educated guess at making something people want. You build something. You talk to customers. You iterate.

Then one day, suddenly, you have customers, you have revenue. You know how many people are using each feature. You can break your funnel down into discrete steps. You have charts going up/flat/down. You have data.

Surely this – data – is the ultimate truth finding tool? We can now know what’s true. No more guessing. No more darkness. Certainty. Light. Maybe we can even use science to be even more certain?

Enter: A/B testing. A/B testing is tech’s name for a Randomized Controlled Trial. You have a control (A) and a test (B). We split the population in two, give some portion of them the control (A) and the remainder a version with just one variable changed, the test (B). With some basic statistics, we can be certain that any movement in the numbers is significant and causal.

The way A/B testing usually first enters this picture is this. You have some hunch that something is broken, that it could be better with a change. It could be anything: copy on your marketing site, how your signup flow works, how you price your product. Let’s say it’s copy on the marketing site, you think a better headline would convert better. You make the change. Signups go up 20%. And someone says – but how can you really be sure? What if it’s just random? Next time, can we run a test?

No. Next time, you should not run a test.

The most obvious issue with A/B testing in startups is that it's slow. Depending on the magnitude of your desired change you’ll need many thousands of people in each arm of your test. This might be possible in weeks or months near the top of your funnel. As you get further into your funnel, it becomes impossible to get meaningful significance in a reasonable amount of time.

But that’s not the biggest problem. The biggest problem with A/B testing is it offers false precision. The complexity of the questions startups grapple with can rarely be reduced to a single testable variable. If you’re trying to decide whether to focus on market segment A or B, maybe you update your website headline to speak more directly to segment B. You run a test. Conversion drops 20%. Ok, A, it must be, right? Well… what if you’re getting much more traffic for segment A than B today? Or what if it was just a bad headline? What if you are getting enough traffic and it was a good headline, but your signup flow doesn’t speak to this segment? I could go on.

If instead you went and spoke with 10 customers in segment A and B, and those in segment B told you that really their pain is so great. They’ve wanted your product forever. They need it. And if everyone in segment A was lukewarm. Would anyone question that this was just random? That next time you should be more scientific?

That’s what’s funny. We are comfortable dealing with qualitative data in soft, intuitive ways. But how the most impactful individuals work with quantitative data is similar. Through years of building a company, founders, leaders, and the early team build a deep model of their problem space. When you see a metric spike, after making a specific change, you have a much better than random shot at determining its significance.

There will be mistakes. But the rapidity of decision making when you treat quantitative data as just another signal is so much higher that it doesn’t matter. Think of it this way: A/B testing might let you make one “correct” decision once a month, without it even if you make 2 wrong decisions and 3 correct decisions a month, you’re ahead. And I bet the odds are much better than that.

Ostensibly, this article is about A/B testing. But it’s not really. What it’s really about is trusting the instincts and model you’ve built of your business. And so when someone tells you to run a test next time, you can confidently tell them no. A/B testing is not for startups.

In traditional business models, products are developed, manufactured, marketed, and sold to ensure profitability for each sale. Imagine you're selling a car for $30,000. The moment that sale is made, you know if you've covered your costs—production, transportation, marketing, and even the salesperson's commission. It's a straightforward transaction. It’s either profitable or not.

But in SaaS, when you acquire a new customer, you're stepping into a zone of initial unprofitability. Upfront costs to acquire that customer—marketing spend, sales team efforts, and onboarding processes—typically exceed the revenue from their first payment. This is where the Triangle of Despair begins to take shape.

This dynamic almost guarantees that a SaaS business will be unprofitable from the outset. It’s like taking a financial belly flop before you can swim in the profit pool. What’s more, the rapid growth we all crave often exacerbates those initial financial setbacks. As you acquire new customers at an accelerated pace, the associated acquisition costs quickly pile up, putting a strain on cash flow. Fantastic. 😅

However, the magic of the SaaS model is that it aligns customer spending with realized value. Compared to a traditional business model, customers pay less to start but keep paying on a fixed schedule as they continue to use the product. Many will be willing to pay more as they realize more value from the product and expand their usage (expansion), creating a fair value exchange.

This recurring revenue model offers a path to higher long-term profitability. Once initial acquisition costs are recovered (CAC), retained customers can be significantly more profitable (LTV). The potential for higher future returns allows the business to invest more in acquiring customers upfront, knowing they’ll be able to reinvest incoming profits into driving growth and increasing market share. That sounds better. 😄

A SaaS business must effectively manage all these dynamics to escape the Triangle of Despair. Earnings are a poor indicator of their profit potential in this pursuit of growth. Instead, we need specific metrics to help investors and operators truly understand the viability of a SaaS business.

We wrote The Guide to SaaS Metrics to help. From CAC to LTV and everything in between, it covers all the metrics you need to measure a SaaS business.

And it’s 100% free.

Take a look and let us know what you think.

But, every time I open LinkedIn, I see a fancy-looking framework for this and a complicated framework for that.

Don't get me wrong. Frameworks are useful. I use some. I've even created my own, like this one for prioritizing product announcements and this one for creating homepages.

However, I find that most frameworks are overcooked. They make something that should be simple, overly complex. In particular, they make the task of developing and communicating marketing strategy look more intimidating than it actually is.

Yet, we feel we need to use them because complexity looks impressive. It evokes a feeling of, "This must be good. Look at how many components there are to it."

My advice: Keep It Stupid Simple.

And the smaller or earlier-stage your business, the simpler it should probably be.

Below is the "one-pager" I wrote for Equals at a time when we needed to reboot our marketing strategy. It answers three simple questions:

1. What problem are we solving and why?

2. What does success look like?

3. What should we do?

This single page was all we needed to align the exec team and start taking action.

If you find this approach works for you, try answering a variation of these questions for everything you do to execute your strategy:

1. What are we doing?

2. Why are we doing it?

3. What does success look like?

I do this for every project. Like our first book, The Ultimate Guide to ARR.

I find it to be a simple, effective way to get everyone on the same page and move quickly from plan to action. Try it with your next project.

If you’ve been following us, you might notice we’ve not done everything we said we’d do. And that’s OK. Things changed, new opportunities emerged, and we adapted.

But I can say we’ve checked off a few big things that have had a significant impact, like rebooting our approach to content and moving this blog to Substack. We've also launched world-class product like Dashboards, which won Product Hunt’s Golden Kitty for Data and Security in 2023. And we’ve shared, very transparently, how we think about and build product—like our new charts.

I'm not suggesting this is a gold standard example of marketing strategy. It's definitely not. My hope is that it simply provides a starting point for those of you who don't know where to start. Or for those of you who, like me, are starting to get "the ick" with all the fancy frameworks out there. 🙊

Marketing can be simple. But, simple ≠ easy.