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Building Blocks of an Efficient SaaS Finance Operations Team




Growth of any kind demands changes in the status quo. SaaS businesses are no exception. As your SaaS business scales, new processes and functions get added.

But while the rest of the organization gets shiny updates and new tools, finance teams are often stuck with decade-old software. Because finance operations are just a back-end function with no direct revenue implications, right?


In my previous post, we spoke about the role of a finance controller in a scaling SaaS business and how they directly impact the revenue cycle. We also enlisted key capabilities finance teams should possess in their arsenal to tackle the challenges faced by a growing SaaS.

In this webinar, Karthik Srinivasan, Director of Finance at Chargebee, shares his insights about driving efficiency in SaaS finance ops teams. I’m summarizing all the actionable takeaways in this post.

With remote teams, a subscription revenue model, and an agile workforce, legacy finance systems and age-old processes just don’t cut it for SaaS companies anymore.

Before we go deeper into techniques to modernize finance operations and how to turn it into a well-oiled machine for SaaS businesses, let’s understand the FinOps function a little more.

The Anatomy of the SaaS FinOps Function

An overall umbrella of the CFO’s duties has two layers: General finance & accounting and financial planning and analysis. The former comes under the purview of finance operations and it consists of Accounts Receivable, Account Payables, and Compliance Regulations.

Structure of finance operations

Accounts Receivable: SaaS businesses require a mechanism to generate invoices and map payments against those invoices on a recurring basis. The Accounts Receivable engine also needs to be integrated with an accounting solution, to facilitate reconciliation.

Accounts Payable: Typically opposite to what Accounts Receivable are, Accounts Payable pertains to handling purchase orders, ensuring payments happen on time, and spend management of all tools and services your SaaS uses.

Compliance Regulations: Compliance requirements can vary depending on the nature of your SaaS business and the geographies you serve. For example, PCI-DSS (Payment Card Industry Data Security Standard) is relevant for SaaS businesses that offer card payments. Additionally, SaaS businesses also have to focus on tax compliance and accounting guidelines like GAAP & ASC 606.

Finance operations teams are expected to work seamlessly across these three buckets, while also keeping a keen eye on important SaaS metrics and Revenue Reporting.

Focus areas for a finance operations team

Growing Pains of SaaS FinOps Teams

When a SaaS business grows, workflows are changed and new tools are often added, but not always with best practices in place. With a multitude of such changes, SaaS finance ops teams have to deal with:

New policies: What used to be ad-hoc changes in finance workflows now become written policies.

Global expansion: When the SaaS ventures into new geographies, multiple currencies, and local tax rules come into the picture.

Complex financial models: Scaling SaaS companies usually experiment a lot with revenue models and pricing.

And most finance teams end up doing this work manually. That’s a dangerous path to tread because this process is error-prone and eventually leads to revenue leakage.

Building Blocks of an Efficient FinOps Team

So how do we plug the revenue leaks and transform the finance operations team into a well-oiled machine for SaaS businesses? We have identified three building blocks to help achieve that.


The first step is to automate the repetitive tasks that are eating into the finance teamʼs time and mind space. The automation should be done in a way that helps a business evolve seamlessly and implement the following changes:

  • Enable data flow between functions when implementing process changes.
  • Serve a higher or a mid/downmarket audience while testing or implementing business model changes.
  • Comply with privacy, taxation, and accounting regulation changes when expanding globally.

As a SaaS company goes through different stages of evolution, revenue workflows get more and more complex. So, we have laid out a framework for four stages of evolution in SaaS finance automation. We will explore each of them in the next section.

The SaaS Finance Ops Maturity Model


With this model, finance ops teams will be able to preempt the requirements for their next stage of SaaS evolution and be equipped for efficiency.

Stage 1: Order to Invoice (<$100K)

This is at the very beginning of the evolution of a SaaS company. FinOps’ key goals at this stage would be to send recurring invoices and ensure payment collection on time, along with basic bookkeeping.  At this stage, automation tools are available at a low cost (or even free). This is probably the best time to experiment with these tools to see what works best.

Stage 2: Order to Cash (<$1M)

As the volume of transactions increases, SaaS businesses at this stage offer more plans with different pricing models. The key focus for FinOps at this stage would be to automate the subscription management with all the upgrades, downgrades, and prorations.

Stage 3: Order to Revenue ($1 to $10M)

When a SaaS company reaches this stage, it is rapidly expanding to new geographies and widening its customer base. The role of finance ops expands to include multi-currency support and tax & payment compliance. Automation at this stage can help recover lost revenue by having a smart dunning mechanism in place.

Stage 4: Lead to Ledger ($10M<)

FinOps’ role at this stage starts right from when a quote is created for a lead and continues until that quote gets converted into an invoice and gets recorded in the books. Not having a sophisticated end to end finance ops workflow at this stage can stunt the organization’s growth. Finance operations at this stage play a key role in reducing the gap between revenue forecast and actual revenue.

Head here to find out the ideal workflow for each of these stages.


The last but the most critical building block is reporting.

The metrics that need to be monitored also need to evolve as with your SaaS business.

At the Order to Invoice stage, tracking standalone metrics such as MRR/ARR will be enough to get a pulse of the business. At the Order to Cash stage, your customer base is expanding and retention is the key to growth. So the FinOps team must keep a close eye on the churn metrics and also on the cash flow to check on the health of the business.

However, as the SaaS business progresses to the Order to Revenue stage, it is important to take the relationship metrics into account. At this stage, LTV, churn rate, expansion MRR, MRR retention cohorts should be tracked to see if the business continues to expand thanks to a higher net MRR that makes up for the customer churn.

In the last layer of measurement at the Lead to Ledger stage, the FinOps teams play a critical role in minimizing revenue leakages that happen across functions. FinOps teams should go over all the workflows and processes with a fine-tooth comb to spot any inefficiencies and leaks. They need to audit everything from the marketing sources to discount campaigns. This requires end-to-end visibility from quote to revenue recognition.

So what does a modernized SaaS finance operations dashboard look like? It must have account summary reports, dunning reports, geography-wise revenue segregation, and metrics that are key to stakeholder management internally as well as externally. In short, this dashboard ties the product, customer, and revenue information together to showcase growth metrics to drive strategy to maximize growth. Check out Chargebee’s RevenueStory to explore subscription analytics and how they can help you grow.

These building blocks will prepare your finance operations team to tackle the changes as your SaaS business rides the growth wave. To get you started, here’s a primer on how you can simplify your SaaS finance operations.

PS: What if I told you that we have compiled a comprehensive list of SaaS finance ops resources in one single place for you? Check our content library exclusive to SaaS finance for actionable insights, expert talks, guides, and more.

A subscription management platform like Chargebee is your partner in growth and helps you manage pricing, automate recurring billing, tax management, reconciliation, and revenue recognition. Understand how Chargebee can help streamline your Finance Operations by scheduling a demo with us today.



We need a new field of AI to combat racial bias




Since widespread protests over racial inequality began, IBM announced it would cancel its facial recognition programs to advance racial equity in law enforcement. Amazon suspended police use of its Rekognition software for one year to “put in place stronger regulations to govern the ethical use of facial recognition technology.”

But we need more than regulatory change; the entire field of artificial intelligence (AI) must mature out of the computer science lab and accept the embrace of the entire community.

We can develop amazing AI that works in the world in largely unbiased ways. But to accomplish this, AI can’t be just a subfield of computer science (CS) and computer engineering (CE), like it is right now. We must create an academic discipline of AI that takes the complexity of human behavior into account. We need to move from computer science-owned AI to computer science-enabled AI. The problems with AI don’t occur in the lab; they occur when scientists move the tech into the real world of people. Training data in the CS lab often lacks the context and complexity of the world you and I inhabit. This flaw perpetuates biases.

AI-powered algorithms have been found to display bias against people of color and against women. In 2014, for example, Amazon found that an AI algorithm it developed to automate headhunting taught itself to bias against female candidates. MIT researchers reported in January 2019 that facial recognition software is less accurate in identifying humans with darker pigmentation. Most recently, in a study late last year by the National Institute of Standards and Technology (NIST), researchers found evidence of racial bias in nearly 200 facial recognition algorithms.

In spite of the countless examples of AI errors, the zeal continues. This is why the IBM and Amazon announcements generated so much positive news coverage. Global use of artificial intelligence grew by 270% from 2015 to 2019, with the market expected to generate revenue of $118.6 billion by 2025. According to Gallup, nearly 90% Americans are already using AI products in their everyday lives – often without even realizing it.

Beyond a 12-month hiatus, we must acknowledge that while building AI is a technology challenge, using AI requires non-software development heavy disciplines such as social science, law and politics. But despite our increasingly ubiquitous use of AI, AI as a field of study is still lumped into the fields of CS and CE. At North Carolina State University, for example, algorithms and AI are taught in the CS program. MIT houses the study of AI under both CS and CE. AI must make it into humanities programs, race and gender studies curricula, and business schools. Let’s develop an AI track in political science departments. In my own program at Georgetown University, we teach AI and Machine Learning concepts to Security Studies students. This needs to become common practice.

Without a broader approach to the professionalization of AI, we will almost certainly perpetuate biases and discriminatory practices in existence today. We just may discriminate at a lower cost — not a noble goal for technology. We require the intentional establishment of a field of AI whose purpose is to understand the development of neural networks and the social contexts into which the technology will be deployed.

In computer engineering, a student studies programming and computer fundamentals. In computer science, they study computational and programmatic theory, including the basis of algorithmic learning. These are solid foundations for the study of AI – but they should only be considered components. These foundations are necessary for understanding the field of AI but not sufficient on their own.

For the population to gain comfort with broad deployment of AI so that tech companies like Amazon and IBM, and countless others, can deploy these innovations, the entire discipline needs to move beyond the CS lab. Those who work in disciplines like psychology, sociology, anthropology and neuroscience are needed. Understanding human behavior patterns, biases in data generation processes are needed. I could not have created the software I developed to identify human trafficking, money laundering and other illicit behaviors without my background in behavioral science.

Responsibly managing machine learning processes is no longer just a desirable component of progress but a necessary one. We have to recognize the pitfalls of human bias and the errors of replicating these biases in the machines of tomorrow, and the social sciences and humanities provide the keys. We can only accomplish this if a new field of AI, encompassing all of these disciplines, is created.


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As Q3 kicks off, four more companies join the $100M ARR club




Welcome back to our $100 million annual recurring revenue (ARR) series, in which we take irregular looks at companies that have reached material scale while still private. The goal of our project is simple: uncovering companies of real worth beyond how they are valued by private investors.

The Exchange is a daily look at startups and the private markets for Extra Crunch subscribers; use code EXCHANGE to get full access and take 25% off your subscription.

It’s all well and good to get a $1 billion valuation, call yourself a unicorn and march around like you invented the internet. But reaching material revenue scale means that, unlike some highly valued companies, you’re actually hard to kill. (And more valuable, and more likely to go public, we reckon.)

Before we dive into today’s new companies, keep in mind that we’ve expanded the type of company that can make it into the $100M ARR club to include companies that reach a $100 million annual run rate pace. Why? Because we don’t only want to collect SaaS companies, and if we could go back in time we’d probably draw a different box around the companies we are tracking.

$100M ARR or bust

If you need to catch up, you can find the two most recent entries in the series here and here. For everyone who’s current, today we are adding Snow Software, A Cloud Guru, Zeta Global and Upgrade to the club. Let’s go!

Snow Software

Just this week, Snow Software announced that it has crossed the $100 million ARR mark, according to a release shared with TechCrunch. The Swedish software asset management company has raised a few private rounds, including a $120 million private equity round in 2017. But, unlike many American companies that make this list, we don’t have a historical record of needing extensive private capital to scale.


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U.S. Fintech Saas Company HighRadius Continues European Expansion with Opening of New Frankfurt, Germany Office




HighRadius, a U.S.-based fintech enterprise Software-as-a-Service (SaaS) company specializing in integrated receivables, announced on Thursday it has continued its European expansion efforts by opening its new Frankfurt, Germany office.

Founded in 2006, HighRadius claims its HighRadius Integrated Receivables platform optimizes cash flow through automation of receivables and payments processes across credit, collections, cash application, deductions, electronic billing and payment processing.

“Powered by the Rivana Artificial Intelligence Engine and Freda Virtual Assistant for Credit-to-Cash, HighRadius Integrated Receivables enables teams to leverage machine learning for accurate decision making and future outcomes. The radiusOne B2B payment network allows suppliers to digitally connect with buyers, closing the loop from supplier receivable processes to buyer payable processes.”]

HighRadius reported it had a 250% increase in bookings, 25 new customers, and a fourfold increase in employees in EMEA in the last 12 months. HighRadius noted that the new office will it to support more customers to accelerate their recovery from the impact of COVID-19.

“The pandemic has increased demand for agile and intelligent credit and collections solutions as organizations focus on maintaining cash flows and strengthening business resilience.”

Speaking about the expansion, Jon Keating, HighRadius’ Vice President and General Manager, added:

“Frankfurt’s position in central Germany makes other parts of the country readily accessible, and its status as the financial center of the country opens up a gateway to a deep pool of talent and relevant partnerships.”


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