The economics of churn reduction: Why analytics for SaaS is so vital for valuation

QuantSpark has worked with PE-backed SaaS businesses since 2016 in providing an end-to-end service offering data engineering, dashboarding, predictive analytics, bespoke tool development, and due diligence support.

We work to make buy-and-build software investment strategies successful by integrating SaaS financial datasets, developing best practice data cubes to enable meaningful revenue snowballs, and then building on this foundational analytics layer to predict and mitigate churn, exploit upsell / cross-sell opportunities, and automate marketing interventions.

The SaaS market will be worth $172bn in 2022

Last year was a big year for SaaS businesses (and their investors), with numerous large acquisitions. Two examples of many: in September 2021, financial software mogul Intuit acquired MailChimp for $12bn and financial services company, Block (formerly Square), planned the acquisition of the well-known ‘pay later’ platform Afterpay for $30bn. Overall it’s estimated that be the end of 2022 the worldwide SaaS market will rise up to $172bn.

Source: Statista, 2021 
* Estimated value

SaaS is valuable due to ARR

SaaS products are accessed over the internet, based on the cloud, and eliminate the need to install complex and expensive hardware or software on premise. For customers this removes the need for costly upgrades and ensures that large upfront licence costs are consigned to the dustbin of history.

From the software provider’s perspective this allows incremental improvements, reduces cost to develop, leverages more sophisticated cloud-based technology, improves interoperability, and most crucially of all creates an extremely valuable business model owing to SaaS subscription-based pricing and Annual Recurring Revenue (ARR).

ARR is sensitive to churn

So, what do investors look for when evaluating the next best company? As you’d expect in 2022, the key for investors is data: clean data which can be easily analysed and explained. For SaaS businesses a crucial metric is Annual Recurring Revenue (ARR) which is utilised by investors to assess the overall performance of the business, as it most accurately exposes the scale and momentum of the company.

ARR = Year Start ARR + (New ARR + Expansion ARR) – Churn ARR

At surface level ARR appears to be an extraordinarily simple financial calculation.

However, this is far from reality as accuracy relies on an intricate understanding of customer and sales data and the ability to link unique transactions to specific revenue categorisations. It’s this decomposition of ARR which allows investors to truly understand the underlying health of the business. Let’s look at a basic example of two companies with the same overall ARR across a three-year period.

Net churn and gross churn

Gross recurring revenue churn is the percentage of total revenue lost from contracts that customers cancelled during the analysis period.

Net recurring revenue churn is the percentage of total revenue lost from canceled contracts, offset by any additional revenue from upsell, cross-sell or seat increases from your remaining customers. 

Company 1 Revenue Snowball: High acquisition combined with low churn

Company 2 Revenue Snowball: Moderate acquisition combined with high churn

What’s the difference?

While both companies have the same $1m growth per year the composition of revenue growth is markedly different: Company 1 has a small level of churn across the three-years and retains revenue from their existing customer base.

Company 2 on the other hand has a much higher level of churn, with lower revenue retention than Company 1. In most cases high churn is attributable to problems with customer satisfaction (or poor product-market fit including value proposition) and/or inefficient sales and marketing activities. This in turn decreases the pool of potential customers for Company 2 for future nurturing programs and reduces the potential for future upsell and cross-sell.

This value-destruction compounds further over time which means in most cases there’s no time to delay churn reduction initiatives.

This example highlights the importance of understanding what lies beneath the overall ARR figure and the role played in generating accurate calculation of churn. Small variances in churn can have a dramatic impact on the overall health of a business.

How should we recognise revenue and calculate churn?

Even if there’s already agreement about the commercial value in understanding and minimising churn, it’s often difficult generate a transparent and logical framework to recognise revenue in a way that withstands financial scrutiny during a due diligence processes.

What’s more, most large consultancies often involved in due diligence refuse to share their “IP” to help explain the workings of their churn calculations which means that transaction advisors find it difficult to scrutinise and trust the churn figures and subsequent valuation of the SaaS business.

Top 5 challenges faced by SaaS businesses in recognising revenue accurately

In our experience, SaaS businesses (especially those that have grown inorganically through buy-and-build strategies) experience a similar set of challenges when analysing recurring revenue:

1. Legacy customers using historic products and pricing models

2. Disagreement in how to recognise revenue across the business i.e. finance and sales use different methodologies and definitions of churn

3. Complex upsell, cross-sell, or up-spin dynamics

4. Inconsistent invoicing between customers including invoice gaps

5. Stealth discounts by sales teams

For example, if a customer cancels their subscription mid-month do you define the point of churn as:

• The moment the customer cancels, or

• When their subscription ends without renewal?

How should you attribute this when calculating churn? To add additional complexity, if you were then to give this customer a partial refund (i.e., 50% back for the half month they didn’t use), how would you categorise this? 

This all might sound academic but in practice getting this wrong could mean you over-reporting churn and in all likelihood this will significantly devalue your business.

As we show in this blogpost, apparently simple ARR calculations frequently depend on a range of assumptions that require careful analysis. Without effective data management and revenue recognition principles, we find SaaS business often fall into a trap of being unable to accurately describe their ARR growth let alone predict customer behaviour.

QuantSpark’s experience supporting SaaS businesses

Since 2016, QuantSpark has supported more than 20 PE-backed SaaS portfolio companies to improve their ability to understand and optimise the drivers of revenue growth. The work we typically undertake includes:

• Revenue recognition analysis: Understanding and documenting the business logic behind revenue recognition through conducting management interviews, historical revenue analysis, and process analysis (sales, marketing, finance)

• Data engineering: Building the underlying data architecture and cloud-based databases to power the revenue recognition calculations and data-cleansing and preparing data for visualisation in Tableau or PowerBI

• Dashboarding: Creating the snowballs (or revenue bridges) to allow detailed analysis of historical recurring revenue behaviour

• Predictive analytics and modelling: Automating the prediction of churn, upsell, and cross-sell in order to inform marketing interventions

• Deal support: Working with the SaaS business to support the due diligence process, evidencing growth in recurring revenues.

References: 

https://www.statista.com/statistics/505243/worldwide-software-as-a-service-revenue/