Data Analytics in SaaS Solutions

A structurally bizarre aspect of working with data is realizing that intricately connected user flows and business processes are artificially broken up during measurement only to rely on heroic data modeling efforts to piece it all back together. In daily organizational workflows, it feels like utilizing chopped up pieces of a whole where the pieces don’t fit as easily like lego blocks or as cohesively like a puzzle. This is the default pattern in organizations. Take a SaaS business for example: the user flow starts with marketing generating leads, which are then funneled into sales, then onboarding, followed by customer success, and eventually churn or renewal. All of these steps are deeply interrelated, yet the data around each of these steps is often captured via different APIs in separate systems, with differing levels of accuracy, time grains, and dimensions, making it impossible to track the full user journey without doing extensive work. To address this challenge, data teams execute valiant data modeling efforts. First, they clean up the raw facts to ensure they are accurate and consistent. Then, they recognize the key entities or dimensions and the associated attributes that give context to these measurements. Once the facts and dimensions are in place, they move on to creating meaningful metrics that represent the business’s key performance indicators (KPIs). At this stage, many organizations end up with a set of reports or dashboards powered by these data models. But even after going through these steps, we still encounter a major challenge: how do we connect these metrics and dimensions into cohesive, unified models that reflects the entire business process? The state of the art for this is additional painstaking work in spreadsheets. This is where metric trees come in as they represent the pinnacle of data modeling. They go beyond the basic elements of facts, dimensions, and metrics to model the complete flow of a business process, illustrating how different metrics interconnect and influence each other across various stages of the business cycle. For instance, in the case of a SaaS business, the metric tree would start with the highest-level output metric, such as revenue, and branch out to show how acquisition metrics (e.g., new customer leads), activation metrics (e.g., onboarding success), retention metrics (e.g., churn rates), and expansion metrics (e.g., upsell and cross-sell) all interconnect. This structure mirrors the actual dynamics of the business and reflects how changes in one area affect other areas, allowing for a more holistic view of operations. In short, metric trees represent the end state evolution of data modeling from fragmented measurements to a unified, connected view of the business.

In my years working with senior executives at growth-stage and mid-market SaaS businesses, one thing is crystal clear: most struggle to leverage GTM data as a legitimate tool to guide their actions and improve performance. Instead, what we often see is passive, reactive reporting, which leaves decision makers to rely on gut instincts rather than actionable insights. Why does this happen? First, businesses aren’t set up to capture the right data. Tech systems are frequently misconfigured by non-experts, and essential processes to track meaningful information are often missing. Data sits in silos across sales, marketing, and customer success, further complicating leadership's ability to see the full picture. Worse, data hygiene issues undermine trust in the numbers, rendering even the most beautiful dashboards useless. Let’s be honest—these companies aren’t short on reports. But what’s the value of data if it’s not actionable? This is where most companies get stuck: with endless metrics but no clarity on how to translate them into actions. As a result, executives are left to make decisions based on intuition, which can backfire and lead to unintended consequences. At scaleMatters, we’ve designed a methodology called Data Drives Action to solve this. Here’s the framework in simple terms: Start by thinking about the actions you can take to improve your Go-to-Market performance. For example you might take actions to change people…such as coaching, training or even terminating. You might take actions to streamline processes with the goal of shortening sales cycles or perhaps improving conversion rates. You might decide to change channels perhaps by reallocating investment away from one channel such as outbound prospecting in favor of another such as paid LinkedIn advertising. And so on... Then, work backward—what insights would guide those actions? Ask yourself, “What questions do I need answered to make informed decisions?” Once you identify the key business questions, you can map out what data is needed and how it should be presented to answer those questions. Lastly, focus on how to source this data. This involves configuring the right tech and processes to capture the necessary information. By starting with the end goal—performance improving actions—and reverse engineering back to the tech and processes businesses can finally turn passive data into a tool for real, performance-driven actions. #gtm #gtmanalytics

While most of the SaaS companies have challenge knowing what exactly their ARR is, it is even more difficult for usage based companies considering the fluctuating nature of actual revenue. Tracking and forecasting ARR for usage-based SaaS companies comes with its unique set of challenges: 1. Complex Usage Metrics: Usage-based companies often have complex usage metrics that vary from customer to customer. Tracking and consolidating this data can be challenging, especially when there are multiple pricing tiers, feature add-ons, or usage-based billing models. 2. Granularity of Data: Usage-based companies need to collect granular data on customer usage to accurately calculate revenue. This requires robust data collection systems and integration with the product or service to capture usage details at a fine-grained level. Handling and analyzing vast amounts of usage data can be daunting. 3. Data Synchronization: The data required to calculate ARR often resides in different systems such as usage tracking tools, billing platforms, and CRM systems. Ensuring data synchronization and accuracy across these systems can be a significant challenge for finance teams. 4. Billing and Revenue Recognition: Usage-based billing introduces complexities in revenue recognition, as revenue is recognized based on actual customer usage. Finance teams must navigate the intricacies of recognizing revenue correctly based on usage patterns, contractual commitments, and billing cycles. 5. Forecasting Accuracy: Forecasting ARR becomes more challenging with usage-based models due to the inherent variability in customer usage. Predicting future usage patterns and accurately forecasting revenue requires sophisticated algorithms, statistical modeling, and a deep understanding of customer behavior. 6. Data Analysis and Insights: Finance teams must analyze usage data alongside billing and contract data to derive meaningful insights about customer behavior, revenue drivers, and trends. This requires advanced analytics capabilities and cross-functional collaboration between finance, product, and sales teams. Overall, successful tracking and forecasting of ARR for usage-based SaaS companies require a combination of robust systems, data integration, accurate revenue recognition methodologies, advanced analytics capabilities, and cross-functional collaboration to navigate the complexities of the usage-based business model effectively. As usage based business model becomes the majority for SaaS companies, more and more companies will face the issue of being able to derive the right ARR at the right time. I would love to hear how you are tackling this problem at your company today. P.S.: FP&A tools like Mantys help companies keep a track of their usage data, revenue and help forecast based on past trends and future possibilities. #saas #usagebased #arr

Ever wondered why despite immense potential, some SaaS companies struggle to scale and achieve profitability? I recently went deep into a compelling discussion that shed light on the vital role of business metrics in SaaS growth. One anecdote stood out: the story of Salsify, a company that enhanced its trajectory by relocating its European headquarters to Lisbon, symbolizing a strategic shift in optimizing operations. The central theme was crystal clear: "If you can't measure it, you cannot improve it." Accurate metrics are not just numbers; they shape strategies, align teams, and spark growth. But what's the secret formula? Key takeaways include: - The Rule of 40: A SaaS company's growth rate and profitability combined should exceed 40%. - Net New ARR: Monitor bookings via net new Annual Recurring Revenue (ARR), encompassing new customer ARR, expansion ARR from existing customers, and losses from churned customers. - Sales Funnel Efficiency: Deploy a holistic funnel that includes onboarding, retention, and expansion. - Sales Team Metrics: Productivity per salesperson and timely hiring are crucial to meet growth targets. - Customer Economics: Balance the Customer Acquisition Cost (CAC) against the Lifetime Value (LTV). Aim for an LTV to CAC ratio of 3:1 and recover CAC within 12-18 months. - Negative Churn: Expansion revenue should ideally outpace revenue losses from churned customers for sustainable growth. Metrics like these can transform a SaaS company from merely surviving to thriving. It's fascinating how strategic measurement and adjustment can turn potential into proven success. How do you leverage metrics to steer your SaaS business towards growth and profitability? Share your experiences and insights! #SaaSMetrics #GrowthStrategy #BusinessAnalytics #SaaS #CustomerRetention #StartupGrowth #ScaleYourBusiness