3 mins read
Imagine you can act today for an event that will happen in the future? No, it isn’t a Disney series or a Netflix fiction movie, predicting and altering the future has moved from a concept to a tangible reality in the real world.
Predictive analytics has become a major part of everyday life, shaping the future of many companies around the world. This allows for the opportunity of these companies to improve their financial performance, ensure sustainable growth, and drive success to name a few.
Particularly, for SaaS (Software as a Service) businesses, staying ahead in a competitive eco-system during current market downturn, requires intelligent predictions and immediate strategic decisions.
Predictive analytics in the field of finance
Within the field of finance, when talking about predictive analytics, it’s going beyond commonly executed financial forecasting and explanatory statistical models or regressions.
Predictive analytics in finance leverages computational methods like Artificial Intelligence (AI) techniques, Machine Learning (ML) models and big data mining to analyze historical financial data, discover patterns, identify trends and predict future scenarios. This enables companies to make data-driven, unbiased decisions and ultimately improving their financial performance.
The finance sector leads the application of predictive analytics driving the exponential growth of the global predictive analytics market which is expected to reach $28.1 billion by the end of 2026 (marketsandmarkets).
With the growing adoption of predictive analytics across industries, SaaS firms should implement these tools to remain profitable in today’s competitive landscape.
In this blog, we will present three use cases whereas a SaaS founder, you can leverage predictive analytics to improve your company’s financial performance and drive growth opportunities.
Financial predictive analytics: 3 use cases in SaaS
1-Recurring Revenues and Cashflow Forecasting
Fluctuating revenues and cash flow forecasts are common in the SaaS business model due to the diverse subscription plans like freemium, monthly, or annual. Manually conducting these forecasts can be a time-consuming task, and carries the risk of making sub-optimal decisions, which can slower the growth of a SaaS Co.
Predictive analytics, on the other hand, utilizes a combination of real time internal and external data, as well as trends, to automatically generate cash flow forecasting models. This automation streamlines the process and empowers SaaS companies to effectively manage their liquidity planning.
By leveraging predictive analytics, SaaS companies can make data-driven decisions that optimize their cash flow, for instance: Decision to secure capital to bridge an anticipated cashflow gap identified through the use of a predictive analytics tool.
Additionally, predictive analytics analyzes customer behavior data from sales, marketing and external sources to provide finance teams predictions of future revenues.
2- Customer Churn and Life time value (LTV) prediction
SaaS businesses use subscription models, making them vulnerable to revenue losses due to churn. Customer churn forecasts are essential to identify “high churn-risk” customers and minimize financial risks.
In addition, these companies struggle to determine their highest-value customers. It now becomes critical for them to predict customer churn, recognize long-term customers, their value, and retain them.
SaaS companies can use predictive analytics tools to study trends and patterns. These tools consider various features like demographics, customer support, product/service usage, and behavioral purchasing actions.
Their objective is to develop churn and LTV predictive models that inform product, sales, and marketing strategies, ultimately driving revenue generation and enhancing financial performance.
3- Detecting and preventing fraud with predictive analytics
SaaS businesses face various fraud risks, including trial abuse, chargeback fraud, data breaches, and unauthorized access.
These risks can be prevented by implementing a real-time predictive analytics model for fraud detection. This approach minimizes manual efforts, reduces the risk of errors, and avoids financial losses associated with fraud, which can be financially burdensome for SaaS companies.
To enhance the effectiveness of predictive analytics in fraud detection, it’s beneficial for a SaaS company to collaborate with domain experts. As they can provide valuable insights and domain-specific knowledge to improve the accuracy and efficiency of the predictive analytics process.
How can Tapline help you?
Tapline offers more than financial performance prediction and management tools for SaaS founders.
By also offering growth capital to early and growth-stage SaaS companies, enabling these clients to trade their future subscriptions for upfront cash. It’s a fast, efficient and non-dilutive way to capitalize a business in 48 hours.
Additionally, Tapline offers a free customized predictive financial dashboard where SaaS founders can sign up to visualize predicted cash flow, MRR, churn, LTV, and other financial metrics.
Based on these financial predictive metrics, SaaS Co’s can request funding to bridge their cash flow gaps or to invest in further growth.
