Machine learning for Saas

In an information-driven world where information is power, Machine Learning (ML) has become an indispensable and invaluable tool for startups of all sizes. However, with more than 50 algorithms available, making the right choice can be difficult. Today we will talk about Machine Learning for SaaS business.

In this post, we will explain how to select the right Machine Learning model to calculate and predict metrics and solve other specific problems that will help and make a difference in your startup. In addition, we will demystify the idea that only two models, linear regression and Random Forest, are sufficient for any project (although it never hurts to have them in your repertoire!).

Machine Learning models for metric evaluation

To begin with, let’s take a quick recap of the main types of ML algorithms:

  1. Supervised learning: Imagine you are an analyst and you know how to classify data and predict certain behaviours. However, instead of doing it manually, you want an algorithm to do it for you. This is where supervised learning comes in. The algorithm works with labelled data, which has all the right answers, and learns to make predictions about new data based on that prior knowledge, which is going to be very helpful for prediction. It’s like having a teacher who teaches you how to classify and predict accurately.
  2. Unsupervised learning: Now let’s assume that you are faced with a bunch of messy data and you have no idea and don’t know how to classify it. This is where unsupervised learning comes in. Algorithms of this type become detectives and find hidden patterns in the data. They may organise the data into groups or clusters based on similarities, or create structures and relationships between the data.
  3. Semi-supervised learning: Sometimes, you find yourself in an in-between situation where you have some labelled data and some unlabelled data. This is where semi-supervised learning comes in. Algorithms of this type leverage the labelled data to learn and then apply that knowledge to the unlabelled data. These algorithms are great when you want to leverage all available data and achieve a more complete analysis.
  4. Reinforcement learning: It’s like a strategic game where you interact with an environment and learn to make smart decisions through trial and error. Imagine you are a video game player and you have to find the best way to advance in the game. You learn from your mistakes, you adapt, and you are always looking for the best possible outcome. Reinforcement learning algorithms work in a similar way, continuously learning and improving as they interact with the environment.

Supervised ML models for data analytics in SaaS startups

Since we are talking about analytics, we will now list the most common models used in data analytics and outline the use cases for each of them.

Let’s look at the 4 main models:

📈 Regression models for the calculation of Kpi.

Have you ever wondered how many page views you need to double your SaaS conversions to sales? Or how many SDR meetings generate 50 new subscriptions per day? Regression is your tool to evaluate these cases. With this model, you can predict continuous numerical values, such as the impact of different actions on your key metrics, forecast future revenue trends and predict the lifetime value of your customers.

Some of its use cases are as follows:

  • How many more steps do users have to take to convert to Premium?
  • Does showing more recommendations positively affect testing?
  • If we increase sending 3 times more notifications, how much will the DAU increase?
  • What is the future revenue trend? How to predict MRR for the next 4 years?
  • LTV prediction

⚖️ Classification Models

Sometimes, we need to make decisions based on categories. Would you like to know if a specific user will churn or stay? Or change their plan? Classification will be of great help in these situations. You can train a model to assign labels to different instances based on their characteristics. Classification can determine whether a user will pass or fail an exam, or it can even predict a person’s gender based on data such as name and height.

Some examples of use could be:

  • Predicting churn in a SaaS: will it help us to lose or keep a particular user?
  • Is someone likely to click on the ad?

👥 Clustering

If you want to know your users in depth and group them according to similar characteristics, clustering is your solution to this task. This way you can segment users into groups with unique characteristics, such as target groups or personas, to better target your marketing campaigns or avoid churn. You can also group customers according to their product preferences, allowing you to offer a personalise

Conclusion on Machine Learning for Saas

What we have shown you are just a few examples of supervised machine learning models that you can use in your startup to implement financial analysis, marketing and product analysis. Although there are many more algorithms, I have presented the most common and suitable ones for these specific tasks. Choosing the right model depends on several factors, but in general, a good strategy is to select 3 to 4 models relevant to your business case and then choose the one that offers the best performance.

At NextScenario we can help you with the analysis, prediction and visualisation of your SaaS business metrics, feel free to contact us.