AI in SaaS

SaaS is evolving and as the number of competitors on the markets is skyrocketing, AI is becoming a decisive differentiator for success. The key challenges that SaaS companies facing is often twofold. On the one side they need to investigate where in their business model those 10x improvement levers through AI are. On the other side their engineering teams need to develop new set of skills to turn vision into action.

Image alt

SAAS Performance Drivers

Here are our top-4 performance drivers for applying AI & analytics in SaaS.

Performance Drivers

SaaS companies can gain advanced insights on their leads and different customer groups through analyzing data at later stages of the customer lifecycle. Through lead scoring models sales people will know on which leads to invest time and how to package personalized offers.


SaaS companies can optimize their pricing strategy by identifying key value items, i.e. products for which price is exceptionally important. In addition, companies can use competitive intelligence through web crawling to adjust their prices dynamically.

Customer Churn

Companies can identify likely churners and identify offers that will convince them to stay.

Recommendation Engine

Companies can predict how customers respond to service offerings & product campaigns based on assessment of historic behavioural patterns.

How to identify Machine Learning Opportunities in your SaaS organization?

Get free access to this lesson by registering for the AI Masterclass & Coaching program.

The SaaS Challenge

As data collection and AI grows in demand, the availability of AI- & ML-services has commoditized and we are seeing a similar development than for cloud computing in the last decade. This implies that the success factor in applying AI for SaaS companies is not to built superior models, but rather to train available data in the best possible way so the models can suite the needs of the business context. This is a major shift in the role model of engineers from writing algorithms to training of applications and come with challenges of its own. Key success factor on this journey is to have training data at sufficient quantity and quality as well as further data sets to test the model prior to moving into production.

AI- & ML- algorithms are in general designed to serve on of the following purposes: - Diagnose a situation / pattern recognition - Predict a future outcome - Prescribe actions in order to optimize along certain dimensions - Personalized user experience Based on their business model and customer value proposition, companies need to identify areas where algorithms have the potential to increase status quo by 10x and also determine what data is needed for this.

Endorsed by

Hear from our customers & partners

Data-driven market leaders
Trust in Scalework.

Subscribe to our newsletter

Get our 'Top-5 traps to look out for when monetizing your data - a reality check by SCALEWORK'


Thank you for subscribing!