This is the first question?

Back to all posts


Posted by Harish on June 1 2020

SaaS companies are looking to leverage machine learning and AI to grow customer engagement, revenues and improve inefficiencies in delivering their products. Below are some of the key use cases of ScoopML’s predictive analytics for the SaaS platforms.

  • 1. Predict Churn
  • 2. Predict Conversion KPIs
  • 3. Predict In-App purchases
  • 4. Build Customer Personas

Determining the value of each customer, including likely future spending and the resulting profit margins, is a vital factor in deciding where to aim personalized marketing efforts. ScoopML can help determine the likely profit each individual customer will generate over the entire customer relationship, as well as when a customer is likely to churn; allowing you to focus your efforts on those likely to be most profitable eventually reducing churn and increasing revenues.

SaaS companies want to know how likely website visitors are to provide contact information, ask for further product details, or purchase a particular product. ScoopML's predictions can offer unprecedented insight into visitor behavior, allowing businesses to optimize their web design and product offerings accordingly.

Retaining and developing existing customers is much more cost-effective than acquiring new ones, and SaaS platforms that effectively encourage purchases from existing customers get the most lifetime value from each customer. Using customer demographic and purchase data, ScoopML can identify which customers will be interested in making in-app purchases and upgrading their plans. Allowing you to determine which touch points will likely result in the desired action.

ScoopML can predict which prospects are likely to become the most profitable clients allowing SaaS platforms to prioritize leads and referrals. Additionally you can build user personas and predict user personas that are of high value. These predictions update in real time and get better over time.