SALT LAKE CITY - eTradeWire -- Dawn Marie Daras, MS, MS, ABD/PhD, Behavioral Data Scientist and former Microsoft & IBM analytics lead, today announced the launch of her Customer Recommendation Engine, a plug-and-play solution designed to help small- and medium-sized businesses (SMBs) predict what their customers are most likely to buy next.

The Customer Recommendation Dashboard, built in Streamlit, leverages advanced machine learning to provide actionable insights with enterprise-level rigor, now accessible at a startup-friendly scale. Businesses can upload their customer data and immediately see product recommendations, complete with probability scores indicating the likelihood of purchase or engagement within the next two weeks.

"Think Netflix or an e-commerce landing page: if a customer buys or likes one product, our engine predicts what they're most likely to enjoy next," said Daras. "We've distilled almost two decades of experience in healthcare, financial fraud, cloud technology, and executive strategy into a tool that SMBs can plug in and start using immediately."

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The demo uses movie data to illustrate the engine's capabilities, but the platform is fully adaptable to any business dataset, from retail to SaaS subscriptions. Users can watch a demo on YouTube and explore the interactive Streamlit dashboard online.

Daras, who programs in R, Python, Scala, SQL, SAS, and SPSS, combines technical rigor with strategic insight, helping organizations turn complex data into measurable business outcomes. "By stripping away unnecessary overhead, SMBs can now access Fortune 500–level analytics without the enterprise price tag," Daras added.

For more information, a live demo, or to watch the YouTube introduction, visit https://youtu.be/X1W4usgaBHQ?si=NXQgozyeCYhJBDhB  or https://movie-recommender-customer.streamlit.app/



About Dawn Marie Daras
Dawn Marie Daras, MS, MS, ABD/PhD, is a Behavioral Data Scientist and former Microsoft & IBM analytics lead. She has translated complex data into actionable insight for nearly two decades across healthcare, financial fraud, technology, and government. A published researcher and former university lecturer, she now focuses on delivering compact, cost-effective analytics solutions for SMBs.