Dynamic price quotation with machine learning in Microsoft Dynamics 365 for Sales

September 28 2017

Picture yourself scanning a travel site for the best deal for your long-waited flight to your dream holiday destination. A question pops into your head: Is the price going to be more convenient tomorrow? And here you are, hesitating for a day, and searching again the day after. And yes, the price is different, more likely it has increased!

What is behind flight price fluctuation, which occurs so apparently at random? Can we predict ticket price quotations and find the best deal? And moreover, can we introduce a similar model into our own Dynamics 365 for Sales solution?

About Stefano Tempesta

Stefano Tempesta is CTO at SXiQ, a Microsoft Regional Director, MVP on Azure, AI and Business Applications, and co-founder of 365 Community, a non-profit organization whose mission is to empower communities around the world with better ICT processes for customer relationship management (CRM) and customer service, using modern technology.
A technology manager with 20 years of international experience, entrepreneur CTO and advisor for start-ups in Australia, Singapore and Switzerland, Stefano is an author, a public speaker, a blogger, an event organizer and an ambassador of beautiful software. His interests extend to microservice architectures, blockchain, IoT and A.I. related technologies.

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