Business Applications Applied AI
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Insights Blogs
Customer Lifetime Value Predictive Model now uses Customer Profile Attributes
When crafting your marketing and sales strategies, personalizing your marketing campaigns, journeys and content to the special traits of your customer segments is an important way to drive engagement and purchases. One method for segmenting your audiences is to determine the potential value of those customers to isolate your high and low predicted value customers and personalize campaigns to reward high value customers and drive up the value of lower value customers. Artificial intelligence (AI) can be used to predict the value of customers to create segments like these.
Ram Hariharan and Will Dubyak We must measure user impact to continue enhancing Copilot User Experience. This post addresses application of A/B testing and the North Star metric to this question. It uses an actual example to demonstrate test set…
By Zewei Xu, Senior Applied Scientist and Will Dubyak, Principal Program Manager In March we announced Dynamics 365 Copilot (opens in new tab) and the Copilot in Power Platform (opens in new tab) which has generated curiosity about how we’ve…
Authors: Will Dubyak, Chhaya Methani With Satya’s copilot announcements (Microsoft Ignite Opening (opens in new tab) )at Ignite in the rear-view mirror, it’s a good time to talk more about the kind of work and creative thinking that made it possible.…
Authors: Kidus Asfaw, Sally Kellaway, and Radha Sayyaparaju When crafting your marketing and sales strategies, personalizing your marketing campaigns, journeys and content to the special traits of your customer segments is an important way to drive engagement and purchases. One…
Author: Allie Giddings and Chhaya Methani In our last blog post, we explained how news is surfaced in Supply Chain Insights and how it can be useful for having better risk visibility. Since then, we’ve made two major updates to…
Authors: Matthew Burruss and Shafiul «Jacky» Islam In an ideal world, machine learning would be a straight path from defining the business use-cases to operationalizing the model, but in reality, the model lifecycle is a continuous loop, as objectives are…
Tommy Guy and Kidus Asfaw We noticed an odd case of nondeterminism in Spark’s randomSplit function, which is often used to generate test/train data splits for Machine Learning training scripts. There are other posts, notably this one (opens in new tab) that…
Explainability
ML models can be a “black box” that make decisions in ways that we don’t understand. Model Explainability is a feature that helps practitioners’ probe how a model makes decisions so they can have more confidence in the results.
Authors: Tommy Guy, Allie Giddings, Chhaya Methani Microsoft Dynamics 365 Supply Chain Insights is a new product that helps predict risks and manage your supply chain through three main functions. It increases visibility of risks affecting your supply chain. It…