Sales Effectiveness in Microsoft Dynamics CRM with Azure IoT and Machine Learning, Part I
How can an organization optimize its sales channels and product targeting by building a 365-degree view of its customers in Dynamics CRM? The answer, and topic of this article, is with the help of Azure IoT and Azure Machine Learning services.
Sales effectiveness
The use case described in this article is the promotion of best-fit products to consumers, in this instance, active cosmetics. The objective is to optimize stock availability among street distributors according to predicted demand, using detailed profiles of consumers.
To do so, we classify those consumers based upon such criteria as:
- common patterns of actions
- age
- gender
- and skin type
Augmenting this data is data from wearable and mobile devices connected to the Azure IoT Hub, such as:
- location
- commuting patterns
- and weather conditions
All this information is then scored and evaluated in Azure Machine Learning to predict the best matching products. Data about sales conversion and customer loyalty is also captured and analyzed with Power BI.
CRM Integration Flow with Azure IoT and Machine Learning
All of the data described above is collected by the Azure IoT platform and stored in the CRM system, providing an analysis dataset for applying a "demand prediction" Machine Learning algorithm that determines:
-
Churn rate, or measure of customer
attrition, defined as the number of customers who discontinue ...
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