String matching algorithms in the presence of abbreviations, such as in Stock Keeping Unit (SKU) product catalogs, remains a relatively unexplored topic. In this paper, we present a unified architecture for SKU search that provides both a real-time suggestion system (based on a Trie data structure) as well as a lower latency search system (making use of character level TF-IDF in combination with language model vector embeddings) where users initiate the search process explicitly. We carry out ablation studies that justify designing a complex search system composed of multiple components to address the delicate trade-off between speed and accuracy. Using SKU search in the Dynamics CRM as an example, we show how our system vastly outperforms, in all aspects, the results provided by the default search engine. Finally, we show how SKU descriptions may be enhanced via generative text models (using gpt-3.5-turbo)

The answer, and topic of this session, is with the help of Azure IoT and Machine Learning services! To achieve the objective, wearable and mobile devices are used and connected to the Azure IoT Hub for collecting information about location, commuting patterns and weather condition. All this information is then scored and evaluated in Azure Machine Learning to predict the best matching products and services. This session focuses on presenting all the technologies used to build the discussed use case, and how to integrate them in an end-to-end fully functional solution in Dynamics CRM.

"If you want to know where to make money over the next two decades, look for companies that are finding ways to automate jobs that are currently being done by humans...that you wouldn't have thought previously could be done by a machine. Truck drivers are one thing and Google as well as Tesla have a great head-start in disrupting that market, but lawyers, doctors, teachers, customer service and sales reps – there are companies that are turning these professions into lines of code, and they're going to make a lot of money." Customer Relationship Management (CRM) software is a roughly 25bn a year market today and Gartner projects that it will be the fastest growing enterprise SaaS segment over the next few years, reaching over 40bn in annual spend in 2019. The importance of this market is being underscored by the all-out war between tech titans Microsoft, Salesforce, and Oracle who have already spent close to 40bn in the past two months on CRM-related acquisitions including LinkedIn ( 26.2bn cash), NetSuite ( 9.3bn cash), and Demandware ( 2.8bn stock). Companies today are striving to leverage what is rapidly approaching the zettabyte scale data loads that customers are uploading to the cloud every year, and most CEOs understand that getting a better customer 360 will be a key driver of their firms' success.

The CRM of today allows for a complete digital transformation, says Daniel Turtledove, regional director, Dynamics CRM at EOH. When the first cave-dweller started selling hand-made clubs for the discerning hunter-gatherer there is a good chance he would follow up with the client to find out how the product performed in the field – hoping to create a lasting relationship and fine-tune his club production process to better service the market. This process, historically, was a tedious, time-consuming endeavour but, over the last few years customer relationship management (CRM) has evolved, using cutting-edge technology to streamline the process and allow for unheard of insight into your existing and potential clientele. To understand this evolution and the value-add cutting edge CRM can deliver, Daniel Turtledove, regional director, Dynamics CRM at EOH, and winner of the Dynamics CRM Partner of the Year award, gave us a tour of modern CRM. CRM gives you out-the-box ability to deliver to your customers on standard things that we take for granted, Turtledove says. "The traditional core that is CRM, is to offer customers a wide variety of services.

Earlier this year I wrote a post that showed how to perform sentiment analysis in Dynamics CRM using Microsoft Azure Text Analytics. Azure Text Analytics makes it incredibly easy to use sentiment analysis (with English text only), but the full Azure Machine Learning offering is much more powerful. In today's post I will show how to create a custom predictive web service in Azure ML and make predictions with it in Dynamics CRM. One of the exciting announcements about Dynamics CRM 2016 is that it includes some sort of integration with Azure ML, so what's the point of this blog post? For this demonstration I am using data from the AdventureWorks data warehouse sample database to build a model to predict whether a contact in CRM is likely to be a bicycle buyer.