Microsoft recently launched its Dynamics CRM 2016 package, announcing that it would use machine learning (ML) to deliver a more personalised, predictive and proactive experience. In this article we’ll explore what this means and why it’s significant.
What is machine learning?
According to this definition from SAS “machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look”.
Though the definition may sound complicated, many of us will already be familiar with examples of machine learning in practice, without having necessarily called it by that name. For example, the way that Netflix produces recommendations based on what users have already watched, or the way Google Now gets to know the user and starts providing useful, relevant and timely information without the need to be prompted, are prime examples of machine learning applied to everyday situations.
What is Microsoft Dynamics CRM 2016?
Microsoft Dynamics CRM 2016 (Version 8.0) is a customer engagement software. It’s an update on the ‘cloud only’ CRM Online 2015 which gives on-premise customers access to theme records, updated navigation menu, turbo forms and other new features. Several of the new features leverage Office 365 and provide a new experience for customer service teams. The release underlines Microsoft’s investment in mobile CRM with a series of significant enhancements, one of which is the capability to tap into Azure Machine Learning.
The trend over recent years has been for consumers to seek out a fix to their problem online, before turning to a customer service agent for help. This has meant that those who do approach customer services will do so when their problem is more complex and they can’t find a solution online, making the customer service agent’s job more difficult. This inevitably results in issues taking longer to address, causing inefficiencies and in some cases inpatient or unhappy customers. With customer services departments needing to evolve, it goes without saying that updates in CRM software should take advantage of all the digital capabilities available out there, machine learning being one of the more suited examples.
How Does Machine Learning Help?
Dynamic CRM 2016’s advanced analytics and its machine learning capabilities via the Cortana Analytics Suite represent a significant step in closing the gap between consumers and the enterprise and will enhance the processes for sales, customer service and social in the following ways:
Sales: Intelligent selling is made possible via machine learning with cross-sell recommendations that allow sales reps to predict which products and services a customer will need during the sales cycle and therefore which products and services to pitch at the appropriate time.
Customer Service: service agents are empowered via ML with knowledge article recommendations that answer questions and allow them to more effectively resolve customer cases and solve problems on the spot. With its integration with Delve, Microsoft’s Smart Search Service, CRM 2016 can also surface relevant trending information that is most relevant to what a person is working on.
Social: Azure Machine Learning leverages the power of social media by integrating social channels to track information via Microsoft Social Engagement. Customer service agents will be able to track and analyse relevant sentiment in real-time and engage directly with customers on Facebook, Twitter and other social media channels, converting issues brought up via social media channels into existing cases, whether that be customer service issues, complaints or new leads.
As customers continue to use social media to voice their opinion about a brand’s product or service, this will become an absolutely essential feature for brands that want to ensure their customer services representation is consistent throughout all channels.
The inclusion of Azure is intended to provide analytics-driven intelligence and a knowledge management system so businesses can record and access data when resolving issues. The machine learning technology attaches to the business receiving a growing amount of data from customer – employee interactions, meaning that the more employees use it, the more it learns and the more accurate information it can provide without being prompted. Machine learning capabilities identify patterns that may have otherwise gone unnoticed, so employees can learn from consistent problems and implement improvements over time.
The launch of this update signifies an important step in how brands, especially retailers, can ensure that the data gathered is put to good use, and that processes are made more efficient, especially those where customer interaction is concerned.
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