by Jelani Harper
LONDON – Other than automating backend data management processes, the most immediately viable way artificial intelligence and machine learning can improve any organization’s data strategy is via its unparalleled propensity for micro-segmentation.
The ability to compartmentalize data in any variety of forms relevant to the business is an essential step towards personalizing user experiences for individualized treatment that maximizes customer interactions.
“If you’re using the same service for everything, then all the customer experience is going to become standardized,” propounded Vizru CEO Ramesh Mahalingam. “AI is about custom services, not about standardizing the same experience.”
This sentiment is true whether analyzing data at scale for micro-segmenting factors for reducing churn with deep learning, compartmentalizing customers for targeted advertising with edge computing, or analyzing employee engagement for optimizing human resources with AI in the workplace.
Increasing employee engagement is a critical means of maximizing enterprise-wide productivity by ensuring workers—particularly non-desk employees, who compromise approximately 80 percent of the world’s workforce—are adhering to organizational policies and procedures. This horizontal need is particularly eminent for multinational, geographically dispersed organizations that need to adeptly implement strategic measures for fulfilling organizational objectives.
According to StaffConnect CEO Bulent Osman, AI’s predictive analytics for micro-segmenting organizations’ employee engagement concerns requires “an infrastructure where you can have multiple unlimited numbers of categories. Within these categories, [you] can define broadly unlimited number of groups. That becomes a very significant, very sophisticated multi-dimensional slicing and dicing of any organization.”
Such micro-segmentation, by extension, becomes the foundation for issuing predictive analytics for how best to optimize employee resources to increase engagement and overall enterprise effectiveness.
Feeding machine learning models
Organizations need copious quantities of labeled training data to successfully leverage machine learning and other predictive analytics models to boost employee engagement. A comprehensive platform tailored for managing employee interactions across geographic locations, producing an illimitable amount of segmentation categories, is influential to developing those models to increase organizational productivity. According to StaffConnect CMO Geraldine Osman, “Predictive [AI analytics] needs to start with data: intelligent data and, to a certain extent, volumes of data. Once we gather that data we can slice and dice it any which way, which feeds the predictive side.”
One of the more effective approaches to expanding employee engagement is to use cloud-based tools implemented via mobile technologies (for non-desk employees) or conventional desktop ones that give workers a comprehensive medium to interact with various mechanisms for training, policy compliance, and other engagement measures.
These implementations idealize employee data for machine learning models by offering real-time and historic analytics for “being able to slice and dice the data in different ways across the organization, and mapping it to really large enterprises”, then using this data for machine learning models, Geraldine Osman said.
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Enterprise sentiment analysis
Employee engagement solutions with granular analytics for segmentation serve as the launching point for AI’s micro-segmentation by functioning as forums for sentiment analysis, much like numerous social media platforms. The difference is the former is entirely contained within organizations, and therefore primed to comply with data governance and security policies.
Once organizations segment engagement data about their employees, they can “now map that to the strategy of [the organization] in terms of their communication strategy, a marketing strategy, and therefore target content and communication down to a very specific, granular level,” Bulent Osman said. The possibilities of this approach are decidedly multitudinous. Bulent Osman mentioned that segmentation opportunities include “groups of new hires, or by location, or by departments, or by a task force to deliver a certain project or initiative, or by different leadership groups that might be around the world, by role, management level, etc.”
In healthcare, for example, the emergency department of the Betsi Cadwaladr University Health Board is deploying employee engagement technologies for timely video training. This platform enables the organization to “track who’s trained, who’s seen the latest materials so they know what percentage of those emergency workers worked on the training on an ongoing basis,” Geraldine Osman commented.
This methodology is especially effective for matters of regulatory compliance, in which organizations want to ensure that employees have acknowledged, read, and ultimately complied with policies for regulations or legal mandates. By keeping all of this information in-house on enterprise solutions designed to facilitate employee engagement, organizations can monitor who’s actually responding to these policies, and derive any number of metrics useful for segmentation that can in turn feed predictive analytics capabilities to further ensure boost productivity.
Micro-segmentation and personalization
Micro-segmentation is pivotal for providing personalized experiences both internal and external to the enterprise. By gauging employee responsiveness to procedures designed to improve overall enterprise effectiveness, organizations can utilize employee engagement platforms to effectively segment their employees. That segmentation serves as the foundation for machine learning’s micro-segmentation capacity to discern how best to optimize employee productivity.
Thus, organizations can learn how to properly motivate employees, spur them to action, and understand what each respective group of workers needs to further enterprise objectives and “to have for the very first time some sort of heat map that looks at engagement, that looks at sentiment analysis, and to be able to know what that is,” Bulent Osman said. “Part of our vision is to take them forward with AI technology, and to be able to almost synthesize this huge, multi-dimensional data warehouse, if you want to call it that, of [employee engagement] information.”
Jelani Harper is an editorial consultant servicing the information technology market, specializing in data-driven applications focused on semantic technologies, data governance and analytics.