Finding True Voice of Employee with Artificial Intelligence

  4 m, 27 s

Though a focus on “Voice of Customer” and other customer analytics is fundamental to generating positive cash flow, Voice of Employee is often perilously overlooked. Your employees (especially customer-facing ones) are a reflection of your enterprise. And while excellent customer service boosts the likelihood of additional revenue, poor customer service comes with a hefty price tag: U.S. businesses sacrifice $62 billion in revenue per year due to bad customer service. Add to this the $4,000 that’s spent, on average, to hire a new employee (keep in mind, this means ONE new employee) and “Voice of Employee” – also known as “Workforce Analytics” or “People Analytics” – takes on a dimension of greater importance.

“Gone are the halcyon days of quarterly employee satisfaction surveys and yearly performance reviews.”

If your employees are having negative experiences within your organization, it’s vital that you catch the attitudinal shift before it seeps into customer relations. On the flip side, examining the aspects of your outstanding employees in terms of customer service is equally crucial to ensuring consistent and positive customer experiences (yes, it goes deeper than just having magnificent UI/UX design).

Going Beyond Quarterly Surveys

Gone are the halcyon days of quarterly employee satisfaction surveys and yearly performance reviews. Certainly, those surveys and reviews still play a part in providing useful feedback and allowing team members to voice their concerns. But they’re not enough. Employee performance reports usually deliver long-term patterns and trends, and structured surveys rarely give people the chance to voice their true opinions.

<Voice of Employee Performance Review>

Trouble is,  it’s difficult to construct cogent surveys that accurately measure employee perceptions. To start with, what are the most important questions? What, if any, are the main themes you’re trying to analyze? This traditional, top-down method of survey design misses the mark. Human resource (HR) departments and managers need to construct their analysis as an inverted pyramid. This is the best (and really, only) path to finding your true Voice of Employee.

Unlocking the wealth in your communication channels

Your communication channels amongst employees and between customers and employees hold a wealth of data, full of detailed information for real-time performance assessments. But manual analysis and siloed data sources create time-consuming and costly challenges. Many companies are stepping in to fill this need, but few are delivering results. Beware the shiny “machine learning platform” offering glitzy dashboards and all the AI bells and whistles. In the end, their cost, complexity, and lack of depth severely limit their ability to deliver worthwhile returns.

Finding True Voice of Employee with Text Analytics

Sentiment analysis and NPS ratings aren’t just for measuring the customer experience. Through the use of text analytics, workforce analytics professionals can accurately and reliably gather the insights they need for effective Voice of Employee initiatives. For example, perhaps your customer service (CS) team is doing an excellent job of responding to customer queries, yet the IT team is slow in responding to requests originating from CS. By analyzing communications between the IT team and CS, and also amongst the IT team members, you can extrapolate one or more sources of the issue. Perhaps it’s a new team member who needs more training on the processes, or maybe the process for communication is no longer functional. In this age of agility as a requirement for enterprise survival, the faster you discover the problem, the more quickly you can enact a plan of action to remedy it.

“In this age of agility as a requirement for enterprise survival, the faster you discover the problem, the more quickly you can enact a plan of action to remedy it.”

Using Machine Learning and AI

Machine learning (ML) and artificial intelligence (AI) are very buzzword-y these days, but for good reason. It’s difficult for humans (without the help of our trusty machines) to aggregate enormous amounts of text from disparate sources, perform a thorough analysis, and then make accurate predictions about the likelihood of employee churn. A properly trained ML model can collect information from all communication systems, detect common churn indicators, and then send out a notification regarding potential problems. This ML-powered early detection system will greatly reduce the costs associated with employee turnover.

“Beware the shiny “machine learning platform” offering glitzy dashboards and all the AI bells and whistles.”

And that’s just one example. Earlier I talked about building a survey using the “inverted pyramid” approach. Natural language processing tools like Lexalytics’ own Semantria make this possible. These tools quickly extract themes and topics based on analysis of real internal communications, to show you the true voice of employees. Rather than assuming a set of shared pain points amidst employees, the prevailing internal issues are based on what they’re actually saying about their experiences.

Additionally, the speed and flexibility of analytics tools like Semantria empower you to include a free response section. This is a great way to encourage authentic responses and increase your ability to develop impactful HR initiatives.

Summary

It’s a fundamental psychological reality that human beings want to be heard. Your surveys, performance reviews, and timely feedback for your employees should reflect this understanding, and once they do, you’ll have the ability to solve human resource problems before they start to flow towards your customers.

Image sources

https://www.pinterest.com/pin/552465079270063242/

http://www.custerian.com/tag/voice-of-employee/

Categories: Artificial Intelligence, Insights, Machine Learning, Special Interest