Machine Learning - the "How"
Machine learning is the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the study and construction of algorithms that can learn from data, and make predictions of potential outcomes.
Machine Learning applied to agent retention
A lot of time is invested in finding the right agents; typical sources include online job boards, network references, and recruiting agencies.
The one common denominator with all of these candidate sources is bias. And while assurances are often provided by the people involved, it is nearly impossible to eliminate subjectivity or emotional drivers from the process.
Let's examine how objective data can be incorporated, not to replace, but to augment, the people-centric process of our most important investment: our customer-facing staff.
Good hires: Understanding the "Why"
If you work in sales, you probably understand why it is just as important to create a "win report", as it is to analyze how a deal was lost. While everyone understands the drivers for learning from one's mistakes, there tends to be a fair amount of inertia when it comes to reviewing our own successes.
But the truth is, understanding the path to success is actually more important to creating sustainable goal attainment than analyzing our failures.
Not only can you create a repeatable process from a successful hiring experience, but you can also find the hidden patterns and relationships that contrast with poor hiring experience much more rapidly and precisely.
Analyzing data: Understanding the "What"
Types of data
The emphasis on hidden patterns in the context of Machine Learning and Data Mining is concentrated around formalizing, explaining and ultimately, visualizing patterns. Essentially, you are classifying data, so it can be leveraged in a more structured fashion.
Data availability: the "When"
Some of the data required to create an ongoing learning process from your HR analytics is available from C.V.s during the screening process, and other key data points are only accessible after agents have been on the job for a reasonable amount of time. It's important to incorporate both into the process of "good hire" analysis.
Examples | pre-hire
- Average employment tenure
- Commute time
- Past employer organizational profile
- Individual contributor or team leader
- Previous experience
- Average frequency of promotions
Examples | post-hire
- Shift worked
- Absenteeism rate
- Job performance trends
Insights are key to discovering ways to report on the massive amounts of data being analyzed in a typical database. By uncovering patterns related to non-trivial, yet often obscure relationships, deeper analyses and trends can be detected.
For instance, what if you found out that 72% of agents that quit within the first 6 months of employment were from a specific vertical industry prior to joining your organization. Would you consider this to be impactful to your hiring process? That decision is entirely up to you, but having access to this kind of information can only empower you to have a clearer view on a potential issue.
Not only can machine learning offer decision support during the hiring process, but incorporating behavioral analysis within the HR process can greatly increase hiring success. thereby reducing attrition rates and increasing employee engagement.
Employee engagement and its impact on attrition
Workplace engagement constantly figures in the top 5 reasons for attrition. While the definition of engagement is relatively broad, the critical elements, after pay rate or remuneration, include workplace dynamics (collaboration, work/life balance, corporate values) and level of interest or motivation with daily tasks.
Forget about running a yearly engagement survey, and concentrate your efforts around making every day as engaging as possible.
When I interviewed one of our leading contact center experts on a recent podcast, he described his Utopian view of the perfect contact center, and it sounded exactly like an episode of the "Price is Right": always a new and fun way to approach work, and every accomplishment tied to a reward.