Machine learning algorithms and predictive models can be hugely beneficial to call center leadership, helping with everything from automating the Quality Assurance process to identifying agents in need of more training, to predicting the future behavior of your customers.
However, the way that machines interpret data and the way that humans do the same are very different. Traditionally, machines tend to excel at the computation of data and following decision trees to an extraordinary level of granularity. Meanwhile, humans are better at taking data and abstracting it based on past experience.
To produce human-useful information from an automated machine learning interface requires bridging the gap between human reasoning and predictive machine reasoning.
Increasing Machine Sophistication to Make Presented Data More Useful
Most predictive analytics models used by companies rely on regression techniques. Basically, collected data is sorted into classes based on preconceived or well-known information. Each data point is mathematically described with a set of numbers and linked together to create feature vectors. By relating the general feature vector patterns that occur within each class of data to the new feature vector, a mathematical comparison is made to determine a likely outcome based on past data.
The issue with this one and done modeling tactic is that its often necessary to adjust the assumptions and observations that went into the initial data model based on new data. By not adjusting to new information that may invalidate old assumptions, these regressive predictive models may prove to be less accurate over time.
This issue can be addressed in one of two ways:
- Relying on a human expert to reconfigure the model based on data collected; and
- Using a dynamic model rather than a static one.
Using a human expert to manually adjust the static regression model may be helpful, but it requires constant time and attention by said expert to keep the model relevant. Creating a dynamic model that can self-correct based on data collected over time makes managing the predictive model much simpler in the long run, but requires a more complicated program.
Making the Data Human-Interpretable
This is where machine learning algorithms are highly useful. Over time, as the algorithm collects results data from thousands of individual cases, it can adjust the predictive model based on that information. This helps make the predictive model more accurate by taking into account real-world results related to the feature vector information that was collected.
The data sets collected by a predictive analytics solution for a call center can be massive, with dozens of feature vectors from across thousands and thousands of calls. To make this data digestible by a human user, the predictive analytics software performs the calculations and assessments automatically in the background, pushing only the relevant data points forward to the user.
By taking the big data of the call center and reducing it into an easily-digestible report, predictive analysis systems can help to bridge the gap between machine and human interpretation to create a powerful decision-making tool for business.