Descriptive vs. predictive analytics: what’s the difference?
Call center analytics tools help gather information from any channel you use to communicate with your customers. Behavior on voice,
SMS messaging, email, and webchat channels all have something to contribute to figuring out what makes your customers tick.
Data from call center metrics of key performance indicators (KPI’s) like call duration and first contact resolution rates clue you in on the kind of service you are delivering. Speech analytics provide a deeper understanding of customer sentiment and agent ability. Business outcomes and CSAT surveys give you insight into past performance.
Think of predictive analytics as historical and proactive; while descriptive analysis is more real-time and reactive. All of these combine to give you a 360-degree view of your call center performance. Figuring out how each piece fits offers you insight to drive change in your organization and improve performance. According to McKinsey, organizations that use analytics can reduce average handle time by 40% and boost KPIs like conversion rates by nearly 50%.
Analytics fall into two categories: descriptive and predictive. Both are used to make informed decisions about the best path forward for your organization. They differ in how they use data.
Descriptive analytics
Descriptive analytics do just what their name implies: they describe the state of your organization. They give you a clear picture of what has happened in your call center.
Descriptive analytics tells you exactly where your organization stands right now. This data is presented by making use of easy-to-interpret infographics like pie charts, linear charts, and bar graphs.
The goal of descriptive analytics is to learn from the past and the best way to do that is to understand it. They are a reactive approach to events that have already happened.
Sales reports, performance analysis, and call volume are all examples of descriptive analytics.
Like descriptive analytics, predictive analytics is concerned with what has happened in your call center. But, they use data from your call center to make predictions about what might happen in the future. They are used to forecast or fill in information gaps.
Humans are creatures of habit. Looking at historical data is an effective way to predict their responses.
Using algorithms and statistical techniques predictive analytics looks for patterns and attempts to model what events might be in your organization’s future.
Predictive analytics tell you where your organization will stand and encourage a proactive approach.
Sentiment analysis, forecasting, contact volume, and credit score analysis are all examples of predictive analytics.
How are they used?
The applications of descriptive and predictive analytics grow by the day. But a few use cases stand out as prime examples of why you need to be using analytics in your call center.
Improve customer service
Examination of metrics like wait time, average handle time, and abandonment rate tell you about the kind of service you are providing your customers. If your metrics are not where you want them to be, you can develop a course of action for improvement.
Uncover the reason behind each subpar metric and create an actionable plan to remedy it.
Provide learning opportunities
Speech analytics and transfer rates can clue you in on when agents might need additional training. Agents that lack the knowledge to resolve a customer’s issue will transfer calls often. Using speech analytics you can pinpoint what information they were lacking that necessitated the transfer.
With this data, you can create training opportunities tailored to fill in knowledge gaps. This results in a more robust workforce able to handle more customer queries.
Make reviews more productive
Speech analytics also creates a more productive review process. It provides a more in-depth analysis of calls. You can analyze intent patterns, overall customer satisfaction, and agent engagement.
With this information, you can predict how particular agents will perform in different situations. This is helpful to know when you design how calls are routed.
Coaching
There is no denying that sometimes interactions in the call center are emotionally charged. This is stressful for your agents and can result in less than stellar customer service.
The emotional analysis uses natural language processing to determine the tone of a conversation. Coaching tools use this data to predict what the next best response might be and provide real-time suggestions to agents. This is incredibly helpful in navigating the emotional landscape of the call center.
Scheduling
Agent scheduling is a tricky task. You want to maintain your bottom line while at the same time make sure there are enough agents available to handle surges in inbound call volume.
Analytics can help with that. Correlating data about call volume and certain events that trigger spikes can help you make informed scheduling decisions.
For example, you might note a surge in calls around 4 pm for a few weeks after the launch of a new product. This piece of information tells you that the next time your organization rolls out a new product or service you should make sure your call center is well staffed around 4 pm for the few weeks following.
Put the puzzle pieces together
Thanks to big data, it’s never been easier to find out everything you might want to know about your customers, agents, and call center processes. Call center analytics make sense of this data to help you make smarter and faster decisions to optimize your call center’s performance.
Data analysis is a puzzle with shifting pieces. Call center analytics tools help you find where each piece fits to see the big picture.
