The field of machine learning has been around for over 60 years and has been used to solve some of the most complex problems companies have ever faced. One area in which machine learning can have a dramatic positive impact is through call center data collection.
Every business is interested in making its customer service experience as efficient and effective as possible because 88% of customers prefer voice calls with a live agent.
Call center data collection can help measure the service experience’s quality and uncover trends that can be used for predictive analysis.
In this blog, we will discuss the eight best call center data collection methods for machine learning.
Call center data is a collection of information related to the performance of a call center. This data can be used to improve the efficiency of the call center and the quality of the customer service it provides.
To properly analyze call center datasets, it’s important to understand the different types of data collected. Common types of data include call volume, duration, customer satisfaction, and agent performance. Once you have a good understanding of the available data, you can begin to analyze it to identify trends and areas for improvement.
1. Call Recording
Call centers have been using call recording for decades. It’s an effective way to track what’s happening on calls and provide customer service representatives with a record of their conversations.
The recordings can identify areas where agents need additional training, improve efficiency, increase agent productivity, or monitor agent compliance with policies and procedures.
2. Speech analytics
Call center analytics providers are increasingly adding speech analytics capabilities to their platforms.
Speech analytics allows companies to analyze customer calls rather than just the duration or length of calls. This provides additional insights into customers’ feelings about the call center’s efficiency — from average handle time to pick-up times. It can help improve customer satisfaction scores and reduce churn rates.
Speech data catalogs allow organizations to search for key phrases within recordings and use them as variables when building predictive models.
3. Text analytics
Text analytics is another data collection method that has gained popularity over the last few years due to advances in machine learning algorithms and extensive data processing capabilities.
Text analytics involves analyzing unstructured text-based data such as emails, social media posts, chat transcripts, and other communications from customers or prospects.
Text analytics often uses natural language processing (NLP) techniques such as sentiment analysis and keyword detection to identify key trends in customer feedback that can be used for predictive modeling.
Surveys allow contact center managers to collect detailed customer feedback about their experiences, what they like and don’t like, and what they would like to see improved.
You can conduct surveys to collect customer data in several ways, including online, over the phone, or in person. To get the most accurate and honest feedback, it’s essential to ensure that the survey questions are clear and concise and that customers feel like their responses will be taken seriously.
5. CSAT surveys
CSAT surveys measure customer satisfaction with a company’s products or services. This data can improve the quality of the products or services offered and identify areas where customers are not satisfied.
CSAT surveys are typically conducted after a customer interaction, such as a phone call, chat, or email. The customer is asked to rate their satisfaction with the interaction on a scale of 1 to 10. The data from these surveys can be analyzed to identify trends and areas for improvement.
NPS (net promoter score) asks customers how likely they are to recommend a company’s products or services to a friend or family member.
The NPS can be used to improve customer satisfaction and loyalty and can be a valuable tool for call centers to track over time. Other data collection methods used in call centers include surveys, customer interviews, and mystery shopping.
Employee Net Promoter Score (eNPS) is a tool used to measure employee satisfaction and engagement. It is based on the Net Promoter Score (NPS). The eNPS questionnaire asks employees to rate their level of agreement with the questions asked.
The eNPS can be used as a tool for data collection to help identify areas for improvement within an organization.
For example, if the eNPS score is low, this may indicate that employees are not satisfied with their work environment or their job responsibilities.
The ticketing system is where most customer interactions begin. It’s where agents enter details like what happened during a call or chatted with a customer and note about the call itself (e.g., whether it was resolved).
Ticketing data can be used to improve the efficiency of call center operators. Additionally, ticketing can help call centers identify trends and issues affecting their customers.
9. WFO & BI
The WFO (Workforce Optimization) system allows agents to take breaks, get paid for overtime hours, clock in/out for shifts, etc.
The BI (Business Intelligence) system is where management can see reports about how many calls have been made over time, average call length per agent, average handle time per agent, etc.
The answer is simple. You can collect training data yourself or outsource data collection to a data collection company.
If you manually collect training data, it’ll not be ideal for three reasons:
Therefore, it’s best to outsource the speech data collection work to experts. They’ll provide refined training data that you’ll use to train your AI platform. It includes all the data that’s relevant to your business.
The impact of machine learning in the call center market is real and measurable. Real-time data capture and machine learning have been married to allow even more efficient call centers. In addition, voice-based solutions have increased throughout North America and continue spreading across the globe.
Vatsal Ghiya is a serial entrepreneur with more than 20 years of experience in healthcare AI software and services. He is the CEO and co-founder of , which enables the on-demand scaling of our platform, processes, and people for companies with the most demanding machine learning and artificial intelligence initiatives.