Call centers generate data like no other department within a company. Information coming in from the outside, such as customer demographics, common questions, and favorite product features are gold mines for marketing, development, and customer engagement. Data gleaned from internal processes such as hold times, how long it takes to resolve an issue, and the number of calls managed per shift provides valuable information for departmental and company management. Best of all, the automated nature of a call center means that almost everything is recorded, cataloged, tracked, and measured with accuracy. As a bonus, it\u2019s available indefinitely for future analysis and decision making.<\/p>\n
Unstructured data can be defined as \u201cinformation, in many different forms, that doesn\u2019t hew to conventional data models.\u201d An example is voice recordings from callers to a call center. The\u00a0efficient call center software<\/a>\u00a0can analyze the tone of voice, content, and emotion. That knowledge can help a representative gauge the caller\u2019s mood even before the conversation starts. This kind of processing is also called speech analysis or natural language processing (NLP).<\/p>\n Call center is something of a misnomer as consumers now interact with companies via social media, email, messaging apps, and more. Text analytics<\/a> programs can evaluate all those forms of communication, looking for themes and potential issues. The software can perform this kind of study on messages going out as well as those coming in.<\/p>\n Customers use multiple avenues to interact with vendors, and it\u2019s critical for businesses to understand those methods. For example, if a customer makes most purchases online, the company will want to make sure the client knows that he or she can get support the same way. Knowledge of how customers interact lets a company put more resources into the most used forms of engagement<\/a>.<\/p>\n Every call center has metrics related to customer service and the engagement experience. The data gathered through the call center makes this easier. Friction points become clear, and areas of difficulty for your agents are easier to spot. Also, all this data is prime material for training new agents, and better-trained agents mean improved customer metrics.<\/p>\n Big Data analysis is a four-stage process:<\/p>\n This involves setting forth a description of all the pathways by which data is collected. You\u2019ll want to include social media, email, and all other forms of communication.<\/p>\n This data involves the transformation of raw data into something manageable and organized. Depending on the nature of the data, this may or may not be a traditional database. Unstructured data requires more work to put into a manageable form.<\/p>\n The data is used to create a hypothetical model for testing and evaluation. Big data gives you a laboratory to try out new ideas in a safe environment.<\/p>\n At this stage, the company takes some action based on the information from the models. The management team has more confidence in decisions based on hard evidence.<\/p>\n Businesses from healthcare to banking to manufacturing are finding uses for the big data collected through their call centers. Whether it\u2019s artificial intelligence, machine learning, or predictive analytics, big data is becoming a priceless resource. If you\u2019re ready to make the most of the information flowing through your call center, you have many options, and Etech Insights<\/a> can help you with data insights for your organization. Contact Etech <\/a>to find your way to improved customer experience and increased profitability through the use of data you\u2019re probably already collecting.<\/p>\n\u2022 Text data analytics :<\/h5>\n
\u2022 Cross channel analytics :<\/h5>\n
\u2022 Quality control :<\/h5>\n
The Process of Using Big Data<\/h4>\n
1. Defining the data sources :<\/h5>\n
2. Data gathering :<\/h5>\n
3. Modeling :<\/h5>\n
4. Deploying a model :<\/h5>\n
Get Started Now<\/h4>\n