Understanding Communications Intelligence and Analytics

Although the technology for speech recognition has existed for many years, it has served only rudimentary functions until recently. The technology, which came out for the first time about 20 years ago, stalled until larger companies began investing both time and resources into it. The Watson machine developed by IBM is a good example of this progress.

The Technology That Powers Call Tracking Analytics
Before a company can utilize data provided by conversation analytics, it must first acquire the technology for natural language processing (NLP) and machine learning. These technologies make it possible to determine who the speaker is as well as the words and phrases he or she is using most often. With the help of programmed analysis, conversation analytics can analyze a conversation and form conclusions about the words as well as the people speaking them.
Conversation analytics couldn’t exist without NLP. Basically, NLP makes it possible for a computer to analyze as well as understand human language. It can also assign meaning to certain words and phrases to provide the end user with real value. When developers use NLP, they can organize and then structure the feedback to complete such tasks as automated summaries, language translation, recognizing named entities, extracting relationships, analyzing sentiments, recognizing speech, and segmenting various topics.

Machine Learning Helps to Advance the Cause of NLP
The machine learning component of conversation analysis constantly acquires new knowledge. Both machine learning and NLP do an excellent job of pulling out patterns that appear often in audio recordings. As agents take one phone call after the next, they help to train the conversation analytics technology to become more precise and accurate in its reporting.

Advanced NLP technology makes it possible for the conversation analytics program to listen to both ends of a conversation in phone calls and immediately transcribe the words exchanged. It knows whether the agent or caller is speaking, determines and tags certain words and phrases, and decides if the call makes a qualified lead or not.

Companies that utilize Call Sumo’s technology can transform and adapt raw technology to solve real problems in business that continue to stump digital marketers. This will change the very way that companies conduct their business. Thanks to conversation analytics, companies have access to information that was out of reach for them in the past. That is because a human needed to physically come to the location to listen to each phone call and assign outcomes to it.

Merging Call Tracking Software with Conversation Analytics

NLP and conversation intelligence provide companies with improved lead quality, customer loyalty, performance of the sales team, and several other metrics. The artificial intelligence (AI) powering it reviews every cue within a call and determines lead quality on the spot. This is true even though it processed thousands of phone calls for the business.

With this technology, businesses can glean information from many parts of a phone call in real time. This includes factors such as call length, how many turns each speaker took, whether either used keywords specific to the industry, and several others.

Call Data Can Improve the Performance of Marketing Campaigns
The automatic classification of leads is another tool in Call Sumo’s conversation analytics tool. It provides a real-time transcript of the call to highlight issues, experiences, and sentiments expressed by customers. Companies that notice a similar theme among the phone calls can then implement changes to improve customer service.

Another feature of conversation intelligence is that it allows users to see which marketing campaigns bring the best leads. Assigning a tracking number to each marketing campaign helps to provide details such as its performance in each channel where it runs.

Phone calls provide a multitude of data, but marketers have been unable to access it until now. The detailed information obtained from calls can help any company improve and maximize its marketing efforts.

Conversation Analytics in the Future
Industry analysts predict that context and sentiment analysis will be the next big thing to develop in the arena of machine learning and NLP. In the future, a machine will determine whether a caller is legitimately satisfied, determine whether the call is for support or sales, and draw out other pertinent details.

The industry is only expected to grow over the next several years. This will provide businesses the opportunity to change algorithms to suit them and analyze the incoming digital signal better. For example, a computer could say in so many words that a customer feels satisfied or not based on certain signals.

The time has come to take your business to the next level. Please contact Call Sumo at your convenience to schedule a free demonstration.

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