Understanding Communications Intelligence and Analytics

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Understanding Communications Intelligence and
Analytics
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• Although the technology for speech recognition
  has existed for many years, it has served only
  rudimentary functions until recently.

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• 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.

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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.

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• 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 couldnt exist without
  NLP. Basically, NLP makes it possible for a
  computer to analyze as well as understand human
  language.

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• 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.

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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.

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• 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.

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• 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.

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• Companies that utilize Call Sumos 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.

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• 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.

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Call Data Can Improve the Performance of
Marketing Campaigns

• The automatic classification of leads is another
  tool in Call Sumos 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.

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• 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.

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• 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.

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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.

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• 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.

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