With the continued creation of more data than ever, it was no surprise that by the end of 2016, we had created more data in the past two years than in the previous 5,000 years. By the end of 2017, we are expected to create even more data than 2015 and 2016 combined. This trend is due, in large part, to the proliferation of IoT and other connected devices. Yet for Telecom, other technology advances will add to the data deluge, including recent advances in Natural Language Processing (NLP).
According to Telecom Reseller, “a significant advance for call centers is the progression of NLP technologies, which have increased the ability of technology to understand unstructured data, such as voice recordings. Call centers can now gain important data every time a customer call is recorded. Combined with call analytics tools, these big data sets can gain valuable insights into customer interactions.”
With NLP systems, telecom companies can compile data not only on what a customer says, but also how they say it, to uncover the age and gender of the caller. In addition, NLP can detect a caller’s emotions and decipher what they want, determine when pleasantness turns to frustration, and even forecast when they are getting ready to churn. This valuable information can be used throughout the entire customer lifecycle to ensure maximum profitability and to get ahead of the competition.
NLP Data and the Customer Lifecycle
NLP data can be used throughout the entire lifecycle of a customer to provide telecom companies valuable insights into their customers, specifically when they are getting ready to leave. Reducing customer churn is crucial to a company’s bottom line, and NLP data can be a significant help.
Combining advanced analytics, including predictive modeling and machine learning, with NLP data can help telecom companies predict future business outcomes. Tracking NLP data, and understanding customers’ real-time activity, presents huge potential revenue generation opportunities by avoiding customer churn and recognizing upsell or cross-sell opportunities. Utilizing NLP data, telecom companies can identify the right offer at the right time for the right customer, which helps increase average revenue per customer, while maintaining a positive customer experience. But before an organization leverages NLP data, they need to check it for quality.
Ensuring NLP Data Quality
Data quality can be solved with a solution suite that has the capability to implement deliberate and repeatable processes to automatically validate 100% of NLP data for accuracy. The suite should continually monitor data to ensure the bad data is immediately flagged and stopped before it impacts any customers.
With a solution suite that considers data quality for structured and unstructured data, in real time, NLP can be operationalized into day-to-day environments. This means when new NLP data becomes available, scores are dynamically updated to provide telecom companies with real-time, actionable results, thus producing immediate business value and return on investment.
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