Mining Data for Hidden Gold With Sentiment Analysis

Gold panning to find hidden gold

Jas Bansal at Kerv Experience explores mining contact centre data for hidden gold using sentiment analysis.

Offering the ability to search customer interaction transcripts and recordings in real time, sentiment analysis can provide contact centre leaders with snapshots of how conversations with customers or prospects are progressing.

In the process it uncovers what they genuinely think about brands, marketing campaigns, products, services, support, and much more. In this blog we drill down into current use cases and benefits.

Understanding Issues at Scale

Sentiment analysis, also referred to as opinion mining, is the process of analysing speech or text to evaluate its underlying emotional tone.

Using natural language processing, it determines how the customer feels (positive, negative, or neutral) throughout a conversation and, perhaps, how their feelings are changing during the interaction.

Powered by AI, speech, and text analytics multiply quality assurance efforts at scale – in ways humans simply can’t – using random, manual call-sampling methods that tend to capture less than two percent of all interactions and often produce incomplete (or unrepresentative) raw data sets.

Two years ago, a survey found 80 percent of organisations transcribed speech or written data. But only a third leveraged such insights to gauge their effect against business objectives. Today, that’s no longer the case.

More Data-Driven Decisions, Less Guesswork

As the technology has matured and become easier to access – assisted by cloud contact centre platforms with open APIs – more contact centres are investing in speech and text analytics for better customer understanding and experiences. Not just across obvious channels like voice and email but, increasingly, social media and messaging.

Latest Gartner research confirmed that 72% of customer service and support leaders expect to deploy or pilot sentiment analysis tools. Among the most popular use cases are:

  • Sales: Using keyword searches to identify leads and learn from meaningful conversations.
  • Marketing: Improving research, messaging and engagement strategies.
  • Complaints: Interpreting customer frustrations and escalating appropriate interactions.
  • Personal Development: Detecting and addressing agent support and coaching needs.
  • Empathy: Proactively monitoring language and behaviours used by agents.
  • Employee Wellbeing: Tracking agent stress and anxiety, especially among remote workers.
  • Product Design: Uncovering customer perceptions and feedback on invaluable features.
  • Support: Identifying service misunderstandings, disconnects and bottlenecks.
  • Compliance: Raising the flag on interactions that stray off-track or could attract auditor’s attention.

Assuring Best Results

Implementing sentiment analysis usually requires an iterative approach. Most successful adopters show preparedness to experiment, evaluate, and refine speech and text solutions as they learn from feedback and usage patterns. Other sound guiding principles are:

  1. Be Clear on Objectives – Define the problem you want to solve along with goals and measures for success, such as accuracy, precision, recall, and user satisfaction.
  2. Don’t Forget the User Experience – Ensure your solution is intuitive, responsive, user-friendly, and people-centric.
  3. Choose the Right Technology – Various speech and text processing tools, libraries, and APIs are available, and today the best of them are in the cloud.
  4. Design With Variability in Mind – Speech and text data can fluctuate through accents, dialects, informal language, and alternative spellings.
  5. Safeguard Privacy and Security – Ensure your solution protects sensitive information and complies with data protection regulations.
  6. Be Honest About Internal Capabilities – Plug resource and skills gaps by engaging a contact centre specialist, releasing IT staff for more analytical work.

So, what does good look like? A McKinsey article cited cost savings of between 20 and 30 percent, customer-satisfaction-score gains of 10 percent or more, and stronger sales as well.

Code-Breaking and Knowing Where to Look

By 2025, IDC estimates there will be 175 zettabytes of data globally (that’s 175 with 21 zeros), with 80% of that unstructured.

That’s a whole lot of dark data where value is hidden down among the weeds or little understood. Sentiment analysis solutions function as code breakers. They’re also a terrific way to counter other growing business concerns like fake product reviews and bot-generated content.

In summary, speech and text analytics offer insights that simply aren’t available from other sources, helping contact centre leaders identify the causes of customer dissatisfaction and opportunities to become more customer-oriented, efficient, and empathetic. And that can only lead to increased satisfaction and loyalty.

Author: Guest Author

Published On: 11th Aug 2023 - Last modified: 15th Aug 2023
Read more about - Industry Insights, ,

Follow Us on LinkedIn

Recommended Articles

A picture of data analysis
Customer Data Analysis – How to Analyse Data in 7 Steps
Sentiment concept with bubble in heart shape
The 8 Best Ways to Use Sentiment Analysis
Sentiment analysis illustration with people and speech bubbles
How to Use Customer Sentiment Analysis to Grow Your Business
Illustration of people analysing customer sentiment
Lexicon vs. Machine Learning Sentiment Analysis