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Customer contact centre analytics for banks


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Customer Contact Center Analytics - Dealing with influx of structured and unstructured data

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Customer contact centre analytics for banks

  1. 1. Customer Contact Centre Analytics for Banks
  2. 2. Introduction Customer service team for Retail Banking handles customer queries or concerns raised through multiple channels – emails, calls, walk-ins, letters, and faxes. Barring high priority cases flagged by persistent or irate customers, most cases aggregated on a periodic basis ranging from daily, weekly, monthly or annually The customer service team attempts to identify any patterns such as spikes or drops in calls based on historical data from the reports Inferences drawn highly dependent on the team’s experience and knowledge of the prevailing conditions such as festive season, tax filing period and so on
  3. 3. Challenge Contact centers receive large streams of data - combination of audio calls and text communication. Making sense of such largely unstructured data and taking real time action is a major challenge Traditional analytics reveal trends about data such as calls received, average hold time, average call duration, resolution rate, inquiry type etc. Reports are mostly reactive - essentially giving a view of what has already occurred
  4. 4. Need A mechanism that will enable the customer service team to  adopt a proactive approach - alerting it to incidents that might occur in foreseeable future  take pre-emptive measures to tackle any such situations Resulting in both rapid turnaround time and better decision making capabilities
  5. 5. Use Case Scenarios Industries are using Emerging Customer Service Analytics to  Isolate revenue-related calls or other forms of communication  Identify agent best practices  Identify areas of gaps in knowledge of contact center personnel  Identify cases for personalized agent coaching and training  Predict root cause of customer dissatisfaction  Identify what characteristics of a contact lead to costly repeat communications  Identify other causes of customer churn e.g. better products and services of competitors  Improved operational efficiency - Optimize call handling and first contact resolution  Personalized cross selling and up selling Source:
  6. 6. Why Sentiment Analytics?
  7. 7. Starting Point on the Analytics Journey Analyze customer interactions  Email requests and conversations  Audio calls - search by keywords  CRM Data Analyze customer transactions  Relationship data  Portfolio data  Transaction data
  8. 8. Analytics will reveal Trends
  9. 9. Big Data powered Analytics Platform Audio Calls IVR SMS IVR Email Original Customer Data Audio to Text Mining Data Mining, Storage and Analysis Big Data Audio Mining Platform • Linguistic Analysis • Intention Analysis • Dependency Analysis • Trend Analysis • Text Mining Departmental Regulatory Management Reports Stakeholders Audio mining platform to convert audio to text. Big Data Analytics solution  Uses transactions and interactions data to derive correlations and dependencies  Reveals trends and patterns to alert team and direct focus on potential situation(s).
  10. 10. Thank You