Customer Experience Analytics
With your customer at the heart of our solution and technology as its back bone, we offer insightful analysis by integrating data across various customer touchpoints.
Customer Sentiment Analysis– Analyse customer interaction data to discover what is on your customer’s mind. Discover customer sentiments in their everyday interactions with your company and convert that into actionable analytics.
Customer Effort - Find out what your customers DO for you. Identify onerous processes that your customers may not like so much. Resolve and provide a delightful experience.
Net Promoter Score – Use predictive modelling to categorise Detractors, Passives and Promoters based on everyday customer interactions.
Customer Touchpoint Map – Create a timeline of customers’ interaction across multiple channels and identify trends
Customer Voice Interaction Analytics
Customer interaction speech data can be converted to text and analysed for
Speaker Diarization – Assess customer and agent conversation separately.
Trending Topics – Identify hot topics that your customers are discussing on a periodic basis. A good starting point in understanding customer satisfaction issues.
Emotion Detection – Detect and flag calls with intense conversations.
Cross talk and No talk time analysis – Cross talk often indicates confusion, altercation or customer dissatisfaction. Analyse and identify such calls and take actions at the right time.
Root Cause Analysis – Capture and resolve trending issues discussed by customers before they snowball and hurt customer experience.
Contact Centre Effectiveness
Agent Scorecard – Track and enhance agent performance through well- designed scorecards.
Compliance & Audit – Integrate compliance and audit process to proactively identify & minimize risk
Natural Language Processing (NLP) Services
Information Extraction – Extract actionable insights from free flowing unstructured conversations
Text Classification – Build classification systems by leveraging various feature-extraction techniques and supervised machine learning algorithms.
Summarization – Generate coherent summaries containing key data points. Find your needle in the haystack.
Sentiment Analysis – Identify and analyse emotions, intent, opinions, feelings and attitude.
Question Answering – Build systems that can automatically answer questions asked by human in a natural language.