Text Analytics :
Focusing on written language—emails, customer feedbacks, emails, web chats, social media comments etc, text analytics can provide quantifiable insights from these highly valuable yet large amounts of data.
How do we do it?
Natural Language Processing (NLP) algorithms are an effective way of making sense of humongous textual data and to quickly classify them into positive, negative and neutral comments. Further analysis can be carried out on each category to find patterns within each, that can be used as guidance for business decisions.
Self-Service Analytics :
As most BPOs facilitate self-servicing options for their customers to fix issues on their own, it is important to quantify the effectiveness of these options. Analytics can be vital in understanding areas within the self-service options that require improvement.
How do we do it?
Text Analysis can be done on huge amounts of customer feedback data to classify them into positive, negative or neutral customer experiences. Other factors such as standard amount of time spent by users to fix an issue by themselves, the number of times users reopen a request etc can also be analysed using our methods, to unveil valuable insights.
Desktop Activity Analytics :
This is an agent-focused technique which managers can use to ensure the compliance of their sub-ordinates to the organization’s protocols. Using this technique, organizations can monitor, capture, and analyse desktop-based activities as well as workflows.
How do we do it?
Using the data collected by Tools that are meant to detect and capture keystrokes on an agent’s desktop, clients can keep track of typical agent activities and applications that are being accessed.
Speech Analytics :
We emphasise on analysing recorded calls to find insights on the nature of the issues that come up frequently, resolution measures suggested, nominal resolution duration etc.
How do we do it?
By leveraging tools that recognize and convert audio into textual messages, a data repository of textual information can be created. Natural Language Processing can be done on this data in order to identify patterns/ key words within these, that point to specific issues. Thus, previously ambiguous data can be quantified into actionable insights.
Predictive Analytics :
Based on historic data available, a wide spectrum of predictions can be made ranging from number of complaints/ requests forecast in a given time period, resolution time forecast for each type of issue to expenses & overheads estimations.
How do we do it?
Regression based and Time-Series based Machine Learning models are highly effective in predictions and forecasts. They utilize historic data to understand patterns and extrapolate scenarios into the future.
