Conversational Analytics consists of integrative applications that help both customer facing employees and domain experts learn and build business workflows, discover facts and increase knowledge from existing data using an AI/ML/RPA Platform.
That’s a mouthful. For the rest of us, Conversational Analytics is the next generation of the legacy dashboard that so many of us have open or will have open a few times during a normal day. Conversational Analytics is more advanced than what some are touting as the next-big-thing: Conversational Dashboards.
Conversational Analytics is how Tinosys helps companies unlock the potential of their data using Artificial Intelligence (AI), Machine Learning (ML) and Robotics Process Automation (RPA).
Today’s dashboards consist of pretty pictures and graphs created from tools such as PowerBI, Tableau, SAP Business Objects, IBM Cognos, Oracle BI, etc. These require a complex big-data infrastructure built on tools like Hadoop, Elastic Search, Splunk, MS Azure, Apache Spark, etc.
Making data usable and adaptable for those colorful pictures and graphs requires highly paid Data Scientists and domain specialists. These complex and expensive infrastructures can only publish limited analytics due to lack of domain expertise bandwidth and usable data.
So, what is Conversational Analytics and how can it help you?
Conversational Analytics has two parts, Natural Language Analytics and Guided Analytics. Pretty simple. The beauty of a CA platform, without going into the complexity of the underlying AI/ML/RPA architecture, is in what firms like Tinosys have built and what a Conversational Analytics platform can accomplish with Natural Language and Guided Analytics. The outcome is increased business operational awareness, successful strategy development and a serious boost to overall Customer Experience levels across all functional units that use one of these platforms.
What They Are
· Natural Language Analytics are unstructured queries to answer high level questions like “How is Sales doing” or “How is Product X performing” or “How is Bob doing” to acquire a high-level overview of a functional area (Sales, Marketing, Ops, HR, Finance, Service, Engineering, IT), a person, product, quality, company CX ranking or sentiment level.
· Guided Analytics is more structured, using drill-down analytics with specific custom fields to identify and analyze gaps and missing data. It is meant to be used by business functional domain experts and IT to deep-dive and understand the data using a simple search box, without needing complex SQL or Analytics Query Language.
How They Help
· Natural Language Analytics helps front-line staff gather questions answered instantly to accomplish daily tasks quicker and more effectively.
· Guided Analytics provides search like tools to explore the data and drill down deeply into data analysis. It helps the IT staff understand data gaps, define the best strategy and plan for new data generation to make the digital transformation of the business effective and successful.
Conversational Analytics helps business have a dialogue with their data to answer common business functional questions.
Here Are Some Conversational Analytics Examples of Everyday Business Questions
– How is my Northeast Sales team doing this Quarter compared to last Quarter by product
– Predict sales for Product X next Quarter
– Predict sales for Product X next quarter in west region
– What are the top three lead generation campaigns for this Quarter
– What is market share for Product X
– What is the call volume for Product X this Quarter
– Predict Support volume for Product X next Quarter
– What is employee sentiment this Quarter
– What is our Turnover rate per Quarter
– What is our Turnover rate per Quarter by department
The ability to engage data without the constraints of pre-built query models uncovers trends, patterns and indicators that have until now been locked into what’s known as dark data. Free your data and maximize whatever goals your business unit is striving to achieve.
Conversational Analytics. It’s time to have a meaningful discussion with your data.