Data Analytics: Why is it Important?
Updated: May 22, 2019
By Licia Wolf
It seems that in every environment, in every organization, data is being analyzed. From marketing products and services to engineering the next electric vehicle to examining world trends, analyzing information from past occurrences helps build strategies for future endeavors and provides intelligent advancements for commerce, science, and technology.
Why is analysis so important to enterprises? Companies want to become more intelligent when it comes to making big decisions about their welfare and growth, and using a quantitative approach with real life data has proven to be an effective tool.
Business Applications for Analytics Tools
Traditional Data Analytics forms the foundation for Business Intelligence (BI), which uses quantitative analyses that enable companies to make informed business decisions to minimize risk and improve outcomes. Organizations can benefit from BI by:
Optimizing internal processes
Enhancing operational efficiency
Gaining competitive advantages
Identifying business issues
Identifying market trends
With the development of Artificial Intelligence (AI) and Machine Learning, a type of analytics called Advanced Analytics is gaining traction among enterprises. This method takes traditional analytics a step further by incorporating the power of computer learning to better forecast future events and behaviors – essentially conducting “what-if” analyses to predict the effects of new strategies and directions. Using a mathematical approach, Advanced Analytics combines classical statistics with methods such as deep learning to identify patterns, correlations, and groupings.
Contact Center Analytics
The use of data analytics for communication interactions is a growing practice in enterprise and dispatch environments.
For enterprises, contact centers represent the face of the business, and top performance in customer service/contact centers has become a high priority. For dispatch services, call takers must be highly responsive and act within strict guidelines to maintain error-free operations. Analyzing calls and other communication interactions not only provides information about issues and how to correct them, but also can aid in planning for upgrades of systems and methods. The overall result can produce improvements in agent or dispatcher quality, increased communication efficiency, and customer satisfaction.
Statistics can be generated for individual agents, departments, locations, or any relevant grouping. Variables such as call volume, call duration, first verses multiple call resolution, and call escalation can be measured, as well as metadata such as the media type (e.g. phone, text, email, chat), time of day, caller location, weather, caller gender, caller’s emotional state, and more.
Metadata – Analysts look at metadata accompanying interactions to provide further insight. For example, a contact center might notice that it received 50% more calls/emails/chats regarding sunblock products over several days, but customer complaints rose 35%. The analyzed data shows that there was a heat wave in several locations at that time, and the average hold and email response time increased 39%. This knowledge can direct managers to implement changes such as scheduling more agents during hot weather periods or implement an efficient chat bot system to accommodate traffic and raise customer satisfaction.
About the Author - Licia Wolf is the Marketing and Communications Manager at HigherGround. She holds a Ph.D., and a professional background in electronics, internet marketing, and print/imaging technology. Click here for more information on Licia and the rest of the HigherGround team.
HigherGround, Inc. provides best-in-class, reliable data capture and interaction storage solutions that enable clients to easily retrieve critical information. Our interaction recording and incident reconstruction solutions transform data into actionable intelligence, allowing optimization of operations, enhanced performance, and cost reduction.