data analytics

How Big Data Analytics Is Gold for Call Centers?

Call centers generate data like no other department within a company. Information coming in from the outside, such as customer demographics, common questions, and favorite product features are gold mines for marketing, development, and customer engagement. Data gleaned from internal processes such as hold times, how long it takes to resolve an issue, and the number of calls managed per shift provides valuable information for departmental and company management. Best of all, the automated nature of a call center means that almost everything is recorded, cataloged, tracked, and measured with accuracy. As a bonus, it’s available indefinitely for future analysis and decision making. How Can Companies Use All This Data? • Unstructured data : Unstructured data can be defined as “information, in many different forms, that doesn’t hew to conventional data models.” An example is voice recordings from callers to a call center. The efficient call center software can analyze the tone of voice, content, and emotion. That knowledge can help a representative gauge the caller’s mood even before the conversation starts. This kind of processing is also called speech analysis or natural language processing (NLP). • Text data analytics : Call center is something of a misnomer as consumers now interact with companies via social media, email, messaging apps, and more. Text analytics programs can evaluate all those forms of communication, looking for themes and potential issues. The software can perform this kind of study on messages going out as well as those coming in. • Cross channel analytics : Customers use multiple avenues to interact with vendors, and it’s critical for businesses to understand those methods. For example, if a customer makes most purchases online, the company will want to make sure the client knows that he or she can get support the same way. Knowledge of how customers interact lets a company put more resources into the most used forms of engagement. • Quality control : Every call center has metrics related to customer service and the engagement experience. The data gathered through the call center makes this easier. Friction points become clear, and areas of difficulty for your agents are easier to spot. Also, all this data is prime material for training new agents, and better-trained agents mean improved customer metrics. The Process of Using Big Data Big Data analysis is a four-stage process: 1. Defining the data sources : This involves setting forth a description of all the pathways by which data is collected. You’ll want to include social media, email, and all other forms of communication. 2. Data gathering : This data involves the transformation of raw data into something manageable and organized. Depending on the nature of the data, this may or may not be a traditional database. Unstructured data requires more work to put into a manageable form. 3. Modeling : The data is used to create a hypothetical model for testing and evaluation. Big data gives you a laboratory to try out new ideas in a safe environment. 4. Deploying a model : At this stage, the company takes some action based on the information from the models. The management team has more confidence in decisions based on hard evidence. Get Started Now Businesses from healthcare to banking to manufacturing are finding uses for the big data collected through their call centers. Whether it’s artificial intelligence, machine learning, or predictive analytics, big data is becoming a priceless resource. If you’re ready to make the most of the information flowing through your call center, you have many options, and Etech Insights can help you with data insights for your organization. Contact Etech to find your way to improved customer experience and increased profitability through the use of data you’re probably already collecting. This blog was earlier published on LinkedIn.

How to Connect Data and Artificial Intelligence for Personalized Touch Points

Artificial intelligence is currently being propagated as the consumer experience champion and for a good reason. The ability and evolution of computer learning have led to improved efficiency, personalization and excellent analysis of big data, thereby transforming the e-commerce landscape and created a standard of expectation from customers. It is no longer feasible to appease the base with stagnant, unresponsive platforms. Patrons expect businesses to be not only accommodating but personable in both physical and digital locations. The expectation of such customization and personalization is only possible and cost-effective through the implementation of automated and analytical systems. However, many companies struggle with implementing such machine learning structures, which is why it is vital to understand how to connect data and AI with business objectives. Identify Problems and Seek Solutions   While big data offers tremendous insight into operations and corporate structure, it does nothing without the computational ability to analyze and produce actionable information succinctly. Incorporating AI into existing products and services is likely the best place for most businesses to start. Review your products, services, and operations to see where machine learning, natural language processing or image recognition could be used to streamline data and eliminate redundancies. Seek Value Not Reinvention   It is easy to get caught up in the technological advancements offered through AI platforms, but it is not wise to embrace it in its entirety. While these systems are excellent tools for data analytics and insights into the consumer experience, they should not be used at the detriment of your business and brand identity. If your model is built on approachability and personable communications with consumers, then implementing chatbots for all consumer interactions will only dilute that image. Do not reinvent the business for the sake of being technologically advanced. Do implement AI strategies that add value and help the company in finding improved efficiency and effectiveness. Know Your Capabilities   A large corporation with many big data sets is going to require more robust solutions than a small business with a limited consumer base and digital footprint. While every company can grow to the point of needing complex analytic tools that are housed in high-security facilities, not all businesses require such extensive technologies currently. Know your capabilities and where you want your business to grow. Don’t over-implement AI systems because it is what the larger corporations are doing. Your computer systems and processes should grow with the business and not necessarily surpass it. Integrate Integral Data   Though big data is the foundation for the success of automated systems, many corporations are plagued by redundancies in these data sets. Before implementing any advanced computer learning system, a business should weed through their data and eliminate any unnecessary or repetitive information. Once a new system is in use, it should only be fed integral data, allowing it to learn what information is valuable. Set Up Tests and Start Small   As mentioned, AI does require training which is why it is essential to manage the implementation of such systems. It is best to start small and tests the results, only moving further when the machine proves its capabilities on limited data sets. This structured implementation can be used in every aspect of your business from the front-end consumer experience to back-end operational usage. No matter the desired result, slow and structured is the path to a successful installation. Big data is only a piece of the implementation puzzle when it comes to artificial intelligence. While AI can help to transform a business into a consumer-focused and efficient machine, it can also inhibit a company’s progress if it is utilized too quickly and with little forethought. Therefore, if you are looking to incorporate advanced analytics and computer learning in your business model, then contact Etech to discuss and discover a customizable solution. This blog was first published on LinkedIn.

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