Nowadays, customers expect constant contact, personalization, prompt response and quick action on their feedback. Conversational analysis is a data analytics method that makes this possible by deriving insights from human speech using Artificial Intelligence. It gives businesses the ability to process both verbal and non-verbal communication and create a rewarding conversational analysis funnel.
All the emails, calls, chats, social media comments and similar customer interactions pave the way to large volumes of unstructured data. When these interactions are equated with structured megadata, customer journey and crucial insights are revealed. When the customer journey is mapped efficiently, the patterns in lexicons and words can be identified which can indicate faults in products and processes, brand perception, etc.
Let’s take the example of an AC company’s reviews on Amazon.com and probe its conversational analysis. Imagine the data is revealing a word ‘buzz’ that is popping up at random places and it seems out of the place. The word is routinely repeated in customer interactions and when the data is reviewed it is found that the AC’s fan is making a buzzing sound when it is working. This finding is used to improve the product which is otherwise performing well according to customer research data.
The most methodical way to improve your products is to listen to your customers and understand what they expect from you. Data analytic solutions allow brands to conduct customer research impeccably and discern the improvements and changes in products and services that make the biggest impact. Designing a new product or service involves many stages of trial and error; big data helps you to eschew guesswork and base your product development on error-free facts and figures. Data practices such as Predictive Analysis and Machine Learning analyse data in real-time, saving businesses time and effort. It allows businesses to respond to customer and market demands immediately and efficiently.
Interactions with your target audience and a better cognizance of customer attitude should form the core of a business’s plan of action. According to studies, 34% of customer service agents lack adequate customer data and 60% of agents feel that they lack the technology and tools to address their audience productively. Conversational Analysis, sentiment analysis and market research backed by data analytic solutions helps organizations to bridge this gap and foster customer loyalty. Using big data, businesses can craft questions that urge customer responses and analyse them to understand what works for them and what they expect the brand to change. By initiating more conversation around the brand and incorporating customer suggestions, businesses can create and improve empathy from customers.
Investigating conversational data with data mining and business intelligence technologies ensures better customer engagement and improved customer service. Using such data techniques, businesses can understand their customer’s preferred medium of service and focus their effort on reaching their audience via the right medium. Studies have shown that 70% of the customers prefer to solicit customer support through Social Media. Using Social Media messages and general trends, brands can get ahead of issues and use positive as well as negative sentiment to benefit the company in the long run. Follow this link to read more about conversational analysis using Artificial Intelligence.
In business, everything boils down to understanding your customers and Site Lantern’s data analytics services are designed to help you achieve that competently. Take a look at our Industry experience to review our services and find the data analytics solution that proffers holistic, actionable and comprehensive analysis of your brand, boosting customer engagement, retention and sales.