Skip to main content

Understanding the voice of customer through AI


Voice of Customers (VOC)?
In Simple terms VOC is “customers voice”, capturing customer feedback, expectations, preferences and opinions about their experience in interacting with your brand or service.




Why VOC is important for any business:
VOC is important for any because it helps companies identify changes, customer insights and new priorities included in customer feedback.

Role of AI in VOC:
Today’s consumer lives in an omnichannel world. Artificial intelligence can be successfully inked to provide an intelligent, convenient and informed customer experience at any point along with the customer journey. This will result in re-fancied customer experiences and end-to-end customer journeys that are integrated and more personal, so that they feel more logical to customers.

Need of AI in VOC:
Customer voice is a competitive driver of growth when successful and the greatest source of risk when failing. Data insights are one of the primary tools for VOC. VOC datasets are messy and the customer behaviours are turbulent. The rules are undefined and the success criteria are obscure. 
At the same time, this intricacy is precisely the reason why AI can unleash so much value across the “customer voice”. Sales agents and employees in other customer service roles cannot be expected to understand a customer’s full history and derive their own observation from it in real time.
Automated systems cannot be programmed by human with rules to handle every conceivable customer history. Delivering a consistent experience across all enterprise touchpoints requires finding patterns across an overwhelming number of data points.
Ways AI being applied to Improve VOC:
Now that we understand what it takes to successfully apply artificial intelligence (AI) in voice of customer, let’s burrow into some of those applications to see how AI is vent disruption across various aspects of VOC by unifying data, providing insights in real-time, and incorporating analytical business context.
Customer service gets a tremendous makeover:
AI’s biggest impact definitely transform customer service by making it automated, fast and hassle-free. As I previously mentioned, Sales agents and employees in other customer-dealing roles cannot be expected to understand a customer’s entire history prior to each conversation. But, artificial intelligence (AI) is now making it possible.
Below AI applications are giving Customer service a makeover
1.Chatbots
2.Virtual Assistants

Predictive Personalization:
Artificial intelligence (AI) is helping businesses create experiences that easily integrate with consumers’ everyday lives.
Customers will no longer change their pattern of communication when interacting with brands in order to satisfy their needs. Smart prediction and customization will make customers feel as if every product or brand experience was sewn just for them.
AI enabled Customer Analytics & provide insights of High Impact Customers:
Excellent customer experience is achieved when a business remembers its customer and treats them with attention & provide respect and consideration throughout their unique customer journey.


Comments

Popular posts from this blog

Are Your Digital Ads Engaging With Audience

Spends on digital advertising continues to rise, and is forecast to top $203bn in 2018. Yet despite continuing growth, marketers from the most valuable global brand lose confidence in digital advertising’s ability when they are not able to create a seamless, personalized experience, in the decisive moments that matter between people, brands and industry . In today's marketing world most people feel they are being bombarded with increasingly intrusive messages. And while that criticism is aimed at advertising as a whole, the consent is that poorly executed digital advertising is propulsive the trend. How can digital marketers ensure that their campaigns run smoothly across all the channels and platforms of their choice, without alienating people along the way? A new approach to audience targeting This article help business to better understand how people’s online behavior affects their attitude towards digital advertising. The results are instructive: they demonstrate ho

Difference between market research and design research

Market research is an effective tool to assist your business planning. It is about collecting information that provides an insight into your customers thinking, buying patterns, and location. In addition, market research can also assist you to monitor market trends and keep an eye on what your competition is doing. There is a variety of data sources to assist you in researching: Customers Competitors Industry Location There are two types of market research to collect data Primary Research : Referred to as information gathered form original source Survey face -to -face Interviews customer feedback Questionares Secondary Research is information and data that has been already collected and analysed by other sources: industry and trade publications social media and websites marketing and consumer lists newspapers and media IBISWorld Design search is knowledge assembled by a small team dedicated full time to the creation of the product in

How do you detect fraud in financial institutions?

Fraud detection is one of the top priorities for major banks and leading financial institutions, which can be addressed using machine learning. In banking, fraud may include falsify checks or using stolen credit cards. Other forms of fraud may involve emphasize losses or causing an accident with the sole intent for the pay-out. With an unlimited and increasing number of ways someone can commit fraud, detection can be difficult to accomplish . Activities such as downsizing, reorganization, moving to new information systems or confront a cybersecurity breach could weaken an organization's ability to detect fraud. This means facilities such as real-time monitoring for frauds is recommended. Organizations should look for fraud in financial transactions, location, devices used, initiated sessions and authentication systems. How do you detect fraud? The basic approach to  fraud detection with an analytic model is to identify possible predictors of fraud associated with know