In this modern era of technology with e-commerce developing at a rapid pace, it is very important to understand customer requirements and details from a business conversation. It is very crucial for customer retention and satisfaction. Extracting key insights from these conversations is very important when it comes to developing their product or solving their issue. Understanding customer feedback, responses, and important details of the product are essential and it would be done using Named entity recognition (NER). For extracting the entities we would be converting the conversations to text using the optimal speech-to-text model. The model would be a two-stage network in which the conversation is converted to text. Then, suitable entities are extracted using robust techniques using a NER BERT transformer model. This will aid in the enrichment of customer experience when there is an issue which is faced by them. If a customer faces a problem he will call and register his complaint. The model will then extract the key features from this conversation which will be necessary to look into the problem. These features would include details like the order number, and the exact problem. All these would be extracted directly from the conversation and this would reduce the effort of going through the conversation again.

Jiminny, a UK headquartered conversation intelligence provider supporting sales teams across the world, has closed a $16.5 million series A funding round. The round was led by Kennet Partners, a transatlantic growth equity firm, adding to its targeted portfolio of technology growth stage investments. Sales teams use Jiminny's platform to record and analyse new and existing customer conversations in real-time. These insights are used by the business to improve performance and ultimately drive revenue and growth. A range of organisations, from FTSE 100 constituent Informa to Europe's leading expense management solution, Pleo, are using the platform.

The real-time contact center intelligence provider Cresta Intelligence Inc. said today it's updating its platform with the addition of new tools that enable smarter business insights and more capable chatbots. Cresta has created an artificial intelligence-powered platform for contact centers that's designed to guide customer service agents on what to say and do at each turn of a customer conversation. For instance, it provides real-time prompts for agents at pivotal moments in a conversation to optimize their response. At the same time, it coaches agents on an ongoing basis to hone their strengths and correct their weaknesses. In this way, Cresta helps to make sure that agents ask the right discovery questions at the beginning of a conversation, so as to solve customer's problems more effectively.

In 2017, we launched Amazon Transcribe, an automatic speech recognition (ASR) service that makes it easy to add speech-to-text capabilities to any application. Today, I'm very happy to announce the availability of Amazon Transcribe Call Analytics, a new feature that lets you easily extract valuable insights from customer conversations with a single API call. Each discussion with potential or existing customers is an opportunity to learn about their needs and expectations. For example, it's important for customer service teams to figure out the main reasons why customers are calling them, and measure customer satisfaction during these calls. Likewise, salespeople try to gauge customer interest, and their reaction to a particular sales pitch.

We live in a world where the globe is connected and communication made easy with the click of a button. In this social age, the evolution of the World Wide Web (www), characterized by the advancement of smartphones and semantic technology, has redefined social media as a retail platform and an indispensable marketing tool. Social media marketing (SMM) is a form of digital marketing that involves sharing content on social media platforms in an attempt to actualize a firm's branding, sales, and web traffic goals. Social media has become essential to helping brands connect to a wider range of customers, establish brand presence, and increase sales both in-store and online. Considering the size of this new virtual market, it comes as no surprise that marketers would choose to use social media to increase brand awareness.

By Ravi Saraogi Telecommunications might not be the first sector that comes to mind when we think of industries that are considered the pioneers in customer experience. During COVID-19, the expectations for customer experience are even more profound and the stress on telecom providers couldn't be higher. While we would not have been able to maintain our ability to work, stay socially connected, learn, shop, get healthcare, and more during the pandemic without the connectivity that telecom companies deliver, the current customer experience has tremendous potential for transformation. Despite the growing global dependency on reliable and fast connectivity, downward trends that have impacted telecom companies for years continue to affect their present performance and hinder their ability to emerge stronger after the crisis. Slow growth due to declining ARPU (average revenue per user), industry saturation, customer churn, competitive disruptions, and the need to achieve return on investment into 5G and other technologies all continue to impact the industry.

With contact centers globally adjusted to the new normal, there are a number of artificial intelligence levers they can pull to deliver increased levels of customer satisfaction and major cost savings that aren't constrained by the limited capabilities of today's chatbots., Agents remain a critical part of the equation. When contact centers leverage the opportunity to use AI effectively, they can reduce the cost of customers switching to competitors due to poor customer service, which already costs U.S. industries $1.6 trillion. The proper utilization of AI will also result in the simplification of the agent experience which delivers an outsized impact on reducing training time and costs by 40%, lower absenteeism, and reduced agent attrition. By using AI across all customer touchpoints--on the web, app, and voice--enterprises can finally achieve the omni experience that has evaded the industry for years.

Today, Amazon Web Services (AWS) announced the general availability of Contact Lens, machine learning powered contact center analytics for Amazon Connect. Amazon Connect is an easy-to-use cloud contact center that helps companies of any size deliver superior customer service at lower cost. With Contact Lens, supervisors and quality assurance managers can easily understand the sentiment, trends, and compliance risks of customer conversations within their contact centers. These insights allow companies to effectively train agents, replicate successful interactions, and identify important customer feedback. With millions of hours of call recordings, contact centers hold valuable insights into the brand perception and customer satisfaction of a company.

When unexpected events occur and customer demand increases what can companies do to smooth out the resulting peaks and troughs? Henry Jinman at EBI.AI outlines four ways conversational AI can help and shares a real-life example. First there were Storms Ciara and Dennis, now Coronavirus. The impacts of the pandemic are changing every minute, causing widespread fear and uncertainty around the world. Frontline teams are under particular pressure as agents struggle to manage surging and often widely fluctuating volumes of customer calls. When disaster strikes, what can businesses do to smooth the peaks and troughs?

"The biggest risk is not taking any risk. In a world that is changing really quickly, the only strategy that is guaranteed to fail is not taking risks." Customer experience is the key to business success and with new technologies emerging and getting popular; it has become imperative for us to stay on track and take risks. Because the only thing constant in the world of customers is change. A business needs to evolve, change, and grow to match customer demands and expectations. We see data science and AI leaving their mark on almost every existing process; it is no longer a farfetched what we saw in those robots taking over the world movies.