Artificial intelligence (AI) is fast developing, and it is already possible to create conversational virtual agents that can understand and respond to a wide range of questions. But, as a business owner, you may be wondering how they differ and which is the best fit for your organizational model. To answer those questions, in this article, we'll compare chatbots and conversational AI. But before we go further first, let's understand what conversational AI is. Conversational AI refers to any technology that allows users to speak or type to it and receive a response.

In this era of abundant digital information, customer satisfaction has become one of the prominent factors in the success of any business. Customers want a one-click solution for almost everything. They tend to get unsatisfied if they have to call about something which they could have done online. Moreover, incoming calls are a high-cost component for any business. Thus, it is essential to develop a framework capable of mining the reasons and motivators behind customer calls. This paper proposes two models. Firstly, an attention-based stacked bidirectional Long Short Term Memory Network followed by Hierarchical Clustering for extracting these reasons from transcripts of inbound calls. Secondly, a set of ensemble models based on probabilities from Support Vector Machines and Logistic Regression. It is capable of detecting factors that led to these calls. Extensive evaluation proves the effectiveness of these models.

For years, businesses have sought to provide customers with more self-service options and increase automation rates in their contact centers using speech-enabled interactive voice response systems (IVRs). They have also invested heavily in developing web chatbots. However, these systems were complicated to develop and required organizations to purchase, host, and manage a vast array of software, hardware, and equipment. Applications were also created in silos, requiring multiple development projects while making it difficult for applications to share data and context. A number of disruptive innovations have made it easier and more affordable to deploy AI-and-speech-enabled self-service.

As more companies roll out digital infrastructure, they are ingesting greater volumes of data that can be used by business analysts to gauge customer intent and boost transactions. Complexity and lack of data scientists have made that transition harder for mid-size firms looking to monetize "dark" data. Machine learning vendors are therefore automating key aspects of data science workflows that would allow domain experts to customize pipelines and algorithms based on specific data types. AutoML approaches are promoted as boosting the quantity and quality of machine learning models produced on, say, a monthly basis. That's among the goals of a new AutoML platform unveiled this week by Stradigi AI.

Customers are connected and mobile-first, and they're in control of their journeys and experiences. The pace of change for consumers and technology has never been faster. Yet, brands are still chasing customer intent through traditional means. The good news is that because of digital, customer signals give away exactly how to better serve them in every moment throughout their journey. Furthermore, in an era of machine learning, marketers can finally shift from trying to keep up with customer intent and instead, predict it.

Retailers are increasingly looking to harness the rewards available from social media. Social platforms are already widely used by retailers in an effort to connect with customers in an engaging and authentic manner. Sentiment analysis is used widely, but studies have shown that its accuracy can be as low as 58 per cent. Furthermore, it typically misses detailed signals such as specific nuances within customer concerns, plus emotional intent. Without intent it is hard to take action.

In recent years, the proliferation of digital technologies has created multiple customer service channels and touchpoints through which citizens can access online government services. Unfortunately, user experience is often overlooked in the design and deployment of these new digital services. Citizens' expectations of service are shaped not only by their interactions with government agencies, but also by their everyday digital experiences. For example, a recent Accenture survey of over 5,000 citizens from five countries found that as they encounter more user-friendly AI solutions in their daily lives, expectations for government use of these technologies increase. In this changing environment, the need for a convenient and seamless customer experience across all engagement channels has never been more pressing.

There is no longer a delineation between digital and traditional customers. As they do, they gain a penchant for modern conveniences, such as speed, utility and real-time assistance. Along the way, they also become more and more impatient and demanding. To engage today's customer takes a modern approach to marketing where advanced technologies and customer optimization set the stage for what I call "adviser brands." Adviser brands represent a shift away from a traditional focus on top-of-the-funnel campaigns and marketing-centric metrics.

Leading-edge marketing organizations are shifting both strategy and culture to prioritize data storage and application to produce actionable insights. Results from a recent survey conducted by MIT Technology Review Insights in association with Google showed that "leaders" (companies that have experienced significant growth in revenue or market share) are more likely than "laggard" organizations to leverage machine learning (ML) to help their marketers better understand customer intent. Armed with insight into customer behaviors, marketers can focus on those customers with high lifetime value, providing the personalized and relevant offers they seek. ML assists marketers in extracting intelligence from the enormous amounts of data their organizations generate daily, enabling certain customers to view the performance of specific marketing campaigns during specific time periods. ML is a powerful tool that uses empirical data to allow marketers to quickly respond to changing market conditions and customer needs by making informed decisions in real time.

The third edition of Drivers for Deploying Speech Analytics 2018, an annual survey commissioned by Uniphore Software Systems, was released today. The survey revealed that 60% of Indian companies deploy speech analytics for marketing initiative support and quick identification of customer intent, equally. This is in line with the finding of the previous year's survey underlining the growing relevance of speech analytics in increasing sales and delivering superior customer experience. Root cause remediation of customer experience failures came a close second with 58%. In North America and South East Asia, the top driver for deploying speech analytics was quick identification of customer intent.