AI's Impact on Improving Customer Experience

Abhinav Anand | July 11, 2022 | 99 views | Read Time : 2 min

AI's Impact on Improving Customer Experience
To enhance the consumer experience, businesses all over the world are experimenting with artificial intelligenace (AI), machine learning, and advanced analytics. Artificial intelligence (AI) is becoming increasingly popular among marketers and salespeople, and it has become a vital tool for businesses that want to offer their customers a hyper-personalized, outstanding experience.

Customer relationship management (CRM) and customer data platform (CDP) software that has been upgraded with AI has made AI accessible to businesses without the exorbitant expenses previously associated with the technology.

When AI and machine learning are used in conjunction for collecting and analyzing social, historical, and behavioral data, brands may develop a much more thorough understanding of their customers. In addition, AI can predict client behavior because it continuously learns from the data it analyzes, in contrast to traditional data analytics tools. As a result, businesses may deliver highly pertinent content, boost sales, and enhance the customer experience.

Predictive Behavior Analysis and Real-time Decision Making

Real-time decisioning is the capacity to act quickly and based on the most up-to-date information available, such as information from a customer's most recent encounter with a company. For instance, Precognitive's Decision-AI uses a combination of AI and machine learning to assess any event in real-time with a response time of less than 200 milliseconds. Precognitive's fraud prevention product includes Decision-AI, which can be implemented using an API on a website.

Marketing to customers can be done more successfully by using real-time decisioning. For example, brands may display highly tailored, pertinent content and offer to clients by utilizing AI and real-time decisioning to discover and comprehend a customer's purpose from the data they produce in real-time.

By providing deeper insights into what has already happened and what can be done to facilitate a sale through suggestions for related products and accessories, AI and predictive analytics are able to go further than historical data alone. This increases the relevance of the customer experience, increases the likelihood that a sale will be made, and increases the emotional connection that the customer has with a brand.

Spotlight

SparkCognition

SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance. Security, Machine Learning, Big Data, Internet of Things (IoT), Cloud, Predictive Analytics, Cognitive, Industrial Internet, Artificial Intelligence, Endpoint Protection, IIoT, and Natural Language Processing.

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Language model systems, often known as text understanding and generation systems, are the newest trend in business. However, not every language model is made equal. A few are starting to take center stage, including massive general-purpose models like OpenAI's GPT-3 and models tailored for specific jobs. There is a third type of model at the edge that is intended to run on Internet of Things devices and workstations but is typically very compressed in size and has few functionalities. Large Language Models Large language models, which can reach tens of petabytes in size, are trained on vast volumes of text data. As a result, they rank among the models with the highest number of parameters, where a "parameter" is a value the model can alter on its own as it gains knowledge. The model's parameters, which are made of components learned from prior training data, fundamentally describe the model's aptitude for solving a particular task, like producing text. Fine-tuned Language Models Compared to their massive language model siblings, fine-tuned models are typically smaller. Examples include OpenAI's Codex, a version of GPT-3 that is specifically tailored for programming jobs. Codex is both smaller than OpenAI and more effective at creating and completing strings of computer code, although it still has billions of parameters. The performance of a model, like its capacity to generate protein sequences or respond to queries, can be improved through fine-tuning. Edge Language Models Edge models, which are intentionally small in size, occasionally take the shape of finely tuned models. To work within certain hardware limits, they are occasionally trained from scratch on modest data sets. In any event, edge models provide several advantages that massive language models simply cannot match, notwithstanding their limitations in some areas. The main factor is cost. There are no cloud usage fees with an edge approach that operates locally and offline. As significant, fine-tuned, and edge language models grow in response to new research, they are likely to encounter hurdles on their way to wider use. For example, compared to training a model from the start, fine-tuning requires less data, but fine-tuning still requires a dataset.

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Low-code and No-code: A Business' New Best Friend

Article | July 5, 2022

Businesses are starting to integrate artificial intelligence (AI) into their workflow in greater numbers as a result of the growth of digital transformation and developments in machine learning (ML). As a result, platforms that need no coding, as well as their low-code counterparts, are becoming more popular. This development is a step toward computer science's long-term objective of automating manual coding. Low-code/no-code AI platforms will be beneficial to businesses in more data-driven industries like marketing, sales, and finance. AI can assist in a variety of ways, including automating invoicing, evaluating reports, making intelligent suggestions, and anticipating churn rates. How Does an Organization Look at Low-code/No-code as the Future? Developers and other tech-related positions are in high demand, particularly in the fields of AI and data science. Organizations have the chance to close the gap with the aid of citizen data scientists who don't require an AI professional to design unique AI solutions for many scenarios, thanks to low-code and no-code AI technologies. The demand for technological solutions and AI technologies is rising significantly as the technological landscape rapidly changes. AI systems, for example, require complex software that uses a lot of code, a variety of frameworks, and the Internet of Things (IoT). One person's capacity to comprehend every technical detail is strained by the array of complicated technology. Software delivery must be timely, effective, and secure while maintaining high standards. Conclusion Low-code AI solutions offer the speed, ease of use, and adaptability of ready-made software solutions while also drastically reducing the time to market for AI solutions and the cost of recruiting software and computer vision engineers. Organizations are free to construct the architecture, functionality, or pipeline that best suits their project, the sky being the limit. However, creating such unique models may be both costly and time-consuming. Therefore, employing low-code/no-code platforms would apply to particular pipeline actions that would streamline and accelerate the processes.

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Spotlight

SparkCognition

SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance. Security, Machine Learning, Big Data, Internet of Things (IoT), Cloud, Predictive Analytics, Cognitive, Industrial Internet, Artificial Intelligence, Endpoint Protection, IIoT, and Natural Language Processing.

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The Kapture team is pleased to announce that Pavit Ponnanna will be stepping into the position of Head of CX (Customer Experience). With cross-functional experience of 21+ years in customer services, operations management, Pavit's strength is customer experience and brings a deep understanding of the CX landscape. Pavit will play an instrumental role in accelerating CX for Kapture. Commenting on his appointment, Vikas Garg: CTO & Co-founder, Kapture, said," We are delighted to have Pavit Ponnanna join us as our Head of Customer Experience. The last few years have been incredible for us and we have witnessed exponential growth across geographies. Our focus is now to evolve into a more structured organisation that's prepared for the planned growth in the coming years. Having Pavit is going to be a big help in this journey." “I am very excited about joining such a competent and capable team. Kapture has been very successful in combining industrial technology with digital solutions and has become a frontrunner in providing a Customer Support Automation platform that provides businesses across industries with all-in-one customer service software. I am certainly looking forward to developing the company further with a clear industrial ambition to change the future of CX,“ said Pavit Ponnanna. About KaptureCRM Kapture, a Customer Relationship Management (CRM) software firm, was established in 2015, on the simple idea of providing a smarter way for businesses to manage customer relationships through a single automated platform. Simply put, their goal is to organise customer information and make it contextually and instantly accessible to all end users at any given time. Keeping customer experience at the forefront of it all.

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Deci Introduces World’s Most Advanced Semantic Segmentation Models

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Deci, the deep learning company harnessing AI to build AI, today announced a new set of industry-leading semantic segmentation models, dubbed DeciSeg. Deci’s proprietary Automated Neural Architecture Construction (AutoNAC) technology automatically generated semantic segmentation models that significantly outperform the most powerful models publicly available, such as the MobileViT released by Apple, and the DeepLab family released by Google. Deci’s models deliver more than 2x lower latency, as well as 3-7% higher accuracy. Semantic segmentation is one of the most widely used computer vision tasks across many business verticals, including automotive, smart cities, healthcare, and consumer applications, and is often required for many edge AI applications. However, significant barriers exist to running semantic segmentation models directly on edge devices, such as high latency and the inability to deploy those models due to their size. With DeciSeg models, semantic segmentation tasks that previously could not be carried out at the edge because they were too resource intensive are now possible. This allows companies to develop new use cases and applications on edge devices, reduce inference costs (since AI practitioners will no longer need to run these tasks in expensive cloud environments), open new markets, and shorten development times. “DeciSegs are an example of the power of Deci’s AutoNAC engine capabilities to generate custom hardware-aware deep learning models with unparalleled performance on any hardware. AI teams can easily use DeciSegs models or leverage Deci’s AutoNAC engine to build and deploy custom models that run real-time computer vision tasks on their edge devices.” said Yonatan Geifman, PhD, co-founder and CEO of Deci. Deci’s platform has a proven-track record in enabling AI at the edge and empowering AI teams to build and deploy production grade deep learning models. Earlier this year, Deci announced the discovery of DeciNets for CPUs, which reduced the gap between a model’s inference performance on a GPU versus a CPU by half, without sacrificing the model’s accuracy, enabling AI to run on lower cost, resource constrained hardware. “In the world of automated deep neural network design and construction, Deci’s AutoNAC technology is a game changer. It uses deep learning to search vast spaces of neural networks for the model most appropriate for a particular task and particular AI chip. In this case, AutoNAC was applied to the Pascal VOC Semantic Segmentation task on NVIDIA’s Jetson Xavier NX™ chip and we are very pleased with the results.” said Ran El-Yaniv, co-founder and Chief Scientist of Deci and Professor of Computer Science at the Technion – Israel Institute of Technology. Deci’s platform is serving customers across industries in various production environments including edge, mobile, data centers and cloud. To learn more about how leading AI teams leverage Deci’s platform to build production grade models and accelerate inference performance, visit here. About Deci Deci enables deep learning to live up to its true potential by using AI to build better AI. With the company's deep learning development platform, AI developers can build, optimize, and deploy faster and more accurate models for any environment including cloud, edge, and mobile, allowing them to revolutionize industries with innovative products. The platform is powered by Deci's proprietary automated Neural Architecture Construction technology (AutoNAC), which automatically generates and optimizes deep learning models' architecture and allows teams to accelerate inference performance, enable new use cases on limited hardware, shorten development cycles and reduce computing costs. Founded by Yonatan Geifman, Jonathan Elial, and Professor Ran El-Yaniv, Deci's team of deep learning engineers and scientists are dedicated to eliminating production-related bottlenecks across the AI lifecycle.

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Seismic and Microsoft partner to power the future of sales with Viva Sales

Seismic | September 26, 2022

Seismic, the global leader in enablement, today announced a new partnership with Microsoft for its seller experience application, Viva Sales. Together, Microsoft and Seismic will transform the future of sales and streamline daily workflows for the modern salesperson. Today’s salespeople use numerous apps and tools in their daily work but are challenged with bringing together the bigger picture across meetings, email, chat, and CRM. Breaking down silos of data, Viva Sales empowers sellers in their flow of work within Microsoft 365 and Teams, reducing busy work and maximizing sellers’ time for the most valuable area of their work – engaging with customers and closing deals. ​​ ​​Embedded within the Viva Sales workflow, Seismic will provide content production, collaboration, task automation, and engagement intelligence for Viva Sales users across the meeting experience to help drive deals and relationships forward. The joint vision of Microsoft Viva Sales and Seismic is to streamline the buyer engagement experience for relationship-based sales teams and increase productivity through preparation, automation, and intelligence. ​​“Microsoft has been one of our longstanding partners and we’ve always had close alignment across our product and go-to-market teams, so we’re thrilled to help launch Viva Sales. “Our leadership in sales enablement, content automation, enablement intelligence, and buyer engagement will perfectly complement the mission of Viva Sales to improve seller productivity and drive revenue. We can’t wait to get started.” ​​ Hayden Stafford, President and Chief Revenue Officer at Seismic Microsoft’s partnership with Seismic for Viva Sales will add AI-powered capabilities for virtual meetings, the key vehicle for modern sales teams to interact with prospects and customers. As the first step in this journey, the Seismic Enablement Cloud™ will provide recommended content and training for follow-up as part of the Viva Sales AI-powered post-meeting call summaries.​​ Looking ahead, sales organizations can expect content and training recommendations, pre-built digital sales rooms, and meeting analysis powered by Seismic. ​​“We’re united with Seismic in our commitment to empower sellers through relevant content and an improved seller experience. Our plan to integrate Seismic with Viva Sales will help sellers have more personalized customer engagements whether they are in the office or on the road, with a helpful assist from the AI-driven insights and content,” said Lori Lamkin, CVP, Dynamics 365 Customer Experience Applications. About Seismic Seismic is the global leader in enablement, helping organizations engage customers, enable teams, and ignite revenue growth. The Seismic Enablement Cloud™ is the most powerful, unified enablement platform that equips customer-facing teams with the right skills, content, tools, and insights to grow and win. From the world’s largest enterprises to startups and small businesses, more than 2,000 organizations around the globe trust Seismic for their enablement needs. Seismic is headquartered in San Diego with offices across North America, Europe, and Australia.

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