In the design of customer relationship management (CRM) systems, accurately identifying customer types and offering personalized services are key to enhancing customer satisfaction and loyalty. However, this process faces the challenge of discerning customer voices and intentions, and general pre-trained automatic speech recognition (ASR) models make it difficult to effectively address industry-specific speech recognition tasks. To address this issue, we innovatively proposed a solution for fine-tuning industry-specific ASR models, which significantly improved the performance of the fine-tuned ASR models in industry applications. Experimental results show that our method substantially improves the crucial auxiliary role of the ASR model in industry CRM systems, and this approach has also been adopted in actual industrial applications.

RPA (Robotic Process Automation) helps automate repetitive tasks performed by users, often across different software solutions. Regardless of the RPA tool chosen, the key problem in automation is analyzing the steps of these tasks. This is usually done by an analyst with the possible participation of the person responsible for the given activity. However, currently there exists no one-size-fits-all description language, which would allow to record, process, and easily automate steps of specific tasks. Every RPA solution uses a different notation, which is not easily human-readable, editable, and which cannot be applied to a different automation platform. Therefore, in this paper, we propose a new eXtensible Robotic Language (XRL) that can be understood by both programmers and non-programmers to automate repetitive business processes.

The advance of Machine Learning (ML) has led to a strong interest in this technology to support decision making. While complex ML models provide predictions that are often more accurate than those of traditional tools, such models often hide the reasoning behind the prediction from their users, which can lead to lower adoption and lack of insight. Motivated by this tension, research has put forth Explainable Artificial Intelligence (XAI) techniques that uncover patterns discovered by ML. Despite the high hopes in both ML and XAI, there is little empirical evidence of the benefits to traditional businesses. To this end, we analyze data on 220,185 customers of an energy retailer, predict cross-purchases with up to 86% correctness (AUC), and show that the XAI method SHAP provides explanations that hold for actual buyers.

Through pattern detection, machine learning is already transforming business processes by making sense of and automatically capturing and filing incoming content. Yet it is only when intelligent process automation is applied with broader enterprise context that global businesses will experience the full value of artificial intelligence, argues Dr John Bates, CEO of SER Group.

My dog died a few years ago. She was only about 7 years old and had a lot of health problems. But one thing I don't miss: my pharmacy bill. That's because Lavender (my wife chose the name, not me) was on no less than seven medications. And a day rarely went by without me a text message from my pharmacist at the CVS near me.

Artificial Intelligence (AI) is everywhere -- in our phones, in our offices, in our cars, and pretty much everything else one can imagine. So, it only makes sense this technology has made inroads into the world of e-commerce as well, which happens to be one of the most popular industries in the world at the moment. It is revolutionizing eCommerce for small and big businesses where it is being used by various retail companies to develop a better understanding of their customers. Despite the popularity of the sector, it is starting to feel the need for better and advanced tools that can help address some of the more complex challenges. You would agree AI-powered commerce enables customer-centric online searches, identifies prospective customers, answers customers' queries, simplifies sales techniques, establishes actual conversations with customers through chatbots, etc. Suffice it to say that AI is more than ready to do that and so much more.

At Contentful we are building a Marketing Growth team for rapid product experiments. We are looking for Data Engineers to power the data and event pipelines that ensure the team can make data driven decisions on the experiments and drive actions across our insight and decisioning tools. You will help design and implement tracking and data pipelines and contribute to designing and building strong data models that enable rapid and reliable decision making. You will work closely with Data Analysts, Product Managers, Engineers and Marketers to ensure the needs and priorities are understood. This role will be focused on the data engineering required to drive our Growth Team experiments including integrating with CRM systems, powering analysis and segmentation, and delivering the information required for reporting and self-serve discovery.

Though your CRM system might be fulfilling all your customer relationship management needs, you might be amused to know how Machine Learning can add wings to your CRM solution. If you are looking to improve overall customer engagement, it is imperative to understand how CRM and machine learning can bring a catalytic change for your business. Machine learning is a lot like (AI) or Artificial intelligence, which assists computers or machines to learn without the need of explicit programming. Its data analysis method automates analytical model building. The technology enables machines to perform new tasks after being programmed using historic data sets.

The pandemic has accelerated the transition to a digital-first environment. As such, software and other cloud services are more important now than ever to ensure teams are in-sync and collaborating effectively. To best meet these goals, CRM systems are leveraging new and emerging technology like AI to save time on manual data entry, and give their teams a competitive advantage. However, not all systems are created equal. It is critical to choose a system that maximizes your efficiency – when it works well, your team's increased productivity enables you to spend more time doing the work that matters (finding and closing opportunities).

Predictive opportunity scoring, predictive lead scoring, predictive analytics for forecast ... [ ] management and guided selling are the top four AI-based technologies B2B selling organizations plan to deploy in the next 12 months, according to Gartner. Sales organizations are under increased pressure to reduce selling costs while stabilizing margins and closing only the most profitable deals. Marketing teams across all industries are under increased pressure to increase the quantity, quality and qualification levels of leads that deliver the highest probability of closing this year. AI-based price and revenue management applications and platforms are proving indispensable in keeping sales, marketing, operations, services, accounting and senior management synchronized with real-time updates to achieve more. McKinsey's Global AI Survey: AI proves its worth, but few scale impact survey provides insights into where AI is making its greatest contributions and reducing expenses.