Salespeople frequently face the dynamic screening decision of whether to persist in a conversation or abandon it to pursue the next lead. Yet, little is known about how these decisions are made, whether they are efficient, or how to improve them. We study these decisions in the context of high-volume outbound sales where leads are ample, but time is scarce and failure is common. We formalize the dynamic screening decision as an optimal stopping problem and develop a generative language model-based sequential decision agent - a stopping agent - that learns whether and when to quit conversations by imitating a retrospectively-inferred optimal stopping policy. Our approach handles high-dimensional textual states, scales to large language models, and works with both open-source and proprietary language models. When applied to calls from a large European telecommunications firm, our stopping agent reduces the time spent on failed calls by 54% while preserving nearly all sales; reallocating the time saved increases expected sales by up to 37%. Upon examining the linguistic cues that drive salespeople's quitting decisions, we find that they tend to overweight a few salient expressions of consumer disinterest and mispredict call failure risk, suggesting cognitive bounds on their ability to make real-time conversational decisions. Our findings highlight the potential of artificial intelligence algorithms to correct cognitively-bounded human decisions and improve salesforce efficiency.

The salesperson hawking Brother printers on Taobao works hard--like, really hard. At any time of the day, even when there's no audience on the Chinese ecommerce platform, the same woman wearing a white shirt and black skirt is always livestreaming, boasting about the various features of different office printers. She has a phone in one hand and often checks it as if to read a sales script or monitor the viewer comments coming in. "My friends, I've gotta plug this game-changing office tool that can double your workplace efficiency, " the salesperson said during one recent broadcast, trying to achieve the delicate balance between friendliness and precision that has come to define the billion-dollar livestream ecommerce industry in China. Occasionally, she greeted the invisible audience.

User willingness is a crucial element in the sales talk process that affects the achievement of the salesperson's or sales system's objectives. Despite the importance of user willingness, to the best of our knowledge, no previous study has addressed the development of automated sales talk dialogue systems that explicitly consider user willingness. A major barrier is the lack of sales talk datasets with reliable user willingness data. Thus, in this study, we developed a user willingness-aware sales talk collection by leveraging the ecological validity concept, which is discussed in the field of human-computer interaction. Our approach focused on three types of user willingness essential in real sales interactions. We created a dialogue environment that closely resembles real-world scenarios to elicit natural user willingness, with participants evaluating their willingness at the utterance level from multiple perspectives. We analyzed the collected data to gain insights into practical user willingness-aware sales talk strategies. In addition, as a practical application of the constructed dataset, we developed and evaluated a sales dialogue system aimed at enhancing the user's intent to purchase.

Large language models (LLMs), like ChatGPT and GPT-4, have demonstrated remarkable abilities in natural language understanding and generation. However, alongside their positive impact on our daily tasks, they can also produce harmful content that negatively affects societal perceptions. To systematically assess the safety of Chinese LLMs, we introduce SuperCLUE-Safety (SC-Safety) - a multi-round adversarial benchmark with 4912 open-ended questions covering more than 20 safety sub-dimensions. Adversarial human-model interactions and conversations significantly increase the challenges compared to existing methods. Experiments on 13 major LLMs supporting Chinese yield the following insights: 1) Closed-source models outperform open-sourced ones in terms of safety; 2) Models released from China demonstrate comparable safety levels to LLMs like GPT-3.5-turbo; 3) Some smaller models with 6B-13B parameters can compete effectively in terms of safety. By introducing SC-Safety, we aim to promote collaborative efforts to create safer and more trustworthy LLMs. The benchmark and findings provide guidance on model selection. Our benchmark can be found at https://www.CLUEbenchmarks.com

The next time you buy a car, it might not be from your standard dealership - it could be from an AI bot. The prediction comes from Johan Sundstrand, the CEO of the Swedish video-tech company Phyron - he believes the change could happen as soon as 2025. He said: 'It's only a matter of time before artificial intelligence (AI) is selling cars as effectively as a human salesperson. 'The speed at which self-learning software is developing and being embraced by retailers means that a fully competent AI-powered sales bot is as close as 18 months away.' Phyron is a Swedish video-tech company that have been developing the world's first fully automated AI-enhanced video solution for the automotive industry Phyron is a Swedish video-tech company that have been developing the world's first fully automated AI-enhanced video solution for the automotive industry. The unique AI software and its algorithms enable Phyron to create videos for car advertisements which can be used on brand or retailer websites, across social media channels and targeted email distribution.

Existing multimodal task-oriented dialog data fails to demonstrate the diverse expressions of user subjective preferences and recommendation acts in the real-life shopping scenario. This paper introduces a new dataset SURE (Multimodal Recommendation Dialog with SUbjective PREference), which contains 12K shopping dialogs in complex store scenes. The data is built in two phases with human annotations to ensure quality and diversity. SURE is well-annotated with subjective preferences and recommendation acts proposed by sales experts. A comprehensive analysis is given to reveal the distinguishing features of SURE. Three benchmark tasks are then proposed on the data to evaluate the capability of multimodal recommendation agents. Based on the SURE, we propose a baseline model, powered by a state-of-the-art multimodal model, for these tasks.

Last month, Microsoft fired a powerful salvo by launching Viva Sales, an application with embedded generative AI technology designed to help salespeople and sales managers draft tailored customer emails, get insights about customers and prospects, and generate recommendations and reminders. A few weeks later, Salesforce (the company) followed by launching Einstein GPT. Sales, with its unstructured, highly variable, people-driven approach, has been a laggard behind functions such as finance, logistics, and marketing when it comes to utilizing digital technologies. But now, sales is primed to quickly become a leading adopter of generative AI -- the form of artificial intelligence used by OpenAI (the company behind ChatGPT) and its competitors. AI-powered systems are on the way to becoming every salesperson's (and every sales manager's) indispensable digital assistant.

Measuring sales performance is a crucial aspect of running a successful business. Accurately tracking and analyzing sales data helps companies understand their strengths and weaknesses, perform forecasts, identify trends, and make informed decisions that drive growth. In this article, I will illuminate how some simple statistical models can be used for measuring sales performance. Whether it is a small or enterprise sales team, simple quantitative techniques can be used to provide valuable sales insights or draw attention to areas of need. After reading this article, you will see various examples how simple models are applied in real life scenarios. Note: All the images in the article were generated by Artificial Intelligence using Stable Diffusion 2.x.

Virtual sales meetings have made it tougher than ever for salespeople to read the room. So, some well funded tech providers are stepping in with a bold sales pitch of their own: that AI can not only help sellers communicate better, but detect the "emotional state" of a deal -- and the people they're selling to. In fact, while AI researchers have attempted to instill human emotion into otherwise cold and calculating robotic machines for decades, sales and customer service software companies including Uniphore and Sybill are building products that use AI in an attempt to help humans understand and respond to human emotion. Virtual meeting powerhouse Zoom also plans to provide similar features in the future. "It's very hard to build rapport in a relationship in that type of environment," said Tim Harris, director of Product Marketing at Uniphore, regarding virtual meetings.

By Vanshika Sharma Artificial intelligence (AI) is already transforming businesses. We've seen significant advancements in the last year alone, and it's not only making sales more accurate but is also reducing the administrative burden on sales reps and revenue operations. With the increasing shift towards digital, businesses have aggressively deployed major tech-backed evolution. Utilizing the advancement of this shift, one of India's leading and only certified carbon neutral agarbathi manufacturers, Cycle Pure Agarbathi, a vertical of N. Ranga Rao and Sons, has implemented AI-powered sales force automation to become a more efficient and connected enterprise. Cycle Pure looks towards AI to achieve better sales execution and retain its dominance in the segment through route optimization, GRV monitoring, competition tracking, and analytics.