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.

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.

Generative artificial intelligence tools like ChatGPT are changing the way that companies and salespeople are communicating with customers for the better, said Clara Shih, CEO of Salesforce's Service Cloud business. "You look at how salespeople work today, and most of them, they dread writing sales emails; they'd much rather be out there with customers," Shih said on CNBC's "Squawk Box" on Thursday. "So they can offload those tasks that are more mundane … they want to focus on engaging with the customer and problem solving." Shih drew a clear line between how generative AI can be utilized by the general person compared to business clients and enterprise users: "We're not talking about writing funny poems, we're talking about writing sales emails and customer service responses that agents can send to get back to customers faster." Earlier this week, Salesforce launched what it called the first generative AI CRM technology, Einstein GPT.

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.

Conventionally, salespeople are responsible for juggling tasks like following a playbook, capturing responses, building rapport and updating a customer relationship management (CRM) system during sales calls. As these tend to be repetitive and time-consuming, tedium can quickly set in. The average salesperson spends more than five hours a week updating CRM records, according to a Dooly survey. In search of a solution, sales tech entrepreneur Eldad Postan-Koren and cybersecurity practitioner Bar Haleva co-created Winn.AI, an AI-powered assistant designed to help sales teams automatically track, capture and update CRM entries. Winn.AI monitors sales calls and records key data, in theory reducing the need for salespeople to note-take themselves.

Winn.AI handles the busywork of sales, so salespeople can focus on selling. Capture meeting notes & customer data in real-time and update your CRM in a click.

We are here today to discuss every detail of machine learning in marketing. When you heard the phrase "artificial intelligence" a few decades ago, the first images that undoubtedly came to mind were robots destroying humankind. These days, this phrase is often associated with good things. Almost everyone comes into contact with machine learning daily. For instance, you might interact with a chatbot on a website, see advertising offers relevant to your interests, or configure a spam filter in your email account. Machine learning allows marketers to decide on important matters quickly using vast data sets. We'll discuss the decisions you can base on big data in this article. A type of artificial intelligence called machine learning use algorithms to make judgments and predictions based on information. It is utilized in various contemporary contexts, including healthcare, banking, and advertising.

In 2020, the U.S. insurance industry was worth a whopping $1.28 trillion. The American insurance industry is one of the largest markets in the world. The massive amount of premiums means there is an astronomical amount of data involved. Without artificial intelligence technology like machine learning, insurance companies will have a near-impossible time processing all that data, which will create greater opportunities for insurance fraud to happen. Insurance data is vast and complex, composed of many individuals with many instances and many factors used in determining the claims.

Chatbots using conversational AI are great for customer service and marketing automation. These chatbots can be used to engage potential customers in meaningful conversations about products and services before they push for a sales pitch (like the annoying telemarketers). Chatbots make it possible to communicate with clients and offer 24/7 services for a fraction of what you would pay for human interaction. Chatbot Conversational AI is more efficient because it can handle hundreds upon hundreds of conversations at once, personalize each one and provide context information to answer questions instantly. This article will explain how chatbots work, and outline six ways that your company can make use of this technology to increase sales by three times without annoying or being intrusive to its customers.