Twitter Facebook LinkedIn Flipboard 0 Remember when “AI” was but a reference to the worst Haley Joel Osment movie? Hopefully, that’s ancient history as far as your organization’s martech stack is concerned. Marketing and sales functions across industries have woken up to the potential of today’s evolving data-driven approaches to operations, and business leaders are not only acknowledging, but embracing the reality that artificial intelligence (AI) is no longer a science fiction fever dream but the key to successful data-driven initiatives. AI can be found in many technologies, and it’s increasingly found in marketing and sales solutions for data-driven activities. There’s a reason more and more product offerings contain aspects of AI, and it’s because artificial intelligence can augment human prowess and expertise, thereby improving an organization’s data-driven marketing and sales performance. AI can be implemented in a variety of ways, and Aberdeen research has identified 5 key applications of AI that Best-in-Class organizations use to improve their performance. (At Aberdeen, “Best-in-Class” means the top 20% of performers in a given research study.) Natural Language Processing Natural language processing (NLP) algorithms allow users to interact with data within the context of how the language functions of their brains naturally work. NLP empowers decision makers who probably aren’t the tech-savviest employees to explore data and make more informed and better decisions. Aberdeen uses NLP to index keywords for pages that users visit when they’re in-market for a purchase and conducting active research on the web. The visits are mapped to product categories to determine what the users are in-market to buy. Natural Language Generation The flip side of NLP is natural language generation (NLG). NLG translates complex data and machine language into context that humans can digest. NLG uses another piece of AI, machine learning, to learn from what it ingests as users ask questions and explore the output provided by NLG. The machine learning component enables the NLG algorithms to continuously improve their recommendations. As guest Paul Roetzer said in a recent episode of The Intelligent Business Show, a reliable litmus test for supposedly intelligent marketing tech is: “Does it get smarter on its own?” True NLG algorithms, containing machine learning capabilities, pass the test. Predictive and Prescriptive Analytics Predictive capabilities used to run off historical data and in static predictive models. Adding AI to established predictive models that parse historical data can help dynamically update those models and improve the quality of predictions and outcomes. Prescriptive analytics are an extension of predictive, and they build on predictive models by recommending courses of action based on the underlying data. AI is a necessary component in prescriptive analytics, because the system must be capable of learning from previous predictive models, mapping them to actual business outcomes, and continuously updating its recommendations based off prior wins or losses. Aberdeen uses machine-learning powered predictive analytics to accurately predict which buyers are in-market, and what their needs are. In fact, in blind tests run by clients, Aberdeen’s intent data is up to 91% accurate in predicting purchase intent of B2B buyers. Streaming Analytics / Internet of Things Machine-generated data is one of the most effective business use cases for AI and machine learning. As countless devices are connected to the Internet of Things (IoT), they are creating massive amounts of streaming data. Humans have the potential and capacity to do great and inspiring things, but capturing and synthesizing intel from billions of terabytes is not one of them. AI is and will continue to be a required tool to process, find, and extract game-changing insights from an otherwise indecipherable trove of information. Do you know which specific companies are currently in-market to buy your product? Wouldn’t it be easier to sell to them if you already knew who they were, what they thought of you, and what they thought of your competitors? Good news – It is now possible to know this, with up to 91% accuracy. Check out Aberdeen’s comprehensive report Demystifying B2B Purchase Intent Data to learn more. Twitter Tweet Facebook Share Email This article originally appeared on Aberdeen Group and has been republished with permission.Find out how to syndicate your content with B2C Author: Kane Pepi Kane Pepi is an experienced financial and cryptocurrency writer with over 2,000+ published articles, guides, and market insights in the public domain. Expert niche subjects include asset valuation and analysis, portfolio management, and the prevention of financial crime. Kane is particularly skilled in explaining complex financial topics in a user-friendlyView full profile ›More by this author:VoIP Basics: Everything Beginners Should Know!Bitcoin Investment, Trading & Mining: The Ultimate Guide for BeginnersIs This a Better Way to Set Your 2020 Goals and Resolutions?