20 Applications Of Artificial Intelligence And Machine Learning In Sales And Marketing Applications
One of the most immediate ways Artificial Intelligence (AI) I can grow business value is in sales and marketing. Generative AI like ChatGPT, DALL·E 2, and many others have dispelled the historic notion that sales and marketing disciplines have are immune to automation because they are highly human disciplines. The emergence of advanced analytics, AI, and Machine Learning (ML) in sales and marketing – and the massive new customer engagement data sets to support them – represents the most significant opportunity to accelerate sales growth since the scale adoption of call centers (40 years ago), CRM (30 years ago), and digital channels (20 years ago) in sales. As evidence, 90 of the top 100 technologies identified by our institute as enabling the modern Commercial Model (chosen from over 3,000) are using advanced analytics, AI, and Machine Learning to mine first and third party customer data to generate more consistent and scalable revenue, profit and share growth.
Sales and marketing executives are waking up to the reality is AI can support the execution and improve the performance of every aspect of the modern growth portfolio . Ninety percent of marketers say they are using AI to improve their customer journeys, revolutionize how they interact with customers and deliver them more compelling experiences according to a recent survey of global executives by BCG. Most CMOs plan to add AI and Machine Learning to their marketing toolkit. And they are putting their money where their mouth is. Over the last seven years, growth leaders have invested between 8% and 10% of their overall marketing budgets on advanced analytics, and plan to continue to invest at those levels over the next three years according to the Duke CMO survey.
The next generation of growth leaders at the tip of the bell curve understand this new reality. They’re already harnessing the power of AI in all aspects of sales and marketing – from paid, earned and owned media, to running the digital selling infrastructure that drives modern selling, to creating, personalizing and managing the data and content that fuels them. In fact, the faculty of our growth institute identified 20 financially viable and immediately executable applications of Artificial Intelligence and Machine Learning in sales and marketing applications. These include:
Go to Market Strategy – Corporate planners are developing more data-driven growth strategies that use advanced analytics to target the best growth opportunities and optimally deploy commercial assets, programs and teams to monetize them with go to market campaigns and sales motions. In the short term, some of the most practical and impactful ways AI algorithms can create value is to help with the basics of market segmentation, targeting, account sizing, prioritization and qualification, and reallocating sales and marketing resources and effort to the right accounts, territories, and markets. These tasks are easier for organizations to execute with limited analytics acumen and data scientists in short supply. “Organizations are dramatically improving sales performance by using AI algorithms and propensity to buy models to help with the basics of account and lead prioritization and qualification and reallocating sales resources to the places they can have the most impact,” reports Professor Leonard Lodish of the Wharton School of Business. As organizations mature, they will increasingly use AI tools to create algorithmically derived customer response models that help take the guesswork and gut feel out of aligning sales resources across geographies, accounts, and business lines.
Marketing Creative and Content Development – Progressive marketers are already using AI’s that use Large Language Models (LLMs) like Chat GPT in content development – ranging from to copywriting to content, meta descriptions, and improving content consistency (e.g. “Chat GPT can you rewrite this post to match the style of the other one”). “Chat GPT gets story structure and has good command over language,” says Kartik Hosanagar, Professor of Operations, Information and Decisions and Marketing at The Wharton School. ”It can dramatically increase the speed in with which one writes. This allows expensive creative and writing teams to focus on more value added tasks like high end creative, story structure, and plot lines. With collaboration, the two working together can be very powerful. Marketing innovators are also using generative AI like DALL·E 2 in a wide range of creative applications from concept development to copywriting to content , meta descriptions, and improving content consistency (e.g. “Rewrite this passage to match the style of the other one”). “We are helping our marketing clients today to use AI in a variety of creative development use cases that save time, resources and deliver value,” says Donald Williams, EVP and Chief Digital Officer of Horizon Media. “For example, we are already using ChatGPT and similar AI platforms to source images for our material to further illustrate creative for brands and provide fuel and inspiration for our art direction.” For example, I was able to make my own soda advertisement in seconds using AI generated by DALL·E 2 is a new AI system that can create realistic images and art from a description in natural language
Personalized Customer Experiences – Every business is trying to differentiate its products, services, and brands through personalized and highly contextual experiences.. Sales managers rank the demand for more relevant and personalized content as the number one factor impacting their ability to sell in a virtual setting according to the Remote Sales Productivity Study. And 80% of organizations are increasing their investment in content and the systems that support its delivery in context, including guided selling, next-best-action, playbooks, recommendation engines, real time scripting, and automated chatbots. AI represents the most viable way to do this quickly, affordably and at scale. “Creating personalized content at scale with digital speed and control is essential in a modern selling model,” says Jaime Punishill, the CMO of nCino. Growth leaders are using AI and ML technologies to deliver their clients personalized experiences by generating customized RFP responses with Intelligent Response Management, Personalized ad campaigns with dynamic content optimization, recommender systems that make tailored product recommendations, and executing Account Based Marketing (ABM) programs at scale to thousands of buyers in hundreds of accounts. For example, over the last two decades, AI leaders like Pandora, Amazon and Netflix have pioneered the development of content-based recommender systems to recommend the next best content, offer, product in ecommerce platforms, marketing web site, customer service chatbots, and sales readiness systems. 75% of Netflix users select films recommended to them by the company’s machine learning algorithms. “Content-based recommender systems like this are valuable because they are very good at identifying and recommending highly relevant content, songs, or products regardless of preconceived notions, popularity, or other influences,” according to Kartik Hosanagar, Professor of Operations, Information and Decisions and Marketing at The Wharton School.
Sales Channel Enhancement –The majority (62%) of high-performing salespeople see a big role for guided selling that ranks potential opportunity value, recommends the best content, and suggests next steps. This is because a wide range of AI tools are now available that make it easier than ever for sales teams prioritize opportunities based on buyer intent, marketing signals, and customer engagement data. These include sales enablement, sales readiness, sales engagement tools that provide real time selling guidance and one: one coaching at scale. For example, finding the right content in the right context at the time to advance the buyer journey – the bread and butter of sales enablement – is a core and immediate value proposition of generative AI tools like Chat GPT. What makes all of these tools valuable is they augment the value of customer facing employees and dramatically simplify the seller experience. “The key to getting the most out of technology is to simplify the day-to-day selling workflow and ensure the tools you invest in get adopted consistently by all sellers,” says Lynne Doherty, the President of Field Operations at Sumo Logic. David Brunner, a pioneer in people-facing AI, echoes this sentiment. “The key to realizing the immense promise technology to make sellers more effective is to use AI to speed and simplify the day-to-day seller experience and improve the usability, utility, and adoption as a key strategic and operational goal of their selling technology deployments,” says David Brunner, CEO and Founder of an AI powered sales enablement platform ModuleQ. Brunner and his co-founder Anupriya Ankolekar have spent the better part of the last twenty years applying PhD level AI and systems design to find ways to add value to sellers by learning about their priorities and then delivering the information, insights, and resources are most useful to them in achieving those priorities.
Digital Engagement Channels. 83% of IT leaders say AI & ML is transforming digital customer engagement. 90% of organizations are using AI to improve their customer journeys, revolutionize how they interact with customers and deliver more compelling experiences through chatbots, marketing automation, mobile apps, and dynamic web sites and landing pages.
Paid, Earned and Owned Media – Marketers are using AI tools to optimize their investment in paid media in real time and improve earned search engine and social marketing performance by brainstorming long-tail content ideas, keywords. For example, Horizon Media– the leading independent media buying agency – has been using AI to optimize paid media and write and refine social marketing scripts, according to Donald Williams, their Chief Digital Officer. The company just launched a new AI platform, Neon, which utilizes proprietary artificial intelligence that buys media smarter than people can. “Neon is designed to increase advertisers’ revenue by 20+% when planning and buying retail media to achieve their maximum revenue outcomes, by using the power of AI to power forecasts to the tactical level and optimizes in-market spend in real-time at the SKU level, citing these explicit capabilities,” says Williams. In paid search, Microsoft and Google are incorporating the AI and large language model (LLM) and Natural Language Processing (NLP) into their search platforms. Microsoft is an investor in Chat GPT and is actively incorporating it into their Bing search engine giving it access the entire Internet. They will also undoubtably leverage the AI in Teams and other platforms used by sales and marketing teams. In parallel, Google is developing Bard AI – which is similar to Chat GPT but built on Google’s Language Model for Dialogue Application (LaMDA).
This list is just the start. The runway for AI innovation in sales and marketing is long. And the number of ways AI-enabled personalization can improve margins, conversion rates, and firm value are myriad.
The AI arms race is on. This “Copernican revolution” is forcing business leaders to reimagine their commercial technology stacks and go-to-market models around AI enabled platforms that aggregate, orchestrate, and monetize first and third party data about customer engagement, product usage and selling performance. It has made the ability to capture and unify customer data and convert it into commercial insights that enable, optimize, and automate cross-functional sales, marketing, and service workflows a fundamental driver of growth and value creation. We call this a Revenue Operating System.
Today, most of this innovation in AI in sales and marketing applications is coming from hyper growth software and technology firms. A big reason for that is that most computer scientists work in technology businesses (31%). But other industries that can benefit from AI in growth applications – automotive, manufacturing, retail, telecommunications, media and pharmaceutical – represent less than 2% of jobs respectively. “We’re hitting a tipping point for artificial intelligence,” writes Professor Ethan Mollick, Associate Professor of Management at Wharton in a recent HBR article. “The businesses that understand the significance of this change — and act on it first — will be at a considerable advantage.”
