How Chat GPT is Accelerating the Adoption of AI In Sales and Marketing Applications  

Chat GPT – a generative Artificial Intelligence (AI) released by San Francisco-based OpenAI – has been making headlines since it was launched on November 30th last year. Mostly because it can be used by students to cheat on homework assignments and exams by producing high-quality written responses to complex questions in a matter of seconds. The AI software is expanding awareness and perceptions of the “art of the possible” when it comes to applications of AI in knowledge fields like medicine, law, computer science, and business. When Wharton Professor Christian Terwiesch asked the question, “Would Chat GPT Get a Wharton MBA?” – the answer was yes.

Wharton management professor Ethan Mollick views the commotion about Chat GPT as a “tipping point” in AI in business applications. “This is a very big deal,” wrote Mollick 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.”

What’s significant is that Chat GPT is “waking up” a broader cross section of business leaders, growth executives, and investors to the vast potential of AI and Machine Learning (ML) to fuel new revenue and profit growth. One of the most immediate ways AI can grow business value is in sales and marketing.

The reason Chat GPT resonates so much with sales and marketing executives is because these disciplines have historically been perceived as highly human – and therefore immune to automation. Marketing requires art, nuance, and creativity. Sales involves convincing, compassion, and relationship building.  What Chat GPT has made painfully clear is the reality that AI has matured to the point where it can support the execution and improve the performance of every aspect of the modern growth portfolio. Right now AI applications are optimizing paid, earned, and owned media channels. They’re creating and personalizing the content that fuels them. And managing the data that runs them.

Customers won’t complain if AI takes a bigger role in customer engagement. Most don’t want human contact at any point in the buyers journey. They value fast, complete, and personalized answers over flattery, fancy meals, and face time.

The next generation of growth leaders are distinguishing themselves because they understand this new reality. They’re already harnessing the power of AI in all aspects of sales and marketing – from marketing strategy to media optimization, to sales enablement. A quick call to papers from the faculty of our growth institute identified 20 financially viable and immediately executable applications of AI and Machine Learning in sales and marketing applications. They include:

Marketing Strategy – Some of the most practical and impactful ways AI algorithms can create value is to help with the basics of market definition, segmentation and resource allocation that underlie marketing strategy. “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.

Sales Channel Enhancement – A wide range of AI tools are now available that make it easier than ever for sales teams to prioritize opportunities based on buyer intent, marketing signals, and customer engagement data. These include sales enablement, sales readiness, and sales engagement tools that provide real time selling guidance and one: one coaching at scale that simplify the seller experience, utility and ease of use. “The key to making sellers more effective is to overlay AI across the selling technology deployment, creating a simple, seamless, hyper-personalized seller experience that arms each seller with the exact right insights at timely moments, precisely targeted to their current context,” says David Brunner, CEO and Founder of an AI powered sales enablement platform ModuleQ, and a pioneer in people-facing AI. 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 most useful to them in achieving those priorities.

Marketing Content Creation – “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 which one writes. This puts low end creative tasks up for grabs and allows expensive creative and writing teams to focus on  high end creative, selling stories and value added tasks. With collaboration, the two working together can be very powerful.” As a result, marketers are already using generative AI like Chat GPT in many aspects of content development – from copywriting to content marketing, meta descriptions, and content consistency (e.g. “Chat GPT can you rewrite this post to match the style of the other one”).

Creative and Concept Development – “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 Chat GPT 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.”  As evidence, even I was able to make my own soda advertisement in seconds using AI generated by DALL·E 2 – a generative AI system that can create realistic images and art from a description in natural language.

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 and keywords. For example, Horizon Media – the leading independent media buying agency –  has 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 over 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,” says Donald Williams, their Chief Digital Officer. In paid search, Microsoft and Google are incorporating the large language model (LLM) and Natural Language Processing (NLP) capabilities of Chat GPT into their search platforms. Microsoft, an investor in Chat GPT, is actively incorporating it into their Bing search engine giving it access to the entire Internet. In response, Google is accelerating the deployment of Bard AI – which is similar to Chat GPT but built on Google’s Language Model for Dialogue Application (LaMDA).

Personalized Customer Experiences – Growth leaders are using AI and ML technologies to deliver their clients personalized experiences in a range of use cases. Generating customized RFP responses with Intelligent Response Management. Personalizing ad campaigns with dynamic content optimization. Developing recommender systems that make tailored product and content recommendations. And executing Account Based Marketing (ABM) programs at scale to thousands of buyers in hundreds of accounts. 

This 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.

This should be no surprise to most sales and marketing executives. But it is.  Why do so many growth leaders struggle to understand the impact AI can have on revenue growth and financial performance?

It’s not for lack of trying. 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. AI hiring grew 32% in the last year. Despite all this effort, the majority of CFOs struggle to prove the financial return on their investment in analytics and most marketing decisions are still not data driven according to the CMOs surveyed by Duke.

“This is a problem of management acumen and talent rather than technology or level of investment,” according to Mary Purk, the Executive Director of the Wharton AI and Analytics for Business (AIAB) program. “If your leaders lack data fluency, analytical acumen and a fundamental understanding of how data driven processes, algorithmic decision-making and fact based planning and resource allocation can improve their business – they will never fully adopt or embrace AI as a driver of growth,” Purk continues. “And those companies will be left behind as their industries pivot and rapidly change to meet the needs of the marketplace.” The other major obstacles preventing businesses from realizing the full potential of AI in commercial applications – the inability to prioritize, direct, de-risk, and allocate resources to the most profitable AI applications – all stem from this talent gap. The experts on our faculty identified six keys to realizing the full potential of AI to generate more scalable, consistent and profitable growth.

1. Build analytics acumen and talent on your management team. Most sales and marketing executives have limited acumen or background in analytics. “Most of the success and failures to harness the power of AI to transform business lies in management’s understanding of how to apply, deploy and direct these powerful tools”, according to Professor Hosanagar, who authored the influential book A Humans Guide to Machine Intelligence. “A computer science and now data science degree has never been the traditional path to becoming a sales and marketing leader in the C suite, but today it is,” adds Purk.

2. Identify focused business cases with high financial impact.  Identifying the most financially viable use cases for AI in sales and marketing requires a good understanding of what AI is good at vs. what humans are good at so these two forces can combine to create value, according to Raghuram Iyengar, Professor of Marketing at Wharton. “In our experience, a primary reason for these mixed-results is a fundamental lack of understanding among marketers and their stakeholders about how and where AI and Machine learning tools are appropriate to address the macro challenges of marketing” reports Professor Iyengar, who teaches executives how to use customer analytics to drive growth using AI and Machine Learning

3. Coach, question, requestion and supervise your AI initiative. Despite the perception created by Chat GPT, AI cannot simply spit out an optimal result for your customers on its own. The more coaching you provide, the specific way you ask it to do things for you, and the number of iterations and refinements to that process can drastically improve its output.  Businesses can create more value through AI by asking better questions than analyzing bigger data sets or simply letting the data “speak” according to Abraham Wyner, Professor of Statistics at Wharton.  “AI is 50% framing the right questions, 48% about domain knowledge about the business problem, and 2% about advanced analytic methods to refine and validate that hypothesis,” says Wyner. This notion of asking and re-asking questions, coaching, training, and affirming value is essential to refining and proving out AI models. This is particularly important in an ever changing market.

4. Train your AI with proprietary and bespoke learning data sets. A natural obstacle to progress is the lack of learning data or clean data to train AI.  In particular, using data and “know how” proprietary to your organization will ensure your commercial AI delivers unique and relevant content, offers, pricing and engagement to buyers. This bespoke data gives AI greater context into the buyer personas, use cases, pain points, preferences and journey in your specific industry. “The most powerful applications of AI in sales and marketing combine bespoke first-party customer engagement data and third-party customer insights with AI models that capture the context and preferences of each individual seller and their target customers, prospects, industries, and use cases,” adds David Brunner. “For example, we see our clients training our People-Facing AI with collaboration data from email, calendars, and contacts, together with account and opportunity data from CRM. They further augment that with news from a variety of robust third-party sources like the London Stock Exchange Group (LSEG).”

5. Manage the quality, accuracy and risk associated with AI outputs – While generative AI applications like Chat GPT are popular in education and consumer applications, business managers need to be mindful of the risks associated with them. In particular accuracy, data security, and context. For example, in its current form Chat GPT is not fully secure. This matters because the customer data and proprietary “know how” that are essential to training customer facing AI applications are also the most valuable financial assets in the modern business. Quality and accuracy of outputs can also be an issue. The data sets in version 3.5 are a year or more behind. And Professor Terwiesch’s analysis found that while Chat GPT did a great job at basic operations management and process analysis questions, it made mistakes in relatively simple math calculations.

“Just like digital technologies have fundamentally transformed business over the past twenty years, AI is set to do the same over the next twenty years,” advises Professor Hosanagar. “AI is no longer just for engineers and data scientists. It’s for everyone. Professionals can no longer afford to have a poor understanding of something so fundamental to business and society today.”

Professor Iyengar will be addressing these issues in more depth at the Wharton Customer Analytics for Growth Using Machine Learning, AI, and Big Data executive education program – which in the spirit of the changes above, is being offered online to senior sales, marketing and analytics professionals.

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