The CMO-CFO Guide: Marketing in the AI Era
Date: May 22, 2024
Time Reading: 5 min.
By: Jeff Spicer
[Note: This article is part 1 of a 3-part series intended to help bring together marketers and financial leaders to tackle challenges associated with modernizing marketing organizations for the AI-Era. Part 2 can be found HERE, and Part 3 can be found HERE.]
Every so often, a breakthrough technology emerges that fundamentally reshapes an entire industry or function. High-frequency trading (HFT), introduced in the 1990s, was one such technology. Previously, stock trades required people—human brokers who researched market conditions and then made decisions that could be slow or miss opportunities. With HFT, trades happen in milliseconds based on algorithms that find profitable opportunities where humans could not and execute huge volumes of trades in milliseconds.
In Marketing, we’re entering a similar era of technology-led disruption with AI and generative AI. There are some strong parallels between HFT and AI, including data-driven decision-making, blazing speed, operating at enormous scale, precision targeting, and untold efficiency. In this series, we’ll examine the state of marketing today and where AI technologies will have the biggest impact. We’ll provide guidance for both Marketers and the CFO on actions to take right now to prepare for AI and things to watch out for.
First, let’s examine marketing today and then consider the rapid AI-driven evolution already underway.
Marketing Today: Digital or Offline, Always Metrics-Driven
Marketing today comprises an entire spectrum of digital and offline activities to help marketers connect with customers and prospects in a personalized way. CMOs have been “modernizing” their marketing for years, implementing technologies, approaches, and data to bring about this one-to-one connection. Today’s marketing requires more than just new capabilities and tools. It requires CMOs to introduce a data-driven mindset and create ROI-driven programs.
These programs require specialists in some key areas:
- Digital teams who leverage social media, websites, third-party sites, texts, and emails, coordinating each with relevant content and hooks
- Content producers who create stories, insights, profiles, and thought-leadership content for use in personalized marketing
- Operations and data specialists who manage databases or procure information about prospects’ and customers’ demographics and behaviors
- A demand-generation team that can build and execute lead-generating programs that cross digital and offline activities
- A tech stack of tools and data to enable the entire Modern Marketing engine
Marketing and its data-driven approach have also ushered in metrics that marketing leaders use to measure impact and performance. A few key indicators that most marketing teams use:
- Customer Acquisition Cost (CAC): The total cost to acquire a new customer over a specific period. CAC helps businesses understand how much they spend to gain a single customer. For instance, if a company spent $1,000 on marketing monthly and acquired 100 new customers, the CAC would be $10 per customer.
- Customer Lifetime Value (CLV): The total revenue a business can expect from a single customer throughout their relationship. This metric helps leaders understand the value of investing in long-term customer relationships. For example, a SaaS company that charges $10,000 in annual subscription with a 3-year contract and annual support fees of $2000 has a CLV of $36,000.
- Return on Advertising Spend (ROAS): This measures the effectiveness of a specific advertising campaign by comparing the revenue generated from the campaign to the cost of the campaign. For example, if a company spends $200 on a Facebook ad campaign that leads to $1,000 in sales, the ROAS would be 5X.
- Cost per lead (CPL): The cost per lead is simply the number of leads generated by a specific activity or campaign divided by the cost of that campaign. If a social media campaign costs $5000 and generates 250 leads, the CPL is $20.
AI and Marketing
AI technologies have been used in marketing for years in areas such as programmatic media buying, where algorithms buy online ads automatically based on what works, analyzing buyer journeys across digital properties, and making recommendations directly to consumers based on data.
More recently, generative AI, or the ability of AI to generate “new” content based on data and algorithms, has exploded into public consciousness. GenAI has been around for well over a decade, but just like HFT, which didn’t really take off until the era of faster processors and high-speed networking, GenAI grew rapidly in the past two years due to the availability of large data sets for training and advances in neural network architectures—essentially how an AI model mimics the way a human brain analyzes and processes information.
Unlike HFT, which gradually increased in prevalence over the course of many years, GenAI is moving rapidly through all layers of marketing. CMOs and their teams are scrambling to stay ahead of the near-daily advances and new tools. At the same time, legal teams are expressing concern about liability, security, and ownership. CEOs wonder if an entire functional division can be automated. And CFOs are curious about how much this entirely new marketing approach will cost and how to determine the ROI.
It helps to understand where in Marketing that AI is already being used and where it has the potential for the most impact. Roughly, according to the ANA*, AI for Marketing falls into six categories:
- Research and Insights
AI can automate the process of gathering and analyzing large amounts of data to extract actionable insights about market trends, customer preferences, and competitor activities. From this, marketing teams can move quickly to implement growth and competition tactics. For example, a SaaS company could use GenAI to analyze customer feedback, product usage data, and user comments to rapidly identify directions in product development.
- Strategy and Planning
AI can assist marketing teams in formulating strategies by predicting market conditions, optimizing resource allocation, and simulating different marketing scenarios to foresee outcomes. Through this analysis, marketing teams can create growth plans, hone the brand, modify creative campaigns, and develop editorial plans faster and more confidently. For example, the marketing team of a manufacturing company might use AI to forecast different pricing strategies and even the messaging associated with potential price increases, helping guide decision-making and creating marketing assets.
- Creative Concepts
AI, particularly GenAI excels at generating creative content such as editorial, visual content, and even music and video production. GenAI can create these assets from prompts and then create entire sets of creative assets that can be used across various channels. An agency, for example, can use GenAI tools to generate dozens of sample marketing assets for a client that align with the client’s brand guidelines. Marketing departments can create explainer videos with custom music and multiple versions of websites and landing pages. The possibilities are literally endless.
- Personalization
GenAI is also good at determining the best channels and timing for content distribution to maximize engagement. AI can personalize content dynamically to suit different segments of the business audience. For example, a company might use GenAI to automate the personalization of whitepapers to different segments of its market to tailor content to each recipient’s specific interests or pain points.
- Optimization
AI tools analyze the performance of marketing campaigns and websites in real-time, providing insights into what’s working and what isn’t. This allows marketers to adjust campaigns and content on the fly to improve overall performance.
- Team Productivity
Just like AI can create and optimize content and campaigns, it can share that knowledge across marketing teams. As AI-driven systems suggest relevant content and automate routine tasks, they have the potential to improve team collaboration by cataloging and sharing various improvements. For example, a consulting firm could implement an AI-powered internal platform that automatically categorizes industry research and then distributes it to relevant teams, increasing their productivity.
Within each of these categories are dozens of uses, large and small, and the number of uses will continue to expand dramatically.
