7.3 Applying Generative AI Across the Marketing Strategy Framework

The marketing environment is a combination of company, customers, and competitors. Marketers usually understand their company and customers well, but often know less than they should about competitors. Generative AI tools help fill this gap. For example, tools can do competitive benchmarking and real-time competitive intelligence. According to the U.S. Chamber of Commerce, generative AI tools help businesses keep tabs on competitors by analyzing search engine optimization (SEO) and content strategy, monitoring social media to evaluate competitive messaging, benchmarking products and services, and analyzing customer sentiment to identify competitive strengths and weaknesses. AI tools can also help marketers scout for emerging technologies and track innovation.

Marketplace analysis is also part of understanding the marketing environment. Thorough marketplace analysis often relies on scenario planning—but traditional approaches have serious drawbacks. Generating enough scenarios, choosing the right ones, and analyzing them thoroughly is a slow, manual process. That’s especially challenging in today’s post‑COVID world, where market conditions shift rapidly. Generative AI tools make this much easier. Drawing on their ability to quickly ingest and analyze huge amounts of data, these tools help marketers generate baseline scenarios, incorporate emerging trends into new ones, brainstorm creative action plans, evaluate the potential impact of those plans, and continuously update as new data arrive.

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Segmentation, targeting, and positioning help companies focus on the most promising customer groups—those who are measurable, accessible, profitable, and a good fit for the company’s strengths. Generative AI tools simplify this process by pulling together behavioral data at scale. Consider Spotify, the global music streaming service, that now has over 650 million users—including more than 250 million paying subscribers. It leverages AI to segment its audience by geography, demographics, listening behavior, and psychographics (e.g., attitudes and values). This enables them to personalize marketing efforts and product features at scale. Spotify also uses generative AI tools to position itself as hyper-personalized, employing generative AI features like:

1. Spotify AI DJ: selects songs for you based on your listening history and profile, and then narrates your music selections with a realistic and conversational voice created by generative AI

2. Discover Weekly: a personalized 30-song playlist offered up each Monday selected to introduce the listener to new artists and music styles

3. Spotify Wrapped: provides listeners with their top 100 songs played over the last year

4. Spotify AI-Powered “Daylists:” gives listeners three unique, AI generated playlists each day with titles like "Midwest Emo Flannel Tuesday Early Morning" and "Witchy Ethereal Tuesday." The quirky titles engage social media and have grown Spotfy’s popularity since its introduction.

Emily Galloway is Spotify’s Head of Product Design for Personalization, explains that generative AI tools enable the company to deeply understand its customers’ behaviors and experiences:

These generative AI tools help Spotify do exactly that. Its AI-driven DJ experience, for instance, curates personalized music that listeners feel connected to in a way traditional algorithms never could. This is a powerful example of how generative AI tools help marketers create deeper, more meaningful connections between brands and customers.

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Generative AI tools enhance every “P” of the marketing mix as well—starting with product development and brand management. Solutions from specialized AI consultancies help product teams identify trends and opportunities. Many of these tools use generative AI to sift data, surface insights, and rapidly prototype ideas—allowing companies to scale innovation without massive internal teams. For example, established players like Accenture Interactive or IBM iX—as well as newer AI-focused firms—help companies apply AI to improve product development.

One pioneer, Black Swan Data, specializes in predictive AI that forecasts future demand. Black Swan uses generative AI tools to pull data from social media conversations and predict new product trends. Its cofounder, Steve King, explains:

PepsiCo, for example, partnered with Black Swan to discover unmet customer needs for a new sparkling water brand. PepsiCo consumed 157 million online beverage conversations, filtered irrelevant content, then identified promising trends—from a desire for natural flavors to demand for functional benefits like hydration and health. Acting on those generative AI-driven insights, PepsiCo created Bubly, a naturally flavored sparkling water with no calories, no added sugars, and no artificial ingredients.

And generative AI tools help with more than product innovation. They also improve creative decisions in branding and messaging. Marc Pritchard, chief brand officer at Procter & Gamble, puts it simply:

That’s exactly what generative AI tools enable marketers to do—set clear goals and let the technology help craft campaigns that reach the right customers.

For example, P&G’s AI tools optimize creative variations at scale. Pampers uses AI Studios—a “neural data network” built on decades of advertising research—to pre-test different creative executions and predict future performance before going live.

Retail media networks like Amazon Advertising now depend on generative AI tools to fine-tune creatives and optimize spending. AI-driven auto‑bidding algorithms automatically adjust bids in real time, analyzing shopper behavior, competitor activity, and inventory levels—ensuring every advertising dollar efficiently reaches your target audience.

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Finally, generative AI tools also enhance key profitability drivers like customer acquisition, retention, sales per customer, and margin.

Customer Acquisition

Customer Retention

Sales per Customer

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