From SEO to GEO: What Retail Marketing Teams Need to Do Now

The search bar isn’t dead, but it’s no longer running the show. For years, retail marketing teams optimized content for search engines that crawled text and hunted for keywords. Now, generative artificial intelligence tools, including ChatGPT, Gemini, and Perplexity, answer shoppers' questions directly, drawing from a much wider pool of user-generated signals. They determine which brands get cited and which get ignored.

This shift from search engine optimization (SEO) to generative engine optimization (GEO) is happening, and retail marketers who treat it as a technical tweak rather than a strategic pivot will find their brands quickly disappearing from conversations at a critical moment.

Here are three steps retail marketing teams should take now to step into and stay in the conversation.

1. Discover: Audit what’s actually being said about you.

Research shows that roughly 75 percent of brand mentions now appear in images, video, and audio rather than exclusively in typed captions, @mentions, or hashtags. Generative engines synthesize what the broader internet is saying across reviews, forums, video transcripts, podcast mentions, social commentary, memes, and more.

The problem is that most retail brands have no clear picture of what the broader internet conversation looks like, especially across video-, image-, and audio-first discovery, because they’re still relying on traditional social listening tools

What does this mean? Say a shopper is watching a YouTube haul video or a TikTok review and hears a brand mentioned repeatedly, or sees a logo on a piece of swag, but the brand never appears in the post's text. Legacy monitoring tools miss this, while generative engines don’t.

Before marketing teams can optimize for GEO, they need to take a full inventory of how and where their brand is being discussed. That means upgrading from social listening tools to social intelligence that captures video, image and audio content — not just text-based feeds — across platforms that range from TikTok to Bitchute, in multiple languages. Only then will brands understand the full picture of where they’re showing up.

2. Improve: Restructure content and tackle the narrative at its source.

Generative engines favor content that's easy to extract and cite. Dense and keyword-heavy copy optimized for traditional search algorithms is a liability. AI models want clarity, structure, and authentic expertise.

Today, that clarity has to start upstream. Much of the narrative shaping what models know about a brand is formed in niche, fast-moving channels like TikTok reviews, YouTube hauls, creator livestreams, subreddits, podcasts, micro-influencer commentaries, and other multimedia formats. With social intelligence-driven awareness and visibility into these conversations, retailers can understand emerging perceptions and guide them.

Understanding the source of these signals helps teams reinforce accurate narratives, correct misconceptions before they spread to large language model (LLM) search platforms, and thoughtfully seed the product attributes and differentiators they want in those channels so they're reflected in generative answers. In other words, GEO begins long before a shopper ever asks a question.

For retail marketing teams, this also means restructuring cornerstone content with modular formatting, including short declarative paragraphs, comparison tables, and direct answers to the questions customers are actually asking. Bringing in structured data markups, like Schema.org and emerging machine-readable standards like llms.txt, gives AI models a clear map of a brand's authority and relevance.

More importantly, GEO rewards substance. If content is created to rank rather than to inform, generative engines will turn their attention elsewhere. Third-party validation, such as earned media coverage, real customer reviews, and credible industry citations that social intelligence can surface, carries much more weight in AI-generated responses than owned channels alone. Building that earned presence is the foundation of GEO visibility.

3. Measure: Shift KPIs from clicks to citations.

Traditional SEO success was measured in clicks and rankings. The goal now is to become the genuine source of truth that an AI model cites and surfaces when a shopper asks a question relevant to the brand’s product category.

Retail marketing teams should start tracking how often their brand appears in multimedia conversations, particularly in the niche channels that LLMs favor, and ensure these are sending the right message across key product and category queries. With that social intelligence-driven visibility, retail teams can shift from reactive monitoring to a more strategic, meaningful approach by actively participating. This includes adding credible, brand-authored information into channels where shoppers are already asking questions. This also empowers teams to directly address and mitigate misleading claims before they harden into digital truths, and supply links, clarifications and authoritative sources that generative engines can reliably cite.

Once they have this cross-platform, multimedia visibility using social intelligence, they can then prove their real return on investment by assigning monetary value to those mentions using earned media value (EMV), which captures the monetary weight of mentions across audio, video, image and text, and better reflects brand resonance than advertising value equivalent (AVE), which measures the value of media coverage by comparing it to the cost of an equivalent advertisement.

The brands that excel in this environment are those that understand that in the machine economy, authenticity is the algorithm. To be successful, they need visibility into every channel, in every format.

Daniella Sampson is the head of marketing at Pendulum, today's social intelligence platform.