Optimize owned assets for trust and engagement—learn about consumer intent data in the article.
The rise of artificial intelligence, specifically generative AI, which includes tools like ChatGPT and Google’s Search Generative Experience, is shifting the marketing landscape, demanding that brands evolve or lose narrative control and market share. The evolution sees brands moving away from channel-centric approaches to putting the spotlight on building authentic consumer connections. One that leverages search intent data-derived consumer insights to truly understand the audience, in order to satisfy them and win trust across the entire buyer’s journey.
Adopting this mindset and implementing an owned asset optimization strategy (OAO) does just that. It tells the compelling story to connect with consumers, but it also generates a richness of content that AI tools, especially the current wave of AI search engine features need. With OAO-driven content brands can influence the output of these tools. More on that later.
First, let’s look at generative AI and its big risk for unprepared brands.
Understanding Generative AI
Generative AI and AI-powered search engines are data-hungry. These tools are trained on massive datasets, an amalgamation of crawled public web, Reddit, Wikipedia, social media, textbooks, and much more. ChatGPT relies on infrequent training and, recently, internet browsing that provides the model more data. It also learns from individual user queries and engagement. Connected tools like Bing Chat and Google Search Generative Experience (SGE) have access to everything on the public web.
Generative AI produces an output based on its training data and the given query posed by the user. ChatGPT has been exposed to billions of pieces of content. From that experience, it builds content or answers questions — generating text it considers to be a reasonable continuation of what came before. It analyzes probabilities of the next word and creates a ranking, picking from the most likely words to build a comprehensible meaning. SGE, from our early experimentation, builds content based on similar modeling, but uses recently cached web pages for sourcing, building an answer and a list of source pages. Traditional SEO page factors likely come into play when signaling what SGE will use.
The Risk To Brand Narrative Control
A major risk facing brands across industries is the loss of control over brand narrative — the vital storytelling process. AI is exacerbating the risk.
Brands are storytellers, communicating their story (brand narrative) to authentically connect with consumers.
AI represents a rival storyteller. It can tell the wrong story (outdated, erroneous, unflattering, etc.) and result in loss of narrative control. Without action, this loss is inevitable.
Brands with unoptimized content infrastructures, unfavorable stories, and the resulting difficulty building authentic connections, risk narrative control erosion as well as declining market share. The war for narrative control has many fronts (impact of consumer empowerment, reviews, social media, competitors, etc.), and AI opens an additional one.
Dangers of Uninfluenced Generative AI
By default, generative AI tools only use training data, user feedback, and in the case of internet-connected tools, live web content. What AI produces tends to reflect what it already knows, has learned about, or has access to.
When users ask about a brand or perform a related action in an AI tool, the output will reflect what’s already out there. If the input is wrong, unflattering, or simply uncontrolled by a brand, AI’s output will be more of the same. This AI loop can create snowballing preferred narrative erosion and reputational harm. Also concerning is AI’s propensity towards producing thin or even hallucinated content when it has thin data to work with.
Owned Asset Optimization Influences Generative AI
Owned asset optimization is a strategy that uses detailed intent data to create consumer behavior insights and then creates an overarching touchpoint infrastructure to deliver what consumers want. The process of creating this infrastructure — a constellation of owned assets featuring brand-controlled content built with real consumer connection in mind — generates the added surplus of content needed to influence AI tools.
Each owned asset is a fully optimized consumer touchpoint designed to deliver value and build trust, but each also acts as a new data source to feed the AI tools. Because owned assets are brand-owned venues featuring brand-controlled content they’re authoritative and likely to positively influence the story AI tools tell about the brand. Additionally, each touchpoint is optimized for AI visibility using injections of direct, high-volume questions and unique, proprietary data and branding.
These optimized assets take the uninfluenced AI output and inject control where there can be chaos.
ChatGPT and OAO
If you build owned assets now, there’s an increased likelihood that they get pulled into the training dataset, informing how ChatGPT understands your brand and what it produces. OpenAI has not updated ChatGPT’s dataset beyond September 2021, but it is likely to happen in the future. OAO helps brands prepare with authoritative, accurate, and contextualized data.
Google SGE and OAO
SGE is still a Search Labs experiment, but our experience with it confirms that owned assets will be able to directly influence what it creates when users submit a query in Google Search. When you ask SGE, it rapidly generates a response. On the right it lists the sources. In a fully implemented OAO strategy, a brand’s owned assets can get pulled into SGE results. This injects brand control into the generative response, pointing consumers to the asset, whether it’s a blog, landing page, or other owned asset type, and encouraging a more favorable, accurate response.
Role of Consumer Intent Data
Owned assets are created and optimized with consumer engagement and authentic trust-building in mind. But how do you know exactly what your audience wants? Consumer intent data, like search intent data, social engagement, and first-party insights, answers that question, and can be applied to all owned assets. Real world consumer behavior data, correctly analyzed and leveraged, tells brands what people want and how to solve their problems. Each owned asset in the network of assets is built with consumer intent in mind, aimed at people within all journey stages.
This is important for two reasons.
First, the touchpoints and content built with consumer intent in mind, and designed to truly help users wherever they are, can actually connect with humans. It garners trust, and influences consumers to make a purchase decision later on. Second, the same content that helps real people solve their pain points and develop deep brand trust also helps AI tools understand your brand, your values, and your story. The more you produce, the more the AI has to pull from, and the higher the likelihood of owned assets influencing the generated narrative.
Pursue Consumer Connections, Win With AI
The takeaway here is: produce a higher volume of data-informed owned assets to pursue authentic connections and the rest will follow. Delighting your audience by knowing what they want and satisfying them with an abundance of useful content also satisfies the AI tools that consumers increasingly rely upon. Tightening up your brand’s control with owned assets in the digital space will pay dividends and result in AI strengthening your position instead of undermining it.
The best path is proactively telling your brand story through owned assets. A laser focus on investing in, growing, and optimizing your network of assets offers more control over your story, strengthening its authority, accuracy, and diversity as well as defending it from competitors on one hand, and AI-generated erosion on the other. Each individual owned asset tells a piece of your story to consumers and to the AI tools that they are fast adopting. Today’s brand marketers must be ready to evolve.
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