Brand equity has become more than just consumer recognition; it’s now also about how both artificial intelligence (AI) and consumers perceive and interact with your brand. Because nowadays, billions of consumers ask AIs, like ChatGPT what the best brands are [1].
The underlying technology that shapes AI’s understanding is text embeddings—mathematical representations of words and phrases that capture their meanings, contexts, and relationships.
By leveraging text embeddings, brands can understand and influence how AI models like ChatGPT perceive them, but also how search engines rank them and how consumers form associations. neuroflash specializes in mapping these embeddings, providing insights into a brand’s current position within AI models and offering strategies to optimize content for better representation and market share.
McDonalds AI representations - to understand and optimize Brand communication
Understanding Text Embeddings and AI Perception
Text embeddings transform textual data into numerical vectors, allowing AI models to process and understand language in a nuanced way. These embeddings capture semantic relationships between words, enabling AI to comprehend context, sentiment, and associations. [2]
For instance, when an AI model processes the term “fast food,” it doesn’t just see isolated words; it recognizes associations with brands like McDonald’s, concepts like convenience, and sentiments like indulgence or guilt. These associations are encoded in the embedding space, influencing how AI generates responses related to your brand.
Text Embeddings in Search Engines and SEO
Search engines like Google utilize text embeddings to enhance search results. By understanding the contextual relevance of content, search algorithms can rank brands, products, and articles more effectively. This means that your brand’s visibility in search results is directly tied to how well your content’s embeddings align with relevant search queries. [3, 8, 9]
Optimizing for SEO now extends beyond keyword stuffing to ensuring that your content’s embeddings reflect desired associations and contexts. This alignment improves your brand’s chances of ranking higher in search results, increasing visibility and potential market share.
Influencing Consumer Associations Through Text Embeddings
Consumers form implicit associations with brands based on various touchpoints—advertisements, social media, word-of-mouth, and more. These associations reside in the consumer’s mind, shaping their perceptions and purchasing decisions. [4]
By crafting content that strategically influences text embeddings, brands can shape these consumer associations. When AI models generate content consumed by users—like personalized recommendations or chat responses—the underlying embeddings affect the messages delivered, further influencing consumer perceptions.
Mapping and Optimizing Embeddings with neuroflash
neuroflash leverages AI and text embeddings to provide brands with a continuous map of their positioning within the embedding space. By analyzing both public data and outputs from proprietary AIs like ChatGPT, neuroflash offers insights into:
- Current Brand Representation: Understanding where your brand stands in AI models and search algorithms.
- Changes Over Time: Tracking how your brand’s embeddings evolve with new content and market trends. [5]
- Optimization Strategies: Identifying how to adjust content to influence embeddings positively, enhancing AI and consumer perceptions.
This comprehensive approach allows brands to see the full picture of their digital presence and take actionable steps to improve their positioning.
Case Study: McDonald's Dominance in the Fast Food "Brain"
McDonald’s exemplifies how strong text embeddings correlate with market dominance. The brand has successfully established numerous associations in both AI models and consumer minds—such as “fast food,” “hamburger,” “Big Mac,” and “Golden Arches.” [6]
These associations are not accidental; they result from strategic content and marketing efforts that reinforce desired embeddings. As a result, when AI models process queries related to fast food, McDonald’s consistently emerges as a prominent entity, influencing both AI-generated content and search engine rankings.
Measuring Brand Equity with Embedding Networks
By mapping McDonald’s embeddings, neuroflash can visualize the brand’s extensive network of associations. This network illustrates how deeply ingrained McDonald’s is in relevant contexts, contributing to high mental availability and market share.
Other brands like Burger King and smaller chains can compare their embedding networks to identify gaps and opportunities. Understanding these differences enables brands to strategize on how to expand their associations and improve their positioning in both AI models and consumer minds.
Market share is connected to higher mental availability
Having more connections between a brand (e.g. McDonald’s) and product topic (e.g. fast food) is and indicator for higher market share. When consumers are in the mood for food, they will more easily think of a brand with more associations and hence be more likely to eat there.
Higher mental availability and ease of purchase are the two keys to a higher market share. This relates to one of the few empirical laws in marketing, double jeopardy, coined by Byron Sharp (Sharp, 2010). In other words, the more paths lead to Rome, the more people will end up at the capital. But how does it “look like” in the consumer’s mind, let’s illustrate.
Below you see the association network surrounding fast food. There are over 2000 words associated with over 400.000 connections with each other. When fast food is likely to make people think about McDonald’s, both words will be connected by a line. The 5 colors indicate clusters of similar associations. Download the full PDF file here (caution, will take a long time to draw all connections).
Market leaders acquire more implicit associations
Mental availability is determined by the quantity and quality of a brand’s associations. Higher brand equity emerges when many relevant connections are present
To visualize the difference between a market leader and a challenger, let’s visualize how many connections various brands have already created in the consumer’s mind and how strong they are.
McDonalds – The market share leader with over 14,036 locations in the US (2017) shows a strong grip on the associations relevant in the fast food market.
Burger-King – Closest competitor to McDonald’s with ca. 7,226 locations and has fewer associations than the market leader, but they are still very well connected in the fast food and restaurant foods semantic space.
Wahlburgers – A small fast food chain with ca. 20 locations has significantly fewer connections. When you think of fast food and you don’t think of Wahlburgers, this is why.
Keep in mind that for a challenger, the ease of purchase is also significantly lower. With only around 20 locations in the US, even if you think of them, you won’t be able to eat there easily.
Strategizing Brand Positioning with Text Embeddings
To influence AI perception, search rankings, and consumer associations, brands should:
- Audit Current Embeddings: Use tools like neuroflash to assess your brand’s current position in the embedding space.
- Identify Target Associations: Determine the associations you want to strengthen or establish based on market goals.
- Optimize Content Strategically: Craft content that naturally incorporates desired associations, enhancing embeddings.
- Monitor Changes Over Time: Continuously track how your embeddings evolve with new content and adjust strategies accordingly.
- Align Across Platforms: Ensure consistency in messaging across all channels to reinforce embeddings in both AI models and consumer perceptions.
By adopting this approach, brands can create a blueprint for influencing AI’s perspective, improving search engine rankings, and strengthening consumer relationships. [7]
Conclusion
As AI plays a significant role in shaping consumer behavior, understanding and influencing text embeddings is crucial. Brands that proactively manage their embeddings can improve their visibility in AI-generated content, climb search engine rankings, and foster stronger consumer associations.
neuroflash is helping Brands of all sizes with insights and tools to navigate the complex embedding space. By bridging the gap between AI understanding and consumer perception, brands can achieve greater market share and sustained success.
References
[1] https://www.similarweb.com/blog/insights/ai-news/chatgpt-beats-summer-slump/
[2] https://www.datacamp.com/blog/what-is-text-embedding-ai
[5] Grippa, F., & Fronzetti Colladon, A. (2020). Brand Intelligence Analytics for the 2020 US Democratic Presidential Primaries. In Computation+ Journalism Symposium (C+ J 2020). Northeastern University. https://arxiv.org/abs/2001.11479
[7] Berger, J., Packard, G., Boghrati, R., Hsu, M., Humphreys, A., Luangrath, A., … & Rocklage, M. (2022). Marketing insights from text analysis. Marketing letters, 33(3), 365-377. https://link.springer.com/article/10.1007/s11002-022-09635-6
[8] https://blog.gigasearch.co/boost-elasticsearchs-relevancy-with-text-embeddings-and-knn-search/
[9] https://www.searchenginejournal.com/llm-embeddings-seo/518297/
Fast food data sources:
https://www.qsrmagazine.com
https://www.partnersforyourhealth.com/fast-food-statistics
https://www.bls.gov/opub/reports/consumer-expenditures/2015/pdf/home.pdf
https://www.eater.com/2015/3/6/8163891/americans-spend-more-restaurants-grocery-stores
https://nypost.com/2017/12108/americans-spend-an-absurd-amount-on-takeout/
