First Published: 20 March, 2024
In this article, we will explore what SOM is, why it has become a recent trend, its importance for marketers, and why it should be considered an essential component of any forward-thinking marketing strategy.
Share of Model (SOM) is a groundbreaking metric that quantifies how large language models (LLMs) perceive a brand in comparison to its competitors. LLMs, such as ChatGPT, Meta's Llama 2, and Microsoft's Copilot, are AI-powered chat programs that are rapidly gaining popularity and are poised to become the primary means by which consumers engage with brands and make purchasing decisions. SOM measures the extent to which these models understand and recommend a brand, considering factors such as brand identity, product offerings, and overall reputation.
SOM is important because LLMs will soon answer billions of search queries each day, shaping consumers' perceptions of brands.
The emergence of SOM as a critical metric can be attributed to several factors. Firstly, the exponential growth of AI and its integration into various aspects of our lives has made it imperative for brands to understand how these technologies perceive and represent them. As consumers increasingly rely on AI-powered chat programs and virtual assistants to answer their queries and guide their decisions, brands that fail to optimise their presence within these models’ risk being overlooked or misrepresented.
Secondly, the sheer volume of data processed by LLMs has made them a valuable source of insights for marketers. These models are trained on vast amounts of data from across the internet, including websites, social media, and customer reviews. By analyzing how brands are represented within this data, marketers can gain a comprehensive understanding of their brand's perception and identify areas for improvement.
The importance of SOM cannot be overstated in today's digital age. As LLMs become the primary interface through which consumers engage with brands, a strong SOM will be crucial for driving brand awareness, consideration, and ultimately, sales. A high SOM indicates that a brand is well-represented and positively perceived by the models, increasing the likelihood that it will be recommended to consumers when they make queries related to the brand's products or services.
Moreover, SOM provides marketers with valuable insights into how their brand is perceived in relation to competitors. By comparing their SOM to that of their rivals, marketers can identify areas where they are excelling or falling short and adjust their strategies accordingly. This comparative analysis can also help brands identify opportunities for differentiation and establish a unique brand identity within the AI-powered landscape.
As a marketing professional, I strongly believe that SOM should be considered an essential component of any comprehensive marketing strategy. The rise of AI and LLMs is not a passing trend, but rather a fundamental shift in how consumers interact with brands and make purchasing decisions. By neglecting to optimise their SOM, brands risk being left behind as their competitors capitalize on this new frontier.
Incorporating SOM into marketing strategies requires a multi-faceted approach. Firstly, brands must ensure that their website and other digital assets are optimized for LLMs. This involves creating high-quality, informative content that accurately represents the brand and its offerings. Secondly, brands must actively engage with LLMs, providing them with regular updates and prompts to ensure that they have the most up-to-date information about the brand.
Additionally, brands should leverage the insights provided by SOM to inform their broader marketing strategies. By identifying areas where their SOM is weak, brands can develop targeted campaigns to address these shortcomings and improve their overall perception within the models. This may involve creating new content, engaging with customers on social media, or even adjusting product offerings to better align with consumer preferences as interpreted by the LLMs.
While the potential benefits of optimizing SOM are significant, there are also challenges and considerations that marketers must be aware of. One of the primary challenges is the constantly evolving nature of LLMs. As these models are continually updated and refined, brands must remain vigilant and adapt their strategies accordingly to maintain a strong SOM.
Another consideration is the potential for bias within the models. LLMs are trained on vast amounts of data, and if this data contains biases or inaccuracies, these may be reflected in the model's outputs. Brands must be aware of these potential biases and work to mitigate them by providing accurate and unbiased information about their products and services.
Finally, it is important to recognize that while SOM is a valuable metric, it should not be the sole focus of a marketing strategy. Traditional metrics such as brand awareness, customer satisfaction, and sales revenue remain critical, and SOM should be viewed as a complementary metric that provides additional insights and opportunities for optimization.
Share of Model (SOM) represents a seismic shift in the world of digital marketing. As AI and large language models become increasingly prevalent in shaping consumer perceptions and decision-making, brands that fail to optimise their SOM risk being left behind. By understanding what SOM is, why it has become a recent trend, and its importance for marketers, brands can develop comprehensive strategies that leverage this new metric to drive brand awareness, consideration, and ultimately, sales.
As a marketing professional, I strongly encourage all brands to consider incorporating SOM into their marketing strategies. By doing so, they can gain a competitive edge in an increasingly AI-powered landscape and ensure that their brand is well-represented and positively perceived by the models that are set to dominate the future of consumer engagement. While there are challenges and considerations to be aware of, the potential benefits of optimizing SOM are too significant to ignore.
Transparency: this article was written by Peter Mangin, but edited with Claude 3 to enhance conciseness and logical sequence, then reviewed by Peter Mangin before publishing.
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