Insights for Brands: Understanding the Shifting Search Query Length in the LLM Era
The world of search is constantly evolving, and the introduction of large language models (LLMs) is set to revolutionize the search experience as we know it. This article delves into the hypothesis that the integration of LLMs into everyday life will fundamentally change how people search. With LLMs capable of providing detailed answers, consumers are expected to move away from simple queries and gravitate towards more complex, conversational searches.
To explore this hypothesis, the author analyzed Google Ads data, as search data from various LLMs like Google’s Gemini and AI Overviews is not yet available. The data spanned from January 2022 to May 2024 and consisted of 41 million search queries and over 83 million impressions. The author divided the data into three buckets: Pre-ChatGPT, 12 months Post-ChatGPT, and 2024 YTD.
The analysis revealed interesting insights into the shifting search behavior of consumers. It was observed that since the introduction of LLM models, consumers have started to shift their search patterns towards longer queries. While the increase in search term length has been slow, there has been a noticeable shift from keywords with 5-6 words to keywords with 7-8 words.
This shift suggests that consumers who previously searched for slightly more complex terms have now become even more specific with additional conversational modifiers in their queries. However, it’s important to note that the majority of keywords still have less than four search terms in the query.
The analysis also looked at the impact of LLMs on metrics such as cost per click (CPC) and click-through rate (CTR). It was found that CPC costs have spiked in the last six months across all search term buckets, with the biggest jumps observed in the 3-6 keyword length buckets. This increase in CPC costs coincided with a decline in CTR, particularly in the 5-7 search term length buckets.
The hypothesis that AI Overviews would drive fewer clicks to traditional ads seems to hold true, as the more detail someone enters into the query, the more likely Google surfaces an AI Overview. Impressions have not decreased, indicating that the search volume is still there. However, it’s crucial for brands to adapt to this new search environment by monitoring search term data, analyzing consumer behavior, and monitoring LLMs.
Tracking search term data allows brands to understand how their specific products and keywords are being influenced and identify any shifts or trends in consumer behavior. This insight can then inform the types of landing pages, ad copy, and content brands create to improve conversion rates. Additionally, monitoring LLMs and understanding how frequently brands appear across different LLMs can provide a competitive edge in the ever-changing search technology landscape.
In a surprising finding, the author discovered that consumers are searching with emojis. While this may not have been something the author had considered, Google is smart enough to interpret and map these emoji-only searches correctly. Depending on the vertical and brand, this could present an untapped opportunity worth exploring.
In conclusion, the rise of LLMs is reshaping the search landscape, and brands need to adapt to this new era. By understanding the shifting search query length and leveraging insights from data analysis, brands can optimize their search strategies and stay ahead in the evolving world of search.
