In MarTech’s “MarTechBot explains it all” feature, we pose a question about marketing to our very own MarTechBot, which is trained on the MarTech website archives and has access to the broader internet.
Q: What are the important metrics to watch as organic search traffic evolves?
As organic search traffic evolves and begins to give way to large language models (LLMs) and AI-driven search experiences, marketers will need to adapt their metrics to effectively measure performance and impact.
Here are some key metrics that will become increasingly important:
1. Engagement metrics: With LLMs providing more direct answers to queries, measuring user engagement will be crucial. Metrics such as time spent on page, scroll depth, and interaction rates with content (e.g., video plays, downloads) will help assess how well content resonates with users.
2. Key events: As highlighted in the shift from conversions to key events in GA4, tracking specific user interactions that are valuable to the business (e.g., newsletter sign-ups, product views) will be essential. This allows marketers to align their reporting with business objectives.
3. Attribution metrics: Understanding the contribution of various channels to conversions will be vital. With LLMs potentially altering traffic sources, data-driven attribution models will help marketers assess the true impact of organic search, social media, and other channels on customer journeys.
4. Customer lifetime value (CLV): As the landscape shifts, understanding the long-term value of customers acquired through different channels will be important. This metric helps in evaluating the effectiveness of marketing strategies in a changing environment.
5. Brand awareness metrics: As LLMs may change how users discover brands, measuring brand awareness through surveys, social media mentions and search volume for branded terms will provide insights into how well a brand is being recognized in the new search paradigm.
Dig deeper: Optimizing LLMs for B2B SEO: An overview
6. Conversion rate optimization (CRO) metrics: With the potential for reduced traffic from traditional organic search, focusing on conversion rates for key actions (e.g., purchases, sign-ups) will be critical. This includes A/B testing and optimizing landing pages to improve performance.
7. Referral traffic analysis: As LLMs may lead to increased “dark traffic” (traffic that is difficult to attribute), analyzing referral sources and understanding how users arrive at a site will be essential for optimizing marketing efforts.
8. Content performance metrics: Tracking how content performs in terms of shares, backlinks and mentions will help marketers understand its relevance and authority in the context of LLMs, which may prioritize high-quality content.
9. Search query insights: Monitoring shifts in search queries and user intent will be crucial. Understanding how users are interacting with LLMs and what types of queries are being made can inform content strategy and SEO efforts.
10. User feedback and satisfaction: Collecting qualitative data through user feedback, surveys and Net Promoter Scores (NPS) will help gauge user satisfaction with the content and overall experience, especially as AI-driven interactions become more prevalent.
By focusing on these metrics, marketers can better navigate the transition from traditional organic search to a landscape increasingly influenced by LLMs and AI technologies. This proactive approach will enable them to optimize their strategies and maintain effectiveness in reaching their target audiences.
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