The method
Paste the following prompt into ChatGPT. Replace the example website with the target site. Ideal for identifying blind spots in data collection for improved marketing and user experience. Works best with publicly accessible sites.
The prompts
Prompt 1
Analyze the website 'www.examplewebsite.com' for common Google Analytics and website tracking implementation mistakes. Focus on areas such as: 1) Missing event tracking for key user interactions (e.g., button clicks, form submissions, video plays). 2) Inaccurate or missing e-commerce tracking (if applicable). 3) Incorrectly configured cross-domain tracking. 4) Duplicate or missing Google Analytics tags. 5) Lack of custom dimensions for user segmentation. 6) Absence of tracking for internal site search. Provide a detailed report outlining each identified issue, its potential impact on data accuracy, and actionable recommendations for resolution. Include specific examples of where the errors are occurring and how to verify the fixes. Prioritize recommendations based on their potential impact on data quality and decision-making. Format the report in a clear and concise manner suitable for technical stakeholders.
Prompt 2
You are an expert web analytics consultant. A client has asked you to review their website 'www.examplewebsite.com' and identify gaps in their current tracking setup. Your goal is to pinpoint areas where they are missing out on valuable user data or collecting data incorrectly. Prepare a comprehensive audit report that covers the following areas: I) Goal Tracking: Are they properly tracking key goals and conversions (e.g., form submissions, purchases, sign-ups)? II) Event Tracking: Are they tracking important user interactions with the website's elements (e.g., button clicks, video plays, file downloads)? III) E-commerce Tracking (if applicable): Is their e-commerce tracking accurately capturing transaction data, product performance, and revenue? IV) User Segmentation: Are they using custom dimensions or segments to understand different user groups and their behavior? V) Data Quality: Are there any data discrepancies or inconsistencies that could be skewing their analysis? For each area, provide specific examples of potential gaps and actionable recommendations for improvement. Be sure to clearly explain the impact of these gaps on their ability to make data-driven decisions.