SEO Memory Lane: Keyword Evolution Tracker

Inspired by the fragmented recall in 'Memento' and the logical deduction of AI in 'I, Robot', 'SEO Memory Lane' is a personal, low-cost tool for SEO professionals and content creators. The core idea is to build a scraper (similar to the 'Salary Insights' project) that focuses on historical SEO data. Instead of salaries, it tracks how search terms and their associated search intents have evolved over time for a given niche.

Story & Concept: Imagine a client who wants to optimize content for a term like 'vegan recipes'. This tool would act as a 'memory' for that keyword. It would periodically scrape search engine results pages (SERPs) for a predefined set of related keywords, noting the top-ranking content, common questions, and even semantic variations that appear. Over time, this builds a 'memory' of how search engine algorithms and user behavior have shifted around that keyword. The scraper would be designed to store data chronologically, allowing users to 'rewind' and see the keyword's 'memory' at different points in time.

How it Works:
1. Niche Selection: The user defines a specific niche or a set of target keywords (e.g., 'sustainable fashion', 'AI ethics', 'remote work tools').
2. Data Collection: A Python-based scraper (using libraries like BeautifulSoup or Scrapy) is configured to periodically visit search engines (e.g., Google, Bing) using these keywords. It captures key information like:
- Top 10-20 ranking URLs.
- 'People Also Ask' (PAA) questions.
- Related searches.
- Snippets/featured snippets content.
- Potentially, the presence of specific content formats (e.g., videos, infographics).
3. Data Storage: This data is stored in a simple, low-cost database (like SQLite or a CSV file) with timestamps, creating a chronological record.
4. Analysis & Visualization: A simple front-end (could be a basic HTML/CSS page or a Flask/Django app) allows users to select a keyword and a date range. It then visualizes the changes, showing:
- Evolution of top-ranking domains.
- Emergence and disappearance of PAA questions.
- Shifts in related search terms.
- Changes in the dominant content types.

Ease of Implementation & Low-Cost: The core scraper can be built with free Python libraries and a local SQLite database. Hosting costs would be minimal, potentially just for a domain name and very cheap shared hosting if a web interface is desired. The niche focus makes it manageable for an individual.

High Earning Potential:
- Consulting: Offer specialized consulting services to businesses who want to understand historical keyword performance and adapt their strategy.
- Content Strategy: Help content creators identify evergreen topics and adapt their content to evolving search intents.
- SaaS Product: As the tool matures, it could be scaled into a niche SaaS product for SEO professionals, offering tiered subscriptions for different levels of historical data access and analysis depth.
- Data Insights: Sell aggregated, anonymized trend data for specific niches to market research firms or larger agencies.