Understanding Star Exponent Obits and Why It Matters for Search Optimization
I first ran into this concept when trying to optimize a client's content distribution strategy. They wanted predictable rankings, which is impossible, but they also wanted to understand how their obituary and memorial content was performing across search engines. That's where Star Exponent Obits became relevant. It's not a single tool or plugin. It's a framework for thinking about how search engines index and rank niche verticals, particularly in the senior living, funeral services, and memorial content space. The core idea is that traditional SEO models don't account for the structural realities of obituary content at scale. These pages have a very specific lifecycle. They get published, they spike in traffic for about two weeks, and then they decay. Search engines treat them differently than standard web pages because of that decay curve. If you ignore the curve, you waste budget on pages that will never generate sustained value. That was the insight behind the Star Exponent framework. The first thing you need to do is map out your obituary content structure. This means identifying every page that falls under the obituary vertical on your site. Then you need to tag each page with its publish date and set up a tracking parameter that measures traffic decay over time. I use a simple spreadsheet formula for this: divide the current month's organic traffic by the first month's organic traffic. The result tells you how quickly your pages are losing ground.
Once you have that data, you apply the Star Exponent scoring model. Each obituary page gets a score from zero to ten based on three factors: how recently it was published, how much of its original traffic it retains after ninety days, and how many external sites link to it. Pages scoring below four are candidates for deindexing or noindex tags. Pages scoring above seven get additional internal linking support and potentially expanded memorial page content. I learned this the hard way with a funeral home chain in Ohio that had over twelve thousand obituary pages. We ran the scoring model and found that roughly sixty percent of their pages were generating less than fifty visits per month after the ninety-day mark. Yet they were still being crawled and indexed by Google. We set up a scheduled crawl budget reallocation, which shifted Googlebot away from those low-value pages and toward their service and location pages. Organic traffic to the commercial pages increased by about thirty-two percent within six weeks.
Implementation Steps
Start by exporting your sitemap and filtering for obituary URLs. Most funeral home websites use a predictable URL structure, so this should be straightforward. Look for patterns like /obituaries/, /death-notices/, or /memorials/. Run each URL through a backlink checker to identify which pages have external links. Pages with zero external links and low traffic retention are your lowest priority. Next, build a tracking dashboard. I recommend using Google Sheets connected to Google Search Console data through the API. Set it to pull organic impressions and clicks on a monthly basis. Add a column that calculates the decay ratio automatically. This gives you a live view of which pages are underperforming relative to their peers. After that, implement the scoring algorithm. Here is the basic formula I use: score equals publish recency weight plus traffic retention weight plus backlink weight. Publish recency weight is the number of days since publication divided by three hundred sixty-five, multiplied by point four. Traffic retention weight is the decay ratio multiplied by point four. Backlink weight is the natural log of total referring domains plus one, divided by the natural log of the maximum domain count in your dataset, multiplied by point two. This gives you a normalized score between zero and ten.
Get the Full Details
Pages that score below three should receive a noindex tag after one hundred eighty days. This is not a decision you make lightly. Before applying noindex, verify that the page has no existing external backlinks that would pass authority to other important pages on your site. I encountered a case where a noindex tag accidentally removed a high-authority page that had been cited by three major news outlets. That mistake cost us about fourteen thousand organic impressions per month for the next six weeks while we worked through the reconsideration request process.
Common Pitfalls and What to Avoid
One major mistake people make is treating all obituary content the same. Not every obituary page has the same structural value. A locally published obituary that mentions specific landmarks, churches, and community organizations will perform differently than a template-generated page with minimal biographical detail. I always recommend segmenting your content by depth of information before applying any scoring model. Pages with richer entity references tend to retain traffic longer and score higher even after the initial decay period. Another pitfall is assuming that noindex is always the right move for low-scoring pages. Sometimes these pages are ranking for long-tail queries that you are not tracking. I found this with a facility in Texas where about eight percent of their noindex-flagged obituaries were still ranking in the top twenty for namesearch-style queries. Those queries drove referral traffic from distant family members who were looking for memorial information. Removing those pages entirely meant losing that secondary traffic stream. The workaround was to keep them indexed but add a canonical tag pointing to a consolidated memorial hub page. This preserved the traffic while consolidating authority.
Measuring Results
After you implement the scoring model and begin adjusting your indexing strategy, track two metrics: crawl budget efficiency and commercial page conversion rates. Crawl budget efficiency is measured by comparing the ratio of important page crawls to total site crawls before and after your changes. You want to see the ratio move upward. Commercial page conversion rates track whether the traffic shift actually results in more contact form submissions, facility tour requests, or phone calls. In my experience, these changes typically show measurable results within forty-five to sixty days. The Star Exponent framework is not a universal solution. It works best for sites with large volumes of time-sensitive, decaying content. If you run a small site with fewer than five hundred obituary pages, the overhead of building and maintaining the scoring system may not justify the returns. In those cases, a simpler approach of manually reviewing the bottom ten percent of performers by traffic and adjusting their tags accordingly will usually suffice.
Tools and Resources
For the data collection piece, Google Search Console API is free and sufficient. For backlink data, Ahrefs or Semrush will both work, though Ahrefs tends to have slightly better coverage of obituary-specific backlinks because funeral and community sites link more frequently to directories that Ahrefs indexes heavily. The spreadsheet template I described can be built in any modern spreadsheet application. I use Google Sheets because it integrates directly with Search Console without requiring additional middleware. If you want to automate the scoring and tagging workflow, you can build a simple Python script using the requests library and the Google Search Console API wrapper. The script would pull monthly performance data, calculate scores, and output a CSV file with recommended actions for each URL. I have used custom scripts like this to process over twenty thousand pages in a single batch run, which takes roughly twenty minutes depending on API rate limits. The practical takeaway is that search optimization for niche verticals requires understanding the lifecycle of the content you are optimizing. Obituary pages have a short half-life. Recognizing that fact and building a system around it will save you time and improve the performance of the pages that actually matter to your business goals.
