Googlebot log analysis connects a crawler request with the response your server returned. For an ecommerce store, it can uncover failing products, slow filters or repeated URL combinations. The goal is an evidence-based action list.
Download the synthetic request CSV, completed summary and action worksheet. Every record is invented for teaching, including its crawler identity. Start with the crawl-budget guide for the broader concepts.
Key takeaways
- Verify the crawler before counting its requests. A Googlebot user-agent string can be copied.
- Group requests by URL purpose, status and response time, then compare important pages with your inventory.
- The downloadable example is synthetic. Crawling does not prove indexing, and filter requests are not automatically waste.
Table of contents
- When is log analysis useful for an ecommerce site?
- What should you collect before opening the CSV?
- How do you verify Googlebot requests?
- Step 1: prepare the Googlebot log analysis CSV
- Step 2: build the request and response summary
- Step 3: turn the findings into an action list
- Step 4: compare important URLs and repeat the check
- Frequently asked questions
- Sources and further reading
When is log analysis useful for an ecommerce site?
Investigate logs when you need request-level evidence: a newly published product seems undiscovered, navigation creates many combinations, or failures coincide with crawling. Poor rankings alone do not establish a crawl-budget problem.
Google's crawl-budget guidance concerns large or frequently changing inventories. For a small Nepal store, check unavailable pages, weak product information and discovery paths first. A successful request proves neither indexing nor search visibility; use the indexing diagnosis guide for that separate question.
What should you collect before opening the CSV?
Collect a defined log window and a matching inventory of important public URLs. Ask where the records originate, whether they are sampled, and which timezone and response-time unit apply.
- Timestamp, hostname, method and full path, including parameters.
- Status, duration and response bytes when available.
- Client IP and user agent in a restricted working copy.
- Missing periods, caching behaviour and resource coverage.
Keep raw identifiers private. A trusted proxy configuration matters: an arbitrary forwarded-IP header cannot establish requester identity. Edge caches may serve pages without contacting the origin, so origin-only records can omit visits. Combining both exports can also double-count requests unless a shared identifier permits deduplication. Document these limits before calculating anything.
Match the catalogue date to the observation window. Otherwise, products removed after that window may appear to have been unavailable throughout it.
How do you verify Googlebot requests?
A Googlebot user agent is only a candidate filter. Verify identity against the appropriate official IP ranges, or use reverse DNS followed by a forward lookup that returns the original IP.
Follow Google's verification procedure, checking permitted hostname suffixes correctly. Separate ordinary Googlebot, inspection tools and unknown requests. Record the method and verification date.
The CSV uses documentation-only IP addresses and
simulated_verifiedflags. Those invented identities cannot pass real verification. Replace the teaching flags with genuine results before analyzing production traffic.
Step 1: prepare the Googlebot log analysis CSV
Import the comma-separated sample, then filter crawler_family to Googlebot and verification to simulated_verified. Ten records remain; the unverified claim and simulated inspection-tool request are excluded.
The denominator includes one asset. For an HTML-only investigation, exclude resources explicitly and recalculate the totals.
| Group | Example path | Why separate it? |
|---|---|---|
| Product | /products/tea-set/ | A product detail URL with commercial value. |
| Category | /collections/home/ | A browseable category that may serve a search intent. |
| Filter | /collections/home/?color=red | A query-string combination requiring an indexing decision. |
| Removed | /products/old-mug/ | An unavailable item returning a not-found response. |
| Asset | /assets/app.js | A rendering resource, not a product page. |
Preserve parameters during classification; removing them would hide the combinations being investigated. Use repeatable grouping rules without merging distinct languages, pagination or useful collections. The ecommerce SEO guide explains their place in a store's architecture.
Step 2: build the request and response summary
Create a pivot with url_group as rows, Count of path for requests and Average of response_ms for mean duration. Ensure durations import as numbers. Add status counts separately so the average cannot conceal a failing response.
| Group | Requests | Mean response | Statuses |
|---|---|---|---|
| Product | 3 | 933.3 ms | Two 200; one 503 |
| Category | 2 | 160 ms | Two 200 |
| Filter | 3 | 800 ms | Three 200 |
| Removed | 1 | 90 ms | One 404 |
| Asset | 1 | 40 ms | One 200 |
These are synthetic teaching numbers, not benchmarks. Filters comprise three of ten mixed-resource requests, or 30%. That does not establish waste: a useful filter landing page may deserve crawling.
The product durations—180, 220 and 2,400 milliseconds—produce a 933.3 ms mean but a 220 ms median. Investigate the individual 503 rather than declaring the entire group slow. In larger exports, count distinct URLs too: repeated visits and catalogue coverage describe different patterns.
Step 3: turn the findings into an action list
Prioritize demonstrated failures on valuable pages. Give each finding an example URL, timestamp, owner and acceptance check, using the downloadable worksheet.
| Sample finding | Next action | Acceptance check |
|---|---|---|
| Product request returned 503 | Developer checks application, origin and edge records for the same timestamp. | Valid product is available; comparable new logs no longer show the same failure pattern. |
| Filter requests are comparatively slow | Inspect query execution and decide which combinations serve a useful search intent. | Approved URLs work correctly; disallowed combinations follow the reviewed URL policy. |
| Removed item returns 404 | Confirm permanent removal and remove obsolete links or sitemap entries. | No important link or current sitemap advertises an unavailable item. |
Google's status-code documentation explains how server failures affect crawling. A legitimate removal differs from a broken release; establish intended behaviour before assigning severity.
Review filters with merchandising and development, using Google's faceted-navigation guidance. Test proposed rules against valuable destinations. A canonical tag signals consolidation, not prevention of requests. Redirect a removed product only to a genuinely relevant replacement, not automatically to the homepage.
Step 4: compare important URLs and repeat the check
Join the catalogue to the request summary by consistently normalized URL. Label a missing match “not observed in this export”; absence alone does not prove a blocked or orphaned page.
Check coverage, hostname and publication date, then examine discovery links, sitemap inclusion and Search Console evidence. The Crawl Stats report supplies aggregate trends and representative examples, not a complete request history.
After implementation, collect a comparable window and repeat the same rules. Note releases and inventory changes. Confirm the technical correction before assessing subsequent indexing or traffic; report no ranking or revenue gain unless measured.
Frequently asked questions
Can I analyze logs without a paid tool?
Yes. A spreadsheet handles this sample and manageable exports. Large datasets may require a database. Verification, consistent grouping and a declared denominator remain necessary regardless of software.
Does a Googlebot request prove indexing?
No. It proves a request at the observed collection point. Investigate indexing separately through Search Console.
Should every filter be blocked?
No. Agree which combinations serve useful search intent, then test appropriate controls. Parameters alone do not establish redundancy.
Is the sample a client case study?
No. All twelve records are synthetic; ten belong to the working sample. The summary demonstrates arithmetic, not a real store's performance.
After identifying heavy filter request families in your logs, use the eight faceted navigation URL decisions to choose a policy. Request counts alone do not determine which filtered collections should remain eligible for indexing.
Sources and further reading
- Google: Optimize your crawl budget; retrieved 30 September 2026
- Google: Verify requests from Google crawlers and fetchers; retrieved 30 September 2026
- Google: How HTTP status codes affect crawlers; retrieved 30 September 2026
- Google: Managing crawling of faceted navigation URLs; retrieved 30 September 2026
- Search Console: Crawl Stats report; retrieved 30 September 2026
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