Digital Ad Attribution Self-Referral Sentinel
$14,915.00 in paid ad spend cannibalizing zero-cost organic brand traffic across across 3 items.
Existing customers logging in clicked paid ads ($6,300.00 spend). Organic position #1 captures 91.2% CTR without ad spend.
Method1Correlated paid ad search queries against Google Search Console Position-1 organic dominance.
2Engineered negative keyword lists to prevent customer service and login query bidding.
| Reference | Description | Cannibalized_Spend |
|---|---|---|
| Exact | Paid Brand Login Cannibalization | $6,300.00 |
| Broad_Match | Customer Support Phone Paid Click Waste | $3,990.00 |
| Phrase_Match | Local Store Locator Paid Broad Match Spill | $4,625.00 |
Google Ads search query reports and Google Search Console organic traffic logs, plus negative keyword lists
Brand query cannibalization report and exact-match negative keyword deployment list (Resolution Dossier, Findings Schedule)
Run Digital Ad Attribution Self-Referral Sentinel free on the last 12 months of files. If it finds money, the fee is 15 percent of what is actually recovered, nothing on what is not. After that, ongoing monitoring is $1,250 a month. 30% goes to the referring partner.
Read in memory for the session, never stored, never used to train a model.
Outputs are computed from your inputs and the tool's rules. Check them before you rely on them.
Free scan. Pay only on cash recovered.
Run Digital Ad Attribution Self-Referral Sentinel free on the last 12 months of files. If it finds money, the fee is 15 percent of what is actually recovered, nothing on what is not. After that, ongoing monitoring is $1,250 a month.
On a sample file: $14,915.00 in paid ad spend cannibalizing zero-cost organic brand traffic across across 3 items.
Why not just use ChatGPT?
Rules that stay current: the rate cards, tariffs, code sets and regulations it checks against are kept up to date for you; a one-off prompt starts from nothing each time. Evidence that stands up: every finding cites the line, the file and the rule, so it holds up with a vendor, a payer or an auditor. The same answer every time: the checks are written rules, not a fresh guess, so this month's result can be compared with last month's. Nobody has to own it: no one inside has to build, test and maintain a home-made tool, and files are not pasted into a public chatbot.
Get a free scan
If you would rather talk it through, write to dev.sathya@baseloom.app; that reaches Dev Sathya, founder.