Anonymous category analysis

How Enterprise Bank made local product conditions legible to AI

Modeled on Enterprise Bank, a cooperative group with about 200 local banks. Shared master content, local rates hidden in dropdowns, comparison answers that flattened the brand. Public pages on their domain. Same HTML for humans and AI.

11→58%Named on local-rate questions, 12 weeks
68→22%Answers that only gave a vague range
2.6xAI crawler hits, first quarter
1,240AI referral sessions, 90 days
The group was not losing every comparison because the product was worse. It was losing because the answer had no local rate to quote, so the model used a national range or skipped the brand.
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Appear category patternEnterprise Bank, anonymized

The Challenge

This is an anonymized pattern study for Enterprise Bank. About 200 local banks share a master product set. Each bank sets its own rates, fees, and branch benefits. Legal has been nervous about duplicate content, so most local condition pages never shipped.

The live product pages keep rates behind client-side dropdowns. Crawlers fetch the shell, not the number. The PDFs that do hold the terms are poorly structured for models. The agreed sequence is to make the website readable first and leave the PDF problem for later.

When someone asks ChatGPT or Perplexity for a mortgage, a savings fee, or a local current-account comparison, the answer often gives a wide range or names a more transparent rival. Some of those losses are commercial. An uncompetitive mortgage rate will still lose a shortlist even after the page exists. The case has to say that out loud.

Appear does not rewrite the existing human site. It publishes new public pages from already-approved facts, on the bank's own domain, with the same HTML for people and crawlers. No cloaking. No AI-only wiki. No llms.txt.

Before Appear

  • Local rates lived in dropdowns and PDFs. Crawlers saw the label, not the number.
  • Local-bank pages stayed unpublished over duplicate-content fear.
  • Comparison answers used a national range or skipped the group.
  • Internal approval could take three months, so new terms aged in review.

After Implementation

  • Public condition pages sit on the bank's domain. Humans and AI get the same HTML.
  • Discovery is hierarchical: product family, then region, then the local bank's terms.
  • Named answers on local-rate questions rose 11% to 58% in 12 weeks.
  • Approved facts can auto-publish. Legal review stays on for anything new.

AI truth

Same buyer questions, first weeks versus week 12. Vague ranges falling is the win, not a second mention score.

Local-rate questions that name the bank+47 pts in 12 weeks
Baseline: 11%Current: 58%
Comparison answers that name a local bank+32 pts
Baseline: 9%Current: 41%
Answers that cite a bank URL+27 pts
Baseline: 6%Current: 33%
Answers that only give a vague range−46 pts
Baseline: 68%Current: 22%

Performance and lead creation

Crawler activity and AI-originated sessions on the new public pages. Media value is a modeled paid-search equivalent, not booked revenue.

AI crawler hits, first quarter2.6x
Baseline: 4,100Current: 10,660
AI referral sessions, 90 daysfrom near zero
Baseline: 90Current: 1,240
Form starts from those sessions5.8%
Baseline: 0.4%Current: 5.8%
Modeled paid-search equivalent€3.10 CPC
Baseline: €280Current: €3,840

Implementation Timeline

Week 1

Map the 200-bank tree

Product families, local exceptions, and the questions people already ask AI. Only approved rates and fees enter the first set.

Weeks 2 to 5

Publish public condition pages

Group pages plus local-bank pages go live on their domain. The existing dropdown UX stays. Humans can open the same URLs.

First quarter

Re-probe the same questions

Local-rate and comparison prompts are asked again. Vague ranges fall. Named answers rise. Mortgage prompts where the rate is uncompetitive still lose the shortlist.

Each quarter after

Refresh when conditions change

Rate updates follow the same cadence as the bank already uses. New claims wait in review. Already-approved facts can ship the same week.

Banking group whose local rates AI still cannot read?

Appear publishes the approved conditions on your domain and re-checks the same buyer questions until the answer names the local bank.

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