AEO EngineBroadview Federal Credit Union Case Study
How a $9 billion New York credit union grew Google search impressions 341% in eight months, moved from average position 14.0 to 6.5 across every query it ranks for, and started earning citations in ChatGPT, Gemini, Perplexity and Google AI Overviews.
Between February 1 and September 30, 2026, AEO Engine helped Broadview Federal Credit Union build a financial content and visibility system that grew monthly Google impressions from 1.57 million to 6.92 million, improved average position from 14.0 to 6.5, put 521 articles into Google results at a blog-wide average position of 6.9, lifted the credit union AI answer mention rate from 5.3% to 8% across 150 tracked questions, and grew tracked form submissions 67.2% against the May 2026 baseline.
Credit union, consumer banking
Feb 1 to Sep 30, 2026 (242 days)
What changed for Broadview Federal Credit Union
Over the 242 days from February 1 to September 30, 2026, broadviewfcu.com appeared in Google search results 29.7 million times and was clicked 775,000 times, according to Google Search Console daily property totals.
Monthly impressions grew from 1.57 million in February to 6.92 million in September, a 341% increase. Average position across every query the site appeared for improved from 14.0 to 6.5 over the same two months. In plain terms, the typical Broadview result moved from the middle of page two to the top half of page one.
The content engine did most of that work. 521 blog articles earned impressions in the period, 24.1 million of them, or 46% of the page-level total, at an impression-weighted average position of 6.9. The eight largest articles drew 16.4 million impressions on their own, all from page one.
On AI answer engines, Broadview was named in answers to 8% of 150 tracked credit union questions in the October 4, 2026 snapshot, up from 5.3% in September, and earned 22 citations across ChatGPT, Gemini, Perplexity and Google AI Overviews. Mortgage and youth savings pages earned 14 of the 22.
Clicks did not grow in step with impressions. Organic clicks stayed inside a band of 87,000 to 115,000 in every full month of the program, and site-wide click-through fell from 6.48% to 1.36% while average position improved. This case study reports that finding too, and the capture program that follows, because that is what a program evaluation is for.
Data used in this case study
- Google Search Console daily property totals, February 1 to September 30, 2026: impressions, clicks, click-through rate and average position
- Google Search Console page-level reporting for the segment, article and utility-page tables
- AEO Engine AI answer engine panel: 150 questions, four engines, 600 answers, point-in-time snapshot taken October 4, 2026
- Broadview Federal Credit Union published branch financials, July 31, 2026, for asset and membership figures
The Results: 4.4x Monthly Visibility, Page Two to Page One, First AI Citations
Average position is the impression-weighted average across every query the site appeared for in the month. AI mention rate is the share of 150 tracked questions where Broadview is named in at least one engine answer, taken as a point-in-time snapshot. It is not a Search Console figure. Form submissions come from the weekly performance report, which compares 28-day periods, so they cover a different window to the monthly search figures.
What changed
Month by month, February to September 2026
Impressions compounded every month of the program while clicks stayed inside a narrow band. That single pattern explains both the headline result and the finding that follows it.
| Month | Impressions | Clicks | Average position |
|---|---|---|---|
| February | 1.57M | 101,890 | 14.0 |
| March | 1.85M | 114,991 | 10.9 |
| April | 2.49M | 97,165 | 11.7 |
| May | 3.14M | 96,952 | 9.1 |
| June | 4.03M | 89,658 | 7.5 |
| July | 4.67M | 93,855 | 7.2 |
| August | 5.06M | 86,600 | 7.4 |
| September | 6.92M | 94,167 | 6.5 |
Source: Google Search Console daily property totals. February is the first full month of the program and September the last.
Forms and Site Activity: Submissions Up 67.2% Against the May 2026 Baseline
Impressions and citations describe how often Broadview is seen. Form submissions describe what happens when someone acts. The weekly performance report tracks both on a rolling 28-day basis, so this table compares the current period against the previous period, the period 90 days earlier, and the May 2026 baseline.
Submission events grew from 60,867 in the May 2026 baseline period to 101,746 in the current 28-day period. Visitors grew from 69,488 to 111,019 and pageviews from 140,765 to 222,682 over the same comparison, and outbound link clicks rose 57.7%.
Tracked site activity, 28-day periods
| Measure | Current | Previous | 90 days ago | Baseline (May 2026) | Change vs baseline |
|---|---|---|---|---|---|
| Visitors | 111,019 | 105,792 | 111,544 | 69,488 | +59.8% |
| Pageviews | 222,682 | 215,622 | 218,823 | 140,765 | +58.2% |
| Form submission events | 101,746 | 91,947 | 89,634 | 60,867 | +67.2% |
| Outbound link clicks | 7,364 | 7,055 | 7,218 | 4,671 | +57.7% |
Source: AEO Engine weekly performance report for broadviewfcu.com, comparing rolling 28-day periods against a May 2026 baseline.
Client Context
Broadview Federal Credit Union is a member-owned credit union headquartered in Albany, New York, formed by the merger of SEFCU and CAP COM. As of July 31, 2026, it reported about $9.4 billion in assets and more than 518,000 members in its published branch financials. It offers checking and savings, auto loans, mortgages and home equity lines of credit, credit cards, youth accounts, insurance and digital banking.
Its website does two jobs at once. It is the front door for members, who arrive for online banking, rates, branch hours and contact details. It is also a publisher of financial education that competes for broad, non-brand questions about credit scores, auto loan rates, credit card billing and household budgets.
The program job was to grow the second role without weakening the first, and to establish where Broadview stood in AI answers against the regional credit unions it competes with for members.
The Financial Search Problem
A credit union search presence is three different businesses that happen to share a domain.
Brand and utility queries carry high click-through and already rank in the top seven, with little room to climb and rising exposure to results-page answers. Product and rate queries carry commercial intent and are contested by national lenders and comparison sites. Education queries carry enormous volume, early-stage intent and a results page where few searchers click any single result.
Most financial institutions publish for the third group, rank for it, and report the total as one number. That hides the problem and the opportunity in the same line. This program measured each group separately, so Broadview could see which pages carried impressions, which carried clicks, and which were beginning to earn AI citations.
- •Financial content is Your Money or Your Life territory. Pages need clear authorship, dates, accurate figures and plain explanations to earn trust from readers, Google and AI engines alike.
- •AI engines name the institutions whose pages explain a product in concrete, quotable terms: who qualifies, what it costs, how it compares.
- •Impressions are a leading indicator. Clicks and assisted applications are the business number, and the report has to say which is which.
- •Brand pages, product pages, education content and structured data all have to tell the same story about who Broadview serves and where.
Goals
Build search visibility at scale
Move broadviewfcu.com from page two to page one across its query set and expand coverage of the financial questions members ask before they apply.
Turn the blog into a measured engine
Publish and maintain a connected library of financial education content, judged by articles earning impressions and page-one positions, not by articles published.
Open the AI answer surface
Establish a baseline for how often Broadview is named in ChatGPT, Gemini, Perplexity and Google AI Overviews, benchmark it against regional peers, then lift it.
Keep the signals separate
Report brand, product, education and AI visibility as distinct lines so growth in one cannot hide weakness in another.
Keep the proof clean
Tie every number to a reporting window, a data source and a plain-English explanation of what it means.
How AEO Engine Built the Visibility System
Mapped the credit union products to the questions members ask first
We mapped Broadview around the questions people search before they apply for a financial product: used car loan rates, how to raise a credit score, how credit card interest and billing cycles work, how credit unions differ from banks, how to cut monthly expenses.
- Defined the core product lines: auto loans, credit cards, mortgages and HELOCs, savings and youth accounts
- Matched each product to the education questions that precede an application
- Prioritized topics with a direct path into a rate or product page
Published financial education content at scale, built for people and AI answers
AEO Engine content engine produced and maintained the library that now holds 521 articles earning impressions. Articles answer the question in the first paragraph, carry clear headings for each sub-question, and link into the relevant product page. Examples include the Used Car Loan Rates Guide, Improving Your Credit Score, Credit Card Billing Cycle, Best Car Interest Rates and How Credit Unions Differ.
- Short-answer openings for the question in the title
- Clean H2 and H3 structure around rates, eligibility, costs and next steps
- Internal links from education pages into auto loan, card, mortgage and savings pages
Structured every page so search engines and AI tools could extract it
Each page was built so a reader, a crawler and an answer engine could identify the topic, the product and the institution behind it without guesswork.
- Schema markup and breadcrumbs on article and product pages
- FAQ sections matched to visible page content
- Consistent entity signals: institution name, location, product names and dates
Optimized the library on a weekly cadence
Content was not published and left. Pages were re-crawled, re-optimized and re-linked as rankings moved, with the biggest-impression articles first.
- Weekly on-page optimization passes across the live library
- Internal link additions as new articles joined a cluster
- Position tracking at the article level, not just the property level
Measured search and AI visibility as separate programs
The evaluation reports Google Search Console daily property totals for the whole site, page-level reporting by segment, and a separate AI answer panel of 150 credit union questions run across four engines and benchmarked against five regional peers.
- Monthly impressions, clicks, click-through and position, February through September
- Segment-level reporting: brand, product, blog, education, other
- AI panel snapshots with mention rate, citations, share of voice and peer comparison
Proof Stack
The proof below shows the four outcomes that mattered most: visibility, position, a working content engine, and a measured AI baseline.
Monthly Google impressions grew from 1.57 million in February to 6.92 million in September 2026.
Source: Google Search Console daily property totals
Average position across all queries improved by 7.5 places between February and September 2026.
Source: Google Search Console daily property totals
The blog library ranks on page one at an impression-weighted average position of 6.9. Eight articles drew 16.4 million impressions.
Source: Google Search Console page-level reporting
The homepage converted 4.6 million impressions at an 11.09% click-through rate, and all five utility pages rank inside the top seven.
Source: Google Search Console page-level reporting
Broadview is named in answers to 12 of 150 tracked questions, up from 5.3%, with citations on all four engines.
Source: AEO Engine AI answer panel, October 4, 2026
Why This Evaluation Is Uncomfortable on Purpose
A program that reports only the numbers that grew cannot fix the ones that did not. Impressions were the easy headline and the funnel was the harder one, so both are here, with the source method behind every figure.
The AI panel is deliberately modest. An 8% mention rate is a baseline, not a trophy, and it sits next to five regional peers who are named more often. That is the number Broadview has to improve, and the only way to improve it is to publish it first.
- •Every figure carries a reporting window and a named source method
- •Brand, product, blog and education performance are reported as separate lines
- •The click-through decline is stated in the FAQ rather than smoothed over
- •Peer mention rates are named, including the ones ahead of Broadview
Why It Worked
The visibility growth was not one lucky ranking. It compounded month after month: 1.57 million impressions in February, 2.49 million in April, 4.03 million in June, 6.92 million in September.
The same discipline is why the evaluation states the click-through decline plainly in its FAQ. A program that reports only the numbers that grew cannot fix the ones that did not.
- Content was produced at a scale a credit union marketing team cannot sustain by hand, and judged by impressions and positions rather than by publishing counts.
- Topic selection matched the questions members ask before they apply, so the library ranks for questions a lender should own.
- Brand, product and education demand were tracked as separate lines so growth in one could not mask weakness in another.
- AI visibility had its own panel, its own snapshot dates and its own peer set, so the baseline is real and the target is measurable.
What We Would Do Next for Broadview: Move From Publishing to Capturing
Five shifts. Each one is aimed at a number on this page, not at general best practice.
- →Rebuild the top of the results page: rewrite titles and snippets on the eight articles holding 16.4 million impressions, targeting 0.5% click-through on the library.
- →Defend the utility queries: audit the brand and rate queries losing click-through, then own the answer box with structured data, FAQ markup and brand-page copy.
- →Make AI citation a workstream: structured answers, comparison tables and source-worthy data on mortgage and youth savings pages, targeting a 20% mention rate.
- →Connect content to product: every top article gets a contextual path into auto loans, HELOC, cards and savings, with assisted applications tracked.
- →Report on clicks and answers, not impressions: impressions are now a leading indicator with a poor conversion record, so clicks and AI mentions become the headline.
Five numbers to hold ourselves to
| Measure | Today | 12-month target | How |
|---|---|---|---|
| Organic clicks per month | 94,167 | 110,000 | Stabilize, then recover on brand |
| Blog click-through rate | 0.15% | 0.40% | Titles, snippets, internal links |
| Homepage click-through | 11.1% | 15.0% | Brand result and page experience |
| AI mention rate | 8% | 20% | Structured answers on cited pages |
| Articles at 0.5% CTR or better | 32 of 182 | 60 of 182 | Snippet rewrite program |
Source: Baselines are the program period, February 1 to September 30, 2026, with September as the monthly reference. Articles are counted among the 182 blog pages with at least 1,000 impressions in the period. Each target is measured with the same method as its baseline.
Explore the system behind this result
These related AEO Engine resources explain the methodology used to build search visibility, AI citations, and qualified conversion paths.
FAQ: Broadview Federal Credit Union AEO Case Study
What was the main result for Broadview Federal Credit Union?
Between February and September 2026, Broadview Federal Credit Union's monthly Google impressions grew from 1.57 million to 6.92 million, a 341% increase, and its average position across all queries improved from 14.0 to 6.5. Over the full program window, February 1 to September 30, 2026, the site was shown 29.7 million times and clicked 775,000 times. Tracked form submissions rose from 60,867 in the May 2026 baseline period to 101,746 in the current 28-day period, a 67.2% increase. Its AI answer mention rate rose from 5.3% to 8% across 150 tracked credit union questions.
Why did impressions grow so much faster than clicks?
Site-wide click-through fell from 6.48% in February to 1.36% in September 2026 while average position improved. The new impressions come mostly from financial education articles ranking on page one for broad, early-stage questions where few searchers click any result, and brand and rate queries are increasingly answered inside the results page. AEO Engine built the next program around click capture: titles, snippets, structured data and paths from content into product pages.
How is the AI mention rate measured?
AEO Engine tracks 150 credit union and consumer banking questions across ChatGPT, Gemini, Perplexity and Google AI Overviews, which produces 600 answers per snapshot. Mention rate is the share of the 150 questions where Broadview Federal Credit Union is named in at least one engine's answer. Citations count the number of times Broadview pages are referenced as sources. Figures are point-in-time snapshots, not Google Search Console data.
What is answer engine optimization for a credit union?
Answer engine optimization, or AEO, is the practice of making a financial institution's pages the ones that AI engines and Google's answer features quote when someone asks about rates, products, eligibility or everyday money questions. For a credit union that means concrete product pages, structured data, clear authorship and dates, education content that answers the question in the first paragraph, and tracking how often the institution is named in AI answers against regional peers.
What makes this an AEO case study rather than a traditional SEO case study?
It reports both surfaces. Traditional SEO reporting stops at impressions, clicks and position. This evaluation adds a 150-question AI answer panel with citation counts by engine, share of voice and a benchmark of five regional credit unions, and it separates brand, product and education traffic so each can be managed on its own.
Where did Broadview's AI citations come from?
Of the 22 citations in the October 4, 2026 snapshot, 10 came from Broadview's Manage My Mortgage pages and 4 from Youth Savings pages. By engine, Gemini and Perplexity each produced 7 references, ChatGPT 6 and Google AI Overviews 2.
Can this work for other credit unions and banks?
The system is repeatable: map products to the questions members ask before they apply, publish answer-first education content at scale, structure every page for extraction, connect content to product pages, and measure search and AI visibility separately. Results depend on existing authority, site structure, compliance review speed and how many product pages give concrete, quotable answers.
How do financial institutions get cited in AI answers?
AI engines cite pages that answer one question completely and in quotable terms: who qualifies, what it costs, how it compares, what changes in 2026. In the Broadview panel, 14 of 22 citations pointed at mortgage and youth savings pages, which are the two product lines where Broadview publishes specific, structured product detail. Depth and specificity on a product page matter more than volume of blog posts.
Want your credit union or bank to show up in Google and AI answers?
Book a free AEO and AI visibility audit. We will show you which financial questions your regional peers are being cited for, where your product pages are missing the structure engines need, and which content clusters can create the fastest path to applications.
- The credit union and banking prompts where your peers are already appearing
- The product lines with the highest citation upside: mortgage, auto, cards, savings
- A 30/60/100-day plan for content, snippets, structured data and product paths
Limited to 5 new audits per month.
More Case Studies
My Rights Law
+217% organic traffic and 9x monthly bookings
Read Case Study →LegalAsk4SAM
#7 to #2 of 72 tracked NY PI firms in 180 days
Read Case Study →B2B EcommerceIconic Displays
+155.8% revenue growth and 749 AI citations
Read Case Study →Marketing AgencyDominate Dental
+286% organic traffic and new AI lead channel
Read Case Study →