HER Appears in 40.4% of AI Answers. Here's Our Five-Step Plan to Improve Its AI Visibility.

Answer Capsule

In November 2026, HER appeared in 273 of 675 AI answers included in the Online Dating benchmark (40.44%). It received 253 recommendations (37.48%), with 13 Top 3 placements and 3 first-place recommendations. A mention means the company appeared in an answer; a recommendation means the answer affirmatively endorsed it.

Our plan compares the company with its competitors, checks conflicting AI claims against reliable sources, and identifies website pages, third-party sources and buyer questions to address. The historical results show how the figures changed across available months. Different monthly samples limit those comparisons, and conflicting AI claims require verification before corrections are proposed.

Current AI visibility

Questions This Section Answers

• How do HER mentions, recommendations and placements compare with its Online Dating peers?

HER recorded 273 mentions (40.44%) and 253 recommendations (37.48%) across 675 answers included in the benchmark. Top 3 placements numbered 13; first-place recommendations numbered 3. Framing among its mentions was 254 positive, 19 neutral and 0 negative.

HER is named in a large share of the answers captured for this benchmark, and most of those mentions carry positive wording. That gives the brand real reach inside AI-generated dating advice, which matters because readers often treat a named app as a viable option even before they compare features.

The gap sits in position rather than presence. HER appears near the top of a shortlist far less often than it is simply mentioned or recommended, so the practical task is to strengthen the reasons an answer would rank it highly, not to chase more raw mentions.

Recommendation, placement and framing are separate measures here. Positive wording does not by itself produce an endorsement, and a strong recommendation rate should not be read as conversion. The agency would treat reach and position as distinct goals with distinct evidence.

What changed since last month

Questions This Section Answers

• How did HER counts and coverage differ across the available monthly snapshots?

From October 2026 to November 2026, HER's mention count rose from 266 to 273, while its recommendation count rose from 228 to 253. The recommendation rate was 32.48% and 37.48% respectively.

Across the recorded months, HER's mention and recommendation counts moved without a single steady direction. The latest month shows more mentions and more recommendations than the month before, while the top-position counts moved the other way. These are separate samples, so the movement describes what was recorded, not why it happened.

The comparability limit matters for planning. Because each month draws on a different set of answers, month-to-month shifts should be read as observations to watch rather than as a trend the agency can act on confidently. The agency would set a baseline and judge future work against consistent measures.

Step 1. Measure

Questions This Section Answers

• What should the agency measure and verify first for HER?

AI answers contained 0 conflicting claims about HER: 0 critical, 0 high, 0 medium, 0 low. Of these, 0 were rated critical or high priority; the critical claims are included in that total. These ratings help prioritize fact-checking. They do not establish that a company or publisher made an error.

No conflicting claims about HER were recorded in the captured answers, so there is no immediate fact-check queue to clear. That is a useful starting position, but it is not proof that every statement about the brand is accurate, and it does not show that any publisher made a mistake.

The agency would still build a visibility comparison and a short list of claims worth verifying against primary sources. This lets the client see where HER stands relative to competitors and confirm the facts that answers rely on before any outreach begins.

Step 2. Make the website consistent

Questions This Section Answers

• Which recorded claim differences about HER should be verified before proposed page edits?

No results are available for this part of the analysis.

Both columns quote AI answers, not verified company facts. Resolve the entity, date and source before deciding whether a correction is warranted; different dates can explain an apparent conflict.

This part of the analysis returned no results, so there are no disputed topics to resolve from the captured evidence. The agency would not treat that absence as a clean bill of health or as a problem to fix.

Instead, the proposed work is to verify the entity, dates and sources behind any claim before deciding whether a page change is warranted. Page briefs would be drafted only after that check, and only where approved facts support them.

Step 3. Align the external narrative

Questions This Section Answers

• Which cited third-party pages are relevant to fact review for HER?

These pages were selected because they relate to the company in the captured answers. Their current contents still need checking before any correction request. A page linked to a conflicting claim is not necessarily its cause, and the same answer may contribute to more than one row.

The cited pages selected for review are mostly third-party roundups and comparison articles, alongside a few competitor homepages. They appear often in answers that mention HER, which makes them worth reading closely, but a citation does not show that a page influenced an answer or contains an error.

The agency would review each page's current contents against verified facts and prepare evidence-backed requests only where a genuine gap exists. Publisher control is never assumed, and no correction would be requested without primary-source confirmation.

Step 4. Optimize priority company pages

Questions This Section Answers

• Which observed HER website pages should receive review briefs?

The company's website was cited 8 times across 4 distinct page addresses; the table shows 4 pages selected for review. Zeros in the answer columns mean no matching answers were recorded. A cited page is not automatically a page that accompanied a recommendation.

HER's own pages appear in the captured answers, including the homepage and a few topic pages, but the counts are small and should be read carefully. A cited page is not automatically a page that accompanied a recommendation, so these figures describe presence rather than influence.

The agency would prepare page briefs that use approved facts and address the questions buyers actually ask. Before proposing any new page, it would check whether existing coverage already answers the question well enough to improve rather than duplicate.

Step 5. Publish around buyer questions

Questions This Section Answers

• Which observed buyer questions support content briefs for HER?

These are recorded answer observations, not search volume, verified demand, or proof of missing website content.

The recorded questions cluster around broad comparisons, such as top dating sites and best apps, plus a Spanish-language query and a question about apps for women. HER appears in answers to several of these, which suggests the brand is already part of the conversation rather than absent from it.

These are answer observations, not search volume or proof of missing content. The agency would turn the recurring questions into content briefs, checking existing pages first, and would write for the buyer's decision rather than for keyword coverage.

Our 90-day action plan

Questions This Section Answers

• How should the agency sequence the proposed HER work and judge completion?

The first month establishes the baseline: a scorecard, a scoped fact-review list and a shortlist of pages and sources, each with an owner and a clear evidence requirement. Nothing is corrected at this stage; the goal is a reliable starting picture.

The second month produces approved page briefs and justified publisher requests, with confirmed edits, no-change decisions and responses recorded separately. The third month adds buyer-question content briefs and a fresh scorecard. Delivered work is tracked apart from measured answer outcomes, and no visibility gain is promised.

Methodology

Questions This Section Answers

• How are HER answer metrics and citation populations defined?

This report covers 2026-11. The exact dates when the original AI answers were collected have not been verified. The visibility rates use 675 answers that met the benchmark's inclusion rules. A company is recommended only when the answer both endorses it and uses positive wording. Top 3 and first-place counts are part of those recommendations, not extra recommendations.

An answer can mention or recommend several companies, so company counts cannot be added to find the number of distinct answers. The net sentiment score is positive minus negative mentions, divided by all company mentions; it describes answer wording, not customer satisfaction.

The wider citation analysis covers 800 captured answers and 4863 appearances of cited URLs. Citation appearances across all captured answers differ from the number of benchmark answers citing a page and the number that also recommend a company. The current contents of cited pages and disputed company claims still need independent verification.

Monthly sample coverage:

The answer samples differ between months. These comparisons show how the recorded results changed; they do not establish why they changed.

The report covers a single reporting month, and the exact collection dates for the original answers are unverified. Visibility rates rest on answers that met the benchmark's inclusion rules, and a company counts as recommended only when an answer endorses it with positive wording.

Answers can mention several companies, so counts cannot be summed into distinct answers. Sentiment describes answer wording, not customer satisfaction. Cited pages and disputed claims still need independent checking, and the monthly samples differ, so comparisons show what changed, not why.

Our first three priorities

The first priority is a baseline scorecard that separates reach from position, so the client can see where HER is named versus where it is ranked highly. This gives later work a fixed reference point and keeps the two goals from being confused.

The second is a fact-review list built from primary sources, with each claim assigned an owner and an evidence requirement. Ambiguous matches stay out of the queue, and no correction proceeds without verification.

The third is a set of buyer-question briefs and a review of the cited roundups, checking existing coverage before proposing anything new. These are proposed work items, not measured outcomes.