A complete page for every agency
Address, opening hours, team, products, reviews: it is the source ChatGPT cites most widely, and the one that most often comes with a local recommendation.
×2.4 first recommendations when it is read AI Barometer · Insurance · Edition #1 · October 2026
Insurance: which agencies do ChatGPT and Gemini recommend, town by town?
When someone asks ChatGPT or Gemini for “an insurer in Laon” or “an agency to insure my shop in Nîmes”, does the AI name a nearby agency, a national brand or a comparison site? We measured where each insurance network's agencies stand in nearly 7,000 answers to local questions, town by town, against mutual insurers, banks, brokers and comparison sites.
Answers collected from July 1, 2026 to October 2, 2026 Published October 2, 2026 ChatGPT and Gemini, queried through their APIs with web search
56% of answers to a local question recommend a specific agency first: its name, address, phone number or agency page.
57% of local answers put agencies from at least two networks side by side.
20–81% depending on the network: how often the AI names one of its agencies when it cites the brand.
3 in 10 only: the same question, asked again a week later, gives the same first recommendation.
01 · Agencies first
Local search: AI recommends an insurance agency first
In 56% of cases, the first recommendation is a specific agency of a network (95% CI 50–61). Next come a brand cited without any agency (28%) and a broker, comparison site or online insurer (11%). The gap between large cities and small towns is clear.
What AI recommends first, by town size
Local questions, % of answers
- All local questions
- Large cities (100,000+)
- Mid-sized towns (20,000–100,000)
- Small towns (under 20,000)
See the data
| A specific network agency | A brand, no agency named | A broker, comparison site or online insurer | No brand | |
|---|---|---|---|---|
| All local questions | 55.6% | 27.7% | 11.0% | 5.7% |
| Large cities (100,000+) | 45.8% | 30.0% | 13.5% | 10.7% |
| Mid-sized towns (20,000–100,000) | 59.0% | 27.3% | 11.0% | 2.8% |
| Small towns (under 20,000) | 61.0% | 25.5% | 9.1% | 4.4% |
What AI recommends first, by need
Local questions, % of answers
- Car and motorbike
- Home
- Health
- Life and disability cover
- Savings and retirement
- Mortgage insurance
- Business insurance
- School insurance
See the data
| A specific network agency | A brand, no agency named | A broker, comparison site or online insurer | No brand | |
|---|---|---|---|---|
| Car and motorbike | 48.1% | 29.5% | 21.6% | 0.8% |
| Home | 63.5% | 27.4% | 7.4% | 1.6% |
| Health | 54.6% | 26.4% | 12.1% | 7.0% |
| Life and disability cover | 61.6% | 28.7% | 2.6% | 7.0% |
| Savings and retirement | 51.1% | 19.8% | 7.9% | 21.2% |
| Mortgage insurance | 30.5% | 17.0% | 43.9% | 8.6% |
| Business insurance | 59.8% | 33.0% | 3.0% | 4.2% |
| School insurance | 60.4% | 36.8% | 1.1% | 1.8% |
69%
of first recommendations go to a specific agency when the user is looking for a provider, versus 25% for a plain information question.
11%
of answers in large cities cite no brand at all, versus 4% in small towns.
44%
of first recommendations on mortgage insurance go to a broker, a comparison site or an online insurer.
What it changesAI local search does not replace the agency: it points to it. But it does not point to every agency, and in large cities it takes sides less often.
02 · Networks head to head
When AI cites an insurance network, does it name its agencies?
On local questions, at least one agency is named in 69% of answers. But networks do not turn their visibility into agency recommendations equally: when the AI cites the brand, it names one of its agencies in 81% of cases for the best-placed network, and in 20% for the least well placed.
Share of local answers that cite the network, and that name one of its agencies
Local questions. Hover over a bar for the conversion rate.
- Allianz 64% agency 48%
- AXA 52% agency 40%
- Groupama 41% agency 28%
- MMA 30% agency 19%
- Generali 24% agency 16%
- MAIF 26% agency 12%
- MAAF 20% agency 11%
- Gan 12% agency 10%
- MACIF 16% agency 9%
- GMF 14% agency 8%
- Matmut 13% agency 7%
- Abeille Assurances 9% agency 6%
- Crédit Agricole 9% agency 5%
- Société Générale 5% agency 3%
- Crédit Mutuel / CIC 10% agency 2%
- BNP Paribas / Cardif 5% agency 2%
Conversion = share of the answers citing the network that also name one of its agencies.
See the data
| Network | Cited | Agency named | 95% CI | Conversion | Agency cited first |
|---|---|---|---|---|---|
| Allianz | 64.2% | 47.7% | 42.0–52.0 | 74% | 20.9% |
| AXA | 52.0% | 39.5% | 35.6–43.2 | 76% | 13.3% |
| Groupama | 40.9% | 28.0% | 24.5–31.8 | 69% | 4.2% |
| MMA | 30.1% | 18.8% | 15.3–22.2 | 63% | 3.1% |
| Generali | 24.4% | 15.7% | 12.2–19.1 | 64% | 1.8% |
| MAIF | 25.9% | 12.1% | 8.8–15.1 | 47% | 2.6% |
| MAAF | 20.3% | 10.8% | 7.9–13.5 | 53% | 0.7% |
| Gan | 11.8% | 9.5% | 6.6–12.9 | 81% | 1.8% |
| MACIF | 16.0% | 8.6% | 6.4–10.5 | 54% | 0.8% |
| GMF | 14.4% | 7.7% | 5.8–10.3 | 54% | 0.8% |
| Matmut | 13.3% | 6.9% | 5.3–8.5 | 52% | 1.0% |
| Abeille Assurances | 8.6% | 6.0% | 4.0–8.3 | 69% | 0.7% |
| Crédit Agricole | 9.4% | 4.6% | 3.3–5.9 | 49% | 0.4% |
| Société Générale | 4.6% | 3.3% | 2.0–4.7 | 72% | 0.2% |
| Crédit Mutuel / CIC | 9.7% | 1.9% | 1.4–2.5 | 20% | 0.3% |
| BNP Paribas / Cardif | 4.9% | 1.6% | 1.1–2.1 | 33% | 0.4% |
81%
of the answers that cite Gan also name one of its agencies, the best conversion among the networks studied.
72%
average conversion for general-agent networks, versus 57% for mutual insurers and 40% for bank insurers.
47%
of local answers name agencies from at least three different networks.
What it changesBeing cited is not enough. On a local question, the brand that wins is the one whose agency the AI knows: its name, its address, its page.
03 · Town by town
In each town, 10 networks compete for the recommendation
We looked, town by town, at which networks get at least one of their agencies recommended. Competition is dense: in the median town, 10 networks have an agency named regularly (in at least 3 answers), and the leading one gathers only 22% of the agencies named.
Share of towns where the network has an agency named regularly
At least 3 local answers name one of its agencies · towns with at least 20 local answers
- Allianz 93%
- AXA 93%
- Groupama 84%
- MMA 75%
- Generali 69%
- MAAF 64%
- MACIF 61%
- MAIF 59%
- Gan 56%
- Matmut 48%
- Abeille Assurances 43%
- Crédit Agricole 43%
- GMF 41%
- Crédit Mutuel / CIC 31%
- Société Générale 26%
- BNP Paribas / Cardif 21%
Share of local answers naming an agency of the network, by town size
Local questions, the ten networks most often named
| Large cities | Mid-sized towns | Small towns | |
|---|---|---|---|
| Allianz | 37% | 50% | 55% |
| AXA | 30% | 44% | 43% |
| Groupama | 21% | 29% | 34% |
| MMA | 10% | 19% | 26% |
| Generali | 18% | 18% | 11% |
| MAIF | 12% | 16% | 8% |
| MAAF | 11% | 13% | 8% |
| Gan | 5% | 9% | 14% |
| MACIF | 8% | 12% | 6% |
| GMF | 6% | 15% | 2% |
63%
of answers in small towns name agencies from at least two networks, versus 46% in large cities.
60%
of local answers in large cities name at least one agency, versus 73% in small towns.
The towns studied are those where Wispra clients operate, Allianz among them: the networks present in these towns are favoured. No result is published town by town.
What it changesThe contest is played out town by town: every agency has its direct competitors, and AI puts them side by side in more than one answer in two.
04 · The brand ranking
Insurer ranking in ChatGPT and Gemini: Allianz, AXA and Groupama lead
An answer cites 4.6 brands from the sector on average. The top three account for 49% of first recommendations.
Share of answers that cite each brand
All questions kept. Hover over a bar for the confidence interval.
- Allianz 63%
- AXA 50%
- Groupama 39%
- MMA 29%
- MAIF 25%
- Generali 24%
- MAAF 20%
- MACIF 16%
- GMF 14%
- Matmut 13%
- Gan 11%
- Crédit Mutuel / CIC 10%
- LeLynx 10%
- Crédit Agricole 9%
- LesFurets 8%
Reading: Allianz is cited in 63% of answers, and first in 25%. The towns studied are those where Wispra clients operate, Allianz among them: networks present locally are favoured (see the method).
See the data
| Brand | Family | Cited | 95% CI | In the top three | First |
|---|---|---|---|---|---|
| Allianz | General-agent networks | 62.5% | 57.5–66.7 | 49.5% | 25.3% |
| AXA | General-agent networks | 49.5% | 45.4–53.0 | 37.0% | 15.2% |
| Groupama | Mutual insurers | 38.8% | 33.9–42.7 | 24.5% | 5.2% |
| MMA | General-agent networks | 28.7% | 24.6–32.8 | 15.7% | 4.3% |
| MAIF | Mutual insurers | 25.2% | 21.6–28.8 | 13.9% | 5.0% |
| Generali | General-agent networks | 23.6% | 20.5–27.3 | 12.2% | 2.7% |
| MAAF | Mutual insurers | 19.6% | 16.8–22.6 | 8.2% | 1.3% |
| MACIF | Mutual insurers | 15.5% | 13.4–17.7 | 6.4% | 1.0% |
| GMF | Mutual insurers | 13.9% | 11.5–16.6 | 6.6% | 1.9% |
| Matmut | Mutual insurers | 13.1% | 11.0–15.2 | 6.8% | 1.8% |
| Gan | General-agent networks | 11.1% | 8.1–14.7 | 6.3% | 1.9% |
| Crédit Mutuel / CIC | Bank insurers | 9.9% | 8.8–11.1 | 5.0% | 1.5% |
| LeLynx | Comparison sites | 9.5% | 7.9–11.1 | 3.5% | 1.6% |
| Crédit Agricole | Bank insurers | 9.3% | 7.9–11.0 | 3.9% | 0.8% |
| LesFurets | Comparison sites | 8.3% | 6.9–9.8 | 3.1% | 1.0% |
05 · Families and needs
Mortgage, health, savings: where insurers are no longer alone
General-agent networks are cited first in 52% of answers and mutual insurers in 20%, although mutual insurers appear in 65% of answers. On car and home insurance, insurers and mutuals fill almost the whole answer; on mortgage insurance, brokers and comparison sites are cited as often as insurers.
Share of answers that cite at least one player of the family
An answer can cite several families.
- General-agent networks 81%
- Mutual insurers 65%
- Bank insurers 29%
- Comparison sites 19%
- Health and protection mutuals 16%
- Brokers 11%
- Online insurers 9%
Share of answers citing at least one player of the family, by need
All questions kept, %
| Agent networks | Mutual insurers | Bank insurers | Health mutuals | Brokers | Comparison sites | Online | |
|---|---|---|---|---|---|---|---|
| Car and motorbike | 91 | 87 | 23 | 2 | 7 | 38 | 36 |
| Home | 91 | 87 | 35 | 4 | 6 | 21 | 10 |
| Health | 65 | 54 | 14 | 54 | 11 | 31 | 5 |
| Life and disability cover | 83 | 60 | 29 | 26 | 9 | 7 | 1 |
| Savings and retirement | 64 | 26 | 34 | 13 | 12 | 5 | 0 |
| Mortgage insurance | 59 | 36 | 41 | 15 | 52 | 43 | 2 |
| Business insurance | 88 | 64 | 30 | 23 | 9 | 8 | 1 |
| School insurance | 87 | 93 | 33 | 8 | 3 | 9 | 3 |
The three brands most cited for each need
| Need | Three most cited brands | Comparison sites |
|---|---|---|
| Car and motorbike | Allianz 75% · AXA 57% · Groupama 57% | 38% |
| Home | Allianz 77% · Groupama 60% · AXA 58% | 21% |
| Health | Allianz 46% · AXA 35% · Harmonie Mutuelle 29% | 31% |
| Life and disability cover | Allianz 62% · AXA 49% · Generali 31% | 7% |
| Savings and retirement | Allianz 40% · AXA 38% · Crédit Agricole 16% | 5% |
| Mortgage insurance | Allianz 44% · Cafpi 43% · Meilleurtaux 34% | 43% |
| Business insurance | Allianz 66% · AXA 57% · MMA 42% | 8% |
| School insurance | Allianz 66% · MAE 57% · Groupama 50% | 9% |
49%
of answers also cite at least one independent local broker or firm (manual coding of 240 answers).
31%
of information questions cite a comparison site, versus 13% when the user is looking for a provider.
64%
of first recommendations go to a general-agent network on business questions, versus 47% for individuals.
What it changesFor a mutual insurer, the issue is not awareness but turning a mention into a recommendation. For an agent network, the advantage is won agency by agency.
06 · Sources and documents
What AI reads before recommending an insurer
More than half of the sources cited by ChatGPT are insurers' own pages: their agencies' pages (28%) and their national websites (27%). 68% of answers with sources cite at least one agency page.
Sources cited by ChatGPT
% of sources cited
- Network agency pages 28%
- Insurers' national websites 27%
- Public and official sites 19%
- Other sites (local brokers and firms…) 12%
- Media and guides 5%
- Comparison sites 5%
- Documents (PDF, policy notices, KIDs) 4%
- Directories and reviews 1%
See the data
| Type of source | Share of sources cited | Answers citing at least one |
|---|---|---|
| Network agency pages | 27.8% | 68.1% |
| Insurers' national websites | 27.1% | 41.6% |
| Public and official sites | 19.0% | 20.7% |
| Other sites (local brokers and firms…) | 11.8% | 22.0% |
| Media and guides | 5.0% | 12.6% |
| Comparison sites | 4.5% | 8.2% |
| Documents (PDF, policy notices, KIDs) | 3.9% | 10.6% |
| Directories and reviews | 0.6% | 2.7% |
How often the insurer is cited first
Depending on what the AI read about it before answering
- Its agency page 33%
- Its national site or a document 24%
- None of the insurer's sources 14%
A read agency page goes with a first-place citation 2.4 times more often. Excluding the most-cited network, the gap remains clear: 20.0% versus 14.0%. This is a correlation: the AI also reads the page of the agency it has chosen.
See the data
| What the AI read about the insurer | Cited first | 95% CI | Excluding the most-cited network | 95% CI |
|---|---|---|---|---|
| Its agency page | 32.9% | 30.6–35.3 | 20.0% | 18.4–22.0 |
| Its national site or a document | 23.6% | 21.6–25.9 | 16.0% | 14.5–17.8 |
| None of the insurer's sources | 14.0% | 13.1–14.9 | 14.0% | 13.2–14.9 |
Documents: 1 answer in 10, nearly 1 in 4 on mortgage insurance
Share of answers citing at least one document (PDF, policy notice, key information document), by need
- Mortgage insurance 23%
- Health 15%
- Savings and retirement 13%
- Life and disability cover 10%
- Business insurance 8%
- Car and motorbike 7%
- Home 6%
- School insurance 4%
37%
of the documents cited are published by insurers, 32% by institutions (France Assureurs, Banque de France, AMF, tax authority…) and 31% by other players.
23%
of answers citing a document give an address, versus 54% for the others: documents are used to explain, not to direct.
4.6%
of ChatGPT answers cite at least one document published by an insurer: notices, key information documents, product sheets.
What it changesAI relies first on what insurers publish themselves. Complete agency pages and accessible, up-to-date product documents are the two sources an insurer fully controls.
07 · ChatGPT and Gemini
Gemini cites more comparison sites and gives fewer addresses
Gemini cites more players per answer (5.3 versus 4.6), cites a comparison site twice as often (38% versus 18%) and relies on directories and review sites in 49% of its answers with sources. It gives an address in only 18% of cases, versus 49% for ChatGPT.
Share of answers that cite the brand, by AI
Gemini observed since July 30, 2026
- Allianz
- AXA
- Groupama
- MMA
- MAIF
- Generali
- MAAF
- Abeille Assurances
- Meilleurtaux
- LesFurets
Gemini is observed over a shorter period and a smaller sample: these gaps are signals, to be confirmed in the next edition.
See the data
| ChatGPT | Gemini | |
|---|---|---|
| Allianz | 64.5% | 42.2% |
| AXA | 50.9% | 35.1% |
| Groupama | 40.5% | 22.4% |
| MMA | 28.1% | 34.6% |
| MAIF | 25.9% | 18.5% |
| Generali | 24.7% | 12.8% |
| MAAF | 19.7% | 18.2% |
| Abeille Assurances | 6.7% | 21.5% |
| Meilleurtaux | 6.6% | 17.6% |
| LesFurets | 7.5% | 16.3% |
What it changesThe same network is not equally visible across AIs. Optimising for ChatGPT (agency pages) is not enough for Gemini, which gives more weight to comparison sites, directories and reviews.
08 · Volatility
An AI recommendation is never secured
Each question is asked again every week. From one week to the next, the answer almost always changes.
29% of cases only give the same first recommendation as a week earlier.
7% of cases only give the same top three.
34% of the brands cited reappear from one week to the next, on average.
49% of first recommendations still go to the same three brands over the whole period.
What it changesA screenshot proves nothing. Knowing whether a network is recommended takes continuous measurement, over many questions and towns.
09 · What to do
Five levers for an agency network
What this edition's data tells networks that want their agencies to be recommended.
Cover every need, not just car and home
On mortgage, health and savings, brokers, comparison sites and banks take the space left open.
Brokers in 52% of mortgage answersPublish readable documents
Notices, key information documents, guides: as text, up to date, not blocked. AI uses them to explain cover.
A document in 23% of mortgage answersExist beyond your own website
Comparison sites, directories, reviews: Gemini relies on them far more than ChatGPT.
Directories and reviews in 49% of Gemini answers with sourcesMeasure every week, agency by agency
The first insurer recommended changes from week to week; only continuous measurement, town by town, shows where to act.
71% of first recommendations change within a weekMethod and limits
How this barometer was built
- Corpus. Nearly 13,000 AI answers collected from July 1, 2026 to October 2, 2026. Nearly 7,500 are kept, from more than 1,500 distinct questions and nearly 140 towns; nearly 7,000 of them answer a local question.
- Questions. They are written as real requests from individuals and businesses: car, home, health, protection, savings, mortgage, business insurance, school insurance. Each question is asked again every week. Each one was classified individually, with the help of an AI: main product, local or not, individual or business, provider search or information.
- No brand in the questions. We excluded every question that names an insurer, a bank, a broker, a comparison site or an agency (26% of search questions), as well as all comparison and brand-perception questions.
- AIs queried. ChatGPT through the OpenAI API (gpt-5-nano with web search, located in France and in the town of the question). Gemini through the Google API (Gemini 3.1 Flash-Lite grounded in Google Search), since July 30, 2026. Calls are made with no account or history: answers are close to those of the consumer apps, without being identical.
- How a brand is counted. A brand is “cited” if it appears in the answer text or in a cited source, and “first” if it is the first brand of the sector in the text, from a dictionary of more than 80 brands in 7 families. Manual double coding of 240 randomly drawn answers gives 99.7% precision, 99.0% recall and 96% agreement on the first brand.
- Named agency. An agency is “named” when the answer designates a specific point of sale of the network: an agency page, an address or phone number right after the brand, or a localised agency name (“MAAF Poissy”, “Groupama agency in Laon”). Checked against manual coding of 90 local answers not used to set the rules: 95% precision, 93% recall. The measure is reliable for ChatGPT; for Gemini it underestimates the agencies named.
- Sources. For ChatGPT, the analysis covers the exact addresses of the pages cited. For Gemini, only the domain is available: agency pages and documents cannot be measured there.
- Towns and margins of error. Town size comes from official populations (geo.api.gouv.fr). 95% confidence intervals are computed by resampling grouped by territory, because answers from the same territory are not independent.
- Limits and conflicts of interest. Wispra works with insurance players, Allianz among them as part of a pilot programme. The towns studied are those where these players operate, and the needs tested reflect their offers. The results therefore describe these territories, not the whole of France, and mechanically favour the networks present locally. No client-specific figure is published: no agency results, no trend. Wispra appears in 2.7% of Gemini answers and in no ChatGPT answer; it is excluded from the rankings.
Frequently asked questions
How were the questions chosen?
They reproduce real requests from individuals and businesses looking for insurance near them, for every need: car, home, health, protection, savings, mortgage, business, school. Every question that named a brand or an agency was excluded: the questions kept name no player at all.
Why does Allianz come first?
Partly because of the panel: the towns studied are those where Wispra clients operate, Allianz among them, and the needs tested reflect their offers. The ranking describes these territories, not the whole of France. The results on local competition (agencies named, conversion, sources) remain comparable between networks, because the questions never name any brand.
What is a “named agency”?
A specific point of sale of a network, designated in the answer by its page, its address, its phone number or a localised name. A plain mention of the brand (“Allianz offers…”) does not count. On a fresh sample checked by hand, the measure finds 93% of the agencies named, with 95% precision.
Which insurers do ChatGPT and Gemini recommend most?
On the brand-free questions of this barometer, the most cited are Allianz (63% of answers), AXA (50%), Groupama (39%), MMA (29%) and MAIF (25%). This ranking describes the towns studied, where Wispra clients operate: it favours the networks present locally.
How can an insurance agency get recommended by ChatGPT?
The data points to three levers: a complete agency page (when the AI has read it, the insurer is cited first 2.4 times more often), a presence on every need (mortgage, health, savings) and readable product documents. The recommendation changes from week to week: only continuous measurement, agency by agency, shows where to act.
Can I get my network's results?
Yes. Wispra measures every week how often each agency of a network is recommended by AI, town by town and against its local competitors, and helps improve it from a central view. Book a demo to see your network's measurement.
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