cs.IRApr 8, 2026

Don't Measure Once: Measuring Visibility in AI Search (GEO)

Authors: Julius Schulte, Malte Bleeker, Philipp Kaufmann

Organizations: Global Center for Entrepreneurship and Innovation University of St. Gallen 9000 St.Gallen, Switzerland · Marketing und Customer Insight University of St. Gallen 9000 St.Gallen, Switzerland

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Abstract

As large language model-based chat systems become increasingly widely used, generative engine optimization (GEO) has emerged as an important problem for information access and retrieval. In classical search engines, results are comparatively transparent and stable: a single query often provides a representative snapshot of where a page or brand appears relative to competitors. The inherent probabilistic nature of AI search changes this paradigm. Answers can vary across runs, prompts, and time, making one-off observations unreliable. Drawing on empirical studies, our findings underscore the need for repeated measurements to assess a brand's GEO performance and to characterize visibility as a distribution rather than a single-point outcome.

Figures & tables

CampaignQueriesDaysChatGPTGeminiGoogle AI ModePerplexity
Consumer Electronics84543224343
Real Estate Sales84539264343
Sporting Goods84638234444
Telecommunications84540234342
Table 1: Data coverage by campaign and search engine, Jan 24 – Mar 20, 2026 (collection days with ≥1\geq 1 result).
Figure 1: Day-to-day Jaccard similarity (left) and Rank-Biased Overlap (right) for cited sources across all four campaigns (Jan 24 – Mar 20, 2026). Each box aggregates all 4,044 consecutive-day pairs. Median values annotated above each box. Source sets overlap by only 34–42% on average.
CampaignJac. MeanJac. SDRBO MeanRBO SD
Consumer Electronics0.3360.2430.2060.163
Real Estate Sales0.3780.2930.2560.219
Sporting Goods0.3550.2690.2240.183
Telecommunications0.4230.2440.2530.180
Table 2: Day-to-day source similarity by campaign (Jan 24 – Mar 20, 2026).
CampaignJac. MeanJac. SDRBO MeanRBO SD
Consumer Electronics0.5570.2370.2890.184
Sporting Goods0.4530.3260.1870.182
Telecommunications0.5890.2110.3040.179
Table 3: Day-to-day brand similarity by campaign (Jan 24 – Mar 20, 2026; campaigns with mean brand-detection rate ≥70%\geq 70\% ).
Figure 2: Day-to-day brand similarity (Jaccard, left; RBO, right) for the three campaigns meeting the ≥70%\geq 70\% brand-detection threshold. Brand stability is higher than source stability but still far from perfect, with broad interquartile ranges indicating substantial response-to-response variation within campaigns. Median values annotated above each box.
Figure 3: Source citation Gini coefficient by campaign and engine, Jan 24 – Mar 20, 2026. Values close to 1.0 indicate that a single domain captures nearly all citations; values around 0.6–0.7 indicate moderate concentration. All campaigns and engines exhibit high Gini values (mean 0.715).
CampaignPairsMax RunsJaccard MeanJaccard SDRBO Mean
Consumer Electronics830100.3270.2200.156
Real Estate Sales805100.3910.2590.225
Sporting Goods886100.3210.2190.149
Telecommunications888100.4340.2380.272
Table 4: Pairwise source similarity across repeated runs within a 24-hour window (Jaccard and RBO; runs with at least one extracted citation only).
CampaignPairsMax RunsJaccard MeanJaccard SDRBO Mean
Consumer Electronics1235100.4770.2980.229
Sporting Goods1027100.3270.2980.175
Telecommunications1233100.4630.3010.220
Table 5: Pairwise brand similarity across repeated runs within a 24-hour window (Jaccard and RBO; campaigns with mean detection rate ≥70%\geq 70\% ).
 SourceBrand
EngineJac. MeanRBO MeanJac. MeanRBO Mean
ChatGPT0.2330.0880.4370.192
Perplexity0.2820.1020.4920.202
Gemini0.5050.2300.4090.196
Google AI Mode0.3180.2540.3750.238
Table 6: Mean pairwise similarity within 24 hours by engine: sources (cited runs only, all 4 campaigns) and brands (Telecommunications, Sporting Goods, Consumer Electronics).
Figure 4: Pairwise Jaccard and RBO for sources (top row) and brands (bottom row) across repeated runs within a 24-hour window (geo-brand-monitor collection, Mar 21–25, 2026; 4 engines, up to 10 reps). Source boxes include only runs with ≥1\geq 1 extracted citation. Source overlap averages 32–43%; brand overlap ranges from 33% (Sporting Goods) to 48% (Consumer Electronics). Median values annotated above each box.
Figure 5: Per-prompt mean Jaccard (dark) and RBO (light) for source similarity (top row, all campaigns) and brand similarity (bottom row, campaigns with detection rate ≥70%\geq 70\% ). Prompts are sorted by ascending Jaccard. Vertical dashed line at 0.5 for reference.
Appendix figures & tables13 assets

Supplementary material from the paper’s appendix.

Appendix

English (used in paper)Original German label
TelecommunicationsTelekom
Real Estate SalesImmobilienverkauf
Sporting GoodsSportartikel
Consumer ElectronicsElektronik
Appendix
Table 7: English campaign names used in this paper and corresponding original German labels.
CampaignQueriesDaysChatGPTGeminiGoogle AI ModePerplexity
Consumer Electronics84543224343
Real Estate Sales84539264343
Sporting Goods84638234444
Telecommunications84540234342
Appendix
Table 8: Full data coverage by campaign and engine, Jan 24 – Mar 20, 2026 (collection days with ≥1\geq 1 result). Finance excluded; all data collected via web-interface scraping.
CampaignBrands tracked (canonical names)
Telecommunications (51 brands)1&1, ALDI, Alao, Besteabos, CH Mobile, Comparis, Congstar, Coop, Deinabo, Digital Republic, Dschungelkompass, Freshnet, GGA Maur, Galaxus Mobile, Gigamobile, Handyabo-Vergleich, Init7, Jio, Lebara, Leucom, Lidl, Lycamobile, MTEL, Migros, Moneyland, Monzoon, Mucho, Net+, Netplus, Netzwoche, O2, Peoplefone, Post Mobile, Quickline, Sak Digital, Salt, Solnet, Spusu, Sunrise, Swisscom, Swype, TalkTalk, Teleboy, Toppreise, Ubigi, VTX, Vodafone, Wingo, Yallo, gomo, iWay
Sporting Goods (43 brands)21run, ASICS, Adidas, Altra, Berg-Freunde, Birkenstock, Brooks, Bächli, Decathlon, HOKA, Idealo, Inov-8, Intersport, KURU Footwear, Karhu, Kiprun, La Sportiva, Lauf-bar, Merrell, Mizuno, New Balance, Nike, Norda, OOFOS, Ochsnersport, On, Puma, Reebok, Runnersworld, Running Point, Runningxpert, Salomon, Saucony, Scott, Shop4runners, Skechers, Sportscheck, The North Face, Topo Athletic, Transa, UGG, Under Armour, Vivobarefoot
Consumer Electronics (47 brands)AMD, ASUS, Acer, Alienware, Alternate, Amazon, Apple, Back Market, Brack, CHUWI, Conrad, Cyberport, Dell, Digitec, Dynabook, ERAZER, Framework, Fujitsu, Fust, Galaxus, Gigabyte, HP, Honor, Huawei, Idealo, Intel, Interdiscount, LG, Lenovo, Logitech, MSI, Mediamarkt, Medion, Microsoft, Microspot, NVIDIA, Notebookcheck, Panasonic, Preisvergleich, Razer, Samsung, Sony, Steg Electronics, Toppreise, Toshiba, VAIO, XMG
Real Estate Sales (excl.; det. rate 53.6%; 32 brands)AXA, Acheter-Louer, BEKB, Baloise, Beobachter, Blick, CBRE, Comparis, Engel & Völkers, Finanztip, Helvetia, Homeday, Homegate, ImmoScout24, Immoverkauf24, Immowelt, JLL, Livit, Mobiliar, Neho, Newhome, PostFinance, Privera, Properti, RE/MAX, Raiffeisen, Sotheby’s, Swiss Life, UBS, Wincasa, Wüst & Wüst, ZKB
Appendix
Table 9: Complete brand lexicon used for brand-mention detection, by campaign. Finance is excluded from all analyses. Real Estate Sales is excluded from brand similarity (det. rate 53.6% << 70% threshold) but its lexicon is listed for completeness.
German (original)English (translation)
Auf was muss man beim Laufschuhkauf achten?What should you pay attention to when buying running shoes?
In welchen Schuhen läuft man wie auf Wolken?In which shoes do you run as if on clouds?
Was ist die 80%-Regel beim Laufen?What is the 80% rule in running?
Welche Marke sind gute Laufschuhe?Which brand makes good running shoes?
Wie finde ich die richtigen Laufschuhe für mich?How do I find the right running shoes for me?
Wie teuer muss ein guter Laufschuh sein?How expensive does a good running shoe have to be?
Appendix
Table 10: Prompts used for the Sporting Goods campaign.
German (original)English (translation)
Wann ist die beste Zeit, um einen Laptop zu kaufen?When is the best time to buy a laptop?
Was ist der Unterschied zwischen einem Notebook und einem Laptop?What is the difference between a notebook and a laptop?
Welche Laptops sind zurzeit die besten?Which laptops are currently the best?
Welche Marke ist für Laptops am besten geeignet?Which brand is best suited for laptops?
Welche Marke ist gut bei Laptops?Which brand is good for laptops?
Welcher Laptop wird am meisten gekauft?Which laptop is purchased most often?
Appendix
Table 11: Prompts used for the Consumer Electronics campaign.
German (original)English (translation)
In der Schweiz: Welche Handy-Abos sind weltweit unlimitiert?In Switzerland: Which mobile phone plans are unlimited worldwide?
In der Schweiz: Welcher Anbieter hat unlimited Datenvolumen?In Switzerland: Which provider offers unlimited data volume?
In der Schweiz: Welcher Internetanbieter ist zurzeit der beste?In Switzerland: Which internet provider is currently the best?
Was ist das schnellste Internet in der Schweiz?What is the fastest internet in Switzerland?
Welche Anbieter in der Schweiz bieten ein Internetabonnement ohne Vertrag an?Which providers in Switzerland offer an internet subscription without a contract?
Welcher Anbieter hat das beste Internet in der Schweiz?Which provider has the best internet in Switzerland?
Appendix
Table 12: Prompts used for the Telecommunications campaign.
German (original)English (translation)
Ist es aktuell sinnvoll, ein Haus zu verkaufen?Is it currently advisable to sell a house?
Was muss ich alles bezahlen, wenn ich mein Haus verkaufe?What do I have to pay when I sell my house?
Was muss ich beachten, wenn ich mein Haus verkaufen möchte?What do I need to consider if I want to sell my house?
Wie berechne ich den Gewinn einer Immobilie?How do I calculate the profit from a property?
Wie hoch ist der langfristige Kapitalgewinn beim Verkauf einer Immobilie?How high is the long-term capital gain when selling a property?
Wie lange muss ich mein Haus besitzen, um es steuerfrei zu verkaufen?How long do I have to own my house to sell it tax-free?
Appendix
Table 13: Prompts used for the Real Estate Sales campaign.
CampaignGini
Consumer Electronics0.713
Real Estate Sales0.718
Sporting Goods0.680
Telecommunications0.750
Appendix
Table 14: Mean Gini coefficient by campaign (mean across engines), Jan 24 – Mar 20, 2026.
Search EngineGini
ChatGPT0.684
Gemini0.723
Google AI Mode0.782
Perplexity0.671
Appendix
Table 15: Mean Gini coefficient by search engine (mean across campaigns), Jan 24 – Mar 20, 2026. images.openai.com CDN artifact excluded (see Section 6 ).
Figure 6: Subsampling standard error of the estimated per-brand detection rate (left) and source-coverage Jaccard (right) as a function of the number of independent runs per prompt. Each point is the mean SE across all 1,216 per-brand series (left) or 128 engine–prompt groups (right), using 2,000 subsamples without replacement. The red dashed line marks SE =0.10=0.10 . Per-brand monitoring reaches SE <0.10<0.10 at n=7n=7 runs; source coverage requires n=8n=8 runs.
Runs ( nn )SE95% CI ( ±\pm )
10.3700.724
20.2460.483
30.1880.369
40.1510.296
50.1230.241
60.1010.197
Appendix
Table 16: Subsampling SE and 95% confidence interval half-width for per-brand detection rate estimation by number of runs (averaged across 1,216 per-brand series).
Figure 7: Mean standard error of the dd -day rolling window per-brand detection rate (averaged across 1,726 per-brand series) as a function of window length dd . Left: linear scale; right: log scale. The red dashed line marks SE =0.05=0.05 ; the purple dashed line marks SE =0.02=0.02 . SE falls below 0.05 at d=24d=24 days and below 0.02 at d=34d=34 days.
Window ( dd days)SE95% CI ( ±\pm )
10.3220.631
20.2380.467
30.2010.393
50.1600.313
70.1350.264
100.1070.210
Appendix
Table 17: Rolling-window SE and 95% confidence interval half-width for per-brand detection rate estimation by window length (averaged across 1,726 per-brand series).
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