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Table 4 User cluster and their usage characteristics

From: Health care public reporting utilization – user clusters, web trails, and usage barriers on Germany’s public reporting portal Weisse-Liste.de

User cluster Share [%] Average number clicks2 Average visit length [sec]2 Time betw. clicks [sec]3 Return visitors [%] Viewed results [%] Search steps/results1 Visit dur. Workday [%] Working hours [%] Desktop usage [%] Access via [%]
Intensive Work Timers 19 15.2 693 45 29 100 0.45 100 100 100 100 search engine
Intensive Free Timers 17 16.4 731 46 13 100 0.42 43 0 100 100 search engine
Diagnosis Translator 13 5.7 182 32 19 - - 100 100 100 100 search engine
Challenged Aborts 12 6.1 255 48 8 - - 53 12 69 100 search engine
Patient Experts 9 16.7 851 54 24 100 0.33 63 12 66 100 direct
Curious 7 14.9 747 49 28 78 0.60 83 53 83 35 payer, 30 media
Professionals 7 15.9 884 53 56 100 0.32 100 100 100 100 direct
Results Mobiles 7 14.5 696 49 8 100 0.50 65 30 0 100 search engine
Explorers 4 13.4 571 47 14 72 0.67 77 48 80 100 health website
Other 5 6.5 456 72 40   - 79 42 74 100 direct
Average User 100 12.7 596 47 22 68 0.42 76 51 83 67 search engine
  1. Clustering based on clickstream data and repeated sampling from data sample from 01/2015 – 05/2015 excluding bounce visits
  2. 1. Search steps required relative to number of results viewed; 2. all clicks or visit lengths in sec per user cluster/number of users in cluster (weighted average); 3. Calculated per session and then averaged for user cluster (simple, non-weighted average)