1 / 24

Mariusz Łapczyński Department of Marketing Research Cracow University of Economics Cracow, Poland

Discovering patterns of users’ behaviours in an e-shop – comparison of consumer buying behaviours in Poland and other European countries. Sylwester Białowąs Department of Market Research and Services Poznań University of Economics Poznań , Poland. Mariusz Łapczyński

kamin
Télécharger la présentation

Mariusz Łapczyński Department of Marketing Research Cracow University of Economics Cracow, Poland

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Discovering patterns of users’ behaviours in an e-shop – comparison of consumer buying behaviours in Poland and other European countries Sylwester Białowąs Department of Market Research and Services Poznań University of Economics Poznań, Poland Mariusz Łapczyński Department of Marketing Research Cracow University of Economics Cracow, Poland The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  2. 1 2 3 4 Web mining Web usage mining Market basket analysis Sequences web mining web content mining web usage mining • personalization • system improvement • site modification • business intelligence • usage characterization agent based approach database approach The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  3. 1 2 3 4 Web mining Web usage mining Market basket analysis Sequences • food sector market • DIY market • clothes • books • telecommunications services • every transactional data? if then The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  4. 1 2 3 4 Web mining Web usage mining Market basket analysis Sequences 2010 2015 2020 2025 • marketing (cross- and up-selling) • web usage mining (web clickstream analysis) • biology and genetics • chemistry • fraud detection The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  5. 1 2 3 4 Research methodology Association rules Sequence analysis product C A and B if then • Algorithms: • Apriori • Charm • FP-growth • Closet • Magnum Opus = B and A product C if then The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  6. 1 2 3 4 Research methodology Association rules Sequence analysis product C A and B if then ≠ B and A product C if then product B was purchased earlier ! The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  7. 1 2 3 4 Results Preprocessing A. rules Dataset Statistics S. rules The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  8. 1 2 3 4 Results Preprocessing A. rules Dataset Statistics S. rules • IP addresses • .net, .com, .biz, .org • Europe The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  9. 1 2 3 4 Results Preprocessing A. rules Dataset Statistics S. rules Faroe Islands Cayman Islands Christmas Island The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  10. 1 2 3 4 Results Preprocessing A. rules Dataset Statistics S. rules The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  11. 1 2 3 4 derived variables Results Preprocessing A. rules Dataset Statistics S. rules colour of top: yellow, grey, colourful … skirt / dress colour of skirt / dress: grey, red, olive… en face / profile price > mean (1=yes, 0=no) length (long /short)

  12. 1 2 3 4 Results Preprocessing A. rules Dataset Statistics S. rules top / center top / left top / right bottom / center bottom / left bottom / right page number (1, 2, 3, 4) The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  13. 1 2 3 4 Results Preprocessing A. rules Dataset Statistics S. rules model of blouse Poland outside of Europe other EU countries non-EU countries The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  14. 1 2 3 4 Results Preprocessing A. rules Dataset Statistics S. rules colours Poland outside of Europe other EU countries non-EU countries The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  15. 1 2 3 4 Results Preprocessing A. rules Dataset Statistics S. rules association rules 8.6 % of all transactions contained items C6 and C17 25% of buyers who chose the top C6 also chose the top C17 the choice of product C6 increases over 6 times the probability of choosing product C17 The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  16. 1 2 3 4 Results the most frequent association rules Preprocessing A. rules Dataset Statistics S. rules • white tunics • short sleeves Poland • sweaters • short sleeves other EU countries non-EU countries • short sleeves • wide belt • colourfulflowers outside of Europe The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  17. 1 2 3 4 Results sequential rules Preprocessing A. rules Dataset Statistics S. rules 39% 38% 40% 38% Poland 23% 23% 20% 17% other EU countries non-EU countries outside of Europe The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  18. 1 2 3 4 Results sequential rules Preprocessing A. rules Dataset Statistics S. rules 39% 44% 38% 38% 46% 40% 45% 38% Poland 30% 23% 23% 28% 20% 24% 17% 21% other EU countries non-EU countries … outside of Europe The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  19. 1 2 3 4 Results sequential rules Preprocessing A. rules Dataset Statistics S. rules 39% 36% 44% 60% 38% 38% 40% 46% 33% 45% 38% Poland 23% 30% 29% 28% 40% 23% 24% 20% 17% 21% other EU countries non-EU countries … outside of Europe The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  20. 1 2 3 4 Results sequential rules Preprocessing A. rules Dataset Statistics S. rules 75% 44% 39% 36% 38% 67% 60% 38% 46% 33% 40% 50% 38% 100% 45% Poland 29% 23% 25% 30% 23% 33% 40% 28% 24% 20% 50% 21% 17% other EU countries non-EU countries … outside of Europe

  21. 1 2 3 4 Results sequential rules Preprocessing A. rules Dataset Statistics S. rules 5 x alternative colours the end of path Poland other EU countries non-EU countries 9 x alternative colours the end of path 3 x alternative colours the end of path 4 x alternative colours the end of path outside of Europe The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  22. 1 2 3 4 Conclusions • web usage mining in e-commerce • problems with data pre-processing / IP addresses • behaviour patterns → appearance and functionality • behaviour patterns → personalized marketing communication • futureresearch • differentlanguageversions • other analytical tools The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  23. Thank you for your attention Sylwester Białowąs sylwester.bialowas@ue.poznan.pl Mariusz Łapczyński lapczynm@uek.krakow.pl The project was financed by a grant from National Science Centre (DEC-2011/01/B/HS4/04758)

  24. 1 2 3 4 Conclusions

More Related