1 / 29

Today

martina-mclean
Télécharger la présentation

Today

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. Using Big Data to investigate the influence of climate and demography on wine consumer habitsAlastair Reed1, Michael Shannon1, Daniel Mathews21 Viticulture and Winemaking, Melbourne Polytechnic Contact: alastairreed@melbournepolytechnic.edu.au2School of Mathematic Sciences, Monash University

  2. Today Background Australian wine retail sector The study Use of Big Data in wine to derive relationships between geography and climate Results Association between temperature, geography and consumer preference Recommendations Ongoing research and implications for future management

  3. Introduction The Australian wine retail sector is a clear duopoly Dominated by two players; Wesfarmers Ltd [19%] and Woolworths Ltd [39%] Data analysis opportunity!

  4. What effects consumer preference? Epigenetics of a varietal decision 1. Visual Label, position, status 2. History Regional bias, personal bias 3. Environment Climatic effects, light levels

  5. Decision Genes Decision Gene expression can be developmentally influenced and/or environmental Shiraz Sauvignon Blanc Activation Sauvignon Blanc sale Shiraz sale

  6. Developmental vs EnvironmentalCase Study: Champagne Online Chardonnay sales in Melbourne, Australia, during 2013

  7. Developmental vs EnvironmentalCase Study: Champagne Warm average Cold average

  8. Developmental vs EnvironmentalCase Study: Champagne NYE Football finals Easter Mother’s Day Tax Returns?

  9. We wish to explain the environmental and developmental… Can we quantify to what degree wine purchase decisions are influenced by the weather? Can we explain to what degree wine purchase decisions are influenced by location on a city-level?

  10. The data… Over 3 million transactions from across Victoria, Australia Closely examined: Shiraz Chardonnay Riesling Sauvignon Blanc Pinot Gris/Grigio Cabernet Sauvignon Merlot Pinot Noir

  11. Wine Purchase DecisionCase Study: Victoria Geographically diverse state Desert in north-west Alpine in the north-east Temperate in the south Melbourne’s Climate Average temperature: 13 – 25°C Extreme temperatures: -2 – 46°C

  12. Consumer decisions cluster into groups

  13. Temperature

  14. Varieties correlate to temperature on a geographic scale Association between relative Sauvignon Blanc (left) and Shiraz (right) sales and temperature, across Australia

  15. All analysed varieties were correlated to temperature on a temporal scale Association between relative Shiraz sales and temperature

  16. All analysed varieties were correlated to temperature on a temporal scale Association between relative Sauvignon Blanc sales and temperature

  17. Google search associates Shiraz to temperature Association between relative fortnightly Google searches and average temperature (excluding Christmas period)

  18. Google search associates Sauvignon Blanc to temperature Association between relative fortnightly Google searches and average temperature (excluding Christmas period)

  19. Link between red wine sales and temperature is consistently stronger than white, except Sauvignon Blanc… *>0.027 **fortnightly

  20. Geography

  21. Decision Gene approach Relative purchase figures can be treated the same as allele frequencies (the frequency of gene variants), where an individual has two alleles for each gene Genotypes: aa = purchase Aa or AA = no purchase We can then use the frequencies to describe the characteristics of a population Comparing the relative frequency of alleles allows populations to be compared using distance-matrices, visualized with traditional phylograms.

  22. Clustering between distinct geographic areas Phylogram generated using the Neighbour-Joining (NJ) method on sales frequencies of 7 varieties across 28 retail outlets (derived using POPTREE2 [Takezaki 2010)

  23. Chardonnay sales contradict the cliché N

  24. High Riesling sales follow SE-NW corridor N

  25. High Riesling sales follow SE-NW corridor N

  26. Demographics roughly align with Chardonnay/Riesling distinction

  27. Sauvignon blanc is most popular in an outer suburban ring N

  28. Summary Significant associations can be made between developmental and environmental factors and consumer preference Temporal and spatial trends can be identified but need further analysis for confirmation We are looking for collaborators to consolidate this research, all welcome! alastairreed@melbournepolytechnic.edu.au

  29. Acknowledgements Special thanks to the Australian Grape and Wine Authority and Melbourne Polytechnic for supporting my attendance at AAWE

More Related