Using Big Data to investigate
the influence of climate and
demography on wine
consumer habits
Alastair Reed1, Michael Shannon1, Daniel Mathews2
1 Viticulture
and Winemaking, Melbourne Polytechnic
Contact: alastairreed@melbournepolytechnic.edu.au
2School
of Mathematic Sciences, Monash University
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
Introduction
The Australian wine retail sector is a clear
duopoly
Dominated by two players; Wesfarmers Ltd [19%] and Woolworths Ltd [39%]
Data analysis opportunity!
Beverage Revenue
Wesfarmers Ltd
$2.0 billion
Woolworths Ltd
$4.1 million
From: Data estimated by IBIS World
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
Decision Genes
Shiraz
Sauvignon Blanc
Activation
Shiraz sale
Sauvignon Blanc sale
Decision Gene expression can be
developmentally influenced and/or
environmental
Developmental vs Environmental
Case Study: Champagne
0.6
0.5
0.4
0.3
0.2
0.1
0
1/1/2013
2/1/2013
3/1/2013
4/1/2013
5/1/2013
6/1/2013
7/1/2013
8/1/2013
9/1/2013
10/1/2013
Online Chardonnay sales in Melbourne, Australia, during 2013
11/1/2013 12/1/2013
Developmental vs Environmental
Case Study: Champagne
0.6
Warm average
Cold average
0.5
0.4
0.3
0.2
0.1
0
1/1/2013
2/1/2013
3/1/2013
4/1/2013
5/1/2013
6/1/2013
7/1/2013
8/1/2013
9/1/2013
10/1/2013
11/1/2013 12/1/2013
Developmental vs Environmental
Case Study: Champagne
NYE
0.6
0.5
Football
finals
Easter
Mother’s
Day
0.4
0.3
0.2
Melbourne
Cup
0.1
0
1/1/2013
Tax
Returns?
2/1/2013
3/1/2013
4/1/2013
5/1/2013
6/1/2013
7/1/2013
8/1/2013
9/1/2013
10/1/2013
11/1/2013 12/1/2013
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 citylevel?
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
Wine Purchase Decision
Case Study: Victoria
Geographically diverse state
Desert in north-west
Alpine in the north-east
Temperate in the south
30
25
Melbourne’s Climate
Average temperature: 13 – 25°C
20
15
10
Extreme temperatures: -2 – 46°C
5
0
1
2
3
4
5
6
7
8
9
10
11
12
Consumer decisions cluster into
groups
Temperature
Varieties correlate to temperature on a
geographic scale
30
y = 353.25x - 52.241
R² = 0.69
30
y = -180.76x + 33.717
R² = 0.41
25
25
20
20
15
15
10
10
5
5
0.18
0.19
0.2
0.21
0.22
0.23
0.05
0.06
0.07
0.08
0.09
0.1
0.11
Association between relative Sauvignon Blanc (left) and Shiraz (right) sales and
temperature, across Australia
0.12
0.13
All analysed varieties were correlated to
temperature on a temporal scale
60.00%
y = -0.0036x + 0.2678
R² = 0.20
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
0
5
10
15
20
25
30
35
40
Association between relative Shiraz sales and temperature
45
All analysed varieties were correlated to
temperature on a temporal scale
35.00%
y = 0.0017x + 0.1195
R² = 0.11
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
0
5
10
15
20
25
30
35
40
Association between relative Sauvignon Blanc sales and temperature
45
Google search associates Shiraz to
temperature
60
y = -0.7793x + 56.795
R² = 0.37
55
Google search (relative)
50
45
40
35
30
25
20
15
10
15
20
25
30
Temperature (°C)
Association between relative fortnightly Google searches and average temperature
(excluding Christmas period)
35
Google search associates
Sauvignon Blanc to temperature
0.22
y = 0.0006x + 0.1445
R² = 0.14
0.2
0.18
0.16
0.14
0.12
0.1
20
25
30
35
40
45
50
55
60
65
Association between relative fortnightly Google searches and average temperature
(excluding Christmas period)
Link between red wine sales and
temperature is consistently stronger
than white, except Sauvignon Blanc…
Proportion of stores
with significant
correlation (r)
Average income**
when significant
correlation
Average income
when insignificant
correlation
Cabernet Sauvignon
0.96 (0.29)
$1632
$1110
Merlot
0.86 (0.26)
$1639
$1436
Pinot Noir
0.57 (0.22)
$1793
$1371
Shiraz
0.98 (0.44)
$1623
$995
Chardonnay
0.45 (0.17)
$1703
$1535
Pinot Gris
0.67 (0.23)
$1765
$1303
Riesling
0.61 (0.25)
$1778
$1352
Sauvignon Blanc
0.96 (0.29)
$1626
$1244
Average
0.76 (0.27)
$1695a
$1294b
*>0.027
**fortnightly
Geography
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.
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)
Chardonnay sales contradict the cliché
N
High Riesling sales follow SE-NW
corridor
N
High Riesling sales follow SE-NW
corridor
N
Demographics roughly align with
Chardonnay/Riesling distinction
Sauvignon blanc is most popular in an
outer suburban ring
N
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
Acknowledgements
Special thanks to the Australian Grape and Wine
Authority and Melbourne Polytechnic for
supporting my attendance at AAWE