An old market research (2009) about selling wine online. Today it should completely reviewed considering the new role of social media. Anyway it is interesting, especially because of the rigorous methodology.

1. How to sell wine onlineHow to sell wine online
Made in Italy
S B fi iSusanna Bonafini
AnnalauraCantella
Cecilia MarchiCecilia Marchi
Dario Pagnoni
Michele SchincagliaMichele Schincaglia
Francesco Travagli
8427 – Market Research

2. AgendaAgenda
1. PHENOMENON AND ISSUES
2. RESEARCH AND BUSINESS GOALS
3. TARGET POPULATION3. TARGET POPULATION
4. SAMPLING: METHOD
5. QUESTIONNAIRE SETTING
6 DATA AUDIT AND OUTLIER DETECTION6. DATA AUDIT AND OUTLIER DETECTION
7. DATA ANALYSIS
A. UNIVARIATE
B. BIVARIATE
C. MULTIVARIATE
1. FACTOR ANALYSIS1. FACTOR ANALYSIS
2. CLUSTER ANALYSIS
3. CONJOINT ANALYSIS
4 DISCRIMINANT ANALYSIS4. DISCRIMINANT ANALYSIS
8. RESULTS
9. MANAGERIAL IMPLICATIONS

3. 1 Phenomenon1. Phenomenon (more)
Quality wine is a cultural product that,
together with design and fashion, gives
h i h “ d i I l ” b d/idemphasis to the “made in Italy” brand/idea .
In recent decades, wine has been the center of
the enogastronomic tourism boom that madethe enogastronomic tourism boom that made
producers grow and offer a complex
experiential product: not only wine, but also
T l i 2008 th It li t i i d t i fl
local culture and values are sold.
Truly, in 2008 the Italian enogastronomic scenario made tourism flows
grow, with a rise from 18% to 20%.
In fact, the visitors interviewed in the tourist place appreciated the Italian, p pp
enogastronomic offer so much that gave it an average evaluation of 8.2
out of 10.*
*Source: http://www.vinitaly.com/about_news.asp?id=3478

4. Phenomenon (continued)
This business doesn’t seem to suffer the
crisis: in Italy it involves between 4 and
6,5 million people fond of wine, with a
total turnover of about 2 5 billiontotal turnover of about 2,5 billion
euro.
Italy is the country where Wine & Foody y
tourism is most widespread , with 140
“Strade del vino e dei Sapori”.
As a result to this growth the sector of wine,
which is traditionally static, has opened its doorsy , p
to new channels, as online selling.
An example is the website Vini24, owned by Sole
24 Ore, and other web portals (e.g. Wine to Wine)
based on the collaboration between different
quality producersquality producers.
Source: http://www.b2b24.ilsole24ore.com/articoli/0,1254,24_ART_96632,00.html?lw=24%3BCHL

5. A glance at Internetg
In 2009 Italy placed 20th in a
EU l ith I t tEU scale, with an Internet
penetration rate of 50.1%
compared to the Europeanp p
average of 60.7%.1
I l h h I l hIn general though, Italy has
been showing record growth
in online sales 2in online sales.
The main Italian users are
young people and well-
educated ones, moreover
58 3% of Italians between 1158,3% of Italians between 11
and 74 year old steadily
access the web. 2
1 Source: http://www.eiaa.net/news 2Source: own elaboration with ISTAT data

6. What about E-commerce?
At the beginning of 2009, GFK Eurisko communicated
during a national convention that 5 millions of Italians
purchased on the Internet and that they were on
average satisfied at 90% of it.1
What’s interesting is that only half of them (52%) hadg y ( )
decided in advance to go online for their shopping, the
other half just happened to do it.1j pp
This means that starting a business online today has great
potentials and maybe it is not so risky as we couldpotentials and, maybe, it is not so risky as we could
presume; people trust the Web.
Nowadays there’s the possibility to bring online alsoNowadays there s the possibility to bring online also
products that we would never think about, for example
winewine.
1Source: www.ecommerceforum.it

7. The issues
Wine -in particular quality wine- is
t diti ll ld th h di t t ttraditionally sold through a direct contact
between customer and reseller, who usually
gi es ad ises abo t the prod cts andgives advises about the products and
transmits its experience.
Internet is not so deeply related to the values
that this kind of product transmits such asthat this kind of product transmits, such as
tradition, culture and natural product.
Hence, the main question we want to answer is the following:
“I i ibl f d f hi h li i“Is it possible for producers of high quality wine to convey
effectively their valuable products through a channel like the
I t t? A d f th ti f h tInternet? And, from the perspective of consumers, what are
the perceived resentments?”

8. Solution to the problem
In order to answer the previous question, we want to analyze some of
th t d l t d t li h i f i
p
the concepts and concerns related to online purchasing of wine.
More specifically we will deal with preferences, desires and opinions of the
potential customers.potential customers.
For our purpose, we need to:
Develop a market research on the potential target population
Understand who our respondents are and how they behave in terms of:
• Wine preferences and purchasing habits
• Internet usage and online purchasing
This way we will try to understand if the online channel could bey y
operable for an experiential product as wine.

9. 2 Research Goals2. Research Goals
1
• Identify the potential market for online wine selling• Identify the potential market for online wine selling
2
• Point out the preferences of the potential consumers• Point out the preferences of the potential consumers
2
p pp p
P i t t th t t f th t ti lP i t t th t t f th t ti l
3
• Point out the resentments of the potential consumers• Point out the resentments of the potential consumers
4
• Segment the demand• Segment the demand
5
• Identify the most important potential segments• Identify the most important potential segments

10. Business Goal
We will take the point of view of wine producers that want to challenge
Internet as a distribution channel, therefore we will use the information
gathered from the research to develop the right strategy in order to hit
h diff t t ill id tifeach different segments we will identify.
This task will comprise setting:
1
• PRODUCTS and SERVICES to be
offered
• PRODUCTS and SERVICES to be
offered
2
• PRICING• PRICING
2
3
• PROMOTION• PROMOTION
3
4
• OPTIMAL WEBSITE structure and
d i
• OPTIMAL WEBSITE structure and
d i4 designdesign

11. 3 Target population3. Target population
• O l i t fi d b i l ti f th it li k t
Italians
• Our goal is to find a business solution for the italian market
Between 18 and 64
• This is the most interesting age range of potential buyers of
wine online
Between 18 and 64
wine online
Internet users
• In order to buy wine online, obviously people should use Internet
• Even if there could be people buying wine without drinking it,
Wine drinkers
E e t e e d be pe p e b y g w e w t t d g t,
we consider only wine drinkers

12. 4. Sampling- Stratified Sample
l l
p g p
1st STEP
In order to stratify the M F TOT
Italian population 2008
y
sample, we divided the
Italian population into
18 - 24 2.197.942 2.099.564 4.297.506
25 - 34 4.077.971 3.999.081 8.077.052
35 - 44 4 895 311 4 840 448 9 735 759
strata according to the
gender and the age range.
35 44 4.895.311 4.840.448 9.735.759
45 - 64 7.559.637 7.853.523 15.413.160
TOT 18.730.861 18.792.616 37.523.477
Source: ISTAT
Italian Internet users 2008
2ND STEP
W f d th b f
M F TOT
18 - 24 1.460.328 1.412.807 2.873.135
2 34 2 3 06 2 039 31 4 396 99We found the number of
Internet users for the
selected strata
25 - 34 2.357.067 2.039.531 4.396.599
35 - 44 2.486.818 1.965.222 4.452.040
45 - 64 2 682 192 1 558 190 4 240 382selected strata. 45 - 64 2.682.192 1.558.190 4.240.382
TOT 8.986.405 6.975.751 15.962.156
Source: ISTAT

13. Sampling - Stratified Sample
3RD STEP
Italian drinking wine 2008
p g p
3RD STEP
Here, we made a basic assumption:
the distribution of wine drinkers within the
M F TOT
18‐24 48% 31% 39%
g
Italian population remains stable also
within the Italian sub-population that uses
Internet
25‐34 65% 41% 53%
35‐44 72% 44% 58%
Internet.
It means assuming that, among the people
who use Internet, we will find as many
i d i k th h l It li
45‐64 79% 48% 63%
TOT 67% 41% 54%
wine drinkers as among the whole Italian
population. Using Internet & drinking wine
Starting from the absolute numbers of step
2 and applying the percentages of wine
M F TOT
18‐24 694.412 435.398 1.129.810
drinkers among the Italian population using
Internet, we found the number of Internet
users that are also wine drinkers.
25‐34 1.524.533 832.972 2.357.505
35‐44 1.793.045 865.661 2.658.706
45‐64 2.123.595 744.822 2.868.417
TOT 6.135.585 2.878.852 9.014.437

14. Optimal sample sizeOptimal sample size
Considering our “budget” and time available, we thought
that the best size of our sample was 200.p
Italians using Internet & drinking wine (percentage)
M F TOTSo we proceeded
Italians using Internet & drinking wine (percentage)
18‐24 8% 5% 13%
25 34 17% 9% 26%
p
by finding the
percentage of 25‐34 17% 9% 26%
35‐44 20% 10% 29%
percentage of
Internet users that
are also wine
45‐64 24% 8% 32%
TOT 68% 32% 100%
are also wine
drinkers.
TOT 68% 32% 100%

15. Optimal sample size (2)
In this way we found the exact number of males and females, for
Optimal sample size (2)
each age group, that were necessary for our research and that
perfectly reflected reality.

16. 5 Questionnaire5. Questionnaire
We structured our questionnaire dividing it into 4
sections:
1. Wine consumption
2. The usage of the online channel
3. The online purchase of wine
4. Personal and behavioral data
We sent more than 300 questionnaires through the web, trying toq g y g
reach different target groups (considering age, gender and social
status).
Within the collected questionnaires we extracted randomly the
exact number of males and females of all age groups that wasexact number of males and females, of all age groups, that was
required by our sample size of 200.

17. Questionnaire PrizeQuestionnaire - Prize
Internet-based questionnaires usually have a particular
disadvantage: the lowest response rate among any other method of
ll ti d t I d t id thi bl d t t t h thcollecting data. In order to avoid this problem and try to catch the
attention of the interviewees, we decided to do a prize-winning
lottery:lottery:
• Our respondents could leave their email
dd t th d f th ti iaddress at the end of the questionnaire,
in order to participate;
• On Dec. 10 we randomly drew 3 emailOn Dec. 10 we randomly drew 3 email
addresses from the list mentioned before
• The winners were contacted through
h i il dd d h dtheir email addresses and the rewards
were delivered in few days
• The other participants were thankedThe other participants were thanked
through an email

18. Respondents’ Profile - OriginRespondents Profile Origin
O li t h i t th t b f l d f lOur sampling technique set the accurate number of males and females per
age range. In order to better understand who are our respondents we
analyzed their personal data:analyzed their personal data:
Origin
Even though our aim was to consider people from all the areas of Italy, the
majority of our respondents turned out to come from the North. We canj y p
address this to research limitations.
Th f d h d ‘E ’ I li ifThe four respondents that answered ‘Estero’ are anyway Italians – even if
originally coming from abroad – and so they are part of our target.

19. Respondents’ Profile - EducationRespondents Profile Education
Educational LevelEducational Level
The results regarding the
educational level are quite
significant: most of them
d l d t h hi h h ldeclared to have an high school
diploma and a slightly lower
percentage declared to have apercentage declared to have a
degree.
Hence, the majority of the
respondents have a quite high
d ti l l l
G SC OO D P O A
DEGREE
educational level.
HIGH SCHOOL DIPLOMA
MIDDLE SCHOOL DIPLOMA

20. Respondents’ Profile - Profession
Profession
p
The profile of the respondents that
concerns the profession shows a
high partition of the sample
between different fieldsbetween different fields.
We can say that the sample is quite
equal subdivided amongequal subdivided among
self-employed, employee, students
and “other”.
Just few of them declared to be
housewives or pensioners.
SELF-EMPLOYED HOUSEWIFE
PO P S O REMPOYEE PENSIONER
STUDENT OTHER

21. Respondents’ Profile- Hobbiesp
120
140
80
100
40
60
0
20
Considering the fact that the respondents had more than one possible
choice, the distribution of hobbies is quite homogeneous among commonchoice, the distribution of hobbies is quite homogeneous among common
activities like Sport, Literature and Music. We will see next that there are
no statistically significant differences in terms of hobbies and behaviors
among our segments, which confirms the fact that wine is appreciated by
everyone.

22. Respondents’ Profile - Preferred Holidaysp y
When asked about holidays, it is
evident that the majority of theevident that the majority of the
respondents chose an
adventurous trip on the road,p
which has almost nothing in
common with wine.
But quite interesting, as we will
see in the following slides is thesee in the following slides, is the
fact that the portion of people
that chose Wine & food tourism
Total relax
On the road
Wine&food tourism
has a higher degree of wine
consumption per month.
Wine&food tourism

23. 6 Data audit6. Data audit
Before starting the analysis of the data we obtained, it is fundamental
t h k if th it bl f th l i Thi fi t f llto check if they are suitable for the analysis. This means, first of all,
that we have to consider all the missing cases and blank values, and then
decide what to do with themdecide what to do with them.
Since our research is considering Internet users, we chose to submit
ti i th h li l tf i d t tour questionnaire through an online platform, in order to create an
initial filter (respondents are, for sure, Internet users).
This platform doesn’t show in the final output all the questionnaires
that were abandoned by the respondents, hence we have no missing
cases.
On the other hand, it was possible for the interviewees to leave some ofp
the multiple choices, that’s why we do have some blank cells.

24. Outlier Detection
Before proceeding with the data analysis we decided toBefore proceeding with the data analysis we decided to
investigate if all the numerical answers to the open questions of
our questionnaires presented extreme cases or outliersour questionnaires presented extreme cases or outliers.
– In case of extreme cases we substituted them with the
average value of the variableaverage value of the variable.
– In case of “acceptable” outliers we decided not to change
h d ifi h lidi dthem and not sacrifice the content validity and accuracy.
The process is shown by the example given by the outlier
detection of Question 2: “How many bottles of wine do you consumedetection of Question 2: How many bottles of wine do you consume
at home within a month?”

25. Outlier Detection - Results
Before
Respondents to
questionnaires n.
23 and 17 declared23 and 17 declared
to consume
respectively 300espectively 300
and 100 bottle of
wine. They
represent
Extreme Cases-
b bl i tprobably input
errors- and we
decided todecided to
substitute them
with the average
value, i.e. 9.

26. Outlier Detection - Results
After
We ran again the
outlier analysisoutlier analysis
and we found out
that the
respondent to the
questionnaire
ld bn. 46 could be
considered an
“acceptable”acceptable
outlier and
though was leftg
unchanged in the
data set.

27. 7 Data Analysis7. Data Analysis
A. Univariate:
F i & Ch t-Frequencies & Charts
-Descriptive Statistics
B. Bivariate:
- Analysis of connections
- Analysis of correlation
- ANOVA
C. Multivariate

28. Univariate Descriptive Statistics
We performed the univariate descriptive statistics in
p
p p
order to better understand who are our potential
customers and how is their purchasing behavior:
Wi f s d h si g h bits1
p g
Wine preferences and purchasing habits1
Internet usage and online purchasing2
Preferences in buying wine online3 y g3
Resentments in buying wine online4

29. Wine preferences and purchasing habits
1 p p g
1 WHAT?
After a brief description of the respondents, we want to
d d h b h i f h i h iunderstand the behavior of them with respect to wine.
When asked which wine
type they liked, our
Red Wine
respondents had more
than one possible
White Wine
Spumante
answer, and this is the
final distribution of
Rosè their preferences: red
wine is the most
frequent type.

30. Wine preferences and purchasing habits
WHERE?
Winer
Manufacturer
Supermarket Others
Wine Bar
Online
0% 5% 10% 15% 20% 25% 30% 35% 40%
The most frequent places where the respondents usually buy wine are directly
the prod cer and the s permarkets this co ld mean that they are eitherthe producer and the supermarkets, this could mean that they are either
interested in quality or they don’t care and choose simple supermarkets. The
online channel obtained less than 5% of answers, which tells us that we do have,
some room to take in the sector.

31. Wine preferences and purchasing habitsp p g
WHEN?
160
180
200
100
120
140
160
60
80
100
0
20
40
D ti ti I di id l ti R t t ith th R t t l A ift
When asked for which occasions they purchase wine, the respondents
Domestic consumption Individual consumption Restaurant with others Restaurant alone As a gift
had more than one choice, and the most frequent answers were
“Domestic consumption” and “Restaurants with others”. The results
show that they usually enjoy wine when they are in good companyshow that they usually enjoy wine when they are in good company,
such as with friends and family members.

32. Wine preferences and purchasing habits
HOW MUCH ( th)?
Observing the Mean Value we can see that our respondents consume approximately 8
HOW MUCH (per month)?
g p pp y
bottles of wine per month with friends and family, while they buy approximately 2
bottles of wine per month for gifts.
We can notice that the Standard Deviation is pretty high, showing an high variability
in the consumption habits of the sample.
The skewness is positive inp
both wine consumption and
wine gifted, this because of a
long right taillong right tail.
From the kurtosis we can
d t d th t th di t ib tiunderstand that the distribution
of both the variables is more
peaked than a normal one.

33. Wine preferences and purchasing habits
WHY?WHY?
Descriptive Statistics
N Range Minimum Maximum Mean Std. Deviation Variance
Wine type 198 9 1 10 8,07 2,460 6,052, , ,
IGT/DOC/DOCG certification 197 9 1 10 7,39 2,586 6,687
Manufacturer reputation 198 9 1 10 6,89 2,218 4,922
Geographical origin 198 9 1 10 6 58 2 583 6 671Geographical origin 198 9 1 10 6,58 2,583 6,671
Alcohol gradation 197 9 1 10 4,69 2,433 5,921
Label attractiveness 196 9 1 10 4,23 2,245 5,039
V lid N (li t i )
The chart shows which are the major characteristics that guide consumers while
Valid N (listwise) 194
j g
choosing their wines and which are strictly related to wine. The higher mean value
outlines that the most important factor is the type of wine and then the quality
certification (IGT, DOC, DOCG), the reputation of the producer and the geographicalcertification (IGT, DOC, DOCG), the reputation of the producer and the geographical
origin of the wine.
Less important characteristics are the alcohol gradation and the label attractivenessLess important characteristics are the alcohol gradation and the label attractiveness.
Standard deviations are similar for all the characteristics.

34. Wine preferences and purchasing habits
WHY2?WHY2?
DescriptiveStatistics
N Range Minimum Maximum Mean Std. Deviation Variance
Only known manufacturers 194 9 1 10 5,64 2,842 8,075
Only wines already tasted 195 9 1 10 4,94 2,810 7,899
Only wines advised by confidants 196 9 1 10 4,59 2,556 6,531
Onlywinesadvisedbyguides/magazines/oth
ers 196 9 1 10 3,98 2,374 5,636
Only wines on sales 194 9 1 10 3,09 2,386 5,691
Only wines from my region 196 9 1 10 2 71 2 129 4 533Only wines from my region 196 9 1 10 2,71 2,129 4,533
Valid N (listwise) 189
The data indicate the reasons that guide consumers through the choice of the wine. The
majority of them prefers to choose wine bacause of the reputation of the producer and
they also prefer wines that have already tasted or for which they were advised. This reflectsy p y y
the need of wine consumers to ensure the quality of wine they are buying.
Anyway, in order to make comparison with statistical significance, we should wait for theAnyway, in order to make comparison with statistical significance, we should wait for the
bivariate analysis (the same worth also for the previous slide and for other similar slides
following in this section).

35. Internet usage and online purchasing
P i d i k2 -Perceived risk-2
The graph shows the
di t ib ti f ti th tdistribution of rating that
our respondents gave for
the perceived risk ofp
online purchases: the
average value (4,72 out of
10) i t hi h10) is not very high.
This information gives us
the confidence to say thaty
Internet is a real potential
channel for our business,
as we had mentioned inas we had mentioned in
the introduction section
of this work.

36. Preferences in buying wine online
W b it f t3 -Website features-
hi h h b i h i i h ld ff d
3
Which are the website’s characteristics that could affect our respondents’
purchases of wine online? According to the mean values, the two most
important ones are the ease of the purchasing and payment process and theimportant ones are the ease of the purchasing and payment process and the
ease of seeking a specific wine within the website list.
Lower importance is given to the spotlight on rebate and layout of the website;
hi l b i f d l hi h l f d d d i ithis last website feature presented also an higher value of standard deviation
than the first ones.
DescriptiveStatistics
N Range Minimum Maximum Mean Std. Deviation Variance
Purchase and payment processes ease
200 9 1 10 8,58 1,648 2,717
Ease to seek a specific wine 200 9 1 10 8,36 1,672 2,794
Feedbacks on desired wine 200 9 1 10 7,30 2,215 4,905
Spotlight on rebate 200 9 1 10 6,43 2,305 5,311
Layout & graphics 200 9 1 10 6,11 2,397 5,747, , ,
Valid N (listwise) 200

37. Preferences in buying wine online
Additional services-Additional services-
The following data refer to the overall characteristics that most influenceThe following data refer to the overall characteristics that most influence
consumers while purchasing wine online. By looking at mean values, the most
important aspects are the availability of special wines and shipping
convenience from the website, immediately followed by the return policies.
Our marketing strategy will probably stress these services, improving and
advertising themadvertising them.
Standard deviations are pretty similar and relatively high.
DescriptiveStatistics
N Range Minimum Maximum Mean
Std.
Deviation Variance
S i l i ' il bilitSpecialwines' availability 200 9 1 10 7,47 2,112 4,461
Shipping convenience 200 9 1 10 7,40 2,385 5,688
Return policies 200 9 1 10 6,72 2,966 8,796
Professional advises 200 9 1 10 5,68 2,862 8,190
Community and blog to exchange feedbacks
200 9 1 10 5,50 2,597 6,744
L b l t i tiLabel customization 200 9 1 10 4,24 2,852 8,133
Valid N (listwise) 200

38. Resentments in buying wine online
-Service characteristics-4 -Service characteristics-
A really important aspect of our analysis is to understand the possible
4
resentments of the customers about wine online purchases. The service that less
induces consumers to buy online is the absence of human contact and the
second one is the scare not to receive the wine purchased Both these negativesecond one is the scare not to receive the wine purchased. Both these negative
aspects are related to the concept of security we noticed also for the wine
purchase behavior, as mentioned previously talking about wine purchasing
attitude.
The possibility that other people could not appreciate customers’ choice of wine
doesn’t seem to influence negatively consumers’ behaviordoesn t seem to influence negatively consumers behavior.
Descriptive Statistics
Std.
N Range Minimum Maximum Mean Deviation Variance
Human contact 200 9 1 10 6,94 2,951 8,706
Scare to not receive the wine 200 9 1 10 5,47 2,841 8,070
Scare to buy a wine that dislike 200 9 1 10 5,16 2,874 8,262
Delivery time too long 200 9 1 10 5,02 2,607 6,798
Scare to make a choice that others could
mock 200 9 1 10 2 60 2 182 4 763mock 200 9 1 10 2,60 2,182 4,763
Valid N (listwise) 200

39. Bivariate Descriptive Statisticsp
In order to understand if there is any association
between the selected variables, we performed the
bivariate analysis through different tools:
Analysis of Connection: Contingency tables1 Analysis of Connection: Contingency tables1
Analysis of Correlation: Scatter plots and tables2
Analysis of Variance: Means by classes3 y y3

40. Analysis of Connection1 y1
This analysis investigates the dependency between two categoricaly g p y g
variables through cross tabulations which show counts and
percentages of the variables’ combinations. The variable used are, for
example: Gender, Range of age, Demographics, Yes/No questions.
We analyzed many combinations of variables that we thought would be
important, but most of them weren’t significant. For Example:
- Age ranges and Online purchases (YES/NO)
- Profession and Online purchases (YES/NO)
- Age ranges/Gender/Profession and Online wine purchases (YES/NO)
A possible cause of the absence of relationship between variables could be the
low number of respondents per each category of the variables considered.

41. Age ranges &
O li h
Fascia_età * Online purchaseCrosstabulation
Online purchase
Si No Total
Online purchases
(YES/NO)
Fascia_età 18-24 Count 14 11 25
% within Fascia_età
56,0% 44,0% 100,0%
% within Online
16 1% 9 7% 12 5%
purchase
16,1% 9,7% 12,5%
% of Total 7,0% 5,5% 12,5%
25-34 Count 22 30 52
% within Fascia_età
42 3% 57 7% 100 0%
SymmetricMeasures
Value Approx. Sig.
42,3% 57,7% 100,0%
% within Online
purchase
25,3% 26,5% 26,0%
% of Total 11,0% 15,0% 26,0%
pp g
NominalbyNominal Phi ,151 ,207
Cramer's V ,151 ,207
NofValid Cases
200
Cramer’s V index signal some
35-44 Count 29 30 59
% within Fascia_età
49,2% 50,8% 100,0%
% within Online
33 3% 26 5% 29 5%
200
statistical dependence between the
“Fascia d’età” and “Online purchase”,
but the Approx Sig is too high: the
purchase
33,3% 26,5% 29,5%
% of Total 14,5% 15,0% 29,5%
45-64 Count 22 42 64
% within Fascia età but the Approx. Sig. is too high: the
overall dependent relation between the
variables is not significant.
% within Fascia_età
34,4% 65,6% 100,0%
% within Online
purchase
25,3% 37,2% 32,0%
% of Total 11 0% 21 0% 32 0%
Therefore, we will present only the
% of Total 11,0% 21,0% 32,0%
Total Count 87 113 200
% withinFascia_età
43,5% 56,5% 100,0%
% within Online 100 0
significant and satisfactory analysis of
connection…
% within Online
purchase
100,0%
100,0
%
100,0%
% of Total
43,5% 56,5% 100,0%

42. Age ranges & Preferred payment
modalities (online)
The following statistics assess the dependency between the age variable (range)
and the preferred payment method. Chi-Square value is higher than 0 and the
C V i d i hi h th 0 1
Chi-Square Tests
Asymp Sig
Symmetric Measures
Value Approx Sig
Cramer V index is higher than 0,1.
Value df
Asymp. Sig.
(2-sided)
Pearson Chi-Square 2,453E1 12 ,017
Likelihood Ratio 27,382 12 ,007
Value Approx. Sig.
NominalbyNominal Phi ,350 ,017
Cramer's V ,202 ,017
NofValid Cases 200
Linear-by-Linear
Association
10,369 1 ,001
N of Valid Cases 200
a. 6 cells (30,0%) have expected count less than 5. The
minimum expected count is ,88.
The cross tabulation (in the output document) shows the frequencies of respondents’The cross tabulation (in the output document) shows the frequencies of respondents
preferred payment modality with respect to their age. Two main trends can be detected:
-Young/middle age people prefer Prepaid Card and Paypal(quite new payment tools)g g p p p p yp (q p y )
- Middle/older age people prefer Credit Card and Bonifico(more “traditional” ones)

43. Analysis of Correlation2 Analysis of Correlation2
This analysis aims to determine if there is a significant linear relationshipy g
between two quantitative variables such as Monthly wine consumption,
Minutes spent to purchase wine, Age, Number of online purchases, Willingness to
pay, etc.
As we did with the previous analysis, we combined many variables which couldp y , y
be significant, if correlated, but most of them weren’t statistically
significant. For example:
- Age and Willingness to pay for private consumption/gift/restaurant
- Willingness to pay for a gift and Monthly wine gifted
- Monthly wine gifted and Minutes spent to purchase wine for gift
- Number of online purchases and Acceptable delivery price
Th f ill p t l th l i f ti l t fTherefore, we will present only the analysis of connection relevant for our
marketing goals…

44. Monthly wine consumption &
M thl i ift dMonthly wine gifted
The correlation between the
variables is significant (the p-
value is 0), although the R2 isvalue is 0), although the R is
low.
The number of bottles of
i d d h dwine consumed and purchased
for gifts within a month are
positively correlated.positively correlated.
(See also the following slide)
This information could be used to address specific promotions to the “loyalp p y
customers” that buy lot of bottles online.

45. Age & Monthly wine
i d ifconsumption and gift
Correlations
Age
Monthly wine
consumption
Monthly wine
gifted
Age Pearson
Correlation 1,000 ,250** ,191**
Sig. (2-tailed) ,000 ,007
N 200 000 200 200
The correlation
between wine
purchasing &N 200,000 200 200
Monthly wine
consumption
Pearson
Correlation ,250** 1,000 ,531**
Sig. (2-tailed) ,000 ,000
purchasing &
consumption and
age is slightlyg ( ) , ,
N 200 200,000 200
Monthly wine gifted Pearson
Correlation ,191** ,531** 1,000
g g y
positive and
significant.
Sig. (2-tailed) ,007 ,000
N 200 200 200,000
**. Correlation is significant at the 0.01 level (2-tailed).
This information tells us that as the age of our respondents increases also wine
consumption does. Older people could therefore be more likely to buy wine
more often.

46. Analysis of Variance3
Following, two analysis of variance. We examined many other variables but we didn’t
l
y3
find any interesting values in term of significance and Eta Squared.
Monthly wine consumption & Preferred holiday type
As we could imagine, the monthly wine
ti i hi h f l th t
y p y yp
consumption is higher for people that
declare to prefer an enogastronomic
holiday.y
Even if the Eta Squared is low -and so the strength of the effects of the preferredEven if the Eta Squared is low and so the strength of the effects of the preferred
holiday type on the monthly wine consumption is weak- the ANOVA table shows
that the p-value is significant.
It is therefore possible to conclude that the difference between means of
consumption of wine with respect to the 3 types of holiday is significant.

47. Perceived risk for online purchasesp
Respondents who experienced online purchasing rated
Report
Perceived risk for online purchases p p p g
lower (almost 1 point) the perceived risk to buy online.
Since the number of respondent who has never
purchased online is really high the issue of the
p
Online
purchase Mean N Std. Deviation
Si 3,91 87 2,009
purchased online is really high, the issue of the
perceived risk should be taken into consideration when
developing the business.
No 5,34 113 2,408
Total 4,72 200 2,348
ANOVA Table
Sum of
Squares df Mean Square F Sig.q q g
Perceived risk for online
purchases * Online
purchase
BetweenGroups (Combined) 100,269 1 100,269 19,923 ,000
Within Groups 996,486 198 5,033
Total 1096,755 199
The average risk perceived among people who
purchased online and who didn’t is significant.
Measures ofAssociation
p g
Eta squared is low, so the strength of the effects of
having purchased online and the perceived risk of
Eta Eta Squared
Perceived risk for
online purchases *
Online purchase
,302 ,091
having purchased online and the perceived risk of
online payment methods is weak.

48. Multivariate
i i i iDescriptive Statistics
The multivariate statistic techniques allow us to go
more in depth in the analysis of our respondents inmore in depth in the analysis of our respondents in
order to reach the research goals
Factor Analysis1
Cluster Analysis2 Cluster Analysis
C j i A l i
2
3 Conjoint Analysis3
Discriminant Analysis4

49. Factor Analysis1
In order to start the behavioral segmentation of our respondents and then
classify them into different segments, we chose to run a factor analysis of some
variables: they were too correlated with each other and would have increased
the complexity of our work.
Therefore, we selected four questions that we were particularly interested in,Therefore, we selected four questions that we were particularly interested in,
and managed to reduce their number without losing significance.
The questions were: n. 21, n.22, n.23, n. 8:
─ Layout & graphics
E h ifi i
─ Scared to buy a branded wine that I don't like
H i i d h l b l h h ld─ Ease to choose a specific wine
─ Spotlight on rebate
─ Feedbacks on desired wine
P h d t
─ Hesitation towards the label that others could
mock
─ Scare not to receive the wine
E t l l d li ti─ Purchase and payment ease
─ Special wines' availability
─ Shipping convenience
Return policies
─ Extremely long delivery time
─ Human contact
─ Origin
Gradation (alc%/vol)─ Return policies
─ Return policies
─ Community and blog to exchange
feedbacks
─ Gradation (alc%/vol)
─ Producer
─ Label
─ Typefeedbacks
─ Professional advises
─ Label customization
─ Type
─ Certification

50. First try: error
The first time we ran the analysis, we weren’t satisfied with its output.
In fact, the factors we obtained were affected by three major problems:
• “1/3 Th ” Th t t l b f i bl i d i th l i (22)• 1/3 Theory : The total number of variables comprised in the analysis (22)
divided by 3 didn’t give the number of factors (6)
• The percentage of explained variability was too low (59.14%)
Percentage of explained variability
p g p y ( )
• The Scree plot was not satisfactory
Percentage of explained variability

51. What did we change?What did we change?
At this point we decided to go through all the variables of theAt this point, we decided to go through all the variables of the
selected questions, to understand which ones were the most
interesting for our purpose.g p p
Through this process we eliminated some of them, because they
weren’t related to our research as the others.
These variables are:
• n 8 1 (Origin)• n. 8_1 (Origin)
• n. 8_2 (Gradation: alc%/vol)
• n. 8_5 (Type)_ ( yp )
• n. 23_2 (Hesitation towards the label that others could mock)
• n. 23_5 (Human contact)

52. Second try: ok! (more)y
Without the variables listed here theWithout the variables listed here, the
output of the analysis was satisfactory
under all the aspects (see next slide):
• “1/3 Theory”: The total number of
variables comprised in the analysis (17)
di id d b 3 i t l thdivided by 3 gave approximately the
number of factors (6)
• The percentage of explained variabilityThe percentage of explained variability
was high enough (66,61%)
• The Scree plot was not quite satisfactory
• The Eigen value was fine (1,01)
• The quote of overall variability
explained by each output was high
• The correlation structure was well
d fi ddefined

53. Second try: ok! (continued)
The following table shows precisely the information explained in the
previous slide
y
p

54. Optimal factors
These factors comprise all the concepts we are most interested in, which are: the elements people
consider when purchasing wine, the external influences (being them human or related to the brand)
and the additional services that could affect the purchasing process.
Rotated Component Matrixa
Component
Website features Additional services Delivery Aesthetics External advises Quality
Layout & graphics 740Layout & graphics ,740
Ease to choose a specific wine ,690
Spotlight on rebate ,670
Purchase and payment ease 615 322 316Purchase and payment ease ,615 ,322 ,316
Feedbacks on desired wine ,538 ,426
Shipping convenience ,852
Special wines' availability ,704
Return policies ,589 ,361
Scare not to receive the wine ,845
Extremely long delivery time ,759
Label customization ,698
Scared to buy a branded wine
th t I d 't lik
,407 ,698
that I don't like
Label attractiveness -,311 ,598
Community and blog to
exchange feedbacks
,850
Professional advises 324 652 328Professional advises ,324 ,652 ,328
IGT/DOC/DOCG certification ,768
Manufacturer reputation ,692

55. Description of final factors
Factor 1:
Website
f
Website components that affect the consumers when
purchasing a product and that could improve or worsen the
p
features process.
Factor 2:
Additi l Services that could add value to the core product, and that
Additional
services
Services that could add value to the core product, and that
normally make the difference when building loyalty.
Factor 3:
Delivery
Usual concerns of consumers when choosing Internet to
purchase products.
Factor 4:
Aesthetics
Subjective reactions when purchasing an experience good like
wine, they are mainly related to brand image and social status.y y g
Factor 5:
External Opinions and suggestions that could influence consumers’
External
advises
p gg
choices.
Factor 6:
Quality
Elements that usually assure the actual quality of wine.

56. Cluster analysis2
Once obtained the factors, we were able to run a cluster
analysis aimed to classify our consumers into relatively
homogeneous groups, based on the factorized set of
variables considered.
Being our sample relativley big, we ran the cluster
analysis with the k-means method.y
But, before doing it, we used the hierarchical methodg
on a sample of 61 cases to identify the optimal number of
clusters and initial seeds to run the k-means.

57. Hierarchical with sampleHierarchical with sample
After observing the dendrogram we conclude that good numbers ofAfter observing the dendrogram, we conclude that good numbers of
clusters could be either 3 or 6.

58. Final choice: 3 clusters ( )Final choice: 3 clusters (more)
Considering both the analysis of the dendrogram and the nature of our
market research, we decided that 3 could be a good number of clusters (3
segments of customers is enough for an online business).
Hence, we saved the initial seeds found through the hierarchical cluster
l i th lanalysis on the sample.
Then, we used the initial seeds to run a k-means analysis on all the cases,
considering as cluster number 3considering as cluster number 3.

59. Final choice: 3 clusters
As we expected after deciding a number of clusters relatively lower than
Final choice: 3 clusters (continued)
As we expected after deciding a number of clusters relatively lower than
the number of factors, one of these turned to be not very significant for
the analysis.y
In particular , Website Features seems to be not very relevant for thepa ticula , Website Features see s to be ot ve y eleva t fo the
creation of our clusters.

60. Final choice: 3 clustersFinal choice: 3 clusters (continued)
The analysis of the ANOVA suggests to increase the number of
clusters.
Anyway though, the dendrogram suggests a solution of 3 or 6 clusters.
Considering our marketing objectives and the nature of our business,
we believe that a 3 clusters are still the best solution.
So, finally, we found 3 relevant
l h ill b d ib d iclusters, that will be described in
the following slides.

61. Cluster analysisCluster analysis
- Final cluster centers -
The final cluster centers table
shows a significantshows a significant
characterization

62. General description of clustersGeneral description of clusters
• Insensible to additional services, external
i fl s d d li o ditio s Th
Quality seekers influences and delivery conditions. They
just want the best quality.
Q y
(cluster 1)
• Slightly sensible to additional services, theyLow involved Slightly sensible to additional services, they
are not emotionally involved in the wine
purchasing process
Low involved
(cluster 2)
• Favorable to the services connected to buy
li d ibl i d hWeb confidents online and sensible to community and other
external advices. They also care about
quality.
Web confidents
(cluster 3) q y

63. Detailed description of clustersp
In the next slides we will analyze our segments in a more detailed way, iny g y
order to try and see how they differ from each other and thus how we can
hit them with our marketing strategy.
This is aimed to avoid the problem of the current online wine resellers,
that don’t know who their customers are.
Analyzing the main demographical and behavioral information (e.g.
hobby, preferred holiday type), we observed that there are no statistically
significant differences among the three clusters we identified.
This is probably due to the fact that, in Italy, wine is a traditional element,
it is part of our culture. Both young and older people enjoy it, no matter
what their life habits are. If we were dealing with other markets, like the
United States we probably would have found less people that drink wineUnited States, we probably would have found less people that drink wine
as often as Italians do, and in fewer occasions.
Anyway there something that differentiate the clusters we will discussAnyway, there something that differentiate the clusters; we will discuss
it in the following slides.

64. Cluster 1: Quality seekers
M F TOT 18-24 25-34 35-44 45-64 TOT
Sample size
29%M F TOT
68% 32% 100%
18 24 25 34 35 44 45 64 TOT
7% 30% 24% 39% 100%
As in the other clusters, the percentage of women is about half of the percentage of
men. This is also because of the characteristics of the target population (italiansmen. This is also because of the characteristics of the target population (italians
drinking wine and using Internet) for the selected strata.
The majority of the people is between 45 64 years old and only very few are young
F h t i i th i ttit d Q lit k i l i t t d i
The majority of the people is between 45-64 years old and only very few are young
(18-24). So, as we could presume, Quality seekers tend to be older.
For what is concerning their attitudes, Quality seekers are mainly interested in
purchasing the best quality of wine, and therefore pay significantly more attention to:
-Wine certifications (IGT/DOC/DOCG)( )
- Wine producer’s reputation
On the other hand, they consider less important additional services offered by the
website.

65. Cluster 2: Low involved
M F TOT 18-24 25-34 35-44 45-64 TOT
Sample size
M F TOT
72% 28% 100%
18 24 25 34 35 44 45 64 TOT
21% 15% 25% 39% 100%
20%
Thi i th l t ith th hi h ti f l b t dThis is the cluster with the higher proportion of people between 18 and 24
year old. It is also the smallest cluster.
The peculiarity of the Low involved is that they are not concerned
about particular aspects related to wine and website They didn't gaveabout particular aspects related to wine and website. They didn t gave
significantly high rating of importance to any of the question we
asked, showing low interest and involvement in the topic.g p
As it’s very clear especially from the Radar graph, they are not
concerned at all about quality elements, but consider a lot potential
ddi i l i ff d b h b iadditional services offered by the website.

66. Cluster 3: Web Confidents
M F TOT 18-24 25-34 35-44 45-64 TOT
Sample size
M F TOT
67% 33% 100%
18 24 25 34 35 44 45 64 TOT
13% 27% 32% 28% 100%
51%
The age distribution of the clusters' members show that the majority of
them are middle-aged: 59% of them are between 25 and 44 years old.
l h l h h b f d hBeing also the cluster with the bigger size, Web confidents seem to be the
right people to target.
This cluster is the most interesting one among all the others, because it gave high
ratings of importance to wine and website features.
With respect to the other clusters, they are really sensitive to:p , y y
- Special wines availability
- External advises (feedbacks on blogs and professional advices)
Moreover, the average of their preferences is quite high for almost all the variables.
H t p di t hi h l t p b l t ?How to predict which cluster a person belongs to?
We will see it with the discriminant analysis, after the conjoint analysis…

67. Conjoint analysis3 j y
We decided to use a conjoint analysis in order to better understand
th f f l t d diff t ibl t tthe preferences of our sample toward different possible structures
of the wine online purchase.
We selected a set of attributes and levels represented as follows:
Discount
We selected a set of attributes and levels represented as follows:
Payment: Delivery: Discount
•10% per ordini
superiori a 20
Payment:
• Carta di
credito/prepagata
Delivery:
•1 giorno
lavorativo superiori a 20
bottiglie
• 10% i t
credito/prepagata
• Contrassegno
t l
lavorativo
• 2-3 giorni
lavorativi • 10% su acquisto
successivo ogni 3
ordini
postale
•PayPal
lavorativi
• 4-5 giorni
l i i ordini
• Sconti sui vini del
mese proposti
lavorativi
mese proposti

68. Conjoint analysis - ResultsConjoint analysis Results
With th tt ib t d th ibl l l f h fWith three attributes and three possible levels for each of
them, spss figured out 9 possible product configurations,
hi h h b l t d b lwhich have been evaluated by our sample.

69. Output InterpretationOutput Interpretation
• The most relevant attribute is the payment method: 49 17• The most relevant attribute is the payment method: 49,17
• In particular, the prepaid/credit card system (“carta di credito/
prepagata”) positively influences the global judgement of the selling
proposition, while Cash-on-delivery (“contrassegno postale”) and
PayPal impact less because of their minor importance values.
Analyzing the utilities of our respondents we can figure out
the best scenario:
1 Rebates on
Credit/prepayed
card
1
Day-
delivery
Rebates on
monthly
suggested winesy gg

70. Conjoint Gender
To evaluate how the perception of the attribute varies according to the personal
Classification variable
data of the interviewee, we chose to run again the analysis considering
demographical variables: Gender, Age and Educational Level.

71. Output InterpretationOutput Interpretation
-Gender-
• Comparing the results of the importance values we find no
big differences between males and females, however it’s
possible to note that the payment method attribute remains
the most important for both genders, even if with slightly
different weights.
• Comparing the utility results we note that females
perceived Credit card payment more important than menperceived Credit card payment more important than men
did.
• On the other hand, for males the presence of discounts on
the monthly suggested wines has -slightly- more
importance.

72. Conjoint Age (18-34)j g ( )

73. Conjoint Age (35-64)j g ( )

74. Output InterpretationOutput Interpretation
-Age-
• Comparing results, it’s clear that our segmentsp g g
consider “payment” the most important component.
We want to highlights that this attribute is particularlyg g p y
important for people between 45-64.
• Moreover, the same age class perceive respect theMoreover, the same age class perceive respect the
other age classes the “delivery” attribute (12,392) as
the less important.the less important.
Ed i l L lEducational Level
We run the conjoint also splitting for the educational levels, but we
found that both the utilities and the importance values were similarfound that both the utilities and the importance values were similar
among the different educational levels.

75. Linear discriminant analysis4 Linear discriminant analysis4
“Often you have measured different groups of respondents on
i i bl Di i i l i i f lmany metric variables. Disriminant analysis is a useful way
to answer the questions... Are the group different? ...On what
i bl h diff ? C I di hi hvariable are they most different? ...Can I predict which group
a person belongs to using these variables?”
Jamie Baker-Prewitt,,
vice President,
decision sciences,,
Burke Inc.

76. Linear discriminant analysisLinear discriminant analysis
Do the different market segments (clusters) differ in their
structure of preferences about the characteristics?
• Independent variable market segments (clusters)• Independent variable: market segments (clusters)
• Dependent variables:
– Geographical origin
– Alcohol gradationAlcohol gradation
– Producer’s reputation
L b l tt ti– Label attractiveness
– Wine type (red, white, sparkling, Rosé)
– IGT/DOC/DOCG certification

77. Linear discriminant analysisLinear discriminant analysis
• The valid cases considered in the analysis are 194 in total• The valid cases considered in the analysis are 194 in total
• Just 1 case lacked the evaluation in at least one of the
discriminant variables (missing data)

78. Linear discriminant analysisLinear discriminant analysis
• Only the variable “Geographical origin” is not significant
Th t i ifi t i bl i th “IGT/DOC/DOCG• The most significant variable is the “IGT/DOC/DOCG
certification”

79. Linear discriminant analysisLinear discriminant analysis
The Eigen values show that the first function explains the 92,4% of
the variance, while the second one explains the residual 7,6%
Obviously, the coefficient of canonical correlation Eta is higher for the
first function.
Looking at Wilks’ Lambda, it is notable the significance of the two
discriminant functions.

80. Linear discriminant analysisLinear discriminant analysis
1° Function: certification, producer’s reputation and wine type
2° Function: label attractiveness, alchool gradation and geographical
i iorigin
Rotated pooled within-groups correlations between discriminating
variables and standardized canonical discriminant functions .
Variables ordered by size of correlation within function.
*.Largest absolute correlation between each variable and anyg y
discriminant function

81. Li di i i t l iLinear discriminant analysis
With the rotated structure matrix and the functions at group centroids
we can create a map, in order to better understand the findings of the
discriminant analysis.
1°function X axis wine quality indicators
°f i Y i l f ifi d f2°function Y axis relevance of specific product features

82. Discriminant analysis findingsDiscriminant analysis findings
Do the different market segments differ in their structure of
preferences about the wine characteristics?
C di hi h l b l ?Can we predict which cluster a person belongs to?
• We analized the discriminating power of the preference towards
different characteristics of wine
• With a brief questionnaire -that should be submitted to the website
k h f f h h d husers- asking the preferences for the wine characteristics used in the
analysis, we can presume the cluster each user belongs to
• In this way, we can address to each client the right selling proposition
and marketing mix according to his cluster membershipand marketing mix, according to his cluster membership

83. Linear discriminant analysisLinear discriminant analysis
Wine Characteristics Clusters
Label attractiveness
Quality seekers
IGT/DOC/DOCG
certificationcertification
Wine type
Geographical origin
Low involved
Manufacturer
reputation
Alcohol gradation Web Confidents
X axis Wine quality indicators
Y axis Relevance of specific product features

84. Linear discriminant analysisLinear discriminant analysis
Wine Characteristics Clusters
Label attractiveness
Quality seekers
IGT/DOC/DOCG
certificationcertification
Wine type
Geographical origin
Low involved
Manufacturer
reputation
Web & wine lovers
Alcohol gradation
Web & wine lovers
X axis Wine quality indicators
Y axis Relevance of specific product features

85. 8 RESULTS
FINDINGS) P t ti l k t f li i lli
8. RESULTS
FINDINGS) Potential market for online wine selling
Th t ti l k t f b i i it h t iThe potential market for our business is quite heterogeneous in
terms of gender, age, origin or profession: from one side, Internet
is accessible basically to everyone; from the other wine drinkersis accessible basically to everyone; from the other, wine drinkers
don’t seem to have peculiar characteristics.
We detected just positive correlation between age and wineWe detected just positive correlation between age and wine
consumption (per month) and a slight relationship between age
and preferred payment method online.and p efe ed pay ent ethod online.
The online channel is not very common for wine selling (less than
5% of our sample declared to experienced it) but it has goodp p ) g
potential because 43,5% of respondents used Internet for
purchasing. Moreover the risk perceived for the online purchasing
in general is rated 4,72 (out of 10), so relatively low.

86. FINDINGS) Preferences of the potential consumers (more)) p ( )
Preferred wine type: considering that it was a multiple choice answer, the
distribution of preferences in quite homogeneous among red, white and
( h h d h li k d) Th i i ’spumante (even though red was the most clicked). The wine type it’s
also the major characteristic that guide consumers while choosing
their winestheir wines.
Anyway, before taking decision, a view of the trend along the time should
be consideredbe considered.
Most frequent consumption occasions are meals at home or restaurantMost frequent consumption occasions are meals at home or restaurant,
always with other people (family or friends), but also for gifts. People
use to drink almost 8 bottles per month and purchase for gifts almostp p g
2, in average.

87. FINDINGS) Preferences of the potential consumers (continued)
When choosing wine, people give importance to the wine type, but also quality
certifications, producer’s reputation and advises of trusted people.
) p ( )
p p p p
The last element could be interesting if we consider the potential of web 2.0
instrument. Interaction among users, but also between the (trusted with
good reputation) firm, will be very important.
W ld l i i i l t k f i t f “Wi b k”!We could also imagine a social network of wine, a sort of Winebook !
Respondents of our questionnaire who prefer food & wine tourism consumeRespondents of our questionnaire who prefer food & wine tourism consume
significantly more wine (per month) so the association between these two
variables could be exploited for communication and advertising.
And what about online wine purchasing preferences?
- People declared very important the ease of purchasing process and of seeking
a specific wine.
Th l hi h t f i t t th il bilit f i l i- They also gave high rates of importance to the availability of special wines,
the shipping convenience from the website, return policies.

88. FINDINGS) Resentments of the potential consumers) p
Our respondents assess that their major resentments regarding Internet
channel are:
• the absence of human contact
• the scare not to receive wine purchased
B h f hi ifi f h i d bBoth of this resentments are not specific for the wine product category but
reflect the general distrust attitude toward the online world. So it seams
that there aren’t, for wine, significant more resentments than in other, , g
industry.
This is also a matter of “generational consumption”. We can presume that,
with the passing of time, resentments will decrease.
Anyway it is very important to build a trusted brand well known to theAnyway it is very important to build a trusted brand, well known to the
costumers.

89. FINDINGS) Demand segmentationFINDINGS) Demand segmentation
W i d f b h i l i b did ’ b iWe tried to perform a behavioral segmentation but we didn’t obtain a
satisfactory result , maybe also because our sample was quite little. Moreover,
we realized that there are no particular elements that differ wine drinkers inp
Italy. Thus we decided to perform a demographical segmentation but again
ended up with poor results.
Finally we chose to perform a segmentation using significant factors, and
realized that it was the best solution since this method really comprisesrealized that it was the best solution, since this method really comprises
what’s important for our business.
Through the factor analysis we obtained 6 factors that summarize all the aspects
of purchasing wine online (quality, aesthetics, external advises, website
features, delivery, additional services) and according to them we created 3
clusters, called: Web confidents, Quality seekers, Low involved.

90. FINDINGS) Potential and most important segmentsFINDINGS) Potential and most important segments
We analyzed a sample representative of the Italians that are wine drinkers and
Internet users, but it is unrealistic to think that all of them would buy winey
online.
Therefore, considering our three clusters, we think that efforts should be, g ,
addressed to hit cluster n. 1 and 3: Quality seekers and Web Confindents.
Even if they are different in terms of internal preferences and behaviors, they
l th t i ifi t d ibl t i d b h t i tiare also the most significant and sensible to wine and web characteristics.
Afterwards we will give suggestions to create the best online platform possible,
in order to attract the consumers that are looking for an innovative and easy
way to get their quality wine.
We will not consider to invest money on Low Involved people.

91. Limitations of our analysisLimitations of our analysis
Before moving on with our final consideration on the managerial
i li ti f thi k ti h t t t iimplication of this marketing research, we want to repeat once again
the limitation of our analysis:
• Time constraints
• Budget limitationsBudget li itations
• Questionnaires have been distributed to relatives and friends,
trying also to reach more people from different areas of Italy But intrying also to reach more people from different areas of Italy. But in
the end the majority of people turned out to be from the North of
Italy anyway.taly a yway.
• This is our first marketing research!

92. 9. Managerial Implications9. Managerial Implications
The best business idea
Starting form the analysis of our data, we want try to outline some of
the characteristics of the online selling that would match better with
The best business idea
the characteristics of the online selling that would match better with
the segments identified, according to their habits, preferences and
needs.
We are conducting this analysis of the managerial/marketingWe are conducting this analysis of the managerial/marketing
implications from the point of view of :
• PRODUCTS and SERVICES offered
• PRICINGPRICING
• PROMOTION
• OPTIMAL WEBSITE structure and designOPTIMAL WEBSITE structure and design

93. PRODUCT and SERVICES
Everyday offer and Special wines
In the website, consumers should find the same assortment of a real wine
cellar, especially because one of our clusters is really fond of wine (Quality
seekers) and we want to make sure that they trust the high standard of itseekers) and we want to make sure that they trust the high standard of it.
Anyway a selection of specific wines should be created and proposed togetherAnyway, a selection of specific wines should be created and proposed together
with advices of experts.
Therefore we suggest to rely only on experienced and well-known producers, to
propose a wide offer that comprises both everyday and special occasions’
b lbottles.
I dditi i ti l t ( Ch i t l h bi thd fIn addition, in particular moments (e.g. Christmas, seasonal changes, birthday of
customers) some peculiar wines should be offered, in order to enhance
loyalty or just please the clients.y y j p

94. PRODUCT and SERVICES
Ease of finding wines
One of the first behavioural output of our analysis is that the majority of
the respondents considers as the starting point of the decisional process
the type of wine they want to buy (white, red, sparkling, Rosé) and,
moreover, one of the most important characteristics of the website is the
f ki ifi iease of seeking a specific wine.
Considering these two outputs, the
website should make a clear
di i i f diff idistinction of different wine types:
in this way customers will feel guided
through the choice and will be helpedthrough the choice and will be helped
in order to find the desired product
without wasting time searching on theg g
website.

95. PRODUCT and SERVICES
Feeling of human contact and experience
One more evident result of our analysis is that the majority of our respondents
prefers to choose the wine basing on the reputation of the producer.
F thi d t il d i f ti b t th d h ld b id dFor this reason detailed information about the producers should be provided
and, moreover, it should be possible to contact them directly: in this way
costumers will feel free to ask them questions regarding the product and willq g g p
solve all doubts they have with regard to their purchase.
Moreover we suggest to give the opportunity to get real backups from a
sommelier, who will share its experience through a dedicated blog.
A id b f ld l i i i l k f i fAs said before, we could also imagine a social network of wine, a sort of
“Winebook”, a Facebook of wine!

96. PRODUCT and SERVICESPRODUCT and SERVICES
Payment process
Since the prepaid/credit card system positively influences the global judgment
of consumers, and since one of the characteristics of the website that have a
strong impact on consumer behavior is purchase and payment ease, we
strongly believe that the website has to be organized in such way that a safestrongly believe that the website has to be organized in such way that a safe
payment is granted.
Moreover, the process doesn’t have to be too complicated: our data show that this
attribute is particularly important for people between 45-64 and we don’t
t t t b k th h f thi t t th t ld b l kill dwant to set back the purchases of this target group that could be less skilled
with the use of technologies.
Hence, we propose to set an easy process, that grants security and that allow
payments through prepaid and credit cards, in order to satisfy both young
and older people.

97. PRODUCT and SERVICESPRODUCT and SERVICES
Delivery and return policies
From the analysis resulted that our respondents prefer the shortest delivery time
possible, that is one working day. The only ones that didn’t worry a lot about
this aspect were people between 45-64 year old.
Moreover, the clusters we chose to address showed to be quite interested also
about return policies – if you buy more than one bottle of the same wine but,
while you open the first one, you don’t like it, you can return the unbroachedwhile you open the first one, you don t like it, you can return the unbroached
bottles for free.
Thus, our suggestion is to engage a reliable shipping company to grant the safest
delivery of wine, and to offer return policies to manage situations of damaged
b ttl ti fi d tbottles or unsatisfied customers.
This will also appease those consumers that are scared about not receiving theirThis will also appease those consumers that are scared about not receiving their
purchases, and that don’t fully trust Internet.

98. PRICING
Unfortunately, our marketing research doesn’t allow us to gain relevant
consideration about the pricing strategy.
For sure, from one hand, the online channel allow to cut costs. In this way it
would be possible use a strategy of lower prices.p gy p
On the other hand, wine use the pricing also a as a signal for the quality: a lower
price could be perceived as a lower quality.
So, the basic price should be the same of the traditional channel. But it would be
ibl k i l ipossible to make special promotions.
M i i h i it f th b it t ifi l t it ld bMoreover, assigning each visitors of the website to a specific cluster it would be
possible to adopt a customized pricing strategy for him/her.

99. PROMOTIONSPROMOTIONS
O l i tli th t th b f i h i thOur analysis outlines that the number of wine purchaser increases among those
people who prefer an Wine and Food tourism.
So, sponsorships and co-marketing activities with travel agencies or holiday
farms could be for sure a success, bringing advantages both to the website and to
the other company.
Often tourists visit a region and fall in love with the local food and wine. If we
promote to them the wine website, they will be very happy to order and continue
drinking wine also from they home!drinking wine also from they home!
In addition, we suggest to cooperate with some restaurants: if they purchase from, gg p y p
the website cellar, the website could be advertised on their wine selection
menu (“Carta dei vini”).

100. OPTIMAL WEBSITE DESIGNOPTIMAL WEBSITE DESIGN
In conclusion, we would like to point out some of the elements that an optimal websitep p
should have to potentially and successfully sell wine.
Si l d i t ti b l t• Simple and interactive web-layout
• History section dedicated to wine producers information and contacts
• Website section dedicated to special wine & offering (promotions discounts and freeWebsite section dedicated to special wine & offering (promotions, discounts and free
deliveries )
• Website section called “Talk to the Sommelier” where consumers can share information
d b i h l lk di l i li d li fand comments about wines; they can also talk directly to a specialized sommelier for
advices and tips
• Website section dedicated to all the events linked to wine and enogastronomicg
experiences all over Italy
• Web tool to make an easy research of different wines based on wine’s name, producer’s
name origin wine type and all these attributes combinedname, origin , wine type and all these attributes combined
• Web tool, such as shopping carts and personal account, to make an easy purchase and
payment processe and always keep track of personal orders, payment and delivery
status of the product purchased

101. CONCLUSIONSCONCLUSIONS
“It is well to remember that there are five reasons for drinking wine:f f g
the arrival of a friend, one’s present or future thirst, the excellence
of the wine, or any other reason”
L i bLatin proverb
“Wine is a bottled poetry”
Robert Louis Stevenson