2. Segmentation of buyers:
1. By age
2. By gender (men, women, kids)
3. By income (low, middle, high)
By income level
By savings level
4. By family size
5. By lifestyle
Sportsmen
Housewives
Officeworkers/Freelancers/Unemployed
Businessmen
white collars/blue collars
Travelers
Fashionists
6. By nationality
7. By race
8. By religion
9. By role
Fashion movers
Influencers
Decision makers
Buyers
End users
Segmentation of products:
1. By season (winter, spring, summer, autumn, in-between)
2. By material used
3. By style
4. By color
5. By geographic region
6. By brand
7. By price
8. By production method
3. Mass production
Individualmanufacture
9. By distribution method:
Stores
E-commerce (websites, socialmedia, landing page
By catalogue
10. By theme:
Wedding
Military
Work shoes
Medicine
Big sizeetc.
11. By price orientation
Standard price
Stocked price (low costbrands, pastyear collections)
Reduced price
12. By elasticity
Sales promotion of shoeware
I. Customization (3D printing, individual manufacture)
II. Physicaland nonphysicaldiscounts (cumulativepoints;
buy 2, get 3rd free; buy 2 for a price of 1; holiday,
special events price offs, birthday gifts; free
complimentary goods; discount for a family of 4 people,
for aged people; students cards, freeshop cards after
filling in questionnaireetc.)
III. Credit payment options
IV. Huge warranty period (morethan rivals offer)
V. Aromamarketing (specialfragrances in a showroom),
audiomarketing (surrounded music), tactile marketing
(chocolates in a vasein a showroom, things that a
potential buyer wants to touch), visualmarketing
(design of a showroom, showcases/racks).
Warmlight enhances the presenceof leather goods,
jeans and suites benefit froma cold light. It’s preferable
4. to have a ratio of 80% neutral colors against20% of
bright specific themed colors. Citrus fragrancearouses
happiness, lavender and green tee alleviate and
tranquilize, vanilla and ambergris – providereliability
and comfort, flower scents –romance and adventure. As
for a tactile preferences wood is perfect for showcases.
VI. On screen video placed in a shop with new collection
or/and fashion week shows demonstration.
VII. Social media stunts:
Reviews in facebook, twitter, vkontakte, themed
media, review sites
Coupons, promo actions
Quizzes
Feedback forms (upon a firstrun of a future
advertising, new collection involve target
audience in a process of evaluation, advice in
order to commence a dialogue with a potential
customer as a primary goal.
Instagramas sale’s platform(5-6 hash tags
underneath a photo)
VIII. Partner’s mutualbenefit actions.
(Buy and get a sales coupon in a massagesaloon,
romantic night in a restaurantfor a couple, free ticket
for movie, exhibition etc.)
IX. Neurolinguistic programming elements:
The more efforts a buyer applies the more
valuable acquisition is (let a customer trade off;
persuadehim to buy a best bid; cause a frenzy,
artificial shortage)
Productinvolving (when a customer participates
in a product, when he can alter a product
according to his preferences)
Creation of inferiority (you are shortof smth..)
Desireof distraction (selling goods at the airport
for instance to destruct people from upcoming
flight)
5. Strategy “We are not finished yet and you will
extra get…”
X. Advertising
With a live model. Highlights:
A model blends with environmentby color and on contrary advertised shoe
is underlined
Any action draws attention (crossing legs when sitting down)
Elimination of competitor (only legs, feet are left)
Without a live model. Highlights:
Addition of importance attributes (gypsumbustnearby – an element of
classicalculture; flowers symbolizebeauty, femininity and tenderness;
wooden texture – closeness to nature; apples –temptation, horses –
aristocratism, crocodiles, lizards and snakes –exotica; cats and leopards –
elegance and grace
Action as well (flying sneaker for instance)
Examples of using SPSS IBM Statistics
1. Compare two means (Independentand two-paired t-tests)
Initial data
20 shoe shops in Dubai and Abu Dhabi, their selling records per week
before and after test (for instancelaunch of promotiveadvertising
campaign of louboutins)
Null hypothesis1: Placeof selling doesn’taffect the quantity of louboutins
sold.
Null hypothesis2: Advertising campaign failed and didn’t affect the sales.
Shops Region Pretest Posttest
1 1,00 2000,00 2700,00
2 1,00 1400,00 1534,00
3 1,00 3455,00 3806,00
4 2,00 437,00 657,00
5 2,00 630,00 754,00
6 2,00 865,00 945,00
7 1,00 2567,00 3235,00
8 2,00 395,00 298,00
9 1,00 1234,00 2345,00
10 1,00 3214,00 5348,00
11 1,00 5463,00 5550,00
6. 12 2,00 626,00 822,00
13 1,00 1235,00 1356,00
14 2,00 371,00 426,00
15 2,00 143,00 175,00
16 2,00 68,00 79,00
17 2,00 101,00 167,00
18 2,00 352,00 406,00
19 1,00 1111,00 1645,00
20 2,00 631,00 743,00
Group Statistics
Region N Mean
Std.
Deviation
Std.
Error
Mean
Qty of
louboutins
sold per
w eek
Dubai 9,00 2408,7 1445,82 481,94
Abu Dhabi
11,00 419,91 252,87 76,24
Independent Samples Test
Levene's Test for
Equality of
Variances t-test for Equality of Means
F Sig. t df
Sig.
(2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Low er Upper
Qty of
louboutins
sold per
w eek
Equal
variances
assumed
13,84 0,0016 4,51 18,00 0,0003 1988,87 441,44 1061,45 2916,29
Equal
variances
not
assumed
4,08 8,40 0,0032 1988,87 487,93 872,98 3104,76
As we can see fromthe tables Equal variances are not assumed (Sig <0.05) and
null hypothesis1 can be rejected (Sig. 2 tailed<0.05).
Outcome1: Place of selling does affect the quantity of goods sold and mean value
for Dubai is much larger that for Abu-Dhabi.
Paired Samples Statistics
Mean N
Std.
Deviatio
n
Std.
Error
Mean
Pair 1 Qty of louboutins sold per
w eek 1314,90 20,00 1394,40 311,80
Qty of louboutins sold per
w eek 1649,55 20,00 1673,98 374,31
7. Paired Samples Correlations
N
Correlatio
n Sig.
Pair 1 Qty of louboutins sold per
w eek& Qty of louboutins
sold per w eek 20,00 0,96 0,0000
Paired Samples Test
Paired Differences
t df
Sig.
(2-
tailed
)Mean
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Low er Upper
Pair 1 Qty of louboutens sold per
w eek- Qty of louboutins
sold per w eek
-334,65 516,83 115,57 -576,54 -92,76 -2,90
19,0
0
0,009
As we can see fromthe tables above two means differ unessential but t-test
shows us that null hypothesis can be rejected (Sig. 2-tailed<0,05).
Outcome2: Advertising campaign affected the sales (increased it).
2. Compare two means in more than 2 clusters.
Initial data
4 shops, 10 brands sold in each shop
Null hypothesis: all luxury brands contributethe same shareinto a total qty
of goods sold.
Shop Brand Qty,pcs
Shop1 Gucci 3
Shop1 Miu-miu 6
Shop1 Stuart Weitzman 2
Shop1 Brain Atwood 7
Shop1
Alexandra
Mcqueen 4
Shop1 WalterSteiger 33
Shop1
Christian
Louboutin 2
Shop1 JimmyChoo 2
Shop1 ManoloBlahnik 6
Shop1 LouisVoitton 12
Shop2 Gucci 3
Shop2 Miu-miu 5
Shop2 Stuart Weitzman 1
Shop2 Brain Atwood 8
Shop2
Alexandra
Mcqueen 4
12. As we can see fromthe tables null hypothesis can be rejected (ANOVA
sig.<0.05) butweshould conduct Posthoc test to identify which brand
contributes the mostshare. According to Tamhane test it’s a Louis Voitton
brand (differences are the largest).
Outcome: all luxury brands contributedifferent shareinto a total qty of
goods sold and Louis Voitton brand expels the others.
3. Multiple regression.
Initial data
Monthly costfor advertising in newspapers
Monthly costfor advertising on the radio
SEO optimization costs
Paper catalogues production cost
Total marketing expenditures
Tasks
Find out if above mentioned initiatives representthe major shareof overall
marketing expenditures, comprisea regression model, determine the major
influencer.
Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 ,858a
,736 ,619 2670,01769
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 179111192,822 4 44777798,206 6,281 ,011b
Residual 64160950,035 9 7128994,448
Total 243272142,857 13
14. Papercatalogues 14 14 14 14 14
As we can see fromthe tables above multicollinearity is not observed (correlation
coefficients between dependants are less than 0.7; tolerance more than 0.1, VIF
less than 10); population is normal distributed (according to the plot); outliers are
not identified (Cook’s distanceis less that 1, case wisediagnostics didn’t show any
outlier).
Outcome. All 4 predictors contribute significantly in overall marketing
expenditures (determination coefficient=0,736: that means that regression model
describes influence for 73.6%). Monthly costfor advertising in newspapers and
monthly costfor advertising on the radio influence 3 times larger than SEO
optimization cost on overallexpenditures.
Regression model: y=38177+0.686x1+0.297x2+1,188x3-0.211x4 where
Y1- Total marketing expenditures
X1- Monthly costfor advertising in newspapers
X2- Monthly costfor advertising on the radio
