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Non-scale related competitivnes
Moscow, 4th of February 2011
Igor Maroša
2A.T. Kearney 43/01.2011/18733p
Regional retailing has a perspective in the next strategicperiod
1. Food retail market consolidation levels depend heavily on size of population and GDP per capita
2. Russian giants will slow down their growth, Russian market is becoming less interesting forglobal retailers
3. Key to maintain market position and profitability is to find competitive edge in non-scale related areas
4. Understanding the store, adapting assortment and pricing strategies are the key pillars to build local/regional competitive edge
5. Stores need to be understood from the perspective of consumers and competitors
6. Once understanding your stores, assortment can be adapted on store/cluster level
7. Smart pricing can help you be competitive and maintain margin
3A.T. Kearney 43/01.2011/18733p
Food retail market consolidation:
Food retail market consolidation levels depend heavily on size of population and GDP per capita
0
5
10
15
20
25
30
35
40
45
50
55
60
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90
United kingdom
United arab emirates
Ukraine
Turkey
Switzerland
SwedenSpain
Slovenia
Slovakia
Serbia
Russia
Romania
Poland
Norway
Netherlands
GDP/PPP per capita
Japan
Italy
India
Hungary
Greece
Germany
France
FinlandDenmark
Czech republic
Croatia
China
Canada
Bulgaria
Market conc.
United states of America
Brazil
Belgium
Mexico
Sources: Planet retail, A.T. Kearney, www.infoplease.com
Bubble size represents the size of the population
4A.T. Kearney 43/01.2011/18733p
Russian giants will slow down their growth, Russianmarket is becoming less interesting for global retailers
Russian retailers YOY projected selling space growth:
2007 2008 2009 2010
2 3 2 10
Russia ranking on GRDI(1):
(1) GRDI – Global retail development Index by A.T. KearneySource: VTB Capital, A.T. Kearney
Regional retailers have their window of opportunities open for the followingstrategic period
0%
5%
10%
15%
20%
25%
30%
35%
2010F 2012F2011F
31.7%
6.7%5.2%
10.5%9.5%
8.7%8.0%
22.7%
6.2%
16.2%
24.8%
11.0%
13.3%11.7%
17.7%
15.3%
7.4%
10.1%
12.0%
8.4%9.2%
2013F 2014F 2015F 2016F
DixyMagnit X5
Additional consolidation barriers:
• Country size
• Dispersed urban areas, low logisticssynergies
• Only 40% of modern trade formats
5A.T. Kearney 43/01.2011/18733p
Key to maintain market position and profitability is to find competitive edge in non-scale related areas
Opera-
tional
efficiency
Compe-
titive
edge
• Create alliances especially on private label
Sourcing LogisticsCategory
managementFormats and
MarketingRetail
ops/service
• Manage complexity
• Outsourcing vs. insourcingdecisions
• Manage complexity in assortment
• Manage inventory
• Adapt communication strategy to local/regional specifics
• Take the advantage of understanding local/regional labor market
• Use and promote local sources extensively
• Take advantage of understanding local habbits/tastes
• Service level vs. cost
• Use logistics as additional potential service
• Smart pricing
• Localized assortment
• Focused promotions
• Understand locations and adapt formats/types accordingly
• Stress local/regional characteristics
• Adapt service levels to local/regional habbits
• Add services with high value added (home delivery, pick&pay…)
Non-scale related focus areas of local and regional retailers:
6A.T. Kearney 43/01.2011/18733p
Understanding the store, adapting assortment and pricing strategies are the key pillars to build local/regional competitive edge
Pricing
Assortment
Understanding of stores
29,90Introduce the price elasticity concept to the assortment
Introduce store/cluster based assortmnet structures
Who are your competitors? Who are your customers?
7A.T. Kearney 43/01.2011/18733p
Store service area
Stores need to be understood from the perspective of consumers and competitors
Client store service area;primary (black) and secondary (red)
• Each store has a primary, secondary and sometimes even a tertiary service area defined
• Demographic data can be linked to service area
• Competitors can be clasified by different dimension:
– Formats (discount vs. SM vs. HM
– Distance (primary vs secondary vs. terciary
Ho
useh
old
co
mp
osit
ion
Average household income
Employed cluster model with constraints
1
2
3
Model segment; every client store will land in one of the segments. A store cluster is formed by multiple model segments
With kids
Without kids
Mixed
Low
Mediu
m
Hig
h
High
Low
8A.T. Kearney 43/01.2011/18733p
Once understanding your stores, assortment can be adapted on store/cluster level
“Clean” the assor
-tment
Build the assortmnet
ladder
Distribute right
SKU‟s to the right stores
9A.T. Kearney 43/01.2011/18733p
PAQ analysis reveals the item-level NSV and AGM performance within a category in order to be able to make “cleaning” decisions
Total Canned Meat and Fish
…-03-03-01 Tuna …-02-01-01 Meat Pate …-02-03-01 Fish Pate & Spreads
PAQ Analyses by SBS3 & Top SBS6 Categories(12.2008 – 11.2009, M, %-NPV & %-AGM )
20%
% of
NPV
Performing
% of
AGM
Acceptable
Questionable
50% 80%
14%
45%
77%
5 SKU„s
19 (1) SKU„s
49 (3) SKU„s
257 (227) SKU„s
% of
AGM
1
3
9 (2)
41(51)
30% 52% 81%
14%
38%
74%
P
A
Q
% of
NPV
% of
AGM 63(98)
21% 51% 80%
20%
50%
78%
P
AQ
% of
NPV
% of
AGM
1
4
4
19 (9)
30% 52% 81%
18%
57%
80%
P
A Q
% of
NPV
2
6
14 (2)
Example: Canned Meat & Fish
Active
Non-active (..)
Source: client data-warehouse, A.T. Kearney
10A.T. Kearney 43/01.2011/18733p
The assortment matrix helps to structure the category accross various dimensions
• Currently the deodorant assortment in market formats consists of 211 (active) SKU„s
• Within that range there are only four private label products
• There is a significant amount of non-active items (247) that were sold during the 12 month under consideration –overall those represent 12% of NPV
• The client generates most of it‘s revenues with low-to-mid priced products
• According to client data Spar tends to have a smaller assortment with comparable/ slightly higher prices in the deodorant category
1-0,4%-
[2 / 0,2%]
-
1-0,6%-
[-]
1
-
-
-
-
4-1,4%-
[1 / 0,0%]
1
-
-
-
-
-
-
NPV
Price
X - # of SKU‘s
% - Share in NPV
[..] - Inactive ass.
Y - # of Spar SKU‘s
71 (4) -14,8%-
[198 / 7,9%]
34
9-7,0%-
[1 / 0,7%]
6
2-2,6%-
[-]
1
1-1,9%-
[-]
1
16.549 32.847 49.145
4,41
2,38
3,40
82-19,9%-
[31 / 3,3%]
52
30-23,4%-
[-]
22
6-8,1%-
[-]
5
4-7,9%-
[-]
3
A.T. Kearney Assortment Matrix(12.2008 – 11.2009, €, M)
Source: client data-warehouse, A.T. Kearney
Example: Deodorants
5,42
1,37
250 65.443
Normalized Price
11A.T. Kearney 43/01.2011/18733p
The assortment scatter helps us to optimize the distribution of SKU‟s on store level
0
60
120
180
240
300
360
420
480# of stores sold
LN
NPV
Note: (1) Only active products consideredSource: client data warehouse, A.T. Kearney
Deodorant Scatter Plot (12/2008– 11/2009, MNE1))
AGM (%)[Ø-40,0%]
# of months sold
12
11
10
9
8
7
4
3
2
Example: Deodorants
12A.T. Kearney 43/01.2011/18733p
Smart pricing combines competitivness, perception andelasticity to optimize volume sales, value sales and margin
Pricing in retail
Competitvness – who am
I competing against, what is
the distance range
Perception – how do
my consumers perceive
my price position
Elasticity – how
sensitive are my
consumers towards
price in different
categories
13A.T. Kearney 43/01.2011/18733p
The cheapest retailer is… (% of consumers)
Price gap to Retailer A (% of average price difference)
0%
10%
20%
30%
40%
50%
60%
Retailer A Retailer B Retailer C
Price perception does not always coincide with actual price competitiveness
Source: A.T. Kearney example
2004 20092005 2006 2007 2008-10%
-8%
-6%
-4%
-2%
0%
2%
Retailer A Retailer B Retailer C
2004 20092005 2006 2007 2008
Price competitiveness vs. Price perception
Retailers are often neglecting other price perception elements: in-store positioning andpromotion management
Price competitiveness Index,2004–2009
Price perception,2004 – 2009
14A.T. Kearney 43/01.2011/18733p
Value Meaning
E = 0 Perfectly inelastic.
−1 < E < 0 Relatively inelastic.
E = −1 Unit (or unitary) elastic.
−∞ < E < −1 Relatively elastic.
E = −∞ Perfectly elastic.
Price Elasticity and its Impact on Revenues
Pri
ce
x
Price elasticity (elasticity of demand) is the measure of responsiveness in the product quantity demanded as a result of change in price of the same product. It is calculated as
Ed =% Change in quantity demanded
% Change in price
As a price of an
article in the elastic
range decreases,
revenue increases.
Example: E = -13,4
As a price of an article
in the inelastic range
decreases, revenue
decreases. Example:
E = -0,21
x
Sale
sP
rice
x
x
Sale
s
Price elasticity reflects how consumers react to a change in price of a single item
Source: A.T. Kearney, client
Price Elasticity Elasticity
Inelasticity
15A.T. Kearney 43/01.2011/18733p
Target price positioning is segmented according to item elasticity: KVI = Competitor B+ 1 %, Inelastic = Competitor B + 10%
1) Average of all articles in the elasticity rangeSource: A.T. Kearney example
Seasonal/Apparel
0
KVI Destination cat. Inelastic
Rank of items by volume
% g
ap
to
co
mp
eti
tio
n
-10
10
Quantity
sold
Today
FocusKVI price position
Inelastic price position
∆ AGM € ∆ NPV € ∆ AGM %∆ price KVI(1) # KVI
∆ price inel.(1) # Inel.
Balance1% above retailer B
10% above Retailer B
0.15% 1.43% -0.47% -2.5% 5,261 +3% 13,632
3. Target price position
• As retailer A has a larger market share and a worse cost structure, price-war should be avoided
• Price competitiveness should be improved on items, where consumers perceive the difference (KVIs)
• Inelastic items should compensate for the margin loss prices should be increased
Neutral margin Gain marginInvest margin
Target
16A.T. Kearney 43/01.2011/18733p
Regional retailers have a window of opportunity open,
Invest into areas with low level of modern trade formats
Operational excellence is a must
Analyze and understand your consumer
Localize assortment
Promote regionality
Keep price competitiveness with an eye on a margin
Thank you!