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

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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

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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

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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:

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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?

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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

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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

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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

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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

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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

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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

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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

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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

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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!