Retail Tracking
Objective and Applications

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Objective
- Provide an accurate assessment of the
performance of products in terms of sales (volume
and value) and availability (distribution)
Applications
- Monitor brand health and performance
- Monitor competition
- Understand market opportunities and threats
- Develop market strategies
- Set goals and targets
- Evaluate sales, distribution and business
performance
- Evaluate performance of individuals - business
leaders, marketing and sales personnel (?)
Market Size / Share analysis - key input for market strategy
Chart analysing firm’s growth across region
MAT
Growing faster than
category, gaining
market share
KR
30
CN
Market $
25
Firm
($)
ID Growing slower than
category, losing
market share.
Firm’s Value Growth
%
20
15
HK
10
VN
JP
MY
5
TH
Market Value Growth%
0
(5)
0
SG
(5)
TW
5
10
15
20
25
30
Variety of approaches to Retail Tracking
Continuous measurement
of sales
…
Consumer Panel measures
purchases brought home by
the buyers
Retail Index measures
the pipeline here
Richer data
that yields
Insights
Most efficient and accurate
method of measuring and
monitoring market
movements
Data Pooling
Low cost but
unreliable
Homes
Retail
Outlets
Shoppers
…
Manufacturers
Width and depth … basic components of sales
Distribution
Penetration
% of Stores
% of homes
buying
Retail
Index
Consumer
Panel
Rate Of Sale
Sales per Store
Volume per buyer
Sales
Sales
No of stores distributing x Sales per store
No of homes buying x Volume per buyer
Retail Measurement
Arthur C. Nielsen
(1897-1980)
Retail Measurement Services
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Nielsen, IRI, intage, Aztec
FMCG products
GfK
Consumer Durables – Brown & White goods
Pharmaceutical – Medicines / Tonics
IMS
Vision Care
Pet foods – pet shops
Chinese Tonics – CMHs
Writing instruments
Computers and Peripherals
Mobile phones / Telecom products
Hardware and Sanitary ware
How it works?
Retail Measurement Services
a Six Steps process
Design & Development of service
1. Define Universe
2. Undertake Census
3. Establish Sample
Service Cycle … repeated every period
4. Collect Data
5. Process / Validate Data
6. Analyse, Interpret, Act
1. Defining the Universe
TOTAL
REGIONS
Upper Trade
Supermarkets
Metros
Other
Lower Trade
Convenience
Metros
Other
Medical
Provision
Stores
Urban
Misc.
Stores
Rural
Retail Universe in China
Villages??

4 Key City (SH/BJ/GZ/CD)
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23 A City
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254 B City
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343 C City
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1,327 D City
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18,787 Towns
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16,621 Townships
Universe in Singapore
… a precise definition of stores to be covered
Supermarkets
Scan
Medical
Scan/Audit
NTUC FairPrice
Scan
Watson’s
Scan
Liberty
Scan
Guardian (DF)
Scan
Cold Storage
Scan
Unity
Scan
Market Place, Jasons
Scan
National Health Care
Scan
Shop & Save
Scan
Chinese Med Halls
Audit
Carrefour
Scan
Giant
Scan
Mini-Markets
Audit
Sheng Siong
Scan
Econ
Independents
Audit
Audit
C-Stores
Scan
7 Eleven
Scan
Cheers
Scan
Esso, Mobil
Scan
Shell
Scan
Provision
Coffee Shops
Hawker Stalls
NITE Entertainment
Sundry Kiosks
Audit
Audit
Audit
Audit
Audit
BP / SPC
Caltex
Scan
Scan
Not Covered  Not in Universe
Coverage Gap
Hotels, bars,
schools,
exports, etc...
Coverage Gap
due to:
1. Difference in
Sales area and
measurement
Universe
2. Pick-up
Client
Primary Sales = 2,000,000
Agency
Sales Estimate = 1,800,000
2. Census
Retail Census - Objectives

Identify different outlet types and quantify
number of outlets in a Universe by type
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Provide key statistics (selection criteria) for
setting up a representative panel / sample
China 2009: Modern trade accounts for barely 3.5% of outlets yet contributes
to 64% of sales.
2009: 3.289 Million Stores (Urban – Cities and Towns)
FMCG Outlet (%)
FMCG Sales (% value)
Modern
3.5%
Traditional
35.6%
Traditional
96.5%
Modern
64.4%
China: Modern trade boom in 2004 and 2005
51807
+14%
National Towns and Cities
(73,907 stores)
45343
+20%
37925
+3%
+2%
13789
13117 13339
+13%
1189
1346
+12%
+22%
+27%
6799
5577
4399
1512
Hypermarkets
Supermarkets
2004
Minimarkets
2005
2006
CVS
China
China: 2008-2009: Modern trade continues to grow, but at a
slower pace than the mid 2000s when it grew by 16%
4%
84363
80728
National Towns and Cities
(115,415 stores + 8.5%)
10%
27%
10%
9224
14484
11591
17353
1912 2108
Hypermarkets
Supermarkets
2008
2009
Minimarkets
CVS
China
Retail Census – Store Details Collected
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Name
Address (incl. directions)
Telephone number
Shop Type
Presence of Air-conditioning
Refrigeration Facilities
Number of hours open per day
Monthly Turnover
Sales Mix : floor space/turnover
1. Define Universe
2. Undertake Census
3. Establish Sample
More dots
Less dots
Setting Up the Sample
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Retail Index Samples are:
- Stratified Random Samples
- Disproportionate
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Stratified Random Samples provide greater accuracy
than a Simple Random Sample of the same size
Disproportionate Samples ensure that the key trade
sectors affecting the total market are given greater
importance to being correct
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Determining the Sample Size
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The precision (Sampling Error) of our estimates
depends on : - The variation in the population.
- The sample design.
- The size of the sample
(Note : universe size is not mentioned).
Bias is prevented by : - A randomised selection process.
- Eradication of systematic errors.
How good is the data?
4. Collect the data
Data Collection in Manual Audit
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Auditors visit each sample store every
month
Variations in time between visits is adjusted
to a 31 day cycle
Information collected from the store :
1. Stocks in the outlet (inside the shop &
storeroom)
2. Retail selling price from the tags
3. Purchases made by the outlet since last visit
(31 days)
Monthly Audit cycle
Assuming Jan is initial audit for a new category
Month
Feb
Mar
Apr
- 31 days -
- 28 days -
- 31 days -
Jan
First
Route
Opening
Stock for Feb
Second
Stocks, Purchases
Feb - Closing Stock
Mar - Opening Stock
……….
Third
Stocks
Purchases
Fourth
Stocks
Purchases
Scan Data
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Sales data obtained directly from the POS
terminals.
Usually weekly data
Cycle should ideally coincide with the chain’s
promotional week
Provides volume / value sales and price
Does not provide stock / out-of-stock /
distribution or purchase data.
1. Define Universe
2. Undertake Census
3. Establish Sample
4. Collect Data
5. Process / Expand Data
How we derive Sales
Purchases
(Invoices)
Stocks
Consumer Purchases
Consumers
Computing Sales
The “Audit Principle”
Opening Stock
Less
Closing Stock
Purchases
Credit Returns and Transfers
Equals
Consumer Purchases
Less
Plus
Computing Sales for March
25th Feb Audit
Stocks = 500
(opening stock for March)
(closing stock for Feb)
25th Mar Audit
Stocks = 700
(closing stock for March)
Purchases = 1200
Sales (25th Feb to 25th Mar) = Opening Stock – Closing Stock + Purchases
= 500 – 700 + 1200 = 1000
Process / Expand the data
Simplified example - Numeric Estimation
Sample
Universe
Sales in
sample
Estimated
Sales
Supermarket
100
1000 (*10)
528
5,280
Provision
120
2400 (*20)
50
1,000
Mini-Market
200
800 (*4)
400
1,600
TOTAL
420
4200
7,880
1. Define Universe
2. Undertake Census
3. Establish Sample
4. Collect Data
5. Process / Expand Data
6. Analyse, Interpret, Act
Basic Facts
The data is essentially
3-Dimensional
Product
Market
Time
… Data Types
Product
Segment
Manufacturer
Brand
SKU (item level)
Market
National
Regional
Trade Sectors
Account Specific
Time
Annual (MAT)
Monthly
Weekly(Scan services)
Data Types
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Volume Sales and Shares
Value Sales and Shares
Retailer Purchases
Retail Stock
Forward Stock
Price
Distribution - In-Stock,
Out of Stock,
Purchase
(shown as Numeric, Weighted)
Retail Index - Reported Data Types
Sales Volume
The sales volume for the audit period.
Sales Value
The sales value for the audit period.
Year-to-date (YTD) Sales Volume (Value)
The sum of the total sales volume (value) from the beginning of year
to the present audit period.
Moving Annual Total (MAT) Sales Volume (Value)
The sum of the total sales volume (value) for the past 12 months …
ending with the current audit period.
Retail Index - Reported Data Types
Average Price
Average Price is calculated as follows:
Total Sales Value Divided Total Sales Volume
Retailer Purchases
The volume of purchase made by retailers in the audit
period. Returns to wholesalers and wholesale sales
are offset from this figure.
Retailer purchases not reported in Scan Channels
Retail Index - Reported Data Types
Stock
Includes all retailer stock whether on display, on the shop
floor space or stock room.
Forward Stock
Stock in the store’s selling area which can easily be
accessed by customers. For example stock placed inside
chillers, freezers, cabinets or on shelf.
Stock Cover (Stock cover Days)
Number of days the current stock cover would last
assuming that sales continues at the same rate.
Stocks, Forward Stocks & Stock Cover not reported in Scan
Channels
Stock Cover
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Forward Stock = 100 units
Backroom stock = 200 units
Sales = 600 units per month
How many days will stock last?
Stock Cover Days = Total Stock / Sales rate
Total Stock = Forward + Back = 100+200 = 300
Sales Rate = Sales per day = 600 /30 = 20
Stock Cover Days = (100+200)/(600/30)
300/20 = 15 days
Numeric Distribution
Numeric % of stores handling product
Universe: 10 Stores
Distribution
Numeric distribution = 60%
Weighted Distribution
Share % of category sales by handlers
Universe: 3 Store, $50,000 category sales
1
Stores
2
3
Distribution
Category sales
$20,000
$10,000
$20,000
Product is handled by stores accounting for 80% of Product
Category sales.
Weighted Distribution = 80%
Numeric & Weighted Distribution
Store
% of Sales
Brand 1
Brand 2
Brand 3
A
40%
B
30%
C
20%
D
10%
Numeric Distribution
75%
50%
50%
Weighted Distribution
60%
70%
30%
Comparison of Weighted and Numeric distribution provides an
understanding of the quality of distribution
Stock Out and Loss of Distribution
Stocked ...
In stock Distribution
Period
1
2
3
4
Either sales or purchases, but no stocks …
Out of Stock Distribution
Purchase
Closing Stock
Distribution
Stock Out
Yes
Yes
Yes
No
No
No
Yes
-
No sales, no purchases, no stocks …
No Distribution
Stock Out and Loss of Distribution
Period
1
2
3
4
Purchase
Closing Stock
Distribution
Stock Out
Yes
Yes
Yes
?
No
No
Yes
?
Now what …
Is it still loss of distribution??
What Benefits?
Benefits – Market Understanding

Market Structure
- Size, Growth, Segments, Opportunities and threats.
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Channel performance
- Channel development and the impact on your channel strategies.
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Brand Health
- Your brand Growth, Share, Distribution, Price.

Competition
- The key players, their activities, Strengths & Weaknesses.
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Sales Evaluation
- Distribution, Stocks, Stock Cover, Stockouts…
These are fundamental to formulating Marketing
Strategies and Sales Plans
Coverage
Theoretical background
Why suppliers’ shipments differ ...
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Different Universe
Pipeline
- Lag between shipment and consumer purchase
- Irregular shipment patterns
- Investment buying
- Promotions
- Changes in Warehouse inventory or pipeline
- Product life cycle
Parallels … as well as exports
Inaccuracies in measurement such as when the product’s
distribution is low
Different Universe: Coverage is usually less than 100%, since the Total
Shipment from the factory can reach Markets not covered by research agency
“Real World”
Department
Stores
Cash & Carry(Makro)
Agency’s Universe
Army
Hypermarkets
Schools
Supermarkets
Construction
Sites
Hospitals
/ Pharmacies
Convenience Stores
Transient Hawkers
Mini-markets
Excluded
Geographic
Areas
Provision Stores
Exports
“Measured World”
Coffee Shops/Rest
Permanent Hawkers
Fresh and Weekend
Markets
Direct
Selling
Specialised
Shops
Hotels/Cafe
Pipeline effect
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Pipeline is directly affected by distribution methods
- Direct Delivery reduces Pipeline delay,
- Elaborate structure of Wholesalers, Agents, Distributors
and Concessionaires lengthens the Pipeline effect.
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Geography, size of country affects pipeline
Pipeline is also affected by product
re-purchasing rate
- Products with “short shelf life” tend to have shorter
pipeline than “long life” ones.
?
Universe, Pipeline, Coverage
Imports
Exports
Retailer’s
Warehouse
Shipments
Retailer
Covered
Sales
Retailer
Factory
Cash Van
Retailer
Wholesaler
Retailer
Warehouse Club
/ Cash & Carry
Hotels, Bars....
Street Hawkers
Consumer
Conflict Resolution
Examples: Kraft changes distribution network
Example: Expected Coverage = 80%
“Real World”
100%
Cash & Carry(Makro)
80%
Department
Stores
Agency’s Universe
Army
Hypermarkets
Schools
Supermarkets
Construction
Sites
Hospitals
/ Pharmacies
Convenience Stores
Transient Hawkers
Mini-markets
Excluded
Geographic
Areas
Provision Stores
Exports
80%: Based on
supplier’s records /
judgement
“Measured World”
Coffee Shops/Rest
Permanent Hawkers
Fresh and Weekend
Markets (Wet)
Direct
Selling
Specialised
Shops
Hotels/Cafe
Coverage and Pick-Up
“Real World”
(All outlets)
Agency’s Universe
100%
% of shipments to
Universe picked up by
measurement service
72%
80%: Based on
supplier’s records /
judgement
“Measured World”
80%
Pick-Up
= 72/80
= 90%
Coverage - Summary
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Expected Coverage is the proportion of total
shipments that are delivered into the
measured universe. (80%)
Coverage is the measured purchase volume
as a proportion of total shipments. (72%)
Pick Up is measured purchase volume as a
proportion of expected coverage. (90%)