Stas Sushkov is a market planning and store chain development expert with Pizza Hut International. He led analysis and strategic development for over 10,000 Pizza Hut restaurants globally. Some of his responsibilities included developing market planning standards and methodologies, implementing a store lifecycle management software, and developing penetration strategies for new Pizza Hut concepts. He has extensive experience conducting market planning for Pizza Hut in various countries around the world.

1. Stas Sushkov
Market Planning and Store Chain Development Expert
LinkedIn Profile

2. Global Market Planner
Yum! Brands. Pizza Hut Int.
(2014 - Present)
 Led the analysis and strategic development for Pizza Hut International
(10,000+ restaurants in 100+ countries).
 Developed market planning global standards and methodologies
 Implemented Store Lifecycle Management Software
 Penetration strategy development for the New PH Concept

3. Guiding Pizza Hut With Precise Market
Planning
UK
TURKEY
RUSSIA
CANADA
GERMANY
BRAZIL
JAPAN
S. AFRICA
INDONESIA
MEXICO
FRANCE
INDIA
COMPLETEDPROGRESSOPPORTUNITY
ADVANCEDMARKETPLANS
UK
TURKEY
RUSSIA
CANADA
GERMANY
BRAZIL
JAPAN
S. AFRICA
INDONESIA
MEXICO
FRANCE
INDIA…
Mapping Top Markets
 Map and analyze existing stores
 Analyze city demographics
 Analyze Competitor/ generators/ traffic
 Field Research - City trip and Street View
Maps
 Identify and map Hot Spots
 Identify and map potential Trade Zones
 Define Asset Portfolio
 Build Stores and Repeat
Market planning Process

4. Market Planning
Trips Taken
To research
regional cities’
potential and
identify proposed
Hot Spots and
Trade Zones
Picture
BRAZIL Market Planning Case study
Top Cities
Selected and
PrioritizedDesktop research
for city potential
has been
performed
100+ existing PH stores. Country potential 1000+ stores.
The goal is develop a market planning methodology for Top-20 cities
Hot Spots Mapped
in SLMS
Identified potential
HS were analyzed
and then mapped
in GIS
Potential TZ
Mapped in SLMS
Potential TZ have
been created
based on 8-min
drive-time
catchments, AB
class pop and HH
data
Picture
São Paulo (Metro)
Salvador
Brasília
Fortaleza
Belo Horizonte
Manaus
Curitiba
Recife
Porto Alegre

5. Taiwan Case study
Existing TZ Digitized
Restaurant
delivery maps
were scanned and
digitized for
upload to GIS
Picture
Uncovered High
Exist. TZ Density
Hard to find areas
for new potential
TZs
Picture
200 existing PH stores. Country potential ~350 stores.
The goal is to identify potential TZ to convince FZ to open more stores.
Store and TZ Data
Analyzed
High TZ size 30k
hh instead of 20k
Low Delivery
Trans > 28%
Low correlation
between TZ size
(hh) and sales
Picture
Existing TZ Size
Reduction Suggested
To give space for
new TZs and
stores
Penetration to tier
3 cities was
suggested as well
Picture

6. DATA TOOLS KNOWLEDGE
FUEL ENGINE PEOPLE
SOFTWARE LAUNCH PROJECT:
Cloud GIS/Store Lifecycle Management
System
GIS
Analysis
Project
Managem
ent
Asset
Data
Base

7. SLMS/GiS Implementation
12 Business Units. 2 Brands. Budget $1.5M+…
• W Europe
• CE Europe
• Africa
• Russia
• Turkey
• SOPAC
• Asia
• Thailand
• UK
• Canada
• LA&C
• MENA

8. • Analyzed existing stores in
selected markets
• Developed site and TZ selection
criteria
• Designed New Concept Tiering
strategy
New Asset Type Concept Launch

9. Yum! Restaurants Russia
Market Planner
(2010-2014)
 Witnessed and contributed to the tremendous success of Yum! Brands
in Russia.
 Drove net unit growth by development of solid strategic plans.
 Set-up and led the market planning and analytical process.
 Created Market plans, analyzed sites, submitted new store CAPEXes,
 Developed multiple analytical tools (sales forecasting, site scoring,
mall ranking and others)
 Trained and hand-overed the process to the right successor.

10. 14 19
39
70
114
2010 2011 2012 2013 2014
Yum! Russia New Store Builds
Contributed to Significant Growth of New
Units in Russia

11. Established and Led Market Mapping
Process in Russia
• 40+ market plans of cities
Russia and CIS countries
• Developed the methodology
• Set-up TZ and site criteria
• Trained the team how to
apply and how to update
market plan
• 250+ stores were opened in
identified TZ

12. Cloud GIS
Market Planner
RE managers in the office RE managers in the field
Leadership Team
Set-up Cloud GIS for the Development Team

13. Compiled CAPEXes and submitted
to the Investment Committee
Trade Zone Location
Trade Zone
Population Density
Generators and Competition Sales Estimations
Market Plan w Trade Zone Priority

14. Designed and Executed Restaurant Guest
Surveys (RTZ Studies)
TZ Guest Origin Map
Guests Trip Origin and Trip Destination
Guests Trip Origin and Trip Destination

15. Developed Store Sales Estimation Models
Carry-Out Orders Map Delivery Orders Map
New Site Sales Estimations
Based on RTZ results and delivery addresses analysis identified density of carry-out and
delivery order. Developed formula that estimates store transactions and sales.

16. Developed Traffic Counts and Capture Rate
Studies

17. Designed and Launched Site/Store
Scorecarding Process
71
Site/store assessment tool
• Mobile iOS-based GPS-supported
scorecard
• iForm Platform
• Custom variables and LOVs
• Site/Store Scoring Formula
• Sales Estimation Formula
• Auto sync and data upload

18. Developed the City Scoring methodology of the Russian cites.
Ranked, prioritized and estimated potential of 2,500 towns and cities.
Provided In-depth Analytics

19. Store Seating Optimization Analysis
113 170 255
74 111 166
62 94 140
0
50
100
150
200
250
Mon
Mon
Mon
Mon
Tue
Tue
Tue
Tue
Wed
Wed
Wed
Wed
Thu
Thu
Thu
Thu
Fri
Fri
Fri
Fri
Sat
Sat
Sat
Sun
Sun
Sun
Moscow Flagship Store
Transactions Flow During the
Week
2000
1500
1250
20min 30min 45min
Carry out 10%
Occupancy
rate
1,7 seats per person
Daily Transactions
Seating time
Trans per day 1950
Seats 299
Store Data
Provided In-depth Analytics

20. GIS Support to Local Store Marketing
Online and Mobile LSM Maps for Store Managers
MAP LINK
LSM Manual for New GMs

21. Geodemographics
 For research purposes designed and developed Geodemographic
Classification of US population based on Census 2000 data.
 Provided GIS and geomarketing analysis (customer profiling,
customer segmentation, retail trade area evaluation, site selection,
marketing campaign planning) using the classification results

22. Demographic and
GIS data selection
Variables selection
Variables
standardization
Clustering
(K-means)
Identification of
cluster numbers
Interpretation,
testing and
mapping of
clusters
Methodology of
USA Geo-Demographic Segmentation
See for More Details

23.  7 US Population Groups
 18 Sub-groups (Clusters)
Segmentation Outcomes

24. Identified Clusters Description
Cluster 5.1- Upscale Couples
“Upscale Couples” is a cluster of rich people with large
share of men and women between 45 and 65 who live in
suburban areas within the vicinity of large US cities.
Their incomes are two times higher than US average
Significant share of these
group members is self-
employed
Those who work prefer to use personal
vehicle to get to work, and majority of them
leave home between 8-10am.

25. Clusters Mapping