The webinar discusses challenges facing grocery retailers, emphasizing the importance of insight-driven analytics to enhance profit and revenue. Key strategies include optimizing assortment, pricing, and promotional efforts, alongside leveraging mobile technology and social media to adapt to consumer needs. The S Group, a key case study, highlights their operational successes and customer-centric approach, focusing on lower prices, high customer satisfaction, and effective supply chain management.

1. Ilkka Alarotu, Jim Hertel, Cyndy Renfrow,
S Group Grocery Trade Willard Bishop SAS Institute Inc.

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4. About Retail TouchPoints
 Launched in 2007
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5. Panelists
Ilkka Alarotu Jim Hertel Cyndy Renfrow
Director, Customer Managing Partner Senior Director, EMEA
Oriented Assortment Willard Bishop Retail/FMCG Practice
and Pricing - Food and SAS Institute Inc.
Daily Consumer Goods
S Group Grocery Trade
Moderator
Debbie Hauss
Editor-in-Chief
Retail TouchPoints

6. Unique Challenges Facing Grocery Retailers
 Commodity pricing pressures
 Decreased household wallet share due to fewer
shopping trips
 Increased competition from non-grocery retailers,
including deep discounters
 Empowered consumers
 Numerous in-store departments
 Fresh food management
 Mountains of SKUs and UPCs
 Need for a highly optimized workforce
 Siloed internal business processes

7. Insight-Driven Analytics Boost Grocery
Profits And Revenue
Insight-driven retailing can break down the barriers/silos and provide
transparency across the value chain
Supermarket retailers can expect to increase same-store sales 5% to10%
when becoming more shopper-centered
– Jim Hertel, Willard Bishop
Mobile Technology and Social Media will greatly impact the grocery
business and analytics will play a key role. “Location-based information …
represents the most important single feature of mobile.”
– Richard Hastings, Global Hunter Securities
Retailers must institute “outside-in” thinking in order to analyze customer
sentiment and deliver actionable insights

8. Insight-Driven Analytics Boost Grocery
Profits And Revenue
Smart retailers will respond with a focused
optimization strategy featuring 4 key components:
1. Assortment Optimization
2. Campaign Planning, Marketing and
Promotion Optimization
3. Pricing Optimization
4. Demand Forecasting

9. 10/12/11 SOK Päivittäistavarakaupan ketjuohjaus 9

10. 1. S Group in brief
2. Value Chain Optimization: Big Picture
3. Space Optimization
4. Price Optimization
5. Interactive social media services
6. Summary
10

11. 1. S Group in brief
2. Value Chain Optimization: Big Picture
3. Space Optimization
4. Price Optimization
5. Interactive social media services
6. Summary
11

12. – The S Group consists of 22 regional cooperatives and
the SOK Corporation, which is owned by the
cooperatives.
– S Group has operations throughout Finland, in the
Baltic Countries, and in St. Petersburg, Russia.
– Retail sales: EUR 12,267 million (+5,0 %)
– Profit before exceptional items: EUR 296 million
– Bonuses paid: EUR 333 million
– 1,636 outlets
– 39,646 employees
SOK Grocery Trade Chain Management 12

13. Grocery sales Service station stores Department stores
and fuel sales and speciality goods
Hotels and restaurants Cars and Agribusiness
automotive goods
Other S Group’s restaurants S Group’s car dealerships
13

14. “Higher quality at a lower price”
S Group leads the industry with consistently
lower prices, wide variety and high customer
satisfaction.
14

15. – Every Day Low Price (EDLP)
requires Every Day Low Costs (EDLC)
– Best demand-based assortment
always (continuously) available
– Easiest place to shop in all locations
15

16. 16

17. 17

18. 1. S Group in brief
2. Value Chain Optimization: Big Picture
3. Space Optimization
4. Price Optimization
5. Interactive social media services
6. Summary
18

19. 1. Forecast 2. Forecast 3. Forecast
•
• S Group total / • Store / Product / • Store / Product / Day
Product / Period Week
• 28 days before
Optimization
Customer-oriented Store
assortment planning Space/product Replenishment
Pricing optimization: In-store efficiency Labour planning
volume scenarios Logistics costs Logistics operations
Sourcing Display methods
Automated ordering
Delivery Pack size
Pricing
Vendors: Vendors:
Vendors:
Auctions ->Orders Labour plans
Logistics plans
Raw material Production drivers
Inventory levels
purchasing Logistics
Production plans
Capacity operations
4 – 8 months 2 months 21 days – 48 hours
19

20. Orders for vendors
2. Forecast
3. Forecast
• Drivers:
• Store / Product / • Store
specific Store / Product /
Week space allocation Day
• 28 days before
1. Forecast • 100% Shelf
availability
Optimization
• S Group total / Display methods Tentative Order
Product / Period Picking and delivery
efficiency pack sizes
In-store Store
(warehouse /
supplier) Recommended
merchandizing Replenishment
facings / System
Wastage product
level Replenishment
Inventory cycle Order
level at store (logistical costs)
Sourcing Decisions
Assortment
Decisions Space optimizer
Pricing Decisions
Store Specific Space in units /
Planograms Product / Store
4 – 8 months 2 months 21 days – 48 hours
20

21. 1. S Group in brief
2. Value Chain Optimization: Big Picture
3. Space Optimization
4. Price Optimization
5. Interactive social media services
6. Summary
21

22. Base drivers
1. Shelf availability (100 % availability)
2. Store specific sales forecast by product
3. Store specific space allocation by category
Business performance optimization
1. Efficiency in store activities
• Labour cost of shelving
2. Logistics efficiency
• Drop size
• Drop times
• Store replenishment cycles
• Warehouse picking
3. Vendor efficiency
• Order volume of delivery pack sizes
4. Total wastage in supply chain
Customer centricity = Best assortment – Easy to shop
Cost reduction in supply chain
22

23. Analyzing,Reporting
Store layout, total area
Total space utilization
Store layout incl. sections
- sales area
- auxiliary area
- concept development
Sales area
Macro level allocation
Allocation of sections to product categories - effectiveness of space
- development of allocation
Space optimization on store/category level recommendations
Display groups Micro space allocation
- effectiveness of space
allocation for products
Automated production of store specific planograms
- assortment optimization
Space optimization on store/product level
Automated replenishment
process
23

24. 40,000 store-based
planograms per
assortment period
24

25. 1. S Group in brief
2. Value Chain Optimization: Big Picture
3. Space Optimization
4. Price Optimization
5. Interactive social media services
6. Summary
25

26. Lower prices, better margin
1. More forecasting power: Price elasticity
calculations
– When product A price changes, what happens to the product A
volume
– When product A price changes, what happens to the product B
volume
2. More analytical power
– What is the best combination of prices:
– Brand leader price is fixed (competition). There also are rules for economic products.
What are optimum prices for other products than brand leader to get best margin
possible?
– How we could use customer owner information to improve our
pricing?
– Best customers: what do they buy and what they do not ?
26

27. Scenario -example
– Maximize Margin (%) such that
– Units sold is not sacrificed (Units sold ≥ Units sold LY)
– Competitor price rules are not violated
10/12/11 SOK Grocery trade chain management 27

28. 1. S Group in brief
2. Value Chain Optimization: Big Picture
3. Store Localization
4. Space Optimization
5. Price Optimization
6. Interactive social media services
7. Summary
28

29. Foodie.fm - Facebook for groceries
Personalized product and recipe recommendations
Answers to a simple question: What would I buy and eat today?
Unique server technology for personalized product and recipe
recommendations.
Empower people to consume smarter
Foodie tailors recommendations based on your preferences. Use
relevancy search to find products that fit your preferences and
taste profile.
Social grocery discovery
Social interaction influences consumer behavior. Connect family
to co-shop and share. Follow friends to discover new ideas and
recommendations.
29

30. Foodie.fm – Social shopping where ever you are
Mobile Facebook & Web SMS & Email
iPhone, Nokia & Android
Shopping
list:
CocaCola,
Arla
Cheese,
Milk
Widgets
Retailer & checkout
assortment link
30

31. 31

32. Benefits
– Social media speeds up fragmentation of consumer
needs: Personalization technology provides a state of
the art way to meet the unique needs of consumers.
– Get the shopper before they buy: Service enables very
transparent and interactive way of being part of everyday
life of consumers. Create link to checkout from social
discovery.
– Analytics: Ability to feed back analytics from social
shopping process.
32

33. 1. S Group in brief
2. Value Chain Optimization: Big Picture
3. Store Localization
4. Space Optimization
5. Price Optimization
6. Interactive social media services
7. Summary
33

34. Summary
– Best demand-based assortment and availability
every day, every hour, every store
– We are ambitious in developing world class
productivity and business analytics
– Value Chain Optimization connects all operations
together to get the maximum output
– Social media speeds up fragmentation of consumer
needs: we need a new level of personalized digital
and mobile services for our individual customers
34

35. Jim Hertel
Managing Partner
September 2011

36. There are Dramatic Differences Between “Best”
and “Worst” Front Page Items
Top 10
Bottom 10
Source: Willard Bishop, Chain 12-store transaction data, for 12 weeks beginning 12-31-2008 to 3-18-2009.
Shopper-centered merchants select Front-Page promotions based on Top Shopper appeal
Copyright © 2011. All Rights Reserved. 36

37. Good Loyalty Programs Invite Collaboration
External (Trading Partners) Internal
 Shopper insights  Shopper insights
 Data access  Behavioral
 Custom research  Attitudinal
 Shopper marketing  Shopper marketing: internally funded
 New vendor resources investment  Shopper-centered merchandising
 Marketing services foundation
 Efficient and effective demand
generation
 Gaining full value starts with placing the shopper at the center
 Retail trading partners will add to retailers’ investments, accelerating impact
Copyright © 2011. All Rights Reserved. 37

38. Over the Past Two Decades, Traditional Supermarket
Retailers Have Lost Significant Ground
Market Share by Segment
Source: Willard Bishop
Copyright © 2011. All Rights Reserved. 38

39. Accelerating Growth
through Innovation
SAS Grocery Case Studies
October 12, 2011
Company Confidential - For Internal Use Only
Copyright © 2009, SAS Institute Inc. All rights reserved. Copyright © 2010, SAS Institute Inc. All rights reserved.

40. Consumers are increasingly difficult to keep
up with…
 In Store
Catalogue
Web
Email Social
4500 212 3344
Loyalty
Program
Mobile
Call Centre Direct Mail
Credit
40
Copyright © 2010, SAS Institute Inc. All rights reserved.

41. How do we break our operational silos
How do we offer personalized Should we open alternate
down to better server our customers How do we improve
service to our customers? channels?
coming to us across the Omni-channel? profitability?
How do we reposition with Where do we open more
Why are we losing share of
deep discounters coming in to stores? Which format?
wallet to our competition?
our market?
How do we innovate and create a unique differentiation? Should we open alternate
channels?
How do we listen to our How do we continue to
customers to better understand maintain market dominance?
what they want next? How do we provide better value?
41
Copyright © 2010, SAS Institute Inc. All rights reserved.

42. Engineering the Solution with SAS
Grocers must successfully execute a combination of these five fundamental
processes to be competitive.
Localize assortments
Segment and engage shoppers
Add stores to markets Rationalize store space and
Target market
Close non-performers assortments
Optimize prices
Remodel to retain and attract Optimize Promotions
SAS Enablers
 Regular Price Optimization
Analytics and ESRI ArcGIS  Analytics  Category Management
 Promotions Optimization
 Customer Insights  Assortment Planning
 Marketing Automation
 Marketing Optimization  SKU Rationalization
 Promotions Optimization  Assortment Scaling
 Customer Sentiment Analysis  Space Management
SAS Forecasting – Predictive Analytics
42
Copyright © 2010, SAS Institute Inc. All rights reserved.

43. Creating Automated Continuous Dialogue
SAS Marketing Operations SAS Media Mix Analytics
Management
SAS Marketing Automation/Optimization
Customer Sentiment Analysis
Omni-Channel Marketing
Store level data
Inventory Data
Pricing Data
Offline Customer History
Content Server
SAS Real-time Decision Manager
Online Customer History
43
SAS for Customer Experience Analytics
Copyright © 2010, SAS Institute Inc. All rights reserved.

44. Dynamic Intelligence-Driven Personalization
Headline Driven by Search
Text Offer Driven by Data
MRM
Price Offer Driven by Data
Product Driven by Data
MA/MO
RDM
CXA
Real-time Dynamic Collection Technology
44
Copyright © 2010, SAS Institute Inc. All rights reserved.

45. Innovation…
45
Copyright © 2010, SAS Institute Inc. All rights reserved.

46. Applying SAS Innovation in Grocery
46
Copyright © 2010, SAS Institute Inc. All rights reserved.

47. Leveraging Customer Insight to Generate
Competitive Advantage
 Company
Large U.S. national grocer with over 1,500 stores and in
excess of $ 50 billion in sales.
 Opportunity
Create “competitive advantage that is better allowing the
company …. to target customers’ needs and wants” – Deborah
Weinswig, Citigroup Global Markets, August 10, 2008
 Results
 “The promotions in these (retailer) mailings have a 40%
redemption rate.…which compares to an industry average of
2%” – Deborah Weinswig, Citigroup Global Markets, August 10, 2008
 “Helped (retailer) boost sales by 5%” – Business Week,
September 22, 2008
47
Copyright © 2010, SAS Institute Inc. All rights reserved.

48. Gain Greater Customer Insight,
Loyalty and Spend with SAS
 Challenge
Large North American grocer was facing increased competition,
customer defection and eroding margins. They wanted to gain better
customer insight, which would drive better merchandising decisions
and offers, and increase share of customer’s wallet.
 Solution
Licensed SAS Retail Intelligence Solutions to better understand its
customer segments and buying patterns by analyzing all point-of-sale
data and customer data.
 Results
Using SAS, the retailer has gained greater customer insight to drive
better merchandising and promotion decisions. The projected ROI is a
3-5% increase in sales.
48
Copyright © 2010, SAS Institute Inc. All rights reserved.

49. Delivering Margin Performance
“Rising Prices are Here to Stay”
A long-standing regional supermarket chain doing
business in the U.S. under multiple banners has
always stood for real quality and value. Their
reputation stands not only on always delivering a
quality product, but on constantly striving to find new
ways to improve that quality
Business Issue Solutions Results
• Ability to deliver competitive  Leverage SAS to create a rules  Increased the total margin
pricing to customers while based regular price optimization performance of pilot categories by
protecting profit margins for the tool that considers (Retailer’s) 2.5 % to 8%
company. competition in creating price
elasticity curves. as developed by
Deloitte LLP.
49
Copyright © 2010, SAS Institute Inc. All rights reserved.

50. Improved Forecasting and Customer Insight
Lead to Precise Pricing
A U.S. regional retailer with more than 1,000
stores doing business across 13 banners.
Business Issue Solutions Results
 Increased competition and eroding SAS® Revenue Optimization  Better forecasting to inform
margins precise pricing
 SAS® Regular Price Optimization
 Growing number of customer  Establishment of comprehensive
choices made it difficult to retain  SAS® Promotion Optimization product and price relationships to
customers and increase loyalty SAS® Shopper Insights improve category management
 Increase responsiveness to  Multiple margin and market
market conditions strategies
 Maximize margins and market  Expected return 2-5% increase in
share margin
50
Copyright © 2010, SAS Institute Inc. All rights reserved.

51. Increased Store Comps while Reducing
Inventory Levels
A fortune 500 Retailer with more than 5,000
stores is one of the largest, fastest- growing
retailers in the United States. It offers both
quality brand name and private-label
products at value prices.
Business Issue Solutions Results
 Sustain rapid company growth SAS® Integrated Merchandise  Adjust Merchandise Assortments
Planning quickly to meet consumer demand
 Continue to know customers
 Integrated Merchandise Planning  Long term sustainable results
 Desire to improve customers’ Suite including Merchandise expected from new program /
shopping experience Financial Planning and Assortment platform
 Desire to add optimization and Planning
 Short-term results achieved
analytics SAS® Business Intelligence in this economy include:
 Desire to tailor assortments,  Includes ad-hoc and standard  Maintain IMU
pricing, store formats; move away reporting, built-in analytics and  Increase inventory turn
from “one size fits all” graphics  Increase GMROI
51
 SAS® Demand Forecasting
Copyright © 2010, SAS Institute Inc. All rights reserved.

52. Gaining Accurate Visibility into
Forecasted Demand
Supermarket chain in Europe has
established itself as a leader in quality
and choice, combining the convenience
of a supermarket with the service of a
specialist shop.
Challenge Solution Results
SAS® Demand Forecasting
 To utilize forecasting analytics to  Forecasting business processes  Improved margins with highly
view spatial performance. that account for demand accurate forecasts
differences across all stores,
 To create optimized assortments geographies and product lines  Automated exception based
tailored to market requirements forecast management reduces the
 Includes models for basic product need for manual inputs and
 To provide numerous lifecycle and seasonal products. forecast updates
 Create weekly forecasts/ updates  Forecast Performance reporting to
automatically with exception validate forecasting decisions
 Forecasts incorporate operational  Enable users to identify the right
updates (re-alignments, re- forecasting level for item/ locations
classifications, promotions, for specific operational need
introductions, etc.) 52
Copyright © 2010, SAS Institute Inc. All rights reserved.

53. Personalized Offers
Supermarket chain in Europe has
successfully targeted their marketing
campaigns to customer profiles
supporting tailored promotions.
Business Issue Solutions Results
 Need to Streamline process  SAS® Shopper Insights  Integrated Analytics
 Need to replace current CRM  SAS® Campaign Management  Highly Targeted, Personalized
system Offers
 High total cost of ownership  More Automated Campaigns
 Campaign management platform  Consistent process for all types of
not tightly integrated with customer campaigns
analytic models
 Streamlined process because of
integrated analytics reduced time
and effort and increased user
satisfaction
 Lower cost infrastructure
53
Copyright © 2010, SAS Institute Inc. All rights reserved.

54. Customer Insight Drives Customer Loyalty
and Greater Share of Wallet
Large North American grocery store
chain addresses eroding margins
and customer attrition with SAS
Analytics.
Business Issue Solution Results
 Increased competition and eroding SAS® Shopper Insights  Using SAS, the customer gains
margins. greater customer insight to drive
better merchandising decisions,
 Growing number of customer which will in turn increase
choices made it difficult to retain customer loyalty and spend.
customers and increase loyalty.
 The expected ROI is a 3-5%
 Want to gain better customer percent increase in customer
insight, which would drive better spend or revenue.
merchandising and marketing
decisions
 Drive offers to increase share of
customer’s wallet
 Need to prevent decline in
revenues and profit. 54
Copyright © 2010, SAS Institute Inc. All rights reserved.

55. Value of Predictive Analytics and Optimization
• Assortment Optimization: National U.S. general merchandise
chain
– 5% to 15% sales increase, margin up 2% to 3%
• Regular Price Optimization: Sales 1+% - 3%, margin +2% - 5%
• Assortment Optimization: European department store chain
– 5% sales increase via average basket growth
• Promotion Optimization: Sales + 1% - 12%, margin +5% - 20%
• Markdown Optimization: Sales + 0 - 5%, margin +6 – 10%
- Gartner (Former AMR Research)
“(Customer Insight) helped major grocer boost sales by 5%”
- Business Week
55
Copyright © 2010, SAS Institute Inc. All rights reserved.

56. Enabling The Infusion Of Deep Consumer
Understanding Into Planning Decisions
Action
Pricing Strategy
Brand
Assort-
Pricing ment
Tailored Assortments
Services Service
Insight
Communi
Promo -
cations Customized Interaction
Layout
Location & Space
Effective Promotions
Bricks & Direct Mail/
Web/Internet
Mortar Catalog
Customers Customers
Customers
Optimized Planograms
Cross Channel Customers
56
Copyright © 2010, SAS Institute Inc. All rights reserved.

57. Q&A Session
Your GoToWebinar Attendee Viewer is made of 2 parts:
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Viewer
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2.
Control
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Type
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58. Q&A | Panelists
Ilkka Alarotu Jim Hertel Cyndy Renfrow
Director, Customer Managing Partner Senior Director, EMEA
Oriented Assortment Willard Bishop Retail/FMCG Practice
and Pricing - Food and SAS Institute Inc.
Daily Consumer Goods
S Group Grocery Trade
Debbie Hauss
Editor-in-Chief
Retail TouchPoints

59. Thanks for attending!
You can download this
presentation & view the
webinar On-Demand here:
http://rtou.ch/optimize-shopper-insights

60. Contact the Panelists
Contact Ilkka at: Contact Jim at: Contact Cyndy at:
Ilkka.alarotu@sok.fi Jim.Hertel@willardbishop.com Cyndy.Renfrow@sas.com