1. NEXT GENERATION
BUSINESS AND RETAIL ANALYTICS
TECHNOLOGIES AND TECHNIQUES
FOR
BUSINESS INTELLIGENCE & PERFORMANCE MANAGEMENT
WEBINAR PRESENTED ON JUNE 24, 2009
HOSTED BY:
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2. Presenters
Michael Beller Alan Barnett
10 years of retail and CPG 25 years of retail management
executive management experience with Steve and
COO Barry’s, Levitz Furniture,
Loehmann’s, Victoria’s Secret
CIO
Stores, and Barney’s New York
EVP of Strategy
Management Merchandising
15 years of management Planning
consulting experience helping Information Technology
clients with operations and IT Frequent speaker at retail
strategy, planning, and industry events on systems,
execution merchandising and planning
© 2009 LIGHTSHIP PARTNERS LLC 2

3. Learning Objectives
• Understand limitations of current Business Intelligence tools
• Discover how next generation tools for business and retail analytics can
supplement and enhance current BI environments
• Identify vendors and characteristics of next generation Business Analytics
tools
• Review industry trends for retail analytics that will benefit from next
generation BA tools
• Learn how companies are using next generation BA tools
© 2009 LIGHTSHIP PARTNERS LLC 3

4. Agenda
• Business analytics vs. business intelligence
• Challenges for current BA environments
IT Limitations
Business Impact
• Next generation BA vendors and tools
Business trends
Technology trends
• Trends in retail analytics
• Case Studies
• Questions and Answers
© 2009 LIGHTSHIP PARTNERS LLC 4

5. BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE
Business analytics is more than just traditional business
intelligence and reporting
Business Intelligence Business Analytics
• Oriented to standard and consistent • Oriented towards ad-hoc analysis of
metrics and analysis past performance
• Focused on dashboards and pre- • Focused on interactive and
defined reports investigative analysis by end users
• Primarily answers predefined • Used to derive new insights and
questions understanding
• Provides end users indirect raw • Explore the unknown and discover
data access through cubes, reports, new patterns
and summarized data
• Relies on low-level data to provide
• Exception based reporting visibility to unexpected activity
© 2009 LIGHTSHIP PARTNERS LLC 5

6. BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE
Part of routine daily, monthly, and
quarterly processes – not a sporadic or
exception based exercise
“Peel the onion” – answers to some questions generate
more questions – dive deeper and deeper into the data
Explore the unknown, search for new
patterns and new findings and new metrics
Investigate exceptions and
anomalies, research hypotheses
Gain broader and deeper
insight and understanding
into past performance
Stay focused on goal to improve
business planning and overall
business performance
© 2009 LIGHTSHIP PARTNERS LLC 6

7. BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE
Business Analytics provides end users tools and data to
explore and develop broader and deeper business insight
“there are $8B (yes, billion) of
• What is business analytics? internally developed analytic
applications with Excel as
Continuous iterative exploration and investigation
their front end. The BI players
of past business performance treat the output to Excel as a
to gain insight and drive business planning feature” [3]
• What impacts and drives business analytics?
The quantity and detail of critical business transaction and related data
combined with powerful and flexible data analysis tools
• How do you improve business analytics?
Use next generation technologies to lower data warehousing and IT infrastructure
costs,
Store larger amounts of historical data at granular levels of detail, and
Provide ad-hoc analysis and data mining without IT development efforts.
© 2009 LIGHTSHIP PARTNERS LLC 7

8. CHALLENGES FOR CURRENT BA ENVIRONMENTS
Organizations struggle to aggregate sufficient breadth and
depth of data for thorough Business Analytics
• Level of granularity Detailed POS transaction data,
EOD inventory data per SKU
Transaction data is summarized and per store, and detailed pricing
aggregated for analysis data are often limited
• Historical context
Technical constraints often lead to
less than optimal data retention One major retailer only
maintains 1 month of POS
• Consolidated view data and 1 year of detailed
Data warehouses often focus on inventory data online for
closely related systems, not enterprise ad-hoc analysis
views
Multiple disparate data silos
Point-of-sale (POS) transactions
Websites “80% of companies
Credit programs
Loyalty programs use three or more
Enterprise resource planning (ERP) business intelligence
Merchandise and financial plans (BI) products” [1]
Other, e.g., weather, competitor, etc.
© 2009 LIGHTSHIP PARTNERS LLC 8

9. CHALLENGES FOR CURRENT BA ENVIRONMENTS
Traditional data analysis and reporting tools are oriented to
IT developers and difficult to modify at the speed of business
• Complex tier of tools
ETL and EAI platforms
Data warehouses
Dashboards and reports
Ad-hoc analysis
• Costly
Capital
Effort Complexity leads to fragile
Duration systems and long lead times
for changes
• Oriented to IT
Cumbersome for end users
Puts IT in the middle
© 2009 LIGHTSHIP PARTNERS LLC 9

10. CHALLENGES FOR CURRENT BA ENVIRONMENTS
Current BI environments pose numerous challenges for
Business Analytics and impact quality of business planning
• Understanding of past performance
leads to quality of future planning “the only way to make a
difference with analytics is
• End users often develop cursory to take a cross-functional,
and summary level insight into cross-product, cross-
customer approach” [5]
business performance which leads
to sub optimal plans
• BI tools have multiple versions of
the truth Point of Pain:
“changing a merchandise hierarchy,
Uncertainty
for example, can create a near
Wasted effort monumental challenge”
© 2009 LIGHTSHIP PARTNERS LLC 10

11. NEXT GENERATION BA VENDORS AND TOOLS
The BA market is dynamic, rapidly expanding and poised for
high growth and adoption beyond early adopters
Business trends Technology trends
• Companies look to leverage • Massively scalable data and
investments in ERP and legacy processing clouds for data
systems aggregation, storage, and analysis
• Economic environment driving low • SaaS and managed service offerings
risk projects with quick payback for low cost quick payback projects
• Existing data warehouse and Minimal, if any, capital
reporting systems have limitations Fast implementation
Cost • Next generation tools, portals, and
Flexibility visualization for data analysis and
presentation
Data Quantity and Granularity
© 2009 LIGHTSHIP PARTNERS LLC 11

12. NEXT GENERATION BA VENDORS AND TOOLS
Next generation BA vendors and tools address current
limitations and complement existing environments
• Data granularity, history, and
consolidation
Columnar, in-memory, and other
database technologies require
minimal data modeling and can load
diverse and complex data, e.g. tlogs
and plans
• Technology cost, complexity, and end
user access
SaaS and managed service require
minimal initial cost
Cloud storage and processing enable
massive scalability at reasonable cost
SAP, Oracle, and IBM purchased three major BI vendors (Business Objects, Hyperion, and
Cognos) within months of one another – a clear sign of the importance of both BI and BA
© 2009 LIGHTSHIP PARTNERS LLC 12

13. NEXT GENERATION BA VENDORS AND TOOLS
Why are companies adopting new SaaS BI solutions?
Source: BeyeNetwork Research Report – May 2009
© 2009 LIGHTSHIP PARTNERS LLC 13

14. NEXT GENERATION BA VENDORS AND TOOLS
By one expert estimate, there are 2 new players entering the
BI and BA market every week
© 2009 LIGHTSHIP PARTNERS LLC 14

15. TRENDS IN RETAIL ANALYTICS
Trends for “intelligent” analytics across the retail industry
will benefit from next generation BA tools
Assortment Optimization
“retail is a data-intensive
Localization and Clustering industry, and taking
Pricing Optimization advantage of all that data
to operate and manage the
Supply Chain Analytics business better requires
Customer-Driven Marketing analytics” [5]
Marketing Mix Modeling
Multi-Channel Analytics and Data Integration
Loss Prevention - Fraud Detection and Prevention
Workforce Analytics
Activity (Task) Performance Management
Expense Management
Real Estate Optimization
Source: Realizing the Potential of Retail Analytics [5]
© 2009 LIGHTSHIP PARTNERS LLC 15

16. CASE STUDIES
Many retailers (and businesses in general) have deployed
next generation BA tools and achieved outstanding results
• Improved local control and performance management at regional building supply retailer
• Improved budgeting, planning, and reporting at cookie and muffin manufacturer, distributor, and retailer by
integrating data from spreadsheets
• Improved collaboration across multi-channel men’s apparel merchant by integrating data across multiple
channels
• Improved analysis and understanding across all functions for nationwide mobile entertainment and phone
retailer
• Performance Benchmark for Retail POS Data
• Improved labor and promotional planning across 155 UK pubs by consolidating data across systems
• Improved loyalty marketing and promotional spending for regional grocer through better understanding of
customer
• Improved sales and promotional spending for discount retailer through deeper understanding of customer
behaviors
• Improved margins and sales through real time price testing and optimization for specialty apparel retailer
• Improved alignment of workforce incentives and replenishment logic to improve profits costs for supermarket
© 2009 LIGHTSHIP PARTNERS LLC 16

17. CASE STUDIES
Improved local control and performance management at
regional building supply retailer
• Family owned regional building supply business with 87 stores
across 5 states and $450MM in sales
• Challenges
Accountability for performance at each retail store
Providing store managers with a tool they can use to view and analyze
monthly profit and loss numbers
Creating a corporate-wide scorecard to track performance against goals
“We selected Host
• Solution Analytics for their cost-
Provide store managers with access to budget vs. actual data in real-time effective software
via a browser-based “Excel look alike” which enables us to
Deliver a Web-based mechanism for each manager to track performance more accurately project
against goals
our revenue, and create
Perform top down and bottoms up budgeting dynamically
a new level of
• Benefits accountability at the
Decentralized organization now has a centralized repository for all retail store level”
budget and actual information
Rick Bell, Budget
The accountable store managers have increased their performance and
receive bonuses for improvements Manager
Source: http://www.hostanalytics.com/Files/Case%20Study%20-%20McCoys.pdf
© 2009 LIGHTSHIP PARTNERS LLC 17

18. CASE STUDIES
Improved budgeting and planning at cookie manufacturer,
distributor, & retailer by eliminating spreadsheets
• Nationwide manufacturer, distributor, and retailer of muffins and
cookies with 5 plants and 51 sales centers
• Challenge
Needed better consistency and completeness to planning and budgeting
Budget data existed in “hundreds of huge spreadsheets linked together”
Cumbersome to search through and, for traveling sales staff, “took a long time
to open on a remote connection”
Finance leadership strictly limited the number of users
Mass of dispersed, inconsistent data held in the many Excel spreadsheets “We have a lot
• Solution more detail than
SaaS budgeting, planning, and reporting system we ever had in
Web access for 125 users across 51 nationwide sales centers Excel, and it makes
for a more useful
• Benefits
plan”
Level of detail that plans and budgets now include
Analysts can go into much greater depth
Increased flexibility also enables coordination across functions
Source: http://www.hostanalytics.com/Files/CaseStudies/HA_casestudy_spunk_v4.pdf
© 2009 LIGHTSHIP PARTNERS LLC 18

19. CASE STUDIES
Improved collaboration across multi-channel men’s apparel
merchant by integrating data across multiple channels
• Men’s multi-channel apparel merchant with 600+ stores
• Challenge
Lacked real time visibility into the performance of
operational functions,
customer behavior,
product sales,
channel management, and
vendor relationships across 600 stores, catalog and Web channels
Poor operating and financial performance
Systems were antiquated; users unhappy with reporting
• Solution
SaaS solution implemented in 6 weeks
• Benefits
Oco reduced total reports from 153 to less than 20 drill down reports
All users now viewing same reports and talking same language
Improved margins 3.5% points
Source: http://www.oco-inc.com/pdf/cs-multichannel-retailer.pdf
© 2009 LIGHTSHIP PARTNERS LLC 19

20. CASE STUDIES
Improved analysis and understanding across all functions for
nationwide mobile entertainment and phone retailer
• Largest national independent retailer of mobile entertainment & wireless phones
• Challenge
“wanted to take sales data and flip it every which way and backward to drive the
business”
No satisfactory way to meet everyone's reporting needs
• Solution
Business intelligence solution from PivotLink
“We didn't want a
Deployed system to more than 125 sales, merchandising, and administrative
employees for daily use solution that built
• Results static data cubes
from the data we
Flexible analytics that meet the needs of all business users, including executives,
sales and regional managers, sales staff, and merchandising clerks loaded. The fact
Reports customizable by business users on the fly that PivotLink
No longer need for IT to develop time-consuming, custom SQL reports could do it on the
Integration of data from multiple systems, including GERS point-of-sale, Oracle fly was amazing”
financial, and ADP HR
Ability to do budget analysis, eliminating the need to invest in more Oracle licenses
Source: http://www.pivotlink.com/customers/car-toys
© 2009 LIGHTSHIP PARTNERS LLC 20

21. CASE STUDIES
Performance Benchmark for Retail POS Data
• The benchmark environment consisted of
23 billion “point of sale” (EPOS) transactions
24 million customer records and over 660,000 product records
Standard hardware and system software
• This represented 2 years of transactional data for the retailer
• Simple queries designed to make the database read every single record in the
database and examine it for a match for a given parameter
Read 2.3 billion records in 0.5 seconds and 23 billion records in less than 1 second
• Complex queries aimed at discovering the propensity of groups of customers to
buy products, e.g., “For the set of customers I am interested in, find who, in the
given period, bought one of the products I am interested in and then tell me what
else they bought in the same product category?”
Processed 2.3 billion records in 6 seconds and 23 billion records in 10 seconds
Source: http://www.kognitio.com/kognitio_library/downloads/cs_retailer.pdf
© 2009 LIGHTSHIP PARTNERS LLC 21

22. CASE STUDIES – RETAIL LABOR COST SAVINGS AND IMPROVED PROMOTIONS
Improved labor and promotional planning across 155 UK
pubs by consolidating data across systems
• Leading UK pub company with 155 pubs
• The Challenge
Leading UK pub company TCG wanted to improve understanding
and decision making related to 4 key questions
Are labor costs too high?
Are the promotions successful in driving profit?
Are they employing too many bar staff?
Have they got their food and drink mix right?
"By doing such a simple
• The Solution correlation as matching
sales data to staffing
Aggregate data from POS, inventory stock, general ledger,
budgets, forecasts, health and safety, and timesheets levels, we have already
realized significant cost
Use Kognitio to perform ad-hoc analytics and correlate savings. The return on our
performance data to understand costs and profits related to labor investment is
and promotions tremendous."
• The ROI Robert George, finance
director, TCG
Improved labor scheduling and promotions reducing costs and
increasing revenue
Source: http://www.kognitio.com/casestudies/pdf/casestudy_tcg.pdf
© 2009 LIGHTSHIP PARTNERS LLC 22

23. CASE STUDY
Improved loyalty marketing and promotional spending for
regional grocer through better understanding of customer
Solution Results
• Analysis revealed that
• Hosted service – no on premise
70% of sales is driven by 25% of their
hardware of software customers
Trip frequency, not basket size, sets the best
• Raw data logs transferred via FTP to shoppers apart
1010data • Better understanding led to comprehensive
shopper-centric marketing program:
• End users access data via web Target promotions to better customers –
resulting in dramatically more efficient
browser and existing tools to promotional spend. Identified cherry-picking
leverage current tools and minimize Focus new-customer acquisition efforts to
attract the best shoppers determined by
training analysis of demographic and behavioral
characteristics
Tailor shopping experience to best shoppers
by analyzing their categories shopped,
preferred brands, days/times shopped, etc.
© 2009 LIGHTSHIP PARTNERS LLC 23

24. CASE STUDY
Improved sales and promotional spending for discount
retailer through deeper understanding of customer behaviors
Environment and Solution Results
• Discount retailer implemented • Better understanding of detailed
1010data to provide market basket interactions between purchases and
merchandising changes
insights to merchandising and
promotional business areas • Better decision making led to 100% ROI in
first month through:
8,400 stores, $10+ billion in sales Assortments are now designed with
Years of POS data – 10 billion an understanding of which brands
maintain loyal followings and which
records are easily substituted
• Live in 5 weeks In-store product placement
encourages cross-purchasing
• Dynamic pre-built reports rolled out
Coupon limits and thresholds now
to 115 users in merchandising, achieve the desired effect while
marketing, supply chain and store reducing promotional expenses
operations Affinity analysis led to more effective
promotional spend
© 2009 LIGHTSHIP PARTNERS LLC 24

25. CASE STUDIES
Improved margins and sales through real time price testing
and optimization for specialty apparel retailer
• Specialty apparel retailer
• Price change testing
Daily reporting and analysis by product (dept/class/style) and store groups
Over 400 classes consisting of in excess of 1,000 style / coordinate groups
3 test groups mirrored by 3 control groups
• End result in the span of 6 weeks
Comp store sales trend changed from down 40% to even
Gross Margin improved from approximately 32% to 40% of sales
© 2009 LIGHTSHIP PARTNERS LLC 25

26. CASE STUDIES
Improved alignment of workforce incentives and
replenishment logic to improve profits costs for supermarket
• Large European supermarket chain
• Challenge
Store managers consistently overrode auto-replenishment system
Was something wrong with the auto-replenishment system?
Why were they deviating from the systemic recommendation?
Were store managers adding value, or should they accept system orders?
• Solution
Analyzed sample granular data from 5 stores which received replenishment orders 6
days/week
Examined daily style sales and 1.1MM replenishment orders at the item level for 52
weeks and store manager incentive criteria for approximately 26 sku’s
• Results
Determined
Incentive misaligned with Auto-Replenish system optimization criteria
Managers balanced labor costs, space, and segregated reorder pattern of best sellers
Developed regression models to assess performance with respect to workload balance
and inventory levels and apply on a door by door basis
Source: “Ordering Behavior in Retail Stores and Implications for Automated Replenishment” [6]
© 2009 LIGHTSHIP PARTNERS LLC 26

27. QUESTIONS?
© 2009 LIGHTSHIP PARTNERS LLC 27

28. MIKE BELLER MBELLER@LIGHTSHIPPARTNERS.COM
ALAN BARNETT ABARNETT@LIGHTSHIPPARTNERS.COM
WWW.LIGHTSHIPPARTNERS.COM
THANK YOU!
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© 2009 LIGHTSHIP PARTNERS LLC 28

29. End Notes and References
1. Kelly, Jeff. “Key considerations for business intelligence platform consolidation.”
searchdatamanagement.techtarget.com, February 17, 2009. http://tinyurl.com/lr4usk .
2. Kirk, Jeremy. “'Analytics' buzzword needs careful definition.” InfoWorld.com, February 7, 2006.
http://www.infoworld.com/t/data-management/analytics-buzzword-needs-careful-definition-567 .
3. Gnatovich, Rock. “Business Intelligence Versus Business Analytics--What's the Difference?” CIO.com,
February 27, 2006.
http://www.cio.com/article/18095/Business_Intelligence_Versus_Business_Analytics_What_s_the_Differenc
e_?page=1 .
4. Hagerty, John. “AMR Research Outlook: The New BI Landscape.” AMRresearch.com, December 19, 2008.
http://www.amrresearch.com/Content/View.aspx?compURI=tcm%3a7-
39121&title=AMR+Research+Outlook%3a+The+New+BI+Landscape.
5. Thomas H. Davenport. “Realizing the Potential of Retail Analytics.” Babson Working Knowledge Research
Center, June 2009.
6. van Donselaar, K.H.; Gaur, V.; van Woensel, T.; Broekmeulen, R. A. C. M.; Fransoo, J. C.; “Ordering Behavior in
Retail Stores and Implications for Automated Replenishment” Revised working paper dated May 12, 2009;
first version: January 31, 2006. http://papers.ssrn.com/abstract=1410095
7. Imhoff, Claudio, and Colin White. “Pay as You Go: SaaS Business Intelligence and Data Management,” May 20,
2009. http://www.b-eye-research.com/
© 2009 LIGHTSHIP PARTNERS LLC 29