Next Generation Business And Retail Analytics Webinar
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Transcript of Next Generation Business And Retail Analytics Webinar
NEXT GENERATION
BUSINESS AND RETAIL ANALYTICS
TECHNOLOGIES AND TECHNIQUES
FOR
BUSINESS INTELLIGENCE & PERFORMANCE MANAGEMENT
WEBINAR PRESENTED ON JUNE 24, 2009
HOSTED BY:
This work is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported License.To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/.
Presenters
Michael Beller
10 years of retail and CPG executive management
COO
CIO
EVP of Strategy Management
15 years of management consulting experience helping clients with operations and IT strategy, planning, and execution
Alan Barnett
25 years of retail management experience with Steve and Barry’s, Levitz Furniture, Loehmann’s, Victoria’s Secret Stores, and Barney’s New York
Merchandising
Planning
Information Technology
Frequent speaker at retail industry events on systems, merchandising and planning
© 2009 LIGHTSHIP PARTNERS LLC 2
• 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
Learning Objectives
• 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
Agenda
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Business analytics is more than just traditional business intelligence and reporting
Business Intelligence
• Oriented to standard and consistent metrics and analysis
• Focused on dashboards and pre-defined reports
• Primarily answers predefined questions
• Provides end users indirect raw data access through cubes, reports, and summarized data
• Exception based reporting
Business Analytics
• Oriented towards ad-hoc analysis of past performance
• Focused on interactive and investigative analysis by end users
• Used to derive new insights and understanding
• Explore the unknown and discover new patterns
• Relies on low-level data to provide visibility to unexpected activity
BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE
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BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE
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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
• What is business analytics?
Continuous iterative exploration and investigationof past business performance to gain insight and drive business planning
• What impacts and drives business analytics?
The quantity and detail of critical business transaction and related datacombined 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.
BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE
Business Analytics provides end users tools and data to explore and develop broader and deeper business insight
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“there are $8B (yes, billion) of internally developed analytic applications with Excel as their front end. The BI players treat the output to Excel as a feature” [3]
• Level of granularity
Transaction data is summarized and aggregated for analysis
• Historical context
Technical constraints often lead to less than optimal data retention
• Consolidated view
Data warehouses often focus on closely related systems, not enterprise views
Multiple disparate data silosPoint-of-sale (POS) transactionsWebsitesCredit programsLoyalty programsEnterprise resource planning (ERP)Merchandise and financial plansOther, e.g., weather, competitor, etc.
CHALLENGES FOR CURRENT BA ENVIRONMENTS
Organizations struggle to aggregate sufficient breadth and depth of data for thorough Business Analytics
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“80% of companies use three or more business intelligence (BI) products” [1]
One major retailer only maintains 1 month of POS data and 1 year of detailed inventory data online for ad-hoc analysis
Detailed POS transaction data, EOD inventory data per SKU per store, and detailed pricing data are often limited
• Complex tier of tools
ETL and EAI platforms
Data warehouses
Dashboards and reports
Ad-hoc analysis
• CostlyCapital
Effort
Duration
• Oriented to IT
Cumbersome for end users
Puts IT in the middle
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
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Complexity leads to fragile systems and long lead times for changes
• Understanding of past performance leads to quality of future planning
• End users often develop cursory and summary level insight into business performance which leads to sub optimal plans
• BI tools have multiple versions of the truth
Uncertainty
Wasted effort
CHALLENGES FOR CURRENT BA ENVIRONMENTS
Current BI environments pose numerous challenges for Business Analytics and impact quality of business planning
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“the only way to make a difference with analytics is to take a cross-functional, cross-product, cross-customer approach” [5]
Point of Pain:“changing a merchandise hierarchy, for example, can create a near monumental challenge”
The BA market is dynamic, rapidly expanding and poised for high growth and adoption beyond early adopters
Business trends
• Companies look to leverage investments in ERP and legacy systems
• Economic environment driving low risk projects with quick payback
• Existing data warehouse and reporting systems have limitations
Cost
Flexibility
Data Quantity and Granularity
Technology trends
• Massively scalable data and processing clouds for data aggregation, storage, and analysis
• SaaS and managed service offerings for low cost quick payback projects
Minimal, if any, capital
Fast implementation
• Next generation tools, portals, and visualization for data analysis and presentation
NEXT GENERATION BA VENDORS AND TOOLS
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• 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. tlogsand 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
NEXT GENERATION BA VENDORS AND TOOLS
Next generation BA vendors and tools address current limitations and complement existing environments
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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
Why are companies adopting new SaaS BI solutions?
NEXT GENERATION BA VENDORS AND TOOLS
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Source: BeyeNetwork Research Report – May 2009
By one expert estimate, there are 2 new players entering the BI and BA market every week
NEXT GENERATION BA VENDORS AND TOOLS
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Trends for “intelligent” analytics across the retail industry will benefit from next generation BA tools
TRENDS IN RETAIL ANALYTICS
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Area Analytical Process Yesterday / Today Trend for Tomorrow
Merchandising PlanningAllocationPricing
Seas / Mon / Wk - Class Chain, AttributePreplanned Assort/ LY / TrendInstinctive / Packages
Int. Product/Store/AssortPlus Attribute & VelocityRegional, History & Tests
Assortment Management
LocalizationPlan-o-gramPricing
One or two Dimensions1 per chain or per Sq FtRegular or Mrkdwn - One fits all
Micro MerchandisingMultiple: Cluster or storeAdjust to local selling
Inventory Management
ReplenishmentSupply Chain
Excel, Key item, Package -limited rulesMinimize time to shelf
Multi-Rule sets, Velocity, Other constraints
Marketing OutreachMarketing Mix
Traditional CRM = R-F-MAnniversaries, Deals
Customer Driven & ProfitMarket Basket, Cross Shop
Store WorkforceTask ManagementSite Selection
Excel & Package Labor SchedulerElectronic trackingDemo/Psycho, Like store, Tenants, etc
Integrated Mkt, Merch, ActIntegrated, Plan & ReportCredit reports & other 3rd party data,
Financial BudgetingExpense ManagementLoss Prevention
Limited Criteria & by SiloMonthlyPackage or manually Ad Hoc
Integrated, Dept & CriteriaReal time, detail richReal time, low cost option
• Improved local control and performance management at regional building supply retailer
• Improved collaboration across multi-channel men’s apparel merchant by integrating data across multiple channels
• Reduced costs while increasing sales, profits, and in-stock rates for high end outdoor adventure retailer
• Improved sales and promotional spending for discount retailer through deeper understanding of customer behaviors
• Performance Benchmark for Retail POS Data
• Improved loyalty marketing and promotional spending for regional grocer through better understanding of customer
• Improved budgeting, planning, and reporting at cookie and muffin manufacturer, distributor, and retailer by integrating data from spreadsheets
• Improved analysis and understanding across all functions for nationwide mobile entertainment and phone retailer
• Improved labor and promotional planning across 155 UK pubs by consolidating data across systems
• 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
CASE STUDIES
Many retailers (and businesses in general) have deployed next generation BA tools and achieved outstanding results
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“retail is a data-intensive industry, and taking advantage of all that data to operate and manage the business better requires analytics” [5]
• 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
• Solution
Provide store managers with access to budget vs. actual data in real-time via a browser-based “Excel look alike”
Deliver a Web-based mechanism for each manager to track performance against goals
Perform top down and bottoms up budgeting dynamically
• Benefits
Decentralized organization now has a centralized repository for all budget and actual information
The accountable store managers have increased their performance and receive bonuses for improvements
CASE STUDIES
Improved local control and performance management at regional building supply retailer
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“We selected Host Analytics for their cost-effective software which enables us to more accurately project our revenue, and create a new level of accountability at the retail store level” Rick Bell, Budget Manager
Source: http://www.hostanalytics.com/Files/Case%20Study%20-%20McCoys.pdf
• 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
CASE STUDIES
Improved collaboration across multi-channel men’s apparel merchant by integrating data across multiple channels
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Source: http://www.oco-inc.com/pdf/cs-multichannel-retailer.pdf
• National outdoor adventure retailer
• Challenge
Find a business intelligence solution
Enable employees and vendors to make more effective and profitable decisions
Have the ability to synthesize and drill into critical performance data
• Solution
Business intelligence solution from PivotLink
Deployed system to 375 REI and vendor employees
• Results
Reduced costs for critical performance analytics
9% sales increase and 1.6% increase in profit
Improved in-stock rates, resulting in more satisfied customers
Buying decisions based on what’s selling and what’s not
Ability for business users to slice and dice data any way they need
Significantly improved communications with largest-volume suppliers
CASE STUDIES
Advanced analytics solution dramatically reduces costs while increasing sales, profits, and in-stock rates for retailer
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Source: http://www.pivotlink.com/customers/REI
“PivotLink marries up all data in one place where people can get at it very, very easily”
“Looking at the data, we could see relationships we couldn’t see before. It was very empowering.”
Improved sales and promotional spending for discount retailer through deeper understanding of customer behaviors
Environment and Solution
• Discount retailer implemented 1010data to provide market basket insights to merchandising and promotional business areas
8,400 stores, $10+ billion in sales
Years of POS data – 10 billion records
• Live in 5 weeks
• Dynamic pre-built reports rolled out to 115 users in merchandising, marketing, supply chain and store operations
Results
• Better understanding of detailed interactions between purchases and merchandising changes
• Better decision making led to 100% ROI in first month through:
Assortments are now designed with an understanding of which brands maintain loyal followings and which are easily substituted
In-store product placement encourages cross-purchasing
Coupon limits and thresholds now achieve the desired effect while reducing promotional expenses
Affinity analysis led to more effective promotional spend
CASE STUDY
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• 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
CASE STUDIES
Performance Benchmark for Retail POS Data
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Source: http://www.kognitio.com/kognitio_library/downloads/cs_retailer.pdf
Improved loyalty marketing and promotional spending for regional grocer through better understanding of customer
Solution
• Hosted service – no on premise hardware of software
• Raw data logs transferred via FTP to 1010data
• End users access data via web browser and existing tools to leverage current tools and minimize training
Results
• Analysis revealed that
70% of sales is driven by 25% of their customers
Trip frequency, not basket size, sets the best shoppers apart
• Better understanding led to comprehensive shopper-centric marketing program:
Target promotions to better customers –resulting in dramatically more efficient promotional spend. Identified cherry-picking
Focus new-customer acquisition efforts to attract the best shoppers determined by analysis of demographic and behavioral characteristics
Tailor shopping experience to best shoppers by analyzing their categories shopped, preferred brands, days/times shopped, etc.
CASE STUDY
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• 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
• Solution
SaaS budgeting, planning, and reporting system
Web access for 125 users across 51 nationwide sales centers
• Benefits
Level of detail that plans and budgets now include
Analysts can go into much greater depth
Increased flexibility also enables coordination across functions
CASE STUDIES
Improved budgeting and planning at cookie manufacturer, distributor, & retailer by eliminating spreadsheets
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Source: http://www.hostanalytics.com/Files/CaseStudies/HA_casestudy_spunk_v4.pdf
“We have a lot more detail than we ever had in Excel, and it makes for a more useful plan”
• 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
Deployed system to more than 125 sales, merchandising, and administrative employees for daily use
• Results
Flexible analytics that meet the needs of all business users, including executives, sales and regional managers, sales staff, and merchandising clerks
Reports customizable by business users on the fly
No longer need for IT to develop time-consuming, custom SQL reports
Integration of data from multiple systems, including GERS point-of-sale, Oracle financial, and ADP HR
Ability to do budget analysis, eliminating the need to invest in more Oracle licenses
CASE STUDIES
Improved analysis and understanding across all functions for nationwide mobile entertainment and phone retailer
© 2009 LIGHTSHIP PARTNERS LLC 24
Source: http://www.pivotlink.com/customers/car-toys
“We didn't want a solution that built static data cubes from the data we loaded. The fact that PivotLinkcould do it on the fly was amazing”
• 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?
• The Solution
Aggregate data from POS, inventory stock, general ledger, budgets, forecasts, health and safety, and timesheets
Use Kognitio to perform ad-hoc analytics and correlate performance data to understand costs and profits related to labor and promotions
• The ROI
Improved labor scheduling and promotions reducing costs and increasing revenue
CASE STUDIES – RETAIL LABOR COST SAVINGS AND IMPROVED PROMOTIONS
Improved labor and promotional planning across 155 UK pubs by consolidating data across systems
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Source: http://www.kognitio.com/casestudies/pdf/casestudy_tcg.pdf
"By doing such a simple correlation as matching sales data to staffing levels, we have already realized significant cost savings. The return on our investment is tremendous." Robert George, finance director, TCG
• 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
CASE STUDIES
Improved margins and sales through real time price testing and optimization for specialty apparel retailer
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• Large European supermarket chain
• Challenge
Store managers consistently overrode auto-replenishment systemWas 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
DeterminedIncentive misaligned with Auto-Replenish system optimization criteriaManagers 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
CASE STUDIES
Improved alignment of workforce incentives and replenishment logic to improve profits costs for supermarket
© 2009 LIGHTSHIP PARTNERS LLC 27
Source: “Ordering Behavior in Retail Stores and Implications for Automated Replenishment” [6]
QUESTIONS?
© 2009 LIGHTSHIP PARTNERS LLC 28
THANK YOU!
MIKE BELLER [email protected]
ALAN BARNETT [email protected]
WWW.LIGHTSHIPPARTNERS.COM
© 2009 LIGHTSHIP PARTNERS LLC 29
This work is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported License.To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/.
Lightship Partners LLC, Lightship Partners LLC (stylized), Lightship Partners LLC Compass Rose are trademarks or service marks of Lightship Partners LLC in the U.S. and other countries. Any other unmarked trademarks contained herein are the property of their respective owners. All rights reserved.
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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/
End Notes and References
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