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 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

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

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

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

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

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

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

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

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

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

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

TRENDS IN RETAIL ANALYTICS Trends for “intelligent” analytics across the retail industry will benefit from next generation BA tools Area Analytical Process Yesterday / Today Trend for Tomorrow Merchandising Planning Seas / Mon / Wk - Class Chain, Attribute Int. Product/Store/Assort Allocation Preplanned Assort/ LY / Trend Plus Attribute & Velocity Pricing Instinctive / Packages Regional, History & Tests Assortment Localization One or two Dimensions Micro Merchandising Management Plan-o-gram 1 per chain or per Sq Ft Multiple: Cluster or store Pricing Regular or Mrkdwn - One fits all Adjust to local selling Inventory Replenishment Excel, Key item, Package -limited rules Multi-Rule sets, Velocity, Management Supply Chain Minimize time to shelf Other constraints Marketing Outreach Traditional CRM = R-F-M Customer Driven & Profit Marketing Mix Anniversaries, Deals Market Basket, Cross Shop Store Workforce Excel & Package Labor Scheduler Integrated Mkt, Merch, Act Task Management Electronic tracking Integrated, Plan & Report Site Selection Demo/Psycho, Like store, Tenants, etc Credit reports & other 3rd party data, Financial Budgeting Limited Criteria & by Silo Integrated, Dept & Criteria Expense Management Monthly Real time, detail rich Loss Prevention Package or manually Ad Hoc Real time, low cost option © 2009 LIGHTSHIP PARTNERS LLC 15

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 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 “retail is a data- • Performance Benchmark for Retail POS Data intensive industry, and taking • Improved loyalty marketing and promotional spending for regional grocer advantage of all that through better understanding of customer data to operate and • Improved budgeting, planning, and reporting at cookie and muffin manufacturer, manage the business distributor, and retailer by integrating data from spreadsheets better requires • Improved analysis and understanding across all functions for nationwide analytics” [5] 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 © 2009 LIGHTSHIP PARTNERS LLC 16

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

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 18

CASE STUDIES Advanced analytics solution dramatically reduces costs while increasing sales, profits, and in-stock rates for retailer • 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 “PivotLink marries up all data Deployed system to 375 REI and vendor employees in one place where people can get at it very, very easily” • Results Reduced costs for critical performance analytics “Looking at the 9% sales increase and 1.6% increase in profit data, we could see Improved in-stock rates, resulting in more satisfied customers relationships we Buying decisions based on what’s selling and what’s not couldn’t see before. Ability for business users to slice and dice data any way they need It was very Significantly improved communications with largest-volume suppliers empowering.” Source: http://www.pivotlink.com/customers/REI © 2009 LIGHTSHIP PARTNERS LLC 19

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 20

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

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 22

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 23

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 24

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 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 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 27

MIKE BELLER MBELLER@LIGHTSHIPPARTNERS.COM ALAN BARNETT ABARNETT@LIGHTSHIPPARTNERS.COM WWW.LIGHTSHIPPARTNERS.COM THANK YOU! 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. © 2009 LIGHTSHIP PARTNERS LLC 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 30

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