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:

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Presenters Michael Beller

Alan Barnett

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

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

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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

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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

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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 metrics and analysis

• Oriented towards ad-hoc analysis of past performance

• Focused on dashboards and predefined reports

• Focused on interactive and investigative analysis by end users

• Primarily answers predefined questions

• Used to derive new insights and understanding

• Provides end users indirect raw data access through cubes, reports, and summarized data

• Explore the unknown and discover new patterns

• Exception based reporting

• Relies on low-level data to provide visibility to unexpected activity

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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

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BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE

Business Analytics provides end users tools and data to explore and develop broader and deeper business insight • What is business analytics? Continuous iterative exploration and investigation of past business performance to gain insight and drive business planning

“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]

• 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

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CHALLENGES FOR CURRENT BA ENVIRONMENTS

Organizations struggle to aggregate sufficient breadth and depth of data for thorough Business Analytics • 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 silos Point-of-sale (POS) transactions Websites Credit programs Loyalty programs Enterprise resource planning (ERP) Merchandise and financial plans Other, e.g., weather, competitor, etc.

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Detailed POS transaction data, EOD inventory data per SKU per store, and detailed pricing data are often limited One major retailer only maintains 1 month of POS data and 1 year of detailed inventory data online for ad-hoc analysis

“80% of companies use three or more business intelligence (BI) products” [1]

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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 Duration

• Oriented to IT

Complexity leads to fragile systems and long lead times for changes

Cumbersome for end users Puts IT in the middle

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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 to take a cross-functional, cross-product, crosscustomer approach” [5]

• 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

Point of Pain: “changing a merchandise hierarchy, for example, can create a near monumental challenge”

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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 investments in ERP and legacy systems

• Massively scalable data and processing clouds for data aggregation, storage, and analysis

• Economic environment driving low risk projects with quick payback

• SaaS and managed service offerings for low cost quick payback projects

• Existing data warehouse and reporting systems have limitations Cost Flexibility Data Quantity and Granularity

Minimal, if any, capital Fast implementation

• Next generation tools, portals, and visualization for data analysis and presentation

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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

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NEXT GENERATION BA VENDORS AND TOOLS

Why are companies adopting new SaaS BI solutions?

Source: BeyeNetwork Research Report – May 2009

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NEXT GENERATION BA VENDORS AND TOOLS

By one expert estimate, there are 2 new players entering the BI and BA market every week

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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 Allocation Pricing

Seas / Mon / Wk - Class Chain, Attribute Preplanned Assort/ LY / Trend Instinctive / Packages

Int. Product/Store/Assort Plus Attribute & Velocity Regional, History & Tests

Assortment Management

Localization Plan-o-gram Pricing

One or two Dimensions 1 per chain or per Sq Ft Regular or Mrkdwn - One fits all

Micro Merchandising Multiple: Cluster or store Adjust to local selling

Inventory Management

Replenishment Supply Chain

Excel, Key item, Package -limited rules Minimize time to shelf

Multi-Rule sets, Velocity, Other constraints

Marketing

Outreach Marketing Mix

Traditional CRM = R-F-M Anniversaries, Deals

Customer Driven & Profit Market Basket, Cross Shop

Store

Workforce Task Management Site Selection

Excel & Package Labor Scheduler Electronic tracking Demo/Psycho, Like store, Tenants, etc

Integrated Mkt, Merch, Act Integrated, Plan & Report Credit reports & other 3rd party data,

Financial

Budgeting Expense Management Loss Prevention

Limited Criteria & by Silo Monthly Package or manually Ad Hoc

Integrated, Dept & Criteria Real time, detail rich Real time, low cost option

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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 • 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

“retail is a dataintensive industry, and taking advantage of all that data to operate and manage the business better requires analytics” [5]

• 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

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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

• 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

“We selected Host Analytics for their costeffective 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

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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

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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 Deployed system to 375 REI and vendor employees

• Results

“PivotLink marries up all data in one place where people can get at it very, very easily”

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

“Looking at the data, we could see relationships we couldn’t see before. It was very empowering.”

Source: http://www.pivotlink.com/customers/REI

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CASE STUDY

Improved sales and promotional spending for discount retailer through deeper understanding of customer behaviors Environment and Solution

Results

• Discount retailer implemented 1010data to provide market basket insights to merchandising and promotional business areas

• Better understanding of detailed interactions between purchases and merchandising changes

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

• 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

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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

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CASE STUDY

Improved loyalty marketing and promotional spending for regional grocer through better understanding of customer Solution

Results

• Hosted service – no on premise hardware of software

• Analysis revealed that

• 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

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.

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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 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

more detail than we ever had in Excel, and it makes for a more useful plan”

Source: http://www.hostanalytics.com/Files/CaseStudies/HA_casestudy_spunk_v4.pdf

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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 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

“We didn't want a solution that built static data cubes from the data we loaded. The fact that PivotLink could do it on the fly was amazing”

Source: http://www.pivotlink.com/customers/car-toys

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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?

• 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

"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

Source: http://www.kognitio.com/casestudies/pdf/casestudy_tcg.pdf

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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

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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]

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QUESTIONS?

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MIKE BELLER ALAN BARNETT

[email protected] [email protected]

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.

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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%3a739121&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/

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