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

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


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


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




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 provides end users tools and data to explore and develop broader and deeper business insight


“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


“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


Traditional data analysis and reporting tools are oriented to IT developers and difficult to modify at the speed of business


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


Current BI environments pose numerous challenges for Business Analytics and impact quality of business planning


“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


• 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 address current limitations and complement existing environments


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?



Source: BeyeNetwork Research Report – May 2009

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



Trends for “intelligent” analytics across the retail industry will benefit from next generation BA tools

TRENDS IN RETAIL ANALYTICS


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


“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


“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


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


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


• 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


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


• 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


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


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


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


• 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


Source: “Ordering Behavior in Retail Stores and Implications for Automated Replenishment” [6]

QUESTIONS?


THANK YOU!

MIKE BELLER [email protected]

ALAN BARNETT [email protected]

WWW.LIGHTSHIPPARTNERS.COM


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.





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_Difference_?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/

End Notes and References


http://tinyurl.com/lr4usk

http://www.infoworld.com/t/data-management/analytics-buzzword-needs-careful-definition-567













http://www.cio.com/article/18095/Business_Intelligence_Versus_Business_Analytics_What_s_the_Difference_?page=1

http://www.cio.com/article/18095/Business_Intelligence_Versus_Business_Analytics_What_s_the_Difference_?page=1

http://www.amrresearch.com/Content/View.aspx?compURI=tcm:7-39121&title=AMR+Research+Outlook:+The+New+BI+Landscape



http://papers.ssrn.com/abstract=1410095

http://www.b-eye-research.com/





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Next Generation Business and Retail Analytics Webinar