Competing on Analytics The New Science of Winning Tom Davenport University of Houston ISRC November 15, 2007

Thomas H. Davenport – Competing on Analytics 2 | 2007 © All Rights Reserved. The Planets Are Aligned for Analytics  Powerful IT  Data critical mass  Skills sufficiency  Business need

Thomas H. Davenport – Competing on Analytics 3 | 2007 © All Rights Reserved. What Are Analytics? Analytics What’s the best that can happen? What will happen next? What if these trends continue? Why is this happening? What actions are needed? Where exactly is the problem? How many, how often, where? What happened? Competitive Advantage Degree of Intelligence Reporting Decision Optimization Predictive Analytics Forecasting Statistical models Alerts Query/drill down Ad hoc reports Standard reports

Thomas H. Davenport – Competing on Analytics 4 | 2007 © All Rights Reserved. What Should Organizations Do with Analytics?  Using analytics is good  Finding the best customers, and charging them the right price  Minimizing inventory in supply chains  Allocating costs accurately and understanding how financial performance is driven  Competing on analytics is better  Making analytics and fact-based decisions a key element of strategy and competition

Thomas H. Davenport – Competing on Analytics 5 | 2007 © All Rights Reserved. What Is Analytical Competition About?  Dispassionate analysis  Data and statistics  Computers  Discipline and rigor  Passionate advocacy  Intuition  People  Creativity and insight

Thomas H. Davenport – Competing on Analytics 6 | 2007 © All Rights Reserved. Analytical Competitors Old Hands Polishing Their Edge  Marriott — Revenue management  Wal-Mart — Supply chain analytics  RBC — Cost and customer profitability  P&G — Supply chain  Progressive — Pricing risk

Thomas H. Davenport – Competing on Analytics 7 | 2007 © All Rights Reserved. Analytical Competitors Major Turnaround in Strategy or Culture  Harrah’s — Loyalty and service  Tesco — Loyalty and Internet groceries  MCI — Network pricing  Rogers / Nextel / Verizon Wireless / Cablecom — Customer relationship processes  A’s / Red Sox / Patriots / Rockets — Players for price

Thomas H. Davenport – Competing on Analytics 8 | 2007 © All Rights Reserved. Analytical Competitors Number-Crunchers from Birth  Capital One — “Information-based strategy”  Amazon — Supply chain, advertising, page changes  Yahoo — Pages as controlled experiments  Netflix — Movie preference algorithms

Thomas H. Davenport – Competing on Analytics 9 | 2007 © All Rights Reserved. Analytical Competitors Cut Across Industries Consumer Products Kraft Mars E&J Gallo Financial Services Bank of America Barclay’s Humana Government New York Police Dept. VA Hospitals Army Recruiting Industrial Products Deere Cemex Retail J.C. Penney Best Buy Transport / Travel and Entertainment FedEx Schneider Hilton

Thomas H. Davenport – Competing on Analytics 10 | 2007 © All Rights Reserved. Analytics in Professional Sports  Identify undervalued attributes  Develop new performance metrics  Know when a player is ready to move up  Use your own selection criteria  Assess the ability to work as part of a team  Understand risk better than your competitors  Determine who gets hurt and who gets tired  Who inspires others to play better?  Who drags down the team?

Thomas H. Davenport – Competing on Analytics 11 | 2007 © All Rights Reserved. The Analytical Delta PROGRESS PERFORMANCE PIECES

Thomas H. Davenport – Competing on Analytics 12 | 2007 © All Rights Reserved. STAGE 5: Analytical Competitors STAGE 4:Analytical Companies STAGE 3:Analytical Aspirations STAGE 2:Localized Analytics STAGE 1:Analytically Impaired The Analytical Performance Delta 11/32 firms 6/32 7/32 6/32 2/32 More analytical = higher performance PERFORMANCE

Thomas H. Davenport – Competing on Analytics 13 | 2007 © All Rights Reserved. 15% of top performers versus 3% of low performers indicated that analytical capabilities are a key element of their strategy. No analytical capability Minimal analytical capability Some analytical capability Above average analytical capability Analytic capability is a key element of strategy 12% 0% 33% 8% 27% 37% 19% 47% 9% 10% Source: Accenture Survey of 205/392 companies The Analytical Performance Delta (cont.)

Thomas H. Davenport – Competing on Analytics 14 | 2007 © All Rights Reserved. High Performers Use Analytics 65 % have significant decision-support/analytical capabilities 23% 36value analytical insights to a very large extent 8 77 have above average analytical capability within industry have BI/Data Warehouse modules installed make decisions based on data and analysis use analytics across their entire organization 23 HighLow Performers Top performers have a greater analytical orientation than low performers.

Thomas H. Davenport – Competing on Analytics 15 | 2007 © All Rights Reserved. How Analytical Competitors Make Money  Optimize a distinctive capability or external relationship  Customer relationships, supply chain, HR, R&D, etc.  Harrah’s, Marriott, Amazon, etc.  Understand and take action on the business better  MCI, Sara Lee Bakeries, RBC  Offer analytics to customers as the core offering  Apex Management Group in insurance risk management  Franklin Portfolio Associates in equity portfolio development  Offer analytics to customers to augment existing product or service  SmartSwing in golf clubs  Nielsen/IRI in retail/consumer products

Thomas H. Davenport – Competing on Analytics 16 | 2007 © All Rights Reserved. The Analytical Landscape Is Always Changing  Airlines—letting a business model become obsolete  Baseball teams—on-base percentage becomes over-valued  Capital One—other banks catch up, and they enter a new business

Thomas H. Davenport – Competing on Analytics 17 | 2007 © All Rights Reserved. The Analytical DELTA — Pieces PIECES Data breadth, integration, quality Enterprise approach to managing analytics Leadership passion and commitment Targets first deep, then broad Analysts..... professionals and amateurs

Thomas H. Davenport – Competing on Analytics 18 | 2007 © All Rights Reserved. Data  The prerequisite for everything analytical  Clean, common, integrated  Accessible in a warehouse  Measuring something new and important

Thomas H. Davenport – Competing on Analytics 19 | 2007 © All Rights Reserved. New Metrics / Data Wine Chemistry Run Production Driving Data

Thomas H. Davenport – Competing on Analytics 20 | 2007 © All Rights Reserved. Enterprise  If you’re competing on analytics, it doesn’t make sense to manage them locally  No fiefdoms of data  Avoiding the analytical equivalent of duct tape  Some level of centralized expertise for hard-core analytics  Firms may also need to upgrade hardware and infrastructure

Thomas H. Davenport – Competing on Analytics 21 | 2007 © All Rights Reserved. Enterprise-Wide Customer View Sales MarketingLogisticsService Internal Transaction Web Metrics External Geo-Demo External Attitudinal Types of Data Processes in Which Data Used

Thomas H. Davenport – Competing on Analytics 22 | 2007 © All Rights Reserved. Leadership  Gary Loveman at Harrah’s  “Do we think, or do we know?”  “Three ways to get fired”  Barry Beracha at Sara Lee  “In God we trust, all others bring data”  Jeff Bezos at Amazon  “We never throw away data” “Our CEO is a real data dog” Sara Lee executive

Thomas H. Davenport – Competing on Analytics 23 | 2007 © All Rights Reserved. The Great Divide Is your senior management team committed? Full steam ahead! Hire the people Build the systems Create the processes Prove the value! Run a pilot Measure the benefit Try to spread it

Thomas H. Davenport – Competing on Analytics 24 | 2007 © All Rights Reserved. Targets  With limited analytical resources, pick a major strategic target, with a minor or two  Harrah’s = Loyalty + Service  Patriots = Player selection + TFE  Barclay’s = Asset analysis + Credit cards  UPS = Operations + Customer data  Can also have two primary user group targets  Wal-Mart = Category managers + Suppliers  Owens & Minor = Logistics + Hospitals  Progressive = Actuaries + Customers

Thomas H. Davenport – Competing on Analytics 25 | 2007 © All Rights Reserved. Analysts 5-10% Analytical Professionals — Can create algorithms Analytical Semi-Professionals — Can use visual tools, create simple models Analytical Amateurs — Can use spreadsheets 15-20% 70-80%

Thomas H. Davenport – Competing on Analytics 26 | 2007 © All Rights Reserved. Taking Action Taking Action  Analytics need to be embedded into the machinery of organizational action  Operational decision-making  Business processes  Manager and employee behavior  Customer expectations

Thomas H. Davenport – Competing on Analytics 27 | 2007 © All Rights Reserved. The Analytical DELTA — Progress PROGRESS Success Factor Stage 1 Analytically Impaired Moving to: Stage 2 Localized Analytics Stage 3 Analytical Aspirations Stage 4 Analytical Companies Stage 5 Analytical Competitors Data Inconsistent, poor quality, poorly organized Data useable, but in functional or process silos Organization beginning to create centralized data repository Integrated, accurate, common data in central warehouse Relentless search for new data and metrics Enterprise n/aIslands of data, technology, and expertise Early stages of an enterprise-wide approach Key data, technology and analysts are central-ized or networked All key analytical resources centrally managed Leadership No awareness or interest Only at the function or process level Leaders beginning to recognize importance of analytics Leadership support for analytical competence Strong leadership passion for analytical competition Targets n/aMultiple disconnected targets that may not be strategically important Analytical efforts coalescing behind a small set of targets Analytical activity centered on a few key domains Analytics support the firm’s distinctive capability and strategy Analysts Few skills, and these attached to specific functions Isolated pockets of analysts with no communication Influx of analysts in key target areas Highly capable analysts in central or networked organization World-class professional analysts and attention to analytical amateurs

Thomas H. Davenport – Competing on Analytics 28 | 2007 © All Rights Reserved. Next Steps for Analytics  Continual pursuit of new data types  Real-time action  Content mining, intangibles analytics  Engineering multi-modal decision-making  Model management / analytical resource management / knowledge management

Thomas H. Davenport – Competing on Analytics 29 | 2007 © All Rights Reserved. It Doesn’t Happen Overnight — Start Now!  Takes a while to put data and infrastructure foundation in place, and even longer to develop human capabilities, a fact-based culture, and “success stories”  Barclay’s five-year plan for “Information-Based Customer Management”  UPS — “We’ve been collecting data for six or seven years, but it’s only become usable in the last two or three, with enough time and experience to validate conclusions based on data.”