Competing on Analytics The New Science of Winning

1. Competing on AnalyticsThe New Science of Winning Tom Davenport University of Houston ISRC November 15, 2007

2. The Planets Are Aligned for Analytics • Powerful IT • Data critical mass • Skills sufficiency • Business need

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

4. 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

5. Dispassionate analysis Data and statistics Computers Discipline and rigor What Is Analytical Competition About? • Passionate advocacy • Intuition • People • Creativity and insight

6. 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

7. 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

8. 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

9. 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

10. 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?

11. The Analytical Delta PIECES PERFORMANCE PROGRESS

12. The Analytical Performance Delta STAGE 5: Analytical Competitors STAGE 4: Analytical Companies STAGE 3: Analytical Aspirations STAGE 2: Localized Analytics STAGE 1: Analytically Impaired 11/32 firms PERFORMANCE More analytical = higher performance 6/32 7/32 6/32 2/32

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

14. High Performers Use Analytics 65 % have significant decision-support/analytical capabilities 23% 36 value analytical insights to a very large extent 8 77 have above average analytical capability within industry 33 77 have BI/Data Warehouse modules installed 62 73 make decisions based on data and analysis 51 40 use analytics across their entire organization 23 Top performers have a greater analytical orientation than low performers. High Low Performers Performers

15. 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

16. 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

17. PIECES The Analytical DELTA — Pieces Data . . . . . . . . breadth, integration, quality Enterprise . . . . . . . .approach to managing analytics Leadership . . . . . . . . . . . . passion and commitment Targets . . . . . . . . . . . first deep, then broad Analysts . . . . . professionals and amateurs

18. Data • The prerequisite for everything analytical • Clean, common, integrated • Accessible in a warehouse • Measuring something new and important

19. New Metrics / Data Run Production Wine Chemistry Driving Data

20. 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

21. Enterprise-Wide Customer View Processes in Which Data Used Types of Data Marketing Logistics Service Sales Internal Transaction Web Metrics External Geo-Demo External Attitudinal

22. 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

23. The Great Divide • Full steam ahead! • Hire the people • Build the systems • Create the processes Is your senior management team committed? • Prove the value! • Run a pilot • Measure the benefit • Try to spread it

24. 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 Targets

25. Analysts 5-10% • Analytical Professionals • — Can create algorithms • Analytical Semi-Professionals • — Can use visual tools, create simple models 15-20% • Analytical Amateurs • — Can use spreadsheets 70-80%

26. Taking Action • Analytics need to be embedded into the machinery of organizational action • Operational decision-making • Business processes • Manager and employee behavior • Customer expectations

27. The Analytical DELTA — Progress PROGRESS

28. 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

29. 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.”