1 Business-to-Business Marketing Haas School of Business UC Berkeley Fall 2008 Week 2 Zsolt Katona

2 Where we stand Week 1 –Introduction to B2B and Industrat –Decision 1 Week 2 –R&D and Market Research in Industrat –Surprise –Decision 2 Week 3 –Pricing Strategies in Industrial Markets –Room and Haas, Signode case discussions

3 Industrat Stock Prices

4 R&D in Industrat Internal Transactions Marketing = Profit Center Production Dept. Finance Dept. R&D Dept. Sales Dept. Technical Dept. Corporate Marketing Dept. Manufact. Costs Inventory Holding Costs R&D Expend- diture Technical Support Corporate Communication Expenses Commissions Sales force Expenditure

5 Feasible Ranges of Physical Characteristics 1Resistance (  ) 2Suspension (MSc) 3Frequency (KHz) 4Density (MGC) Min - Max - KOREX 1Convexity (DE) 2Conductivity (MSc) 3Purity (%) 4Max. Energy (MW) LOMEX

6 Korex Technologies SUSPENSIONSUSPENSION RESISTANCE Max. Min. Max. Tech. limit

7 Korex Technologies S U S P E N S I O N (MSc) RESISTANCE (0) Max. Min. Max. Tech. limit

8 Technology Research Know How Infrastructure Gestation Period Critical Mass Minimum number of years Minimum annual investment New projects (products) using a technology Additional Investment

9 Research Technology Cumulative investment Years above minimum investment Status Minimum total investment Proposed total investment Minimum annual investment

10 Research Technology Cumulative invest Years above min inv Status Min total invest Prop total invest Min annual invest NOOKOKNOOK

11 Development Prototype = First Development Project Development Prototype = First Development Project NamePLIA Technology5 Cum Expenditure13050 StatusOK Characteristic 117 Characteristic 2101 Characteristic 343 Characteristic 4224 Base cost100

12 Research and Development Product Development Project name Technology Budget 4 Physical characteristics Production base cost Period T: Project Specification: Period T+1: Message from Development: Project status Feasibility of base cost Additional budget necessary

13 Production Costs Production cost Base cost specified to R&D Cumulative Production

14 Production Costs Production (transfer cost) Cumulative Production Actual Base Cost New Base Cost Specified to R&D Actual Manufacturing Method New Manufacturing Method Experience Transfer Shared Cost Technology Experience

15 Production Costs Production (transfer cost) Cumulative Production Actual Base Cost New Base Cost Specified to R&D Actual Manufacturing Method New Manufacturing Method

16 Development Projects Modify existing KIDU Introduce KIDA Modify KIDU & introduce KIDA Retain base cost Tighten base cost Relax base cost Terminate project Continue PKI33 (same tech and specs) Complete Incomplete PKI33 Project name

17 Brand Modification Period T Product KIDD Period T + 1 Modified KIDD Automatic payment to production department at transfer cost (exceptional loss) KIDD

18 Loans are available

19 Loans For R&D only, line of credit, equal annual payments interest on outstanding balance

20 Loan Repayment Schedule Loan Length Interest Totalzero one two three four five six seven eight nine

21 Collaboration  Negotiations  Outcome  Terms  Process  Implementation  Risk Only within approved periods Licensing and/or joint research Royalties: historically 3%, but deregulated now Negotiated annual minimum royalties Other fund transfers Written contract and approval (arbitration in case of violation) Compatible decisions entered by both parties Government invalidates contract in case of antitrust action

22 Objectives: Understanding Markets Monitoring Identifying threats & opportunities Forecasting Methodology: Analysis of Survey Data Market Segmentation: None, Specified or Statistical Optimum Market Research

23 Survey Types About Supplier (Studies 1 and 2) –Awareness and preference for each supplier by decision maker –Importance of and ratings on technical, commercial aspects and reputation –Ideal Points About Products (Studies 3,5 and 7,8) –Awareness, preference and market share for each product –Importance and ratings of product attributes –Ideal Points Buying process (Study 6) –Who is the decision maker? Market demand and forecast (Studies 4,9) Competitive Information (Study 16)

24 Perceptual maps

25 Prediction Markets 1988 Iowa Electronic Market (IEM) –A University of Iowa experiment –Securities traded are linked to outcomes of political elections (e.g. you get $1 is B. Obama wins the presidential) –Does better than polls (75% of the time) IEM is but one example… –Sports –Movies (HSX) –Financial and Economic data –etc.

26 Examples of Prediction Markets MarketStatusFocusTypical turnover on an event ($US) Iowa Electronic Market Run by University of Iowa Small-scale election markets for US elections. Tens of thousands (limited to $500 positions) TradeSportsFor-profit company Trade in a rich set of political futures, financial contracts, current events, sports and entertainment. Hundreds of Thousands Economic Derivatives Run by Goldman Sachs and Deutsche Bank Large-scale financial market trading in the likely outcome of future economic data releases. Hundreds of Millions NewsfuturesFor-profit company Political, finance, current events and sports markets. Also technology and pharmaceutical futures for specific clients. Virtual currency redeemable for monthly prizes Foresight ExchangeNonprofit research group Political, financial, current events, science and technology events suggested by clients. Virtual currency Hollywood Stock Exchange Owned by Cantor Fitzgerald Success of movies, movie stars, awards, including a related set of complex derivatives and futures. Data used for market research. Virtual currency

27 Predictive performance of the Hollywood Stock Exchange

28 External validity: Saddam security and oil prices.

29 Potential corporate applications More complex securities –Security pays $1 for each additional percentage point in vote. Market price reflects expected proportion of votes earned. –Security pays according to square of percentage vote. Allows to calculate market’s confidence in estimated proportion of votes earned. –Pay $1 if percentage vote is exactly x, y, z…. –What if a contract is contingent on another event (e.g. contract on oil price if Saddam is in power vs. one contingent on the opposite. Difference tells effect of Saddam’s ousting on oil prices.) Prediction markets in the enterprise –Variety of contexts (scheduling, product success, technology trends) –Examples: HP, Intel, Siemens, Google, Eli Lilly, Arcelor… –Information generated is confidential so more incentive to create the market. –Will traditional market research disappear then?

30 Surprise We will run a prediction market There will be 5 securities (one for each firm) The winner security pays 100 points at the end, the others pay 0 Winner: the firm that makes the highest total net marketing contributions in the last three periods “Alternative stock price”

31 Before we go faculty.haas.berkeley.edu/zskatona/teaching.html Industrat teams Decision deadline: 17:00 (set at startup)