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Intelligent Marketing

Intelligent Marketing. Dr Tim King 22 nd November 2006. My CV. Cambridge Computer Lab 1973-1981 Wrote a relational database for Ph.D. Lecturer, University of Bath 1981-1983 R&D Director 1984-1986 Wrote AmigaDOS Founded Perihelion 1986 Distributed OS, embedded systems, database systems

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Intelligent Marketing

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  1. Intelligent Marketing Dr Tim King 22nd November 2006

  2. My CV • Cambridge Computer Lab 1973-1981 • Wrote a relational database for Ph.D. • Lecturer, University of Bath 1981-1983 • R&D Director 1984-1986 • Wrote AmigaDOS • Founded Perihelion 1986 • Distributed OS, embedded systems, database systems • Founded UK Online 1994 • First UK ISP with content • Sold to EasyNet 1996 • Independent consultant • Technical Due Diligence for VCs • Advice for Sainsbury’s, Sony, Home Office etc • Strategy for small companies and following M&As

  3. Intelligent Marketing Using computers to make intelligent use of easily available data • Market drivers • Supply and demand • One size does not fit all • Cost curves • Price sustainability • Marketing techniques • Lock-in and Lock-out • Brands • Up sell • Personalisation • Customer support

  4. Price p Quantity Basic Economics Supply Curve S(p) Demand Curve D(p) p* People pay more for something rare Mass production reduces cost

  5. Market fit • Different versions for different market subsections • Many examples • Travel • First class vs coach • Cars • Audi vs Skoda • Software • “lite” versions with hardware • Psychology • Brand awareness Price p Quantity

  6. Price p Price p Cost Curves Cost p Cost p Quantity Quantity “Microsoft” “General Motors”

  7. Price sustainability Price of iPod 60G Time Period: 20 Dec 2004 to 31 Oct 2005 Red = Highest , Blue = Average, Green=Lowest

  8. Lock-in • Buying something commits you to buying more • Services • Consumables • Complimentary products • Examples: • Operating systems • Ink-jet printers • Mobile phone subsidy • Car services • Frequent flyer

  9. Lock-out • Incumbent tries to maximise switching cost • Loyalty programs • Technology control • Nintendo game cartridges • Sony Playstation DVD formats • Crypto and tamper resistance • Community – its where your friends are • BB, chat for registered users • Hassle • eg email address change

  10. Brand awareness • Single most important piece of data • People buy from a known name • Sense of trust • Marks and Spencer • Perceived value • Cheap reliable airline => Cheap reliable mobile • Peer pressure • Nike, Rolex, Dolce and Gabanna, Ferrari • Brands can expand • Virgin • Active, Atlantic, Books, Brides, Broadband, Cosmetics, Credit card, Drinks, Galactic(!), Games, Holidays, Limobike, Megastore, Mobile, Trains, Wines • Apple • From computers to iPods

  11. Expand the Brand (1) • YouTube • TV adverts • Recycle TV adverts • People send copies of your advert to each other • Risqué adverts not acceptable on TV • TV shows • Trail shows • Repeat the best bits • Music • Shareable • Do-it-yourself MTV

  12. Expand the Brand (2) • Google • Buy your brand name • Coke • Careers • Corporate Responsibility • The Coca-Cola company • Press Centre • Buy your supplier’s brand name • Nike • JDSports • Buy your competitors’ brand name • Ford • Adverts for Seat dealer • Buy your target • Nike (Boycott Nike), Coke (KillerCoke)

  13. Up Sell • Persuade people to buy more • Buy two get one free • When you only wanted one in the first place • Packs of three • When you only want two batteries • Persuade people to buy something else • Dell • Insist that you take extra warranty • Travel sites • Insist that you buy travel insurance

  14. Personalisation (1) • Know your customer • Profile typical users when they visit a web site • Purchase history • Time to make purchase decision • Amount of research done • Profile users through loyalty cards • Nectar • They know everything you have ever bought • Keep in touch with customers • Email newsletters • Lastminute, Maplin • Cookies • Welcome back Tim

  15. Personalisation (2) • Know your customer type • User database • Address/postcode -> socio economic indicator • Gender • Age -> Register with Data Protection Registrar • 60 “bins” 5 classes x 2 genders x 6 age groups • (kids, teens, dinky, married with kids, empty nesters, retired) • Disposable income • Disposable leisure time • Recommendation • People who bought this also bought that • Data from your own site • Amazon really can recommend music or books you might like • Data mining • People who buy this on cold winter Fridays in Slough also buy that

  16. Customer support • Identify meaning of email • Auto-respond with the answer • Classify once human response given • So next time it will auto-respond • Expose database as FAQ • So they don’t send the email at all • Always give the option of human interaction

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