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Mass Customization

Mass Customization. Mass production era is over Power shifted to retailers Trade is more demand driven Responding to aggregate demand changes Now power is shifting to consumer More information available Don’t need more choice, want what they want, when, where and how they want it.

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Mass Customization

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  1. Mass Customization • Mass production era is over • Power shifted to retailers • Trade is more demand driven • Responding to aggregate demand changes • Now power is shifting to consumer • More information available • Don’t need more choice, want what they want, when, where and how they want it HED 460

  2. Mass Customization • Consumer is the driver and technology is the enabler • Hybrid strategy integrating mass production with customization (seemingly incompatible) • Has low unit costs and high degree of automation • High degree of flexibility and builds to order, not to plan • High levels of variety result HED 460

  3. Collaborative Filtering/Intelligent Agents • Product Reviews • Collaborative Filtering • Shopping robots - comparative pricing HED 460

  4. Product Reviews • Select from product categories • Access opinions of reviewers • Reviews come from other customers • Quality control built in with “usefulness” ratings • Some sites provide compensation for reviews • epinoins.com and mysimon.com HED 460

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  8. Collaborative Filtering • Customers are given product recommendations • based on predictive models • classifies customer with others who have similar profiles • resembles segmentation and target marketing (preferences not demographics) • >precision as database grows HED 460

  9. Collaborative Filtering • Software learns about individual behavior • observing real-time click-throughs • recalling past behavior (purchase histories) • customer rates list of relevant items • Technology pools info with others with similar tastes/interests • Amazon.com • Cdnow.com HED 460

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  14. Collaborative Filtering • Works best with large # prod./services • Not good for extensive product lines – unrelated • Not so effective for products that are functionally related (computers/access.) HED 460

  15. Collaborative Filtering Problems • Privacy issues due to the detailed data collected • Gift purchases • Technology could be a vehicle for advertising • some recommendations highlighted more (listed 1st or given more visibility) HED 460

  16. Collaborative Filtering Facts • Software costs up to $500,000 • Payback within 12 months • Tower Records e-mail newsletter • 4.4% response rate - 1st mailing & 15%, 3 months • Repeat purchases • typically 35-40 %, 63% for Amazon with CF • 800.com – 61% sales increase – holiday • Conversion rates > 20% - cross sells HED 460

  17. Rules based vs shoes socks Collab. Filtering shoes other shoes Narrower recommend. Two methods Combination can be good After filtering, then rules: Price Out of stock/previous orders HED 460

  18. Shopping/digital Bots • Taste based product recommendations • brand • price • features • Searches hundreds of competing web sites • Identifies specific online/offline retailers HED 460

  19. Shopping/digital Bots • Durable goods are heavily represented • Analysis carried out at individual level • Some bots charge merchants for listing products • Some bots sell advertising space • BotSpot.com lists nearly 50 bots HED 460

  20. Shopping/digital Bots • MySimon.com • searches 2600 online retailers • searches can be more or less targeted • shopping.yahoo.com • comparison shop and buy at one site • over 10,000 stores • stores advertise or pay a portion of revenue HED 460

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  22. Bot Problems • Web retailers can lower base price to confuse bot and raise hidden costs • select a bot that reports shipping costs, availability, sort results by price • Bot may not list sites that do not advertise • difficult to develop site loyalty since price is central HED 460

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