item
What do I see? Utter, Unfathomable Inefficiency – that is retail as we know it
Have a look at the 2 pictures below taken at a mall-attached large chain retailer. Not a SINGLE customer in the store. Practically every rack had a red and white sale sign on it. Look at the multiple sizes of each item that have to be made available.
Now consider this.
What is the probability of someone walking through the store to this location, finding an article of clothing that is subjectively pleasing and desirable enough for the person to pick it up and consider the price. Consider if this is a nice to have or need to have item. Further consider the price and whether it is higher or lower than the clearing price — the price at which the user (in that particular user’s mind) thinks it is a good deal and decides to buy it. What is known is the quantity of work needed to inventory, merchandise, display all the products. What is not known very well is the probability of a sale for any or all of the items in the store.
Further consider the redundant inventory of similar (or the same) generic products — redundant because multiple stores attached to the same mall carry pretty much the same generic stuff. Even brand names provide little differentiation or value add. And celebrity designers and endorsers such as Kimora, Cindy, Kathy, or even Jaclyn Smith don’t help. The entire Kimora section was just as deserted as the second photo in this bunch.
RTB – real-time bidding may make ad exchanges more efficient, but it still won’t save display (ads)
in response to this mediapost article
http://www.mediapost.com/publications/?fa=Articles.showArticle&art_aid=113621
While RTB will make ad exchanges even more efficient, it may not be that necessary.
RTB depends on 3 things: 1) inventory, which depends on how many people hit the page to generate an impression, 2) clicks, which depend on people clicking something, and 3) bidders, the more niche you get, the fewer bidders there will be. Inventory does not change rapidly. Clicks take time to accumulate (to yield click rates, which are a necessary ingredient in the RTB calculation). And if there are too few bidders the price of the auction “item” won’t appreciate or depreciate much or rapidly. Because of these 3 things, making bidding real-time versus non-real-time (i.e. overnight) may not make it significantly better or move the needle much on efficiency and ROI.
And RTB will still not save “display” ads. The golden age of display was in the mid 90s when people tolerated ads when they read content. They are now trained to avoid looking at the top and right of web pages So while RTB may increase the ROI of display ads by increasing click rates from a percentage with too many zeros to count to something sligtly higher, display ads are still ignored by users and will still not generate measurable business impact for advertisers.
What is Web 3.0? Characteristics of Web 3.0
2009 06 16 What Is Web 3.0 – Presentation Transcript
- What is Web 3.0? Dr. Augustine Fou June 16, 2009. June 16, 2009.
- Evolution of the Internet microprocessor 40 yrs 10 yrs 20 yrs 5 yrs present web internet 2.5 yrs social networks e-commerce 1.5 yrs Web 1.0 Web 2.0 Web 3.0? June 16, 2009.
- Evolution of the “Web” content commerce search social networks social content social search social commerce As each stage reaches critical mass, the next stage is tipped into present June 16, 2009.
- Key Characteristics present web 1.0 web 2.0 web 3.0
- Speedy
- more timely information and more efficient tools to find information
- Collaborative
- actions of users amass, police, and prioritize content
- Trust-worthy
- users establish trust networks and hone trust radars
- Content
- content destination sites and personal portals
- Search
- critical mass of content drives need for search engines
- Commerce
- commerce goes mainstream; digital goods rise
- Ubiquitous
- available at any time, anywhere, through any channel or device
- Individualized
- filtered and shared by friends or trust networks
- Efficient
- relevant and contextual information findable instantly
June 16, 2009.
- Illustrative Examples – retail/shopping present web 1.0 web 2.0 web 3.0
- what friends bought or want to buy
- drag-to-share items which friends know friends are looking for
- item collections
- value in the aggregation
overstock.com amazon.com FB app: MyFaveThings
-
- contextual reviews
- reviews of reviews
- what others bought
- individualized recommendations
June 16, 2009.
- Illustrative Examples – social networks present web 1.0 web 2.0 web 3.0
- aggregates all your online identities
- syndicates all your updates to all social networks
- social actions visible to friends
- trust networks across geography, time, and interests
- collection of personal homepages
geocities.com facebook.com peoplebrowsr.com June 16, 2009.
- Illustrative Examples – restaurant reviews present web 1.0 web 2.0 web 3.0
- Yelp content vetted through a user’s trust network and individual recommendations made based on situation and need, in real-time
- user submitted reviews
- related items based on similarity of user preferences
- infrequent publication
- centralized editorial control
zagat‘s yelp need reco for great Italian + GPS + Yelp 5-star Babbo, been there, love it June 16, 2009.
- Illustrative Examples – photos present web 1.0 web 2.0 web 3.0
- real-time, contextual “do you like this knit shirt?”
- friends give immediate feedback
- share photos with friends and strangers
- enable visitors to tag and comment
- individual albums
kodakgallery.com flickr.com ? June 16, 2009.
- Illustrative Examples – real estate present web 1.0 web 2.0 web 3.0
- information vetted by fellow users, recommended directly an in context
- listings plus relevant information like school zones, comparable sales, alerts
- listings based on parameters
corcoran.com streeteasy.com trulia iphone app June 16, 2009.
- Illustrative Examples – encyclopedia present web 1.0 web 2.0 web 3.0
- content is ubiquitous and available through any channel or device
- trust network proactively forwards relevant info to user who needs it
- created, updated, and edited (policed) by user actions
- digitized version of printed encyclopedia
britannica.com wikipedia.com chacha.com June 16, 2009.
- Illustrative Examples – online coupons present web 1.0 web 2.0 web 3.0
- coupons delivered contextually and proactively when user needs it (without the user even asking for it)
- instant feedback
- community action makes it more accurate and useful for others
- collection of online coupons – value in the aggregation
dealcatcher.com retailmenot.com June 16, 2009.
in-banner commerce
reducing the number of clicks between the inspiration and the action (purchase) usually helps reduce the precipitous drop off of people not completing the desired end-action. in-banner commerce means you can sell the item right in the banner. The user may already be registered with Amazon.com and have their card on file. This can be 1-click purchase in the banner itself — to take advantage of impulse purchases. This works especially well for low cost and low consideration products.
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