SI can believe that Amazon patented a Method and System for Anticipatory Package Shipping (US Patent 8615473) but can’t believe it would use this for more than a small number of items. Nor does it believe the system would be implemented as outlined in the patent as filed, at least in the short term.
It took Amazon 7 years to turn its first profit, and while Prime is currently very profitable to Amazon (which makes $78 more in profit per year per Prime customer, on average, than non-prime customer according to CIRP’s market research – Source), those margins would drop substantially if Amazon started shipping tens, or hundreds, of thousands of packages a year that no one wanted. Amazon does have an efficient distribution network and probably has the absolute best deals with postal and courier services that can be papered, but every shipment costs money and every unnecessary shipment eats into profit. Returns cut into profit margins enough, how much are returned shipments to nowhere going to cost?
Thanks to big data, predictive analytics is getting better by the day, but it’s still hit and miss at a granular level. While it’s pretty easy to use correlation data across a large customer base to predict that you are likely to desire an item, it’s harder to predict whether or not you’d actually buy it, and if you would, at what price point, assuming you don’t already own the product in question. (It’s always telling the doctor he wants books and media he already owns.)
As a result, any predictive analytics at the individual consumer level are going to be hit-and-miss at best. Predictive analytics work best across a large consumer base with a lot of data where one can predict that, on average, 5 in 100 people who match a profile will buy the product from Amazon.
And, from Amazon’s viewpoint, the best use of the predictive analytics is on new releases, as the bulk of sales in many of its categories, and books and media in particular, are in the weeks immediately following a new product release. With the right data and the right algorithms, it can not only predict how many units it is likely to sell against its current customer base, but if the demand is enough, how many in each region that is associated with each distribution center and how the orders will likely track over time on a daily basis.
In this, and only this situation, would anticipatory shipping, and in particular, anticipatory packaging, make sense in the short term. For example, if Scott Adams were to release a new Dilbert book and Amazon predicted 200,000 copies would be sold in the first 3 weeks, and expected that it would get 50,000 of those sales, pre-packaging 40,000 for shipment and then distributing those across it’s DCs such that each DC received a number of books proportionate to the expected sales in the serviced area would be a good idea. All Amazon would have to do to speed up shipment would be to slap the delivery address on the boxes as the orders came in and have them ready to go in the next pickup for local delivery.
In the future, once the system is fine-tuned and its delivery partners have the technology to replace a unique delivery address identifier with a specific delivery address on-the-fly, Amazon can pre-ship a set number of these pre-packaged items to the local post office or delivery company every day, which can, in turn, load those packages onto the appropriate courier truck each morning as the addresses in the system are updated with consumer delivery addresses sent over by Amazon upon each purchase.
But not everyone would get faster shipping service. In order to prevent too many unnecessary shipments and loss, Amazon would have to err on the side of caution and pre-package (and pre-ship) less unit of an item than it expected to sell, and restrict the anticipatory shipping and packaging to only those items expected to have a large sales volume. In most cases, the best Amazon will do is optimize the distribution of inventory across its warehouses. However, this can still take a day (or two) off of average delivery time, so this is still a good start.
Any differing opinions?