Seems Impossible But Your Order is Shipped Before You Order Them, Confidently !
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| Every Second Data is Captured, Analyzed and User Profiling is done for Demand Forecasting, Political Alignment and Other use |
Demand
Forecasting conventionally done on sizable population data collected via survey,
past sales reports, market condition, projected GDP and many more PESTEL
conditions. It is very old and conventional approach used in many industries
including fashion. Earlier data of single customer such as purchases, daily searches,
save for latter, add to wish list, were not available for a long duration of
time. What if data of single customer online orders, daily searches, save for
latter, add to wish list etc are
available for past 10 years?
Consider an example a female customer profile that
buy female dress, suddenly start buying baby diapers. This is very common
practice as a girl become mother; she has to buy baby diapers. Now take a city
where population is 1 million. In that this abrupt change was observed in 100 female
ecommerce profiles. Additionally baby wears diaper from 0 to 4/5 years. Now the
online company can ship diaper to this city without specifying exact address
based on ordering frequency of these 100 female profiles data analysis. This is
just a hypothetical scenario where lots of data is required.
Anticipatory Shipment get advantage of long
duration analysis of a customer profile based on his/her activity on internet, ordering
pattern luxury / middle class/lower middle class, daily searches, orders made
in last 10 years. This kind of data is not available to physical stores.
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| Photo - Amazon Patent for Anticipatory Shipping |
Anticipatory Shipping Frequency system might include
a package for shipment to a delivery address, Selecting a destination
geographical area to which to ship the package, shipping the package to the
destination geographical area without completely specifying the delivery
address at time of shipment, and while the package is in transit, completely
specifying the delivery address for the package. Data that could be analysed to determine customer demand
for a particular pre-shipped package to help decide where to route it
geographically could include historical buying patterns, preferences expressed
explicitly via surveys/questionnaires, demographic data, browsing habits,
wish-lists and so on.




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