Anticipatory Shipping By Amazon

Authors : Nileesha D'Mello & Sachin T Koshy

Key Words: 
  Predictive Analytics , Anticipatory Shipping , Big Data Analytics

One of the many reasons why customers do not prefer online shopping is the longer wait time. It is faster for them to step into a brick and mortar store and buy what they desire. Retailers like Target, offer customers an option to buy online and then pick up the goods at nearest local stores.

E commerce giant, Amazon has tried several ways to improve the customer experience by reducing the delivery time of products. They have come up with a cheaper concept of Anticipatory shipping which is a better solution to the delayed delivery problem than the usage of drones which the company was working earlier.

Amazon uses predictive analytics to determine the customers buying behavior based on their clicks, product searches, time spent on an item, wish list, shopping cart etc. also including the phone conversations with a service executive related to service inquiries. Once Amazon has figured out the possible buying behavior or pattern of the customer, it then ships all these items to a nearby geographical location (also called as a Hub). The final delivery address is not specified, it is added only after a customer makes an order. This helps in cutting short the delivery time by at least a day. So in short even before a customer orders an item, Amazon keeps it ready to be shipped to customers from its local hubs.

Amazon has patented this model and the above fig. represents it. It offers some explanation to how the model might actually work. This model becomes more profitable when the products are in high demand regularly.

Anticipatory Shipping Process:

How this model has benefited the company?

•  It has helped in cutting down logistics and transportation cost. It is possible for Amazon to ship in bulk, as this is cheaper. Instead of using air shipment for an overnight delivery, which is costly, this model works out more economical.
•  It doesn’t lead to loses as it does not right away ship to the customer before the customer orders. It only ships to a common area where people are more likely to buy that item based on their online activities. The company can eventually manage to get itself to compete with brick and mortar stores.
•  Additional source of revenue as they have patented this model.

How it has benefited the customers?

• They can expect a faster delivery of their item. If Amazon manages to perfect its predictive learning ability for a particular customer or a set of people, they can expect their order in as short as one day. 
• This is achieved while customer need not pay extra for this facility as their order is close by. Moreover customers will get products at a cheaper rate as logistics costs has reduced.
•  If the predictive algorithm goes wrong, it leads to huge loss as the logistics cost for shipping the items to and fro to the fulfillment centre is costly. In such cases the products are offered for a cheaper price by offering discounts or giving away as promotional gifts to acquire more customers.

For example, Amazon’s predictive analytics tells them that in Bangalore region, there are a group of students who are looking for books related to predictive analytics. Some of them have them in their wish list; some have it in their cart. Amazon can then ship books related to the search to a nearby location Chennai from their warehouse say, in New Delhi. Once any of them orders, Amazon’s system will help ship the book to that delivery address from the nearest region, i.e. Chennai. By doing so it is possible for Amazon to ship the book within 2-3 days. By the time the customer has ordered, the book has already reached half-way.

(The figures have been quoted from the information patented by Amazon: US 8615473 B2)

A QUICK RECAP

References

Amazon. (2013, Dec 24). Patents US8615473. Retrieved from google.com: https://www.google.com/patents/US8615473

Burg, N. (2014, March 26). Your company can see the future with predictive analytics-2. Retrieved from forbes.com: http://www.forbes.com/sites/sungardas/2014/03/26/your-company-can-see-the-future-with-predictive-analytics-2/

Marr, B. (2014, April 6). Amazon using big data analytics read your mind. Retrieved from smartdatacollective: http://www.smartdatacollective.com/bernardmarr/182796/amazon-using-big-data-analytics-read-your-mind

Nandekar, P. (2014, August 3). Anticipatory Shipping- The Game Changer in E-Commerce. Retrieved from mbaskool.com: http://www.mbaskool.com/business-articles/operations/9930-anticipatory-shipping-the-game-changer-in-e-commerce.html

Ulanoff, L. (2014, jan 27). Amazon knows what you want before you buy it. Retrieved from predictiveanalyticsworld.com: http://www.predictiveanalyticsworld.com/patimes/amazon-knows-what-you-want-before-you-buy-it/