Amazon: Using Big Data Analytics to Read Your Mind

Amazon.com, the Seattle-based ecommerce giant, has always leveraged data. In one of their latest business moves, the company has obtained a patent to ship us goods before we have even made a decision to buy it, purely based on their predictive big data analytics.

Amazon.com, the Seattle-based ecommerce giant, has always leveraged data. In one of their latest business moves, the company has obtained a patent to ship us goods before we have even made a decision to buy it, purely based on their predictive big data analytics.

I don’t think that back in 1995 when Jeff Bezos started the company in a garage, he could have imagined that it would one day grow into a Fortune 500 global retail empire. I believe that the key building blocks of Amazon’s success are their ability to use data and an eye for the right innovations and patents.

In the early days, when Amazon was primarily a book retailer, the company was the first to extensively use algorithms so that it could provide recommendations for customers: “Customers who bought this item, also bought this one…”.  Today, it uses item-to-item collaborative filtering on many data points such as what users have bought before, what they have in their virtual shopping card or wish list, the items they have rated and reviewed, as well as what other similar users have bought, to heavily customize the customer browsing experience.

Another big coup for Amazon was when it obtained the patent for it’s ‘One Click Buy’ feature. This was pure genius and who would have thought a company could ever get a patent for that.

What Amazon has just done is combine the two (strengths in data analytics and it’s instinct for patenting key features) to obtain a patent for what it calls: Anticipatory Shipping.

What Amazon has patented here is the process of shipping an item to a customer in anticipation that this customer will order that product. This means that Amazon believes the big data analytics insights will become so accurate that it can predict who will order what and when. The reason for this is that Amazon wants to be able to deliver products faster. This is also why it negotiated Sunday deliveries and why Amazon started to experiment with unmanned drones that might deliver our parcels in the future and. 

Other, more traditional retailers have long used predictive analytics to ensure the right items are in stock, based on past buying patterns as well as social media analytics and weather predictions. What is new here is that Amazon is taking it to a personal level, predicting the items you might buy. This is different to a local supermarket stocking items that the people in that community might want to buy.

One problem with anticipatory shipping is that Amazon has to get it right. If their big data algorithms get it wrong, then it could potentially lose the company a lot of money because the logistics costs for shipping the product out and then returning it would be lost. The way Amazon proposes to deal with cheaper unwanted items is to either heavily discount them or give them away as a free gift to build customer ‘good will’.

Another problem with anticipatory shipping is the question about how much a company should be allowed to act on the insights gained from analysing our personal behaviours. For example, my wife bought a pregnancy swimsuit from Amazon as a present for one of her friends who was expecting a baby. The problem that followed was that for the following 9 months or so she had to look at pregnancy related recommendations or watch pregnancy related ads. Just imagine if she had to return all those diapers, baby blankets or baby wipes that a predictive anticipatory shipping algorithm might send in the future!

As a big data guy, I am fascinated by the increasingly accurate predictions commercial companies can make about our behaviours. As a consumer I am excited about the prospect that the stuff I order will be with me quicker, because it will already be on its way before I place my order. But as a private individual, I am getting a little concerned about the power predictive analytics puts into the hands of commercial companies.

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