How Amazon Uses Machine Learning to Drive the Customer Experience

I read all 22 Amazon Shareholder letters. 

I wanted to understand how Amazon used machine learning to drive the customer experience. Here's what I learned.

Amazon is a Platform-as-a-Service (PaaS) company that just happens to be a retailer

Jeff Bezos has been consistent about Amazon's goal since the beginning - deliver an amazing customer experience. That means providing vast selection, fast convenience, and price reductions. Amazon has invested in building massive platforms, whether it's fulfillment or cloud computing centers, to support the pillars of selection and convenience. The last pillar, price reductions, is a result of the efficiencies from scaling. 

Here's their playbook: (a) create a platform for their own business needs (b) formalize this platform into an ecosystem by opening it up to 3rd-parties (c) refine this platform into a self-service option that's easy-to-use with an interface. Lather, rinse, repeat. 

An example is Amazon's distribution centers which they built to sell their own products,

We opened distribution and customer service centers in the U.K. and Germany, and in early 1999, announced the lease of a highly-mechanized distribution center of approximately 323,000 square feet in Fernley, Nevada. This latest addition will more than double our total distribution capacity and allows us to even further improve time-to-mailbox for customers. - 1998 Shareholder Letter

Then they launched Fulfillment by Amazon (FBA) which opened up their distribution centers to 3rd-party sellers,

Fulfillment by Amazon is a set of web services APIs that turns our 12 million square foot fulfillment center network into a gigantic and sophisticated computer peripheral. Pay us 45 cents per month per cubic foot of fulfillment center space, and you can stow your products in our network. You make web services calls to alert us to expect inventory to arrive, to tell us to pick and pack one or more items, and to tell us where to ship those items. - 2006 Shareholder Letter

And finally evolved FBA to a self-service user-interface that anyone can use,

...when sellers use FBA, their items become eligible for Amazon Prime, for Super Saver Shipping, and for Amazon returns processing and customer service. FBA is a self-service and comes with an easy-to-use inventory management console as part of Amazon Seller Central. - 2011 Shareholder Letter

Amazon has mastered the ability to invent, refine and scale platforms that enable an unparalleled customer experience. 

Amazon has focused on customer personalization since the beginning

Nowadays, we often take product recommendations for granted. We hear about Netflix's famous recommendation engine which predicts the shows that keep you binge watching or Spotify's ability to introduce you to new artists that you'll love.

Bezos knew that couldn't win against physical bookstores if they tried to duplicate the same experience online. The two distribution channels are different, which means that the customer experience needs to be different. He talks about customer personalization in his very first shareholder letter,
Today, online commerce saves customers money and precious time. Tomorrow, through personalization, online commerce will accelerate the very process of discovery. - 1997 Shareholder letter
Amazon's vast selection meant that they had a larger product catalog than any physical store. More choice is great, but not practical if I can't find what I'm looking for on the website. They've managed to develop the right assets over time to scale product recommendations: unparalleled access to historical data, cloud computing resources, and machine learning recommendation engines.

First, they started with recommending new books. A decade later, they added features such as customer reviews & product discovery like 'customers who bought this item also bought'. In the past decade, the rise of cloud computing allowed Amazon to put their product recommendation engine on steroids - building a search engine that returns fast, relevant results and running hundreds of software programs to personalize a product page, construct a product detail page for a customer visiting, our software calls on between 200 and 300 services to present a highly personalized experience for that customer. - 2009 Shareholder Letter
Amazon has relentlessly focused on turning their vast product selection into the killer customer experience feature of personalization. 

Amazon has evolved from using machine learning to support their products to machine learning as the product

Over time, Amazon's machine learning capabilities went from a support role that augmented decision making to becoming a product in itself.

For example, machine learning for order fulfillment represents augmented decision-making. Computers label and categorize a product, which minimizes errors that cost money & time. However, more recently, Amazon has created machine learning products. The best examples are Alexa, which uses natural language processing to understand human speech, and Amazon Go, which are stores where checkout lines are eliminated with the aid of cameras using computer vision.

True to Amazon form, they're running the playbook I described earlier and turning machine learning into a platform service. They created machine learning models to support their own infrastructure, then they formalized those models into a platform that people can access, such as Alexa's API - Alexa Voice Service (AVS), and now they're in the process of offering pre-packaged deep learning models-as-a-service - Amazon SageMaker - for developers to run on their cloud platform.

It's been decades in the making, but it looks like machine learning modelling may emerge as their new big platform. And one can imagine SageMaker sets them up to remain a top company for the next decade. As Jeff Bezos says, at Amazon, every day is Day 1.

Mythbusters: Were Overzealous Algorithms Responsible for Slow Sales at Loblaw Companies Ltd?

Well, this is new.

Retailers have blamed bad weather for poor sales before, but I don't think I've ever seen a retailer blame bad algorithms.

Loblaw Companies Ltd, Canada's largest grocery and pharmacy chain, which owns the mainstream brand Loblaws and discount brand No Frills, had a soft Q2 performance with same-store revenue growing 0.6%. Their President, Sarah Davis, blamed the performance on algorithms that prioritized increasing profit margins instead of promotional pricing to attract foot traffic. That is, Loblaw chose to increase the revenue from each customer instead of focusing on increasing the number of customers. She says:

We know exactly what we did and what we did was we focused on going for margin improvements...And in the excitement of seeing margin improvements in certain categories as we started to implement some of the algorithms, people were overzealous...You end up with fewer items on promotion in your flyer. 

Are the data scientists at Loblaw really running wild with their overzealous algorithms and causing there to be fewer items on promotion in flyers, and, ultimately, softer sales? 

Davis' statement was a Mythbusters challenge that I couldn't resist, so I did some research. 

I looked at the Ontario flyers for No Frills and their competitors, Food Basics (Metro), and FreshCo (Empire) over the same time period in May 2019 and 2018. I selected a total of 10 items - a mix of produce, meat, and dairy - that were posted in the flyers for all 3 grocers so that I could price compare. I consider an item on promotion if it has the lowest price out of the three grocers. The goal was to determine how many items where No Frills was the price leader in 2019 or 2018.  


Out of the 3 discount grocers, No Frills ranked 2nd place for being the price leader in both 2019 and 2018. This finding leads me to believe that they did not run any more promotions last year when compared to this year, and certainly the impact of algorithms may be overstated. 

There is, however, one qualifier: in 2018, No Frills ran a promotion for their loyalty program, PC Optimum, offering a bonus for spending a certain amount. Could it be that not including this promotion caused slower sales this year?

Well, taking a look at Q2 same-store sales growth for the past 2 years, it's clear that Loblaw Companies was slowing down even in 2018 when compared to Metro & Empire. 

At this point, I would say, myth busted. There was no difference in flyer promotions when comparing last year to this year and same-store sales growth has been slowing since 2018...which means, the data scientists and their algorithms can take a sigh of relief.

So, how can Loblaw Companies turn this around? Here are 3 flyer suggestions.

Be price comparable or price match

Metro and Empire have trained customers over the past year that their stores offer the best prices. The grocer discount channel is a growth area in food retailing, and is becoming even more competitive as Empire aggressively converts their under-performing Sobeys stores into FreshCo locations. No Frills needs to be price comparable.

Highlight fresh food offering

When I reviewed the flyers, I got the sense that Food Basics had a better complete grocery offering by focusing on produce and fresh foods, whereas, No Frills was focused on packaged dry goods. The drop-off between the primary brand and discount brand seems larger for Loblaw, which is a challenge if consumers are switching to discount brands. 

Continue leaning into the direct-to-consumer channel, the PC Optimum loyalty program

PC Optimum may be one of the strongest loyalty programs in Canada, and from my perspective, has surpassed Metro's Air Miles program. A direct-to-consumer brand relationship is quickly becoming one of the few remaining competitive advantages in markets where products are commoditized. Loblaw Companies should continue promoting offers in flyers to acquire new users - whether by mobile app or points card. The data generated will let them put their data scientists in a position to drive top-line business outcomes.


  • I own a long position in Loblaw Companies Ltd.
  • Basket items
    • 2019: Steak, Chicken Breast, Corn, Tomatoes, Watermelon, Sweet Peppers, Mushrooms, Blueberries, Ice Cream, Yogourt
    • 2018: Steak, Chicken Breast, Ribs, Corn, Tomatoes, Watermelon, Sweet Pepper, Mushrooms, Cucumber, Strawberries
  • Empire's quarterly end dates are mid-quarter when compared to the normal fiscal end dates, so I averaged the two quarters that represented Q2
  • I recognize that the flyer data is limited because it's only from 1 week and doesn't include Walmart or Costco