AWS Insiders Podcast: Episode 1 – Transforming Customer Experience with Amazon Personalize

Ankur Mehrotra

Thanks, Rahul. Thanks for having me. Yeah. Over the last few years, you can see that businesses are engaging
with the users more and more through digital channels and that has only accelerated during the pandemic.

Ankur Mehrotra

So every digital touch point provides businesses an opportunity to create an experience for the users that’s
more tailored to their needs. And that’s where Amazon Personalized comes in. It’s an AWS service that helps
businesses create more personalized experiences for the users by creating recommendations that are powered by
machine learning. They’ll be seeing a lot of customer interest in adoption, and a lot of AWS customers are able
to build a high performing personalization system using Amazon Personalized and they’re seeing great results.

Ankur Mehrotra

As consumer habits are changing and the way they’re engaging with different businesses are changing. We are
seeing the need for personalization or the need for creating better user experiences in digital channels
increase, and we’re seeing that happen across different [inaudible 00:03:20 ]. So obviously as retail has been a
great example, and also where Amazon really pioneered personalization in the late nineties. But now we are
seeing the need in all different kinds of workloads.

Ankur Mehrotra

For instance, in banking we are seeing customers or businesses trying to create more personalized experiences
for the users. And we see even in other cases, for example, a very unique case that I will point out to you is,
there’s an app called Calm. It’s an app for meditation and mindfulness. So they use Amazon Personalized to
create recommendations for the users for mindfulness exercises. And they’ve told us how much impact that’s
created during the pandemic. These kind of stories really make our efforts work.

Ankur Mehrotra

So I think this really depends on the kind of business. For instance, in an online retail scenario, businesses
are looking to improve conversion rates, improve revenue for order. So this is where if you can add the right
kind of personalization at the right customer touch points… For example, at the right point in the customer
journey on the retail website, if you can add recommendations that drive more up sales, then you can change, you
can increase your average revenue per order. It just changes from business to business.

Ankur Mehrotra

For example, in the media and entertainment space, if you have a, let’s say video or on-demand application,
that’s where you may want to optimize for the video engagement and video watch time. And the great thing is that
Amazon Personalize gives you an ability to optimize, to specify a metric, that you want to optimize your
recommendations from.

Ankur Mehrotra

That’s right. So the way you can get started with Amazon Personalized is you have your interaction events, which
is interactions between your users and items and these items can be products in your, if you are an online
retail business, these can be products in your catalog. Or if you are, let’s say in media and entertainment,
this can be videos or titles in a catalog. So in the interaction dataset, which sometimes also sends a spark of
click stream dataset, that the most important dataset that you need to start using Amazon Personalize to build a
recommendation system.

Ankur Mehrotra

Then if you have other metadata about your items or users. So for instance, if you have metadata such as product
category or brand about the items in your catalog, then that is optional for you to use, but that can really
improve the quality of your recommendations because Amazon Personalize is able to automatically understand
trends between products that have similar metadata, for instance.

Ankur Mehrotra

Then if you have any information metadata about your users, such as their location, or their preferences, age
group, et cetera, that also helps. So when you start using Amazon Personalize, you take these three types of
datasets, the interactions one being most important, and you can add it to Amazon Personalize, either through a
set of APIs, by just making API calls, or by moving this data through an Amazon S3 bucket. And then once you’ve
done that, you can use a set of APIs to then select your use case or the type of recommendations that you’re
trying to generate. At Reinvent recently we launched more use case specific recommenders for retail on retail
and media entertainment. So if your use case falls within those two buckets, then you can really pick the domain
specific use case within the service through the API.

Ankur Mehrotra

So in retail, for example, you have frequently bought together. So you can directly pick the recommenders that
map to your use case. And if your use case does not fall in the retail or M&E bucket, then you can pick the
recommendation types that most closely match a use case. So for in instance, we have recommendation types such
as, user level recommendations. If you’re trying to generate a set of item recommendations for a particular user
or a personalized ranking, if you have a set of items in mind, but you want to rank them differently for each
user, you can select that what we call as recipe. If you want to create similar items product widget on a
product detail page, then that recipe can be really useful.

Ankur Mehrotra

Then recently we also launched a new type of recommendations called intelligent user segmentation, where we flip
the problem. We say, instead of generating recommendations for a given user you can specify an item or let’s say
an item category or a genre, and generate a segment of users who may be interested in that item or genre. It can
be used with different marketing tools, for email campaigns, ads, and even for other market [inaudible 00:09:17]
that are upper in funnel or in the consideration.

Ankur Mehrotra

So these are the different kind of recommendations that you can choose within Amazon Personalized and it can be
done through a simple API call. And once you do that, then in the background, Amazon Personalized uses machine
learning, trains a bunch of different machine learning models to create recommendations that optimize for these
use cases.

Ankur Mehrotra

Then you can also specify… You have all these knobs and dials that you can use to optimize or augment these
recommendations. We see that our customers don’t want to directly display recommendations that are coming out of
the machine learning model, they want to specify specific rules and constraints on their recommendations.

Ankur Mehrotra

For example, you may want to filter recommendations for a given product category or metrics. So you can do that
easily through a filtering and business rules capability. It’s super easy to retrieve the recommendations
through a low latency API and integrate that into your application or website or whatever experience you’re
trying to create for your users. So that’s basically the end to end flow. You start with this data, you can add
it to the service through APIs. Then you can pick the type of recommendations you want to create.

Ankur Mehrotra

Yeah. That’s absolutely correct, Rahul. I think if you were to implement such a recommendation system on frame,
you would have to think about servers and instances and how you scale. When you’re using Amazon Personalized you
don’t have to have any of your existing infrastructure data on AWS. This is a completely managed service. Which
means all you have to do is haul a set of APIs and you’ve got highly relevant recommendations ready for you to
integrate into your application.

Ankur Mehrotra

Exactly. Given how personalization can directly impact the business output, all businesses should try and use
the service to see how this drives overall improvement in the business metrics.

Ankur Mehrotra

There is a limit where for one type of recommendation, which we also refer to as a recommended or a campaign,
you can have to, to 750,000 items in the catalog. Now that usually is sufficient. You can split it into multiple
recommendation solutions, but then if you need higher limits, then we are happy to work with you to look at your
specific use case and see how we can help you.

Ankur Mehrotra

That’s a great question. We always tell our customers that when you are starting out with Amazon Personalize or
building a personalization system, always think about how you’re going to measure success. In success for you
would mean, an uptake in [consumer 00:14:00] rates or an uptake in conversion rates. When you’re using Amazon
Personalize as a member of service, underneath is training machine learning models, it does give you offline
metrics in terms of how well the system thinks that the machine learning model form. But those are offline
metrics and understand the efficacy of those recommendations to a certain extent through those offline metrics.

Ankur Mehrotra

We always recommend our customers do experiments with real customer traffic to measure the performance against
the business metrics that they’re trying to optimize. So we see the classic way of doing this is, run an AB test
compared to where you have coming from Amazon Personalize, running in an experiment against an experience that
has no recommendations, that’s not personalized, or any baseline.

Ankur Mehrotra

Yeah. You’re right, Rahul. We’re working on making this much more easy for Amazon Personalize customers. We do
have some resources, including documentation blocks that walk our customers through some of the best practices
they should use while setting up AB tests. But one recommendation I would make is, don’t start from scratch.
Don’t start implementing your own AB testing framework just to do this. There are a lot of other… Lot of a AB
testing frameworks out there that are widely used and leverage them. And some of them, we also do have reference
architectures where they can seamlessly integrate with Amazon Personalize and the sample code out there that you
can use. So that’s that’s one advice I would give. If you don’t have an AB testing framework already in place,
try and look around and see what exists out there and what can be used.

Ankur Mehrotra

Sure. When we see customers starting out with a personalization or a recommendation system project, I think it’s
starting out small by building out a proof of concept is the right thing to do. However, sometimes what we see
is that when customers are trying to start small, they take a very small amount of data and try to build a
recommendation system.

Ankur Mehrotra

Now, the challenge is that if you don’t have sufficient data that presents your overall data set well, then the
quality of recommendations that you may get will not be as good as what you may get if you’re using a larger set
of your data with Amazon Personalize. So that is one thing to think about as you’re doing a POC or you’re trying
to build a smaller version of a recommendation system before you scale.

Ankur Mehrotra

So having sufficient data in the right, paying attention to the quality of data is really important as you start
out. That’s the first thing I would say.

Ankur Mehrotra

There is no hard and fast rule as such, but the high level guidance I would say is, make sure… So if let’s say
you are in an eCommerce business, make sure that the data that you’re using is representative of your entire
catalog, or at least all product categories, rather than just picking the data from just one or two categories
and then later you may end up wondering why recommendations for other categories aren’t good enough. And the
same goes for users as well. If let’s say you have users of, if you have different features on a website and
different kinds of users, you want to make sure that’s also representative of your larger user base. It differs
from use case, to use case, but that’s the high level of guidance I would provide.

Ankur Mehrotra

And this does require some experimentation to get right. Yeah. So there are other things that I would say is the
ability to provide click stream events in real time. Consumer habits can change quite rapidly. So when you’re
browsing something, you may be looking for something right now that you were not looking for, let’s say three
days ago.

Ankur Mehrotra

So if your recommendation system is not aware of your recent browsing behavior, then sometimes the
recommendations can end up being not relevant, aligned with what you’re looking for right now. We always advise
our customers to try and use the legal client events capability and stream those events to Amazon Personalize,
so you can do it quite easily, either using an API that the service provides or using the [edifice amplify
00:19:39] as the creative part integrations with Amazon Personalize. That really improves the quality of
recommendations you may receive in to the service. And it can also really make a big difference in the overall
success.

Ankur Mehrotra

Good question. There are two ways to address this. The first one is, which I just mentioned that, if you have
real time events flowing into the service then the ML models will latch onto the latest, the most recent
browsing behavior of your users and change recommendations in real time. So to solve this so that as your
browsing trends and shopping trends start changing due to seasonality, the ML models will quickly adapt to it,
changing its recommendations.

Ankur Mehrotra

The second way to do this, to feed into the service. And when I say data, the historical data. The historically
interactions data that you use to give to the service, and the service uses that are train machinery models. So
in that historical data, if you have sufficient coverage where you have data from previous, let’s say holidays
or seasons built in, and if you have metadata that indicates that yes, this day was a holiday. Let’s say your
trends on Black Friday or Cyber Monday may be different. So if you can annotate your data set by saying, and let
the service know that while this day was actually a holiday, then that can be really used for the service to
learn how consumer behavior and patterns and shopping patterns change during a particular [inaudible 00:22:06].

Ankur Mehrotra

Yeah. So first of all, you’re right, we get this from so many of our customers. The amount of positive impact or
the uptake in the business output that they saw using Amazon Personalize is far, far greater than what they’re
paying for the service. And we’re happy to see that customers are deriving so much value from the service.

Ankur Mehrotra

There aren’t too many ways where you could end up cost over runs with Amazon Personalize, but there are a couple
things I will say. So Amazon Personalize can scale automatically as your traffic increases. Let’s say, if you
have more traffic during the daytime and less traffic during the nighttime, then the service can scale up and
down automatically to provision the right amount of resources to serve your recommendations.

Ankur Mehrotra

However, we also do allow the ability for you to reserves a certain recommendations per second. And that can be
really useful when you’re expecting the traffic to suddenly spike up. So for those cases, the service does
provide you a way to reserve higher, let’s say TPS. So one advice I would give is, think about when you really
need to reserve that provisioning because the increased reserved provisioning you’re also paying more. So use
that feature where you need it. And when it’s business as usual, just rely on the auto scaling features.

Ankur Mehrotra

Yeah, I think that’s always a good idea to keep some alarms to monitor how the overall integration with
Personalize and how the number of feedback calls being made are trending and alerted. If there’s something
that’s out of the ordinary.

Ankur Mehrotra

Sure. Yeah. Yeah. So especially during the pandemic, the consumption of media and entertainment services has
certainly spiked. So we see personalization becoming more and more relevant. One of our AWS customers discovery
a few months ago, they launched their first director consumer streaming service called Discovery Plus. They had
a very tight timeline to build a service end, to end and launch it.

Ankur Mehrotra

They chose Amazon Personalize to bar their recommendations and they were able to do that from absolutely nothing
to this running and production at scale, ready for prime time in a matter of few months, just going from POC to
just integrating it all the way into their system. So that’s been a great story and love working with the team
at Discovery. They’ve been great. They recommended videos and titles and similar videos and other
recommendations that you see in the Discovery Plus app are programs on Personalize.

Ankur Mehrotra

The other one that comes to my mind is Intuit is a data list customer. They’ve bought an app called Mint, which
helps users track their budget and their spending, et cetera. Intuit used Amazon Personalize to create
recommendations within the Mint app for financial offers and recommendations for financial products. It is a
great use case where you’re able to apply the service in all kinds of different use cases. And they’re seeing
great value from it. That’s another one that I really, really like.

Ankur Mehrotra

So we are seeing use cases across different industries and pleasant piece of price to see how well the models
can adapt to different use cases. We’re excited and we’re working on many new features and launches, and it’s
going to be a great year for the service.

Ankur Mehrotra

Thanks, Rahul. It’s always great talking to you.