Tag Archives: Personalization

Amazon Go isn’t about no-checkout. Here’s what we missed.

After long anticipation and a whole year of delay, Amazon Go was finally launched to the general public with much fanfare three weeks ago. Well, that is if you call opening one small store on the ground floor of your giant corporate building – a “launch”.

Amazon Go in Seattle, December 2016.jpgThe prototype Amazon Go store at Day One, Seattle. By SounderBruce – CC BY-SA 4.0

 

The move has reignited the debate about what impact the new technology will have on 2.3M cashiers in the U.S., whose jobs might be eliminated, and on the retail industry as a whole.

But the real question runs deeper. It’s very clear that operating an Amazon Go store comes at a major cost. If all the cost saving is the paychecks of a few cashiers, but the price to pay is installing and maintaining a very large number of advanced sensors – not to mention the initial price of developing the technology – the bottom line is clearly not a profitable one.

Furthermore, if all Amazon wants is to remove the need for cashiers, self-checkout has existed for quite some time and is likely much cheaper to install. Walmart took care to announce that it expanded its “scan and go” app in advance of the Amazon Go launch – yet another alternative. Why is it so critical for Amazon to eliminate the explicit checkout step altogether?

Is it all perhaps just a publicity stunt? Amazon is not known for pulling such stunts; when it launches something big, it’s usually to truly make a strategic move in that direction.

To better understand Amazon’s motivations, we need to go back to how we, the target audience, have been looking at this story, and in particular – the user data story.

If no cashier or RFID readers scan your (virtual) shopping bag, it inherently means that Amazon should have a different way to know what you put in it. Using cameras means it will need even more than that – it will need to know which item you picked up and only looked at, and which item you did decide to purchase eventually.

So, Amazon Go means Amazon should be watching every move you make in the store. In fact, it means Amazon must watch everything you do, or else the whole concept will not work. This requirement that we accept so naturally, this seemingly so obvious conclusion… this is the heart of Amazon’s strategy.

Just think about it: You walk into an Amazon store; your body and face are being scanned and tracked to the point that you can be perfectly recognized; your every move is being tracked and monitored, and all are mapped to a personally identifiable profile tied to your credit card, managed by a powerful private corporation. In any other context, this would trigger a firestorm of privacy and security charges, but for Amazon Go – well, that’s what it takes to deliver on its promise, isn’t it?

What does Amazon gain from this data?

What’s fascinating to notice is that this data enables the transfer of an entire stack of technologies and methodology from online to offline, from the app or site to the brick-and-mortar, which is a dramatic gain. Think about the parallels to what we already came to expect from online –  browse sessions, recommendations, abandoned cart flows… That item you were considering? Amazon now knows you considered it. Data scientists all over the retail world would love to put their hands on such physical, in-store behavioral data.

For now, the technology may be limited to groceries as a first step. But we could expect Amazon to work to expand it – rather than to more locations – to further verticals. Just think of the personalized recommendations and subscription services such a technology could drive in high-end wine stores, as one example.

One indication that may show Amazon is truly after the data rather than the stores themselves will be if Amazon licenses Go to other retailers or small players. This will immediately position it as a data broker. In any case, retailers have yet another good reason to keep a close look on Amazon’s disruptive moves.

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So Long, and Thanks for All the Links

 

Prismatic is shutting down its app.

I’ve been fascinated by algorithmic approaches to information overload for quite some time now. It seemed like one of those places where the Web changed everything, and now we need technology to kick in and make our lives so much easier.

Prismatic_logo,_June_2014Prismatic was one of the more promising attempts to that I’ve seen, and I’ve been a user ever since its launch back in 2012. Every time I opened it, it never failed to find me real gems, especially given the tiny setup it required when I first signed up. Prismatic included explicit feedback controls, but it seemed to excel in using my implicit feedback, which is not trivial at all for a mobile product.

flipboard-logo-iconFlipboard is likely the best alternative out there right now, and its excellent onboarding experience helped me get started quickly with a detailed list of topics to follow. With reasonable ad-powered revenue, which Prismatic seemed to shun for whatever reason, it is also less likely to shut down anytime soon. Prismatic still does a much better job than Flipboard in surfacing high-quality, long-tail, non-mainstream sources; let’s hope Flipboard continues improving to get there.

It seems, though, that news personalization is not such a strong selling point. Recently, Apple moved from a pure personalized play for its Apple News app to also add curated top stories, as its view counts disappointed publishers. In my own experience, even the supposed personalized feed was mostly made up of 3-4 mainstream sources anyway. Let’s hope that this is not where information overload is leading us back to. Democratizing news and getting a balanced and diverse range of opinions and sources is a huge social step forward, that the Web and Social Media have given us. Let’s not go backwards.

Microsoft Israel ReCon 2015 (or: got to start blogging more often…)

Yes, two consecutive posts on the same annual event are not a good sign to my virtual activity level… point taken.

MSILSo 2 weeks ago, Microsoft Israel held its second ReCon conference on Recommendations and Personalization, turning its fine 2014 start into a tradition worth waiting for. This time it was more condensed than last year (good move!) and just as interesting. So here are three highlights I found worth reporting about:

Uri Barash of the hosting team gave the first keynote on Cortana integration in Windows 10, talking about the challenges and principles used. Microsoft places a high empasis on the user’s trust, hence Cortana does not use any interests that are not explicitly written in Cortana’s notebook, validated by the user. If indeed correct, that’s somewhat surprising, as it limits the recommendation quality and moreover – the discovery experience for the user, picking up potential interests from the user’s activity. I’d still presume that all these implicit interests are probably used behind the scenes, to optimize the content from explicit interests.

ibm_logoIBM Haifa Research Labs have been doing work for some years now on enterprise social networks, and mining connections and knowledge from such networks. In ReCon this year, Roy Levin presented a paper to be published in SIGIR’15, titled “Islands in the Stream: A Study of Item Recommendation within an Enterprise Social Stream“. In the paper, they discuss a feature for a personalized newsfeed included in IBM’s enterprise social network “IBM Connections”, and provide some background and the personalized ranking logic for the feed items.

They then move on to describe a survey they have made among users of the product, to analyze their opinions on specific items recommended for them in their newsfeed, similar to Facebook’s newsfeed surveys. Through these surveys, the IBM researchers attempted to identify correlations between various feed item factors, such as post and author popularity, post personalization score, how surprising an item may be to a user and how likely a user is to want such serevdipity, etc. The actual findings are in the paper, but what may actually be even more interesting is the deep dissection in the paper of the internal workings of the ranking model.

Outbrain-logoAnother interesting talk was by Roy Sasson, Chief Data Scientist at Outbrain. Roy delivered a fascinating talk about learning from lack of signals. He began with an outline of general measurement pitfalls, demonstrating them on Outbrain widgets when analyzing low numbers of of clicks on recommended items. Was the widget visible to the user? where was it positioned in the page (areas of blindness)? what items were next to the analyzed item? were they clicked? and so on.

Roy then proceeded to talk about what we may actually be able to learn from lack of sharing to social networks. We all know that content that gets shared a lot on social networks is considered viral, driving a lot of discussion and engagement. But what about content that gets practically no sharing at all? and more precisely, what kind of content gets a lot of views, but no sharing? Well, if you hadn’t guessed already, that will likely be content users are very interested to see, but would not admit to it, namely provocative and adult material. So in a way, leveraging this reverse correlation helped Outbrain automatically identify porn and other sensitive material. This was then not used to filter all of this content out – after all, users do want to view it… but it was used to make sure that the recommendation strip includes only 1-2 such items so they don’t take over the widget, making it seem like this is all Outbrain has to offer. Smart use of data indeed.

Thoughts on Plus

So what’s the deal with Google+? is Google really taking on Facebook? is that a classic “me too” play, or something smarter?

It took me a while to figure out my opinion, but several interesting articles got the stars aligned just right for a split second to make some sense (until some new developments will soon de-align them again :-)).

Take a deep breath. OK, here it comes:

Google+ is Google’s take on Social.

Yes, I know, who would have thought?…
It’s just that Google’s definition of Social is a bit different.

At Facebook (and really, for most of us), Social is about conversations with people you actually know.
At Google, Social is the new alias for Personalization.

It’s pretty simple: Google’s business model has always meant the more I know about you, the better I can monetize through more targeted ads. At first, it was all about the search engine being where you always start your surfing, and Google was well seated. As traffic to social networks grew, culminating with Facebook overtaking Google on March 2010, it became increasingly clear that a larger portion of our information starts being served to us from social networks. Google was left out.

Why was that so important? Google still had tons of searches, an ever-growing email market share, and successful news aggregation and rss reader, among other assets. That’s quite a lot to know about us, isn’t it?

It turned out that the missing link often was the starting point. You would learn about the new thing, the new trend, the new gadget you want to get, while you were out of Google’s reach. By the time you got into the Google web, you may have already got your mind set on what you want to get and even where, making the Google ads a lot less effective.

The Follow versus Friend model is also a huge issue. It means that G+ is about self-publishing and positioning yourself, and not about conversations. That suits Google very well, and is not just a differentiation from FB. This model drives you to follow based on interest, building an interest graph rather than a social graph, and being a lot more useful to profiling you than your social connections.

That interest graph, in turn, makes sure your first encounter with those things that make you tick is inside the Google web. It also links back well to the fine assets that Google holds today, from your docs to your publishing tools. So when Google News announces those funny badges, and you may have thought “Heh, who would want to put these stinking badges on their profiles…” – think again. Their private nature is just fine for Google. It’s a way to ask you to validate your inferred interests: “So tell us, is that interest of yours in US politics that we have inferred from your news reading a real inherent interest, or was it just a transient interest that will melt away after the election?“. Again – big difference for profiling.

Finally, Google+ is positioned to be a professional network. Focusing on interests and having anyone able to follow you, will keep away the teens and lure the self-proclaimed professionals. In that sense, LinkedIn may have more of a reason for concern, at least as the content network it now tries to be. It’s quite likely that G+ does not even aim to unseat Facebook, only to dry it out of its professional appeal, and leave it with what we started with – party/kids photos and keeping track of what those old friends are up to.

I guess I already know what network I’ll be posting a link to this post to…