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How I Wish Amazon Reviews Worked

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Posted by Dr. Pete

This is not a post about SEO. It is, however, a post about the future of search. This surprised even me – when I started writing this piece, it really was just an idea about building a better review. I realized, though, that finding relevant reviews is a useful microcosm of the broader challenge search engines face. Specifically, I want to talk about three S’s – Social, Sentiment, and Semantics, and how each of these pieces fit the search puzzle. Along the way, I might just try to build a better mousetrap.

The Core Problem

Product reviews are great, but on a site as big and popular as, filtering reviews isn’t much easier than filtering Google search results. Here’s the review section for the Kindle Fire:

Kindle Fire on Amazon - 10,859 reviews

That’s right – 10,859 reviews to sort through. Even if I just decide to look at the 5 stars and 1 stars, that’s still 7,208 reviews. If I could click and skim each one of those 7,208 in about 5 seconds, I’ve got roughly 10 hours of enjoyment ahead of me (if I don’t eat or take bathroom breaks). So, how can we make this system better?

(1) The Social Graph

These days our first answer is usually: “SOCIAL!” Social is sexy, and it will solve all our problems with its sexy sexiness. The problem is that we tend to oversimplify. Here’s how we think about Search + Social, in our perfect world:

Search/Social Intersection = Sexy

Unfortunately, it’s not quite so magical. There are two big problems, whether we’re talking about product reviews or organic search results. The first problem is a delicate one. Some of the people that you associate with are – how shall I put it – stupid.

Ok, maybe stupid is a bit harsh, but just because you’re connected to someone doesn’t mean you have a lot in common or share the same tastes. So, we really want to weed out some of the intersection, like Crazy Cousin Larry…

Search/Social Intersection minus Crazy Cousin Larry

It’s surprisingly hard to figure out who we actually sit at the Crazy-Larry table. Computationally, this is a huge challenge. There’s a bigger problem, though. In most cases, especially once we start weeding people out, the picture actually looks more like this:

Real Search/Social Intersection - Very Small

Even with relatively large social circles, the actual overlap of your network and any given search result or product is often so small as to be useless. We can extend our circles to 2nd- and 3rd-degree relationships, but then relevance quickly suffers.

To be fair to Amazon, they’ve found one solution – they elicit user feedback of the reviews themselves as a proxy social signal:

20,396 people thie review helpful

This approach certainly helps, but it mostly weeds out the lowest-quality offerings. Reviews of reviews help control quality, but they don't do much to help us find the most relevant information.

(2) Sentiment Analysis

Reviews are a simple form of sentiment analysis – they help us determine if people view a product positively or negatively. More advanced sentiment analysis uses natural-language processing (NLP) to try to extract the emotional tone of the text.

You may be wondering why we need more advanced sentiment analysis when someone has already told us how they feel on a 1-5 scale. Welcome to what I call “The Cupholder Problem”, something I’ve experienced frequently as a parent trying to buy high-end products on Amazon. Consider this fictional review which is all-too-based in reality:

The Cupholder Problem (fake review)

I’m exaggerating, of course, but the core problem is that reviews are entirely subjective, and sometimes just one feature or problem can ruin a product for someone. Once that text is reduced to a single data point (one star), though, the rest of the information in the content is lost.

Sentiment analysis probably wouldn’t have a dramatic impact on Amazon reviews, but it’s a hot topic in search in general because it can help extract emotional data that’s sometimes lost in a summary (whether it’s a snippet or a star rating). It might be nice to see Amazon institute some kind of sentiment correction process, warning people if the tone of their review doesn’t seem to match the star rating.

(3) Semantic Search

This is where things get interesting (and I promise I’ll get back to sentiment so that the previous section has a point). The phrase “semantic search” has been abused, unfortunately, but the core idea is to get at the meaning and conceptual frameworks behind information. Google Knowledge Graph is probably the most visible, recent attempt to build a system that extracts concepts and even answers, instead of just a list of relevant documents.

How does this help our review problem? Let’s look at the “Thirsty” example again. It’s not a dishonest review or even useless – the problem is that I fundamentally don’t care about cupholders. There are certain features that matter a lot to me (safety, weight, durability), others that I’m only marginally sensitive to (price, color), and some that I don’t care about at all (beverage dispensing capability).

So, what if we could use a relatively simple form of semantic analysis to extract the salient features from reviews for any given product? We might end up with something like this:

Sample Review w/ Feature Extraction

Pardon the uninspired UI, but even the addition of a few relevant features could help customers drill down to what really matters to them, and this could be done with relatively simple semantic analysis. This basic idea also illustrates some of the direction I think search is heading.  Semantic search isn’t just about retrieving concepts; it’s also about understanding the context of our questions.

Here’s an interesting example from Google Australia ( Search for “Broncos colors” and you’ll get this answer widget (hat tip to Brian Whalley for spotting these):

Denver Broncos Colors (

It’s hardly a thing of beauty, but it gets the job done and probably answers the query for 80-90% of searches. This alone is an example of search returning concepts and not just documents, but it gets even more interesting. Now search for “Broncos colours”, using the British spelling (still in You should get this answer:

Brisbane Broncos Colors

The combination of and the Queen’s English now has Google assuming that you meant Australia’s own Brisbane Broncos. This is just one tiny taste of the beginning of search using concepts to both deliver answers and better understand the questions.

(4) Semantics + Sentiment

Let’s bring this back around to my original idea. What if we could combine semantic analysis (feature extraction) and sentiment in Amazon reviews? We could easily envision a system like this:

Reviews with Feature Extraction + Sentiment

I’ve made one small addition – a positive or negative (+/-) sentiment choice next to each feature. Maybe I only want to see products where people spoke highly of the value, or rule out the ones where they bashed the safety. Even a few simple combinations could completely change the way you digest this information.

The Tip of the Penguin

This isn’t the tip of the iceberg – it’s the flea on the wart on the end of the penguin’s nose on the tip of the iceberg. We still think of Knowledge Graph and other semantic search efforts as little more than toys, but they’re building a framework that will revolutionize the way we extract information from the internet over the next five years. I hope this thought exercise has given you a glimpse into how powerful even a few sources of information can be, and why they’re more powerful together than alone. Social doesn’t hold all of the answers, but it is one more essential piece of a richer puzzle.

I’d also like to thank you for humoring my Amazon reviews insanity. To be fair to Amazon, they’ve invested a lot into building better systems, and I’m sure they have fascinating ideas in the pipe. If they’d like to use any of these ideas, I’m happy to sell them for the very reasonable price of ONE MILL-I-ON DOLLARS.

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Is Google Concerned About Amazon Eating Their Lunch?

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Leveling The Playing Field

When monopolies state that they want to “level the playing field” it should be cause for concern.

Groupon is a great example of how this works. After they turned down Google’s buyout offer, Google responded by…

The same deal is slowly progressing in the cell phone market: “we are using compatibility as a club to make them do things we want.”

Leveling Shopping Search

Ahead of the Penguin update Google claimed that they wanted to “level the playing field.” Now that Google shopping has converted into a pay-to-play format & has opted out of participation, Google once again claims that they want to “level the playing field”:

“We are trying to provide a level playing field for retailers,” [Google’s VP of Shopping Sameer Samat] said, adding that there are some companies that have managed to do both tech and retail well. “How’s the rest of the retail world going to hit that bar?”

This quote is particularly disingenuous. For years you could win in search with a niche site by being more focused, having higher quality content & more in-depth reviews. But now even some fairly large sites are getting flushed down the ranking toilet while the biggest sites that syndicate their data displace them (see this graph for an example, as Pricegrabber is the primary source for Yahoo! Shopping).

How Google Drives Businesses to Amazon, eBay & Other Platforms

Google has spent much of the past couple years scrubbing smaller ecommerce sites off the web via the Panda & Penguin updates. Now if small online merchants want an opportunity to engage in Google’s search ecosystem they have a couple options:

  • Ignore it: flat out ignore search until they build a huge brand (it’s worth noting that branding is a higher level function & deep brand investment is too cost intensive for many small niche businesses)
  • Join The Circus: jump through an endless series of hoops, minimizing their product pages & re-configuring their shopping cart
  • PPC: operate at or slightly above the level of a non-functional thin phishing website & pay Google by the click via their new paid inclusion program
  • Ride on a 3rd Party Platform: sell on one of the larger platforms that Google is biasing their algorithms toward & hope that the platform doesn’t cut you out of the loop.

Ignoring search isn’t a lasting option, some of the PPC costs won’t back out for smaller businesses that lack a broad catalog to do repeat sales against to lift lifetime customer value, SEO is getting prohibitively expensive & uncertain. Of these options, a good number of small online merchants are now choosing #4.

Operating an ecommerce store is hard. You have to deal with…

  • sourcing & managing inventory
  • managing employees
  • technical / software issues
  • content creation
  • marketing
  • credit card fraud
  • customer service
  • shipping

Some services help to minimize the pain in many of these areas, but just like people do showrooming offline many also do it online. And one of the biggest incremental costs added to ecommerce over the past couple years has been SEO.

Google’s Barrier to Entry Destroys the Diversity of Online Businesses

How are the smaller merchants to compete with larger ones? Well, for starters, there are some obvious points of influence in the market that Google could address…

  • time spent worrying about Penguin or Panda is time that is not spent on differentiating your offering or building new products & services
  • time spent modifying the source code of your shopping cart to minimize pagecount & consolidate products (and various other “learn PHP on the side” work) is not spent on creating more in-depth editorial
  • time switching carts to one that has the newly needed features (for GoogleBot and ONLY GoogleBot) & aligning your redirects is not spent on outreach and media relations
  • time spent disavowing links that a competitor built into your site is not spent on building new partnerships & other distribution channels outside of search

Ecosystem instability taxes small businesses more than larger ones as they…

The presumption that size = quality is false. A fact which Google only recognizes when it hits their own bottom line.

Anybody Could Have Saw This Coming

About a half-year ago we had a blog post about ‘Branding & The Cycle‘ which stated:

algorithmically brand emphasis will peak in the next year or two as Google comes to appreciate that they have excessively consolidated some markets and made it too hard for themselves to break into those markets. (Recall how Google came up with their QDF algorithm only *after* Google Finance wasn’t able to rank). At that point in time Google will push their own verticals more aggressively & launch some aggressive public relations campaigns about helping small businesses succeed online.

Since that point in time Amazon has made so many great moves to combat Google:

All of that is on top of creating the Kindle Fire, gaining content streaming deals & their existing strong positions in books and e-commerce.

It is unsurprising to see Google mentioning the need to “level the playing field.” They realize that Amazon benefits from many of the same network effects that Google does & now that Amazon is leveraging their position atop e-commerce to get into the online ads game, Google feels the need to mix things up.

If Google was worried about book searches happening on Amazon, how much more worried might they be about a distributed ad network built on Amazon’s data?

Said IgnitionOne CEO Will Margiloff: “I’ve always believed that the best data is conversion data. Who has more conversion data in e-commerce than Amazon?”

“The truth is that they have a singular amount of data that nobody else can touch,” said Jonathan Adams, iCrossing’s U.S. media lead. “Search behavior is not the same as conversion data. These guys have been watching you buy things for … years.”

Amazon also has an opportunity to shift up the funnel, to go after demand-generation ad budgets (i.e. branding dollars) by using its audience data to package targeting segments. It’s easy to imagine these segments as hybrids of Google’s intent-based audience pools and Facebook’s interest-based ones.

Google is in a sticky spot with product search. As they aim to increase monetization by displacing the organic result set they also lose what differentiates them from other online shopping options. If they just list big box then users will learn to pick their favorite and cut Google out of the loop. Many shoppers have been trained to start at even before Google began polluting their results with paid inclusion:

Research firm Forrester reported that 30 percent of U.S. online shoppers in the third quarter began researching their purchase on, compared with 13 percent who started on a search engine such as Google – a reversal from two years earlier when search engines were more popular starting points.

Who will Google partner with in their attempt to disrupt Amazon? Smaller businesses, larger corporations, or a mix of both? Can they succeed? Thoughts?