Product Search: Hard Numbers or Flights of Fancy?
September 16, 2017
I read “Amazon Shakes Up Search, Again.” I was not aware of Amazon’s shaking up search because there are numerous ways to define the term. The write up narrows “search” to people in three countries who buy products or look for product information online. Ah, good, I think.
My hunch is that the “shake up” is related to the data that suggests Amazon has three times as many product searches than Google. The assertion did not “shake” me up because Google’s product search is not particularly useful. I thought that Froogle had a shot at becoming a daughter-of-Amazon, but the GOOG lost interest. Sure, I can search for a product using Google, but the results are often not what I want. Your mileage may vary.
But back to the write up. I noted some factoids which may be useful to those who are giving talks about product search, those who work for a consulting firm and must appear super smart, or folks like me who collect data, no matter how wild or crazy.
Here we go with the “shake up” from 3,100 consumers in the US, Germany, and the UK:
- 72 percent use Amazon to research a product before buying the product
- 51 percent use Amazon as a way to get “alternative ideas”
- 26 percent use Amazon to get information and price when they plan on visiting a real store
- 84 percent of “searchers” in the US use Google
- 71 percent of “searchers” in the US use Amazon
- 36 percent use Facebook in the US use Amazon
- 24 percent use Pinterest in the US use Amazon
- 31 percent use eBay in the US use Amazon
- 80 percent in the UK use Google
- 73 percent use Amazon in the UK
- 9 percent use Bing in France
- 6 percent us Bing in the UK
- 6 percent use Bing in Germany
- 20 percent of searchers use Bing
- Amazon stocks or “carries” 353 million products. Put aside the idea that percentages usually work on a scale of zero to 100, please:
- 59 percent are “health and beauty”
- 57 percent are “music, movies, or games”
- 55 percent are “books”
- 52 percent fashion or clothing
- 46 percent are home appliances
- 40 percent are furniture and home furnishings
- 39 percent are toys
- 34 percent are sports equipment and clothing
- 26 percent are garden equipment and furniture (?)
- 26 percent are food and grocery
- 9 percent are beer, wine and spirits.
So if there are 353 million products and the percentage data are correct, the total percentage of products is 443 percent. I did not the duplicate furniture entry but counted the percentage anyway. Also, there was no value for garden equipment and furniture so I used “26 percent”. Close enough for millennials steeped in new math.
My math teacher (Verna Blackburn) in my freshman year of high school in 1958 had an dunce cap. I think I can suggest one research report author who might have been invited to wear the 24 inch tall cap. The 443 percent would shake up deal Miss Blackburn. She also threw chalk at students when they made errors when solving on the blackboards which were on three walls of her classroom. The fourth wall looked out over asphalt to the smokestacks of the former RG Letourneau mortar factory. Getting math wrong at that outfit could indeed shake up some things.
Stephen E Arnold, September 16, 2017
Google Leans Left with Climate Search Results
September 13, 2017
Most Google users never think about bias and politics when they search or read suggested pages. Many, though, believe that the average Google user is being sold a bill of goods when searching about climate on Google. A recent WUWT investigation discovered that Google is manipulating the search results to favor left-leaning political ideas. WUWT quotes Google as claiming that their ranking is determined by the following criteria: “High-quality information pages on scientific topics should represent a well established scientific consensus on issues where such consensus exists.” (Section 3.2)
The author goes on to explain,
But the allegations of ‘scientific consensus’ are made only in one field – climate alarmism! ‘Scientific consensus’ is almost an oxymoron. The consensus is a decision-making method used outside of science.
Google was set up to be free from bias, but according to their own explanation, they tend to support the most popular opinion which is a dangerous route to take. Would people want a truly impartial system of search, allowing each searcher to evaluate the source for accuracy and ‘scientific consensus’, or do we like to rely on others, and Google, to make the hard decisions for us?
Catherine Lamsfuss, September 13, 2017
Old School Searcher Struggles with Organizing Information
September 7, 2017
I read a write up called “Semantic, Adaptive Search – Now that’s a Mouthful.” I cannot decide if the essay is intended to be humorous, plaintive, or factual. The main idea in the headline is that there is a type of search called “semantic” and “adaptive.” I think I know about the semantic notion. We just completed a six month analysis of syntactic and semantic technology for one of my few remaining clients. (I am semi retired as you may know, but tilting at the semantic and syntactic windmills is great fun.)
The semantic notion has inspired such experts as David Amerland, an enthusiastic proponent of the power of positive thinking and tireless self promotion, to heights of fame. The syntax idea gives experts in linguistics hope for lucrative employment opportunities. But most implementations of these hallowed “techniques” deliver massive computational overhead and outputs which require legions of expensive subject matter experts to keep on track.
The headline is one thing, but the write up is about another topic in my opinion. Here’s the passage I noted:
The basic problem with AI is no vendor is there yet.
Okay, maybe I did not correctly interpret “Semantic, Adaptive Search—Now That’s a Mouthful.” I just wasn’t expecting artificial intelligence, a very SEO type term.
But I was off base. The real subject of the write up seems to be captured in this passage:
I used to be organized, but somehow I lost that admirable trait. I blame it on information overload. Anyway, I now spend quite a bit of time searching for my blogs, white papers, and research, as I have no clue where I filed them. I have resorted to using multiple search criteria. Something I do, which is ridiculous, is repeat the same erroneous search request, because I know it’s there somewhere and the system must have misunderstood, right? So does the system learn from my mistakes, or learn the mistakes? Does anyone know?
Okay, disorganized. I would never have guessed without a title that references semantic and adaptive search, the lead paragraph about artificial intelligence, and this just cited bit of exposition which makes clear that the searcher cannot make the search systems divulge the needed information.
One factoid in the write up is that a searcher will use 2.73 terms per query. I think that number applies to desktop boat anchor searches from the Dark Ages of old school querying. Today, more than 55 percent of queries are from mobile devices. About 20 percent of those are voice based. Other queries just happen because a greater power like Google or Microsoft determines what you “really” wanted is just the ticket. To me, the shift from desktop to mobile makes the number of search terms in a query a tough number to calculate. How does one convert data automatically delivered to a Google Map when one is looking for a route with an old school query with 2.73 terms? Answer: You maybe just use whatever number pops out from a quick Bing or Google search from a laptop and go with the datum in a hit on an ad choked result list.
The confused state of search and content processing vendors is evident in their marketing, their reliance on jargon and mumbo jumbo, and fuzzy thinking about obtaining information to meet a specific information need.
I suppose there is hope. One can embrace a taxonomy and life will be good. On the other hand, disorganization does not bode well for a taxonomy created by a person who cannot locate information.
Well, one can use smart software to generate those terms, the Use Fors and the See Alsos. One can rely on massive amounts of Big Data to save the day. One can allow a busy user of SharePoint to assign terms to his or her content. Many good solutions which make information access a thrilling discipline.
Now where did I put that research for my latest book, “The Dark Web Notebook”? Ah, I know. In a folder called “DWNB Research” on my back up devices with hard copies in a banker’s box labeled “DWNB 2016-2017.”
Call me old fashioned but the semantic, syntactic, artificially intelligent razzmatazz underscores the triumph of jargon over systems and methods which deliver on point results in response to a query from a person who knows that for which he or she seeks.
Plus, I have some capable research librarians to keep me on track. Yep, real humans with MLS degrees, online research expertise, and honest-to-god reference desk experience.
Smart software and jargon requires more than disorganization and arm waving accompanied by toots from the jargon tuba.
Stephen E Arnold, September 7, 2017
Former Google Employee Launches a New Kind of Search
September 1, 2017
We learn about a new approach to internet search from Business Insider’s piece, “Once Google’s Youngest Employee, this Woman Just Unveiled a New Search Company that Might Make Google Worried.” The new platform aims to cut through the traditional results list, which, depending on the search term(s), can take a lot of time to comb through. It also hopes to connect users to information that they didn’t know to search for. Reporter Caroline Cakebread writes:
Led by founder and CEO Falon Fatemi, Node emerged from stealth on Tuesday ready to take on its lofty goal of changing the way we discover information. By using AI to connect you or your business with the right opportunity at the right time, Node wants to ‘accelerate serendipity’ on the web. Node’s patent-pending technology works by indexing people, places, products, and companies instead of web pages, and using this data to connect customers to opportunities. So far, it has half a billion profiles. The AI understands the relationships between people and companies, and can marry its data layer with a customer’s personal data. Node is currently integrated with Salesforce, and customers can ask questions like ‘What company will be most interested in my product?’ Node will tell the customer who or what they need to connect with, why it came up with that answer, and even what to say to make the most of the opportunity. It’s searching without using a search box.
Node began as Fatemi’s personal project, and now her firm has raised $16.3 million in funding so far. She envisions her new tech as the “intelligence layer of the internet,” as Cakebread puts it, and believes any realm of life, from sales strategy to dating options, could benefit from this approach.
Fatemi started at Google while still in college. She wrote an article for Fast Company a couple years ago, “I Joined Google at 19. Here’s What I Learned,” in which she credits her time at Google with installing many of the qualities that have made her a successful entrepreneur. See that article for those lessons learned.
Cynthia Murrell, September 01, 2017
Google and Video Search: Still a Challenge
August 31, 2017
I read “How YouTube Perfected the Feed.” The main idea is that Google used smart software to make YouTube videos easier to find. The trick is not keyword search. Google’s YouTube, which the write up calls a “pillar of the Internet,” uses signals to identify what a person want. Then smart software delivers recommendations. The “new” YouTube’s secret sauce is described this way:
McFadden [a Google wizard] revealed the source of YouTube’s suddenly savvy recommendations: Google Brain, the parent company’s artificial intelligence division, which YouTube began using in 2015. Brain wasn’t YouTube’s first attempt at using AI; the company had applied machine-learning techniques to recommendations before, using a Google-built system known as Sibyl. Brain, however, employs a technique known as unsupervised learning: its algorithms can find relationships between different inputs that software engineers never would have guessed. One of the key things it does is it’s able to generalize,” McFadden said. “Whereas before, if I watch this video from a comedian, our recommendations were pretty good at saying, here’s another one just like it. But the Google Brain model figures out other comedians who are similar but not exactly the same — even more adjacent relationships. It’s able to see patterns that are less obvious.”
The point of the exercise is to generate more ad revenue. With competition from Facebook and others, Google is facing another crack in its control of search. Amazon may generate three times the number of product searches as Google. That’s another problem for the GOOG.
Now the talk about smart software is thrilling to many. For me, I highlighted this statement in the article as quite suggestive about the method:
YouTube’s emphasis on videos related to ones you might like means that its feed consistently seems broader in scope — more curious — than its peers. The further afield YouTube looks for content, the more it feels like an escape from other feeds.
The smart software is not about search. Google is processing signals and looking for similarities. I don’t want to be a grouser, but these themes have peppered Google patent documents and technical papers for many years. In my Google: The Digital Gutenberg I reviewed some of the wonkier video ideas. (By the way, the “Gutenberg” metaphor refers to the automatically generated content which Google outputs in response to user actions. Facebook may be more prolific today, but when I was working on Google: The Digital Gutenberg, Google had the distinction of being the world’s largest digital artifact producer.
Several observations:
First, finding videos remains a difficult information retrieval task. I recall the promising approach of Exalead, before Dassault bought the company and used the technology to reduce its dependence on Autonomy and deploy a way to find nuts and bolts. Exalead converted text to speech, generated some semi-useful metadata, and allowed me to search for a word or phrase. The system would then display links to videos which contained the string. The problem with video search is that it is visual and, to my knowledge, no one has figured out how to have software convert an image to a searchable string. Years ago, I saw a demo from an Israeli company whose software could “watch” a soccer match and flag the goals sometimes. Google’s video search is useful when one looks for words in video titles, video descriptions, video channel names, or the entity producing or starring in the video.
Second, recommendations work reasonably well for digital Walmart-type shoppers. However, many recommendations are off the wall. I bought a bottle of itch reliever spray for my dog. The product was designed for saddle horses. Now Amazon happily shows me boots, bits, and bridles. Other recommendation systems will work the same way. The reason? Signals are given incorrect “weights” and the clustering methods drift away because many smart software methods are “greedy.” (I have a for fee lecture on this subject which is pretty darned interesting and important. Curious? Write benkent2020 at yahoo dot com for info.)
Third, Google’s smart software for video continues to struggle with uploads that are on some pretty dicey topics. I routinely get links to YouTube videos which require me to be over 18. You can check out Google’s filtering for certain content by running queries on both YouTube.com and GoogleVideo.com for “nasheed.” Yep, interesting “promotional” videos are in evidence.
Net net: Talk about smart software creates the impression that great progress in video content access is being made. I agree. There is progress; however, finding videos remains a work in progress.
I suppose Amazon will sell me a horse when it runs out of farm fresh Echoes. Google is recommending videos to me which don’t match what I usually look for. I was curious about non Newtonian fluids. Guess what Google suggested I view? A Chinese table tennis match and my own video.
There you go.
Stephen E Arnold, August 31, 2017
Mobile Search: Has the Desktop Boat Anchor Search Been Cut Loose??
August 29, 2017
We noted “57% of Search Traffic Is Now Mobile, According to Recent Study.” Who knows if the research is statistically valid, if the math is correct, or the questions ones that an academic would okay?
Nevertheless, if we assume the information is mostly on point, the good old days of big screens, mindless Web surfing just to see what’s online, and mostly uncensored information are gone.
We learned:
A webpage of a particular website most likely to show up first in search results will be different 35% of the time, BrightEdge found. ”If brands do not track and optimize for both device channels, they are likely to misunderstand the opportunities and threats affecting them. It is recommended that marketers assess the proportion of their traffic coming from mobile and desktop and adjust their strategy accordingly.
If I understand this passage correctly, one gets to create, support, and tune two Web sites: One for the boat anchor crowd and one for the zippy mobile users. Want the full report? Click here.
Stephen E Arnold, August 29, 2017
Accenture Makes Two Key Acquisitions
August 29, 2017
Whither search innovation? It seems the future of search is now about making what’s available work as best it can. We observe yet another effort to purchase existing search technology and plug it into an existing framework; DMN reports, “Accenture Acquires Brand Learning and Search Technologies.” Brand Learning is a marketing and sales consultancy, and Search Technologies is a technology services firm. Will Accenture, a professional-services firm, work to improve the search and analysis functionalities within their newly acquired tools? DMN’s Managing Editor Elyse Dupre reports:
A press release states that Brand Learning’s advisory team will join the management consulting and industry specialists within Accenture’s Customer and Channels practice. The partnership, according to the press release, will enhance Accenture’s offerings in terms of marketing and sales strategy, organizational design, industry-specific consulting, and HR and leadership.
It is unclear whether the “advisory team” includes any of the talent behind Brand Learning’s software. As for the Search Technologies folks, the article gives us more reason to hope for further innovation. Citing another press release, Dupre notes that company’s API-level data connectors will greatly boost Accenture’s ability to access unstructured data, and continues:
Search Technologies will join the data scientists and engineers within Accenture Analytics. According to the press release, this team will focus on creating solutions that make unstructured content (e.g. social media, video, voice, and audio) easily searchable, which will support data discovery, analytics, and reporting. Accenture’s Global Delivery Network will also add a delivery center in Costa Rica, the release states, which will serve as the home-base for the more than 70 Search Technologies big data engineers who reside there. This team focuses on customer and content analytics, the release explains, and will work with Accenture Interactive’s digital content production and marketing services professionals.
Furthermore, Kamran Khan, president and CEO of Search Technologies, will now lead a new content analytics team that will reside within Accenture Analytics.
Let us hope those 70 engineers are given the freedom and incentive to get creative. Stay tuned.
Cynthia Murrell, August 29, 2017
Google Drops Instant Search as Mobile Use Rises
August 28, 2017
As more and more Googlers turn to mobile devices to access the search giant the Instant Search feature, first introduced in 2010, becomes irrelevant. Originally, this feature was a time saving (albeit milliseconds) feature giving Google users a much-needed edge in search. But that was for desktops. Now that mobile is king, Google is rethinking their strategies.
According The Verge,
…more than half of Google searches happen on mobile, with the scales continually tipping away from desktop as time goes on. On mobile screens, Instant Search doesn’t make as much sense given we use our fingers and virtual buttons to interact with software, and trying to load a results page on top of the onscreen keyboard isn’t exactly good user experience design.
Internet based services recognizing the trend toward mobile use is nothing new, but Google eliminating one of its hallmark features shows that mobile use for search is much more than simply a trend. Always leading the way, Google is making a statement about the direction of search and we expect others to quickly jump on the bandwagon.
Catherine Lamsfuss, August 28, 2017
Google Home Still Knows More
August 21, 2017
Amazon has infiltrated our lives as our main shopping destination. Amazon is also trying to become our best friend, information source, and digital assistant via Alexa. Alexa provides a wealth of services, such as scheduling appointments, filling shopping orders, playing music, answering questions, and more. While Amazon Alexa has a steady stream of users, Ad Week says, “Google Home Is 6 Times More Likely To Answer Your Questions Than Amazon Alexa.”
The company 360i developed software that would determine which digital assistant was more accurate: Google Home or Amazon Alexa. Apparently Google Home is six times more likely to answer a question than Amazon Alexa. 360i arrived at this conclusion by using their software to ask both devices 3,000 questions. Alexa won when it came to questions related to retail information, but Google Home won over all with its search algorithms.
It’s relatively surprising, considering that RBC Capital Markets projects Alexa will drive $10 billion of revenue to Amazon by 2020—not to mention the artificial intelligence-based system currently owns 70 percent of the voice market.
Amazon might be the world’s largest market place, so Alexa would, of course, be the world’s best shopping assistant. The Internet is much larger than shopping and Google scours the entire Web. What does Amazon use to power Alexa’s searches?
Whitney Grace, August 21, 2017
Google Amps Ads to New Annoying Levels
August 17, 2017
Today, Google is synonymous with search, as they’ve worked very hard to ensure. But search has changed, and not always for the good. One of Google’s hallmark principles at the beginning of their existence was to provide an unbiased search engine with any additions only being to enhance the user experience. Nowadays, though, it seems like Google looks like every other search engine, littered with Ads and flashing videos.
Not impressed with these changes, Wired recently called the search giant out on their recent addition of automatically-playing movie trailers, saying ‘enough is enough’.
Showing a few ads in the image search system isn’t a bad thing. But it shows just how much Google’s thinking has changed. Google’s not a scrappy startup anymore. It’s the world’s most valuable company, and its investors want results. And without much serious competition, the risk of customers bolting for another search engine is pretty low.
Wired is spot on, of course, but what if customers did start trickling out to other search engines that adhere to Google’s original principles and ideologies?
Catherine Lamsfuss, August 17, 2017
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