The new year is here! And as an avid reader of PPC Hero and friend of Hanapin Marketing, we’d like to say thanks by offering up the chance to win your way to Hero Conf Los Angeles, April 18-20, 2017, on us!
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Machine learning is helping doctors diagnose things like genetic disorders, Alzheimer’s, and autism faster than ever before. The post Thanks to AI, Computers Can Now See Your Health Problems appeared first on WIRED.
Account Manager Bryan Gaynor has poured all of his expertise into this whitepaper so that programmatic advertising can help you succeed.
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Welcome to our weekly round-up of all the latest news and research from the world of search marketing and beyond.
And a happy 2017 to all of our Search Engine Watch readers! This week, we’ve got a health-conscious New Year’s update from Bing, a new AI-powered search engine which is transforming scientific research, and a look at why the fake information epidemic could be damaging to local search.
A new AI powered search engine is changing how neuroscientists do research
Google’s work in the realm of artificial intelligence and machine learning has succeeded in making web search more intuitive, effective and useful than it’s ever been before. But until now, the same couldn’t be said of scientific research.
That’s all changing with the development of a new, free search engine, Semantic Scholar. Adam Stetzer wrote for Search Engine Watch this week about how the AI-powered search engine is changing the way that neuroscientists do research, using data mining and natural language processing to truly understand the links between research – and what this means for similar search options like Google Scholar.
How Instagram became a powerhouse for social commerce
2016 was a busy year for Instagram, with more users, more brands, and a host of new improvements and features all joining the platform. In November, Instagram tested out a new shopping feature in a bid to woo ecommerce brands and give users a way to shop more visually.
This week, on Search Engine Watch’s sister site ClickZ, Tereza Litsa spoke to Olapic’s Paul Sabria about the steps that Instagram has taken to turn itself into a social commerce powerhouse, and what we can expect from the platform in 2017.
Bing rolls out health-conscious search updates in time for New Year’s resolutions
Bing has rolled out a health-focused update to its search platform just in time for everyone to turn over a leaf in the New Year.
In late November, we saw that Bing had launched a carousel of shopping flyers to promote deals in time for Black Friday. Now whenever you search for “workouts” or “exercises” on Bing, it will deliver a carousel of images which link to a wide variety of exercise options.
Users who search for information on yoga and pilates will also be rewarded with a carousel, and occasionally a how-to video on a specific pose at the top of search. Meanwhile, the Bing app has new updates aimed at making the food search experience “even richer”, including information on calorie counts and low-fat recipes.
Image: Bing blogs
Bing’s new updates are obviously aimed at providing more intuitive, quick answers to users’ search queries in the same way that Google already does with Quick Answers and featured snippets. While they might be on a smaller scale, the tie-in with different times of year such as Black Friday and New Year is a fun way to introduce these features and draw users’ attention to them through the things they are most likely to be searching for.
How the fake information epidemic will hurt local search in 2017
Headlines about the online fake news epidemic have been everywhere since the US Election, particularly if you follow news about publishing or social media. But Wesley Young, Vice President of Public Affairs for the Local Search Association, believes that this problem is set to get worse in 2017 – and that it will be damaging to local search in particular.
In a column for Search Engine Land, Young laid out how the issue of fake news and information can hurt marketers, along with eight ways that false information is currently being used which marketers should be aware of.
“As consumers search for information to help make purchase decisions, uncertainty about the veracity of the information they receive impacts the effectiveness of local search marketing. Online advertising already faces challenges gaining consumer trust, and the proliferation of fake content will only hurt it more. Worse, you may be spending money on advertising that no one ever sees, be competing in an unfair market, suffer from hits to your reputation or pay more than you should for marketing products or services.
Being aware of how false information is being used will help marketers avoid problems and identify when they may be affected, saving them from both headaches and wasted dollars.”
Google clarifies details of its mobile interstitials penalty
As part of Google’s ongoing efforts to improve the experience of browsing the mobile web, a penalty for sites which use annoying mobile interstitials – pop-ups which appear while a website is loading and cover the entire page – is due to take effect next week, beginning on 10th January.
The question of what kind of interstitials, exactly, will incur penalties has been the subject of considerable discussion amongst the SEO community. This week, Google provided some further clarification on the issue in the form of a tweet from Webmaster Trends Analyst John Wu.
He was responding to a query from Kristine Schachinger, technical SEO expert and founder of digital marketing agency The Vetters, about whether the penalty will only affect interstitials which appear when users are navigating from the search results page to a mobile site, or whether it will include interstitials which appear when navigating between pages of the same website.
— John ☆.o(≧▽≦)o.☆ (@JohnMu) January 4, 2017
Schachinger further enquired as to whether the penalty would affect interstitials which appear between an AMP page and a regular site page, to which Mu replied,
“I haven’t seen an interstitial there, but that would be seen the same as site-page -> site-page.”
These companies had a very special CES experience as they all pitched in front of multiple groups of judges on stage at the Sands Expo. The startups were competing for $ 50,000 and being named the winner of the Hardware Battlefield. After many deliberations, our judges narrowed the list down to four finalists: wearable device for pregnant mothers Bloomlife, smart sensors for construction… Read More
Draw with your face? Conference call ping pong? Not any more. Google today quietly revealed that it will shut down the Hangouts API, preventing new apps from being built and shutting off existing apps on April 25th. There was no blog post about this, just an updated FAQ and email notification to developers active on the API, forwarded to us by one of these devs. Some examples of experiences… Read More
Social – TechCrunch
Google Maps helps people navigate from place to place.
In order for it to work effectively, it’s helpful if it can track the location of the device that someone may be using to help them navigate.
It’s interesting how Google tracks your location. I’ve noticed that after I take a photo near a business, Google will sometimes ask if I would like to upload that photo to the business listing for that business. Sometimes the photos aren’t relevant to the business I’ve taken them near, such as a photo of an Agave Plant that I took near a Seaside Market in Cardiff-by-the-Sea, California.
Google seems to like the idea of saving location history for people who might search for different types of businesses, and a recent patent that I wrote about described how Google might start using distances from a location history as a ranking signal (as opposed to a static distance from a desktop computer.) I wrote about that in Google to Use Distance from Mobile Location History for Ranking in Local Search.
If you think about Google tracking individuals’ location histories in a different way, how else can that tracking history be useful to people? You may have noticed that Google now sometimes shows how busy a place might be a different points in the day. That is from tracked location history aggregated. I saw someone ask about this in Twitter today, and it set me trying to find a patent from Google that described the details of how Google might be tracking how busy different businesses might be. I found one.
The patent I found tells us that it is about:
The present disclosure relates generally to determining a latency period at a user destination, and more particularly to methods and systems that rely on user-location history, such as fine-grained user location data, to determine the latency period at a destination of a user. The present disclosure also relates to using latency period data in a variety of applications, including generation of a shopping route for a user.
Google is tracking how busy different businesses are based upon those user locations.
It tells us that being able to provide someone with planning details about a shopping trip can be useful, such as how long the trip to a business might be, as well as how long they might spend there. If someone asks for a chain business, knowing how busy the location is can also be helpful to a user, and the process described in this patent attempts to answer that problem as well. I hadn’t thought of how helpful it could be in the context of chain businesses until I read the patent:
While knowing the travel time and distance to a location is often helpful to a user, the user is left without knowing how busy the nearest location is or whether other, nearby locations are less busy. For example, the user does not know whether visiting a chain location that is slightly further away—but less busy or less crowded—may take less time overall than visiting the chain location that is nearby. Thus, based on travel time to the destination alone, the user may spend more time traveling to and visiting the nearest location than the user would if traveling to and visiting a location that is further away. And in some instances, a user may not care how long it takes to get to a point-of-interest. Rather, the user may desire only to know how long the wait is at a particular point-of-interest or how long it will take the user to pass through the point-of-interest, such as through a checkout line at a retailer. In addition to knowing how long a trip will take, in certain instances a user may wish to know the fastest route or alternate routes. For example, a user with a specific shopping list may desire the best route (or alternate routes) for obtaining the products on the shopping list.
Other information that might be provided include things like wait times at restaurants and how long it is taking people to check out at grocery stores,
Interestingly, fine-grained location history tracked could include the user device in a checkout line at a grocery store, or at the entrance area of a restaurant, or in a line at an amusement park. So, times spent waiting to buy groceries or waiting to be served a meal or time spend waiting for a ride could be reported to others who might consider going to that grocery store, or restaurant or amusement park. Mobile location information history looks like it could be useful.
I’m reminded of Google doing something similar with mobile devices and real time traffic information, which I wrote about in 2006 in the post Ending Gridlock with Google Driving Assistance (Zipdash Re-Emerges). I guess if it worked with traffic time estimates, it might be worth using in other contexts, like grocery store lines or amusement park ride lines.
The patent is referring to this understanding of how busy a business might be as a “latency analysis system”, and tells us that it is based upon receiving location histories for multiple computing devices. The location history can tell how long each person was at a business in addition to telling how busy a business is at different times of a day.
The patent also points out that this latency information can be “real time” in providing current wait periods for restuarants, and so on.
This system can also tell users whether or not a location they might be planning on traveling to is open or closed, or possibly closing soon (or maybe hasn’t opened yet.)
The patent also describes another feature involving having a shopping list for products on your phone, and being able to identify merchants who offer those products and generating a shopping route based upon those products and merchants offering them, an dhow long it would take to buy each item on the list.
If it is compiling a shopping route from your shopping list with locations to buy from, it may attempt to calculate the most efficient route.
In addition to telling us how busy a place may be, Google may also tell us how long we might take when we go some place, like averaging 20 minutes inside of this place:
There are aspects of this system that may use different data sources to reinforce data being collected. For instance, if location history informaiton is being used to track time waiting to check out in a grocery store line, that timing information could possibly be check up on by looking at electronic wallet information associated with purchased involved in a checkout at the grocery store.
The description of the patent provides more details and more examples, and is worth spending time with.
The patent is:
Point-of-interest latency prediction using mobile device location history
Publication number US9470538 B2
Granted on: Oct 18, 2016
Filing date Mar 11, 2015
Priority date Jul 17, 2013
Inventors Dean Kenneth Jackson, Daniel Victor Klein
Original Assignee Google Inc.
A latency analysis system determines a latency period, such as a wait time, at a user destination. To determine the latency period, the latency analysis system receives location history from multiple user devices. With the location histories, the latency analysis system identifies points-of-interest that users have visited and determines the amount of time the user devices were at a point-of-interest. For example, the latency analysis system determines when a user device entered and exited a point-of-interest. Based on the elapsed time between entry and exit, the latency analysis system determines how long the user device was inside the point-of-interest. By averaging elapsed times for multiple user devices, the latency analysis system determines a latency period for the point-of-interest. The latency analysis system then uses the latency period to provide latency-based recommendations to a user. For example, the latency analysis system may determine a shopping route for a user.
People carrying their phones around with them are providing useful information to others. We have in effect become Googlebot crawling the world with our navigation devices turned on. The patent tells us that Google is being careful by trying to avoid sharing and spreading personally identifiable information.
I am happy that Google asks for permission before it uses a photo that I’ve taken near a business before it assumes that the photo is of the business. When you opt in to using location-based services on your phone, you are helping people decide which restaurants to choose to eat at, or grocery store to shop at or amusement part to visit. You are helping track how long people tend to be at a business.
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The post Google Tracking How Busy Places are by Looking at Location Histories appeared first on SEO by the Sea.
Amid the confusion of smart showerheads and selfie drones at CES this year, a handful of companies are working to serve the needs of populations that are frequently overlooked by the proprietors of high tech. You won’t find these devices in every home, but homes with someone disabled by age or misfortune will welcome them more than a new voice-powered fridge. Read More