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The search dilemma: looking beyond Google’s third-party cookie death

April 17, 2021 No Comments

30-second summary:

  • In 2020, majority of the 181.7 billion U.S. dollar revenues came from advertising through Google Sites or its network sites
  • Even though they will be removing the third-party cookie from 2022, the search giant still has a wealth of first-party data from its 270+ products, services, and platforms
  • The Trade Desk’s 20 percent stock price drop is proof of Google’s monopoly and why it shouldn’t enjoy it anymore
  • Google expert, Susan Dolan draws from her rich experience and details the current search scape, insights and predicts future key themes that will arise out of the 3p cookie death

Imagine search as a jungle gym, you automatically imagine Google as the kingpin player on this ground. This has been a reality for decades now and we all know the downside of autonomy which is why the industry now acknowledges a need for regulation. Google announced that it would remove the third-party cookie from 2022. But a lot can happen in a year, 2020 is proof of that! Does this mean that cookies will completely bite the dust? Think again. I dive deep into years of my experience with the web to share some thoughts, observations, and insights on what this really means.

For once, Google is a laggard

Given the monopoly that Google has enjoyed and the list of lawsuits (like the anti-trust one and more) this move is a regulatory step to create a “net-vironment” that feels less like a net and is driven towards transparency and search scape equality.

But Firefox and Safari had already beaten Google to the punch in 2019 and 2020 respectively. Safari had launched the Safari Intelligent Tracking Prevention (ITP) update on March 23, 2020. Firefox had launched its Enhanced Tracking Protection feature in September 2019 to empower and protect users from third-party tracking cookies and crypto miners.

Google’s solution to respect user privacy

Google recently announced that it won’t be using identifiers. Google is developing a ‘Privacy Sandbox’ to ensure that publishers, advertisers, and consumers find a fair middle ground in terms of data control, access, and tracking. The idea is to protect anonymity while still delivering results for advertisers and publishers. The Privacy Sandbox will don the FLoC API that can help with interest-based advertising. Google will not be using fingerprints, PII graphs based on people’s email addresses that other browsers use. Google will move towards a Facebook-like “Lookalike audience” model that will group users for profiling.

Did that raise eyebrows? There’s more.

Don’t be fooled – They still have a lavish spread of first-party data

Google is already rich with clusters of historical, individual unique data that they’ve stored, analyzed, predicted, and mastered over the years and across their platforms and services. These statistics give you a clear sense of the gravity of the situation:

  • Google has 270+ products and services (Source)
  • Among the leading search engines, the worldwide market share of Google in January 2021 was almost 86 percent (Source)
  • In 2020, majority of the 181.7 billion U.S. dollar revenues came from advertising through Google Sites or Google Network Sites (Source)
  • There are 246 million unique Google users in the US (Source)
  • Google Photos has over one billion active users (Source)
  • YouTube has over 1.9 billion active users each month (Source)
  • According to Google statistics, Gmail has more than 1.5 billion active users (Source)
  • A less-known fact, there are more than two million accounts on Google Ads (Source)
  • There are more than 2.9 million companies that use one or more of Google’s marketing services (Source)
  • As of Jan 2021, Google’s branch out into the Android system has won it a whopping 72 percent of the global smartphone operating system market (Source)
  • Google sees 3.5 billion searches per day and 1.2 trillion searches per year worldwide (Source)

Google has an almost-never ending spectrum of products, services, and platforms –

Here’s the complete, exhaustive list of Google’s gigantic umbrella.

Google's 270+ products, services, and platforms

Source: Matrics360

Google already has access to your:

  • Location
  • Search history
  • Credit/debit card details shared on Google Pay
  • Data from businesses (more than 2.9 million!) that use Google services
  • Your device microphone
  • Mobile keyboard (G-board)
  • Apps you download from the Google Playstore and grant access to
  • Device camera, and that’s not even the tip of the iceberg

Google’s decision to eliminate the third-party cookie dropped The Trade Desk’s stock by 20 percent

Nobody should have monopoly and this incident serves as noteworthy proof. Google’s decision to drop 3p cookies shocked The Trade Desk’s stock prices causing a 20 percent slump in their stock value. The Trade Desk is the largest demand-side platform (DSP) and Google’s decision kills the demand for The Trade Desk’s proprietary Unified ID 1.0 (UID 1.0) – a unique asset that chopped out the need for cookie-syncing process and delivered match rate accuracy up to 99 percent.

Google’s statement on not using PII also jeopardizes the fate of The Trade Desk’s Unified ID 2.0. which already has more than 50 million users.

Here’s what Dave Pickles, The Trade Desk’s Co-Founder and Chief Technology Officer had to say,

“Unified ID 2.0 is a broad industry collaboration that includes publishers, advertisers and all players in the ad tech ecosystem.”

“UID provides an opportunity to have conversations with consumers and provide them with the sort of transparency we as an industry have been trying to provide for a really long time.”

Adweek’s March town hall saw advertisers and publishers haunted by the mystery that surrounds Google as Google denied to participate in the event. The industry is growing precarious that Google will use this as a new way to establish market dominance that feeds its own interests.

We love cookies (only when they’re on a plate)

Cookies are annoying because they leave crumbs everywhere… on the internet! Did you know, this is how people feel about being tracked on the web:

  • 72 percent of people feel that almost everything they do online is being tracked by advertisers, technology firms or other companies
  • 81 percent say that the potential risks of data collection outweigh the benefits for them

These stats were originally sourced from Pew Research Center, but the irony, I found these stats on one of Google’s blogs.

On a hunt to escape these cookies or to understand the world’s largest “cookie jar” I checked out YouTube which seemed like a good place to start since it has over 1.9 billion monthly active users. You could visit this link to see how ads are personalized for you – the list is long!

My YouTube curiosity further landed me on this page to see how my cookies are shared (you can opt out of these). Even my least used account had 129 websites on this list, imagine how many sites are accessing your data right now.

Back in 2011 when I was the first to crack the Page rank algorithm, I could already sense the power Google held and where this giant was headed – the playground just wasn’t big enough.

Key themes that will emerge

Bottom line is, the cookie death is opening up conversations for advertising transparency and a web-verse that is user-first, and privacy compliant. Here’s what I foresee happening in search and the digital sphere:

  • Ethical consumer targeting
  • Adtech companies collaborating to find ways that respect their audience’s privacy
  • A more private, personalized web
  • More conversations around how much and what data collection is ethical
  • More user-led choices
  • Rise in the usage of alternative browsers
  • Incentivizing users to voluntarily share their data
  • Better use of technology for good

What do you think about the current climate on the internet? Join the conversation with me on @GoogleExpertUK.

Susan Dolan is a Search Engine Optimization Consultant first to crack the Google PageRank algorithm as confirmed by Eric Schmidt’s office in 2014. Susan is also the CEO of The Peoples Hub which has been built to help people and to love the planet.

The post The search dilemma: looking beyond Google’s third-party cookie death appeared first on Search Engine Watch.

Search Engine Watch


Looking ahead after 2020’s epic M&A spree

December 23, 2020 No Comments

When we examine any year in enterprise M&A, it’s tempting to highlight the biggest, gaudiest deals — and there were plenty of those in 2020. I’ve written about 34 acquisitions so far this year. Of those, 15 were worth $ 1 billion or more, 12 were small enough to not require that the companies disclose the price and the remainder fell somewhere in between.

Four deals involving chip companies coming together totaled over $ 100 billion on their own. While nobody does eye-popping M&A quite like the chip industry, other sectors also offered their own eyebrow-raising deals, led by Salesforce buying Slack earlier this month for $ 27.7 billion.

We are likely to see more industries consolidate the way chips did in 2020, albeit probably not quite as dramatically or expensively.

Yet in spite of the drama of these larger numbers, the most interesting targets to me were the pandemic-driven smaller deals that started popping up in May. Those small acquisitions are the ones that are so insignificant that the company doesn’t have to share the purchase price publicly. They usually involve early-stage companies being absorbed by cash-rich concerns looking for some combination of missing technology or engineering talent in a particular area like security or artificial intelligence.

It was certainly an active year in M&A, and we still might not have seen the last of it. Let’s have a look at why those minor deals were so interesting and how they compared with larger ones, while looking ahead to what 2021 M&A might look like.

Early-stage blues

It’s always hard to know exactly why an early-stage startup would give up its independence by selling to a larger entity, but we can certainly speculate on some of the reasons why this year’s rapid-fire dealing started in May. While we can never know for certain why these companies decided to exit via acquisition, we know that in April, the pandemic hit full force in the United States and the economy began to shut down.

Some startups were particularly vulnerable, especially companies low on cash in the April timeframe. Obviously companies fail when they run out of funding, and we started seeing early-stage startups being scooped up the following month.

We don’t know for sure of course if there is a direct correlation between April’s economic woes and the flurry of deals that started in May, but we can reasonably speculate that there was. For some percentage of them, I’m guessing it was a fire sale or at least a deal made under less than ideal terms. For others, maybe they simply didn’t have the wherewithal to keep going under such adverse economic conditions or the partnerships were just too good to pass up.

It’s worth noting that I didn’t cover any deals in April. But, beginning on May 7, Zoom bought Keybase for its encryption expertise; five days later Atlassian bought Halp for Slack integration; and the day after that VMware bought cloud native security startup Octarine — and we were off and running. Granted the big companies benefited from making these acquisitions, but the timing stood out.


Enterprise – TechCrunch


Yext researches what American customers are looking for throughout the year

November 28, 2019 No Comments

Yext, the Search Experience Cloud company, released new research about American consumer search behavior during the past year. The data, drawn from a sample of more than 400,000 business locations in the United States, revealed new insights about when consumers are searching for and clicking most on businesses across retail, healthcare, financial services, and food, throughout the year.

Among the key findings:

  • Consumers are only getting more active in search: Consumer actions in business listings — driving directions clicks, clicks to call businesses, and more — grew 17% over the past year.
  • Search — and searchers — are getting better: Consumer actions in search grew faster (17%) than search impressions of business listings (10%) over the year, suggesting that customers are finding what they want faster. Whether searchers are learning to use more specific queries or search engines are getting better at understanding those queries, customers are spending less time searching and more time engaging with businesses.
  • Reviews are on the rise: Consumers are leaving more reviews about businesses. Review count per business location grew 27% over the year. In fact, financial services review volume grew 91% per location, the fastest growth of any industry. Businesses are getting savvier about the importance of reviews as well, responding to reviews 47% more than the year prior.

“Some industries are naturally more popular with consumers during certain seasons, but the need for businesses in every category to be in control of their facts online stays important year-round,” said Zahid Zakaria, Senior Director of Insights and Analytics at Yext. “By ensuring their information is accurate across channels — from the search results on their own website to their listings on third-party platforms — businesses can be prepared to capture the wave of customers who are interested in transacting with them, no matter what month it is.”

Yext analyzed when American consumers clicked online listings for various types of businesses throughout the year. The study found:

January | Resolving to stay healthy: With New Year’s resolutions fresh on their minds, and cold and flu season underway, Americans start the year off with visits to the doctor. In January, healthcare organizations see a 17% jump in clicks to their online listings relative to the previous month.

February | Money on their minds: In February and March, tax season is well underway and searches show it. Searching consumers engage with financial services institutions up to 11% more than the annual average.

March | Open house: Starting in March, consumers looking to ring in the season of renewal with a new home turn to search to find real estate agencies. Listings see a 22% average increase in clicks from February to May, complementing studies indicating that spring is a popular season for house hunting and selling.

April | Telecom phones it in: By April, the wave of consumers picking up the latest high-profile smartphone upgrades from the fall has subsided. During this month, clicks to phone carrier and telecommunications provider listings in search drop 14% compared to the month before.

May | May flowers and horsepower: In May, consumers look to capitalize on Memorial Day sales and revamp their rides in time for summer with an average 18% increase in clicks to automotive service search listings relative to the annual average.

June – July | Fun in the sun: Recreation and entertainment listings online — including theaters, sports venues, nightlife, and more — see a surge of consumer interest during the summer months, reaching an average 35% increase in clicks in July relative to the annual average. Clicks to hotel listings also bump up to 20% above the annual average during this time due to summer travel.

August | Back to school: School is just around the corner in August, and parents and students are not just stocking up on clothes, school supplies, gadgets, and other necessities, but also getting their cars in shape for the morning drop-off line at school. Clicks to listings for stores spike to 18% higher than the annual average. Educational services, like tutors and libraries, see clicks to listings increase 18% as well. Clicks to automotive service listings reach 21% above the annual average.

September | Falling into a Habit: As Americans wrap up their vacations and return to their school and work routines, clicks to recreation and entertainment listings take a noticeable dip (18% below the annual average) in September, falling up to 25% below the annual average in November.

October | Hitting the books: With the school year taking off by October, families get serious about grades again and search for tutors and other educational services. Clicks to listings in the education category see a nearly 10% jump relative to September.

November | Pass the Leftovers: During the month of Thanksgiving, hungry consumers prefer to eat in, with clicks to restaurant listings dropping 13% below the annual average.

December | Home for the holidays: In December, revelers celebrate the holidays with their families and opt to bunk with them over paying for lodging. During this month, clicks to hotel listings in search fall to 26% below the annual average.

December & January | The season of giving — and buying: Americans shopping for holiday gifts in December drive clicks to retail listings 11% more than the annual average. After the holiday shopping season ends in January, those clicks plummet an average of nearly 25% from December as consumers take a break from spending and recoup their savings.

The post Yext researches what American customers are looking for throughout the year appeared first on Search Engine Watch.

Search Engine Watch


As Adam Neumann reportedly faces pressure to step down, it’s looking like a fight for life between WeWork and SoftBank

September 22, 2019 No Comments

According to a new WSJ report, certain members of WeWork’s seven-person board, which includes cofounder and CEO Adam Neumann, are planning to pressure Neumann to step down and instead become We’s non-executive chairman. The move, says the outlet, “would allow him to stay stay at the company he built into one of the country’s most valuable startups, but inject fresh leadership to pursue an IPO that would bring We the cash it needs to keep up its torrid growth.”

The WSJ and Bloomberg are reporting that it is SoftBank specifically that wants Neumann to step down. Neither WeWork nor SoftBank is commenting publicly.

It’s a fascinating development, the kind we saw when Uber’s board successfully forced cofounder and longtime CEO Travis Kalanick to abandon his role as CEO. Still, we’d caution against drawing too close a comparison. While the venture firm Benchmark, which spearheaded Kalanick’s ouster, stood to lose billions of dollars if Kalanick dragged down Uber and continued to push off an IPO, Benchmark was not in a do-or-die situation because of its Uber investment.

SoftBank appears to be in more dire straights, making this standoff a particularly meaningful one.

Let’s back up a minute first, though, and consider who is involved and which way this could potentially go. A few days ago,  Business Insider put together a useful cheat sheet about WeWork’s board members that may hint at their allegiance.

1.) Ronald Fisher — who is vice chairman at SoftBank Group after founding SoftBank Capital, a U.S. venture arm of SoftBank — joined SoftBank’s board last year.  He oversees 114 class A shares, each of which carries one vote. Obviously, he’s going to side with SoftBank.

2.) Lewis Frankfort — the chairman of a fitness studio chain called Flywheel Sports — has been a board member of WeWork for roughly five years, and BI says WeWork once loaned him $ 6.3 million, which he repaid in interest earlier this year. We have to think he’d stick with Neumann out of loyalty. At the same time, he doesn’t wield much power unless he has the right to block significant actions at the company (some shareholders get these blocking rights; some don’t.)  What he know: he controls 2 million shares, and 750,000 of them are Class B shares that carry 10 votes each.

3.) Benchmark, which first backed WeWork in 2012, is represented on the board by Bruce Dunlevie, the founding partner of the venture firm. Benchmark owns 32.6 million Class A shares, and could go either way, seemingly. On the one hand, Benchmark doesn’t want to establish a reputation for pushing out founders after the Kalanick debacle, and if it supports SoftBank over Neumann, it risks this exact thing happening. On the other hand, Benchmark might not want to battle with SoftBank if it thinks it has staying power or it’s concerned (suddenly) that it allowed Neumann to amass too much control.

4.) Harvard Business School professor Frances Frei was brought in roughly a minute ago to add a much-need sprinkling of gender diversity to WeWork’s all-male board. Frei’s name first came to be more broadly recognized when she was hired to help address Uber’s battered culture, so presumably she has ties to Benchmark. We’d guess she’ll side with Dunlevie, meaning that we have no idea whose side she will take.

5.) Steven Langman, the cofounder of private equity firm Rhône Group, has ties that go back a ways with Neumann, and he has benefited richly from the association, seemingly. According to an April story in the WSJ, Langman met Neumann through a shared rabbi in its earlier days and joined the board in 2012. He also invested in the company (he owns 2.28 million shares in the company, according to a bond filing). Langman is on both the company’s compensation committee and its succession committee. He also runs a real-estate investment vehicle in partnership with We that buys and develops buildings to then lease back to the co-working company, despite that it raises conflict-of-interest questions. We’d guess he’s on Team Neumann.

6.) John Zhao is the chairman and CEO of Hony Capital, which partnered with SoftBank and WeWork to create a standalone entity called WeWork China back in 2017, and Hony has subsequently poured more capital into that subsidiary. We’re not sure how close Zhao is to SoftBank, but if SoftBank brought Hony into WeWork, we’re guessing he’ll back the Japanese conglomerate on this one. Hony doesn’t own 5 percent or more of WeWork’s parent company so its share holdings aren’t listed publicly.

Neumann, it’s very worth noting, is himself is far more powerful than any of these six individuals. Even after the company recently revised Neumann’s supervoting rights, which gave him 20 times the voting power of ordinary shareholders and now give him 10, he could fire the entire board if he so chooses, notes the WSJ.

Naturally, that wouldn’t be a good look for Neumann, who is already battling growing public perception that, among other negatives for a public company CEO, he smokes a whole lot of pot and that he is delusional, following a WSJ piece that reported Neumann confided to different people his interest in the role of Israel’s prime minister and, more recently, to become president of the world.

All that said, SoftBank is also fast-losing credibility. While its CEO, Masayoshi Son, has been long revered as a visionary, a growing number of sources we’ve spoken to question the viability of his entire Vision Fund operation, and they point to WeWork — whose valuation leaps on the private market, from $ 20 billion to, more recently, $ 47 billion, were entirely a product of SoftBank’s doing — as just one in a costly string of poor calls.

Indeed, despite the roughly $ 10 billion that SoftBank has sunk into WeWork, the financial loss it would take if WeWork falls apart would pale in comparison to the reputation hit Son would suffer, and you can bet there will be ripple effects.

Put another way, given the Vision Fund’s impact on the startup industry over the last few years, there’s a lot more riding on what happens with WeWork than meets the eye. Stay tuned.


Startups – TechCrunch


Looking to become the video-based social network of the gaming world, Medal.tv raises $9 million

September 7, 2019 No Comments

When Medal.tv first launched on the scene, the company was an upstart trying to be the social network for the gaming generation.

Since its debut in February, the clipping and messaging service for gamers has amassed 5 million total users with hundreds of thousands of daily active users. And now it has a $ 9 million new investment from firms, led by Horizons Ventures, the venture capital fund established by Hong Kong multi-billionaire Li Ka-shing.

“We’re seeing sharing of short-form video emerge as a means of self-expression and entertainment for the current generation. We believe Medal’s platform will be a foundation for interactive social experiences beyond what you can find in a game,” says Jonathan Tam, an investor with Horizons Ventures .

Medal sees potential both in its social network and in the ability for game developers to use the platform as a marketing and discovery tool for the gaming audience.

“Friends are the main driver of game discovery, and game developers benefit from shareable games as a result. Medal.tv is trying to enable that without the complexity of streaming,” says Matteo Vallone, the former head of Google Play games in Europe and an angel investor in Medal.

Assets Web 1

It’s a platform that saw investors willing to fork over as much as $ 20 million for the company, according to chief executive Pim de Witte. “There are still too many risks involved to take capital like that,” de Witte says.

Instead, the $ 9 million from Horizons, and previous investors like Makers Fund, will be used to steadily grow the business.

“At Medal, we believe the next big social platform will emerge in gaming, perhaps built on top of short-form content, partially as a result of gaming publishers trying to build their own isolated gaming stores and systems,” said de Witte, in a statement. “That drives social fragmentation in the market and brings out the need for platforms such as Medal and Discord, which unite gamers across games and platforms in a meaningful way.”

As digital gaming becomes the social medium of choice for a generation, new tools that allow consumers to share their virtual experiences will become increasingly common. This phenomenon will only accelerate as more events like the Marshmello concert in Fortnite become the norm.

“Medal has the exciting potential to enable a seamless social exchange of virtual experiences,” says Ryann Lai, an investor from Makers Fund.


Social – TechCrunch


Looking back at Zoom’s IPO with CEO Eric Yuan

May 14, 2019 No Comments

Since the launch of its IPO in mid-April, Zoom stock has skyrocketed, up nearly 30% as of Monday’s open. However, as the company’s valuation continues to tick up, analysts and industry pundits are now diving deeper to try to unravel what the company’s future growth might look like.

TechCrunch’s venture capital ax Kate Clark has been following the story with a close eye and will be sitting down for an exclusive conversation with Zoom CEO Eric Yuan on Wednesday at 10:00 am PT. Eric, Kate and Extra Crunch members will be taking a look back at the company’s listing process and Zoom’s road to IPO.

Tune in to join the conversation and for the opportunity to ask Eric and Kate any and all things Zoom.

To listen to this and all future conference calls, become a member of Extra Crunch. Learn more and try it for free.


Social – TechCrunch


Google Tracking How Busy Places are by Looking at Location Histories

January 7, 2017 No Comments
Getting Ducks in a Row
Getting Ducks in a Row

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.

busy-times-pizza-google

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.

Google Shopping 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:

People Spend Time Google

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.

Abstract

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.

Take-Aways

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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