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Monthly Archives: January 2018

Breaking CRO Best Practices: You Can Do It!

January 31, 2018 No Comments

Explore a few instances in which we ignored CRO best practices and it was an utter success!

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Google Optimize now offers more precision and control for marketers

January 30, 2018 No Comments
Savvy businesses review every step of the customer journey to ensure they are delivering the best experience and to find ways to offer more value. Today, we’re releasing two new features that will make it easier for you to improve each of those steps with the help of Google Optimize and Optimize 360.

AdWords integration: Find the best landing page 

Marketers spend a lot of time optimizing their Search Ads to find the right message that brings the most customers to their site. But that’s just half the equation: Sales also depend on what happens once people reach the site.

The Optimize and AdWords integration we announced in May gives marketers an easy way to change and test the landing pages related to their AdWords ads. This integration is now available in beta for anyone to try. If you’re already an Optimize user, just enable Google Optimize account linking in your AdWords account. (See the instructions in step 2 of our Help Center article.) Then you can create your first landing page test in minutes.

Suppose you want to improve your flower shop’s sales for the keyword “holiday bouquets.” You might use the Optimize visual editor to create two different options for the hero spot on your landing page: a photo of a holiday dinner table centerpiece versus a banner reading “Save 20% on holiday bouquets.” And then you can use Optimize to target your experiment to only show to users who visit your site after searching for “holiday bouquets.”

If the version with the photo performs better, you can test it with other AdWords keywords and campaigns, or try an alternate photo of guests arriving with a bouquet of flowers.

Objectives: More flexibility and control 

Since we released Optimize and Optimize 360, users have been asking us for a way to set more Google Analytics metrics as experiment objectives. Previously,
Optimize users could only select the default experiment objectives built into Optimize (like page views, session duration, or bounces), or select a goal they had already created in Analytics.

With today’s launch, Optimize users no longer need to pre-create a goal in Analytics, they can create the experiment objective right in Optimize:

Build the right objective for your experiment directly in the Optimize UI.

When users build their own objective directly in Optimize, we’ll automatically help them check to see if what they’ve set up is correct.

Plus, users can also set their Optimize experiment to track against things like Event Category or Page URL.

Learn more about Optimize experiment objectives here.

Why do these things matter? 

It’s always good to put more options and control into the hands of our users. A recent study showed that marketing leaders – those who significantly exceeded their top business goal in 2016 – are 1.5X as likely to say that their organizations currently have a clear understanding of their customers’ journeys across channels and devices.1 Testing and experimenting is one way to better understand and improve customer journeys, and that’s what Optimize can help you do best.

>>> Check out these new features in Optimize now<<<

1Econsultancy and Google, “The Customer Experience is Written in Data”, May 2017, U.S.

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Child health advocates call for Facebook to shutter Messenger Kids app

January 30, 2018 No Comments

 The slings and arrows of outrage keep flying at Facebook. Today a coalition of child health advocates has published an open letter addressing CEO Mark Zuckerberg and calling for the company to shutter Messenger Kids: Aka the Snapchat-ish comms app it launched in the US last December — targeted at the under 13s. Read More
Social – TechCrunch

There’s no way the government is building its own 5G network

January 30, 2018 No Comments

 A report this weekend by Axios cited documents from within the National Security Council describing the possibility — nay, inevitability — of a 5G network built and operated by the U.S. government. Officials have since poured cold water on this idea, and really, it was never feasible. Read More

Gadgets – TechCrunch

Confidence in the Workplace: Stand Out. Be Heard. Have Impact.

January 30, 2018 No Comments

In this new webinar, Chief Confidence Officer from the American Confidence Institute, Alyssa Dver, discusses how confidence directly impacts our own and other people’s behaviors, how it can impact your marketing results, and how it works in our brains. Moderated by Hanapin’s Director of HR, Rebecca Reott, you’ll get tips on how to think and market more confidently that will help you in the workplace as well as personally.

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Google’s Mobile Location History

January 30, 2018 No Comments

Google Location History

If you use Google Maps to navigate from place to place, or if you have agreed to be a local guide for Google Maps, there is a chance that you have seen Google Mobile Location history information. There is a Google Account Help page about how to Manage or delete your Location History. The location history page starts off by telling us:

Your Location History helps you get better results and recommendations on Google products. For example, you can see recommendations based on places you’ve visited with signed-in devices or traffic predictions for your daily commute.

You may see this history as your timeline, and there is a Google Help page to View or edit your timeline. This page starts out by telling us:

Your timeline in Google Maps helps you find the places you’ve been and the routes you’ve traveled. Your timeline is private, so only you can see it.

Mobile Location history has been around for a while, and I’ve seen it mentioned in a few Google patents. It may be referred to as a “Mobile location history” because it appears to contain information collected by your mobile device. Here are three posts I’ve written about patents that mention location history and describe processes that depend upon Mobile Location history.

An interesting article that hints at some possible aspects of location history just came out on January 24th, in the post, If you’re using an Android phone, Google may be tracking every move you make.

The timing of the article about location history is interesting given that Google was granted a patent on user location histories the day before that article was published. It focuses upon telling us how location history works:

The present disclosure relates generally to systems and methods for generating a user location history. In particular, the present disclosure is directed to systems and methods for analyzing raw location reports received from one or more devices associated with a user to identify one or more real-world location entities visited by the user.

Techniques that could be used to attempt to determine a location associated with a device can include GPS, IP Addresses, Cell-phone triangulation, Proximity to Wifi Access points, and maybe even power line mapping using device magnetometers.

The patent has an interesting way of looking at location history, which sounds reasonable. I don’t know the latitudes and longitudes of places I visit:

Thus, human perceptions of location history are generally based on time spent at particular locations associated with human experiences and a sense of place, rather than a stream of latitudes and longitudes collected periodically. Therefore, one challenge in creating and maintaining a user location history that is accessible for enhancing one or more services (e.g. search, social, or an API) is to correctly identify particular location entities visited by a user based on raw location reports.

The location history process looks like it involves collecting data from mobile devices in a way that allows it to gather information about places visited, with scores for each of those locations. I have had Google Maps ask me to verify some of the places that I have visited, as if the score it had for those places may not have been sufficient (not high enough of a level of confidence) for it to believe that I had actually been at those places.

The location history patent is:

Systems and methods for generating a user location history
Inventors: Daniel Mark Wyatt, Renaud Bourassa-Denis, Alexander Fabrikant, Tanmay Sanjay Khirwadkar, Prathab Murugesan, Galen Pickard, Jesse Rosenstock, Rob Schonberger, and Anna Teytelman
Assignee: Google LLC
US Patent: 9,877,162
Granted: January 23, 2018
Filed: October 11, 2016


Systems and methods for generating a user location history are provided. One example method includes obtaining a plurality of location reports from one or more devices associated with the user. The method includes clustering the plurality of location reports to form a plurality of segments. The method includes identifying a plurality of location entities for each of the plurality of segments. The method includes determining, for each of the plurality of segments, one or more feature values associated with each of the location entities identified for such segment. The method includes determining, for each of the plurality of segments, a score for each of the plurality of location entities based at least in part on a scoring formula. The method includes selecting one of plurality of locations entities for each of the plurality of segments.

Why generate a location history?

A couple of reasons stand out in the patent’s extended description.

1) The generated user location history can be stored and then later accessed to provide personalized location-influenced search results.
2) As another example, a system implementing the present disclosure can provide the location history to the user via an interactive user interface that allows the user to view, edit, and otherwise interact with a graphical representation of her mobile location history.

I like the interactive user Interface that shows times and distances traveled.

This statement from the patent was interesting, too:

According to another aspect of the present disclosure, a plurality of location entities can be identified for each of the plurality of segments. As an example, map data can be analyzed to identify all location entities that are within a threshold distance from a segment location associated with the segment. Thus, for example, all businesses or other points of interest within 1000 feet of the mean location of all location reports included in a segment can be identified.

Google may track information about locations that appear in that history, such as popularity features, which may include, “a number of social media mentions associated with the location entity being valued; a number of check-ins associated with the location entity being valued; a number of requests for directions to the location entity being valued; and/or and a global popularity rank associated with the location entity being valued.”

Personalization features may also be collected which described previous interactions between the user and the location entity, such as:

1) a number of instances in which the user performed a map click with respect to the location entity being valued;
2) a number of instances in which the user requested directions to the location entity being valued;
3) a number of instances in which the user has checked-in to the location entity being valued;
4) a number of instances in which the user has transacted with the location entity as evidenced by data obtained from a mobile payment system or virtual wallet;
5) a number of instances in which the user has performed a web search query with respect to the location entity being valued.

Other benefits of location history

This next potential feature was one that I tested to see if it was working, querying location history. It didn’t seem to be active at this point:

For example, a user may enter a search query that references the user’s historical location (e.g. “Thai restaurant I ate at last Thursday”). When it is recognized that the search query references the user’s location history, then the user’s location history can be analyzed in light of the search query. Thus, for example, the user location history can be analyzed to identify any Thai restaurants visited on a certain date and then provide such restaurants as results in response to the search query.

The patent refers to a graphical representation of mobile location history, which is available:

As an example, in some implementations, a user reviewing a graphical representation of her location history can indicate that one of the location entities included in her location history is erroneous (e.g. that she did not visit such location). In response, the user can be presented with one or more of the location entities that were identified for the segment for which the incorrect location entity was selected and can be given an opportunity to select a replacement location.

Location History Timeline Interface
A Location History Timeline Interface

In addition to the timeline interface, you can also see a map of places you may have visited:

Timeline with Map Interface
Map Interface

You can see in my screenshot of my timeline, I took a photo of a Kumquat tree I bought yesterday. It gives me a chance to see the photos I took, so that I can edit them, if I would like. The patent tells us this about the user interface:

In other implementations, opportunities to perform other edits, such as deleting, annotating, uploading photographs, providing reviews, etc., can be provided in the interactive user interface. In such fashion, the user can be provided with an interactive tool to explore, control, share, and contribute to her location history.

The patent tells us that it tracks activities that you may have engaged in at specific locations:

In further embodiments of the present disclosure, a location entity can be associated with a user action within the context of a location history. For example, the user action can be making a purchase (e.g. with a digital wallet) or taking a photograph. In particular, in some embodiments, the user action or an item of content generated by the user action (e.g. the photograph or receipt) can be analyzed to assist in identifying the location entity associated with such user action. For example, the analysis of the user action or item of content can contribute to the score determined for each location entity identified for a segment.

I have had the Google Maps application ask me if I would like to contribute photos that I have taken at specific locations, such as at the sunset at Solana Beach. I haven’t used a digital wallet, so I don’t know if that is potentially part of my location history.

The patent describes the timeline feature and the Map feature that I included screenshots from above.

The patent interestingly tells us that location entities may be referred to by the common names of the places they are called, and refers to those as “Semantic Identifiers:

Each location entity can be designated by a semantic identifier (e.g. the common “name” of restaurant, store, monument, etc.), as distinguished from a coordinate-based or location-based identifier. However, in addition to a name, the data associated with a particular location entity can further include the location of the location entity, such as longitude, latitude, and altitude coordinates associated with the location entity.

It’s looking like location history could get smarter:

As an example, an interaction evidenced by search data can include a search query inputted by a user that references a particular location entity. As another example, an interaction evidenced by map data 218 can include a request for directions to a particular location entity or a selection of an icon representing the particular location entity within a mapping application. As yet another example, an interaction evidenced by email data 220 can include flight or hotel reservations to a particular city or lodging or reservations for dinner at a particular restaurant. As another example, an interaction evidenced by social media data 222 can include a check-in, a like, a comment, a follow, a review, or other social media action performed by the user with respect to a particular location entity.

Tracking these interactions is being done under the name “user/location entity interaction extraction,” and it may calculate statistics about such interactions:

Thus, user/location entity interaction extraction module 212 can analyze available data to extract interactions between a user and a location entity. Further, interaction extraction module 212 can maintain statistics regarding aggregate interactions for a location entity with respect to all users for which data is available.

It appears that to get the benefit of being able to access information such as this, you would need to give Google the ability to collect such data.

The patent provides more details about location history, and popularity and other features, and even a little more about personalization. Many aspects of location history have been implemented, while there are some that look like they might have yet to be developed. As can be seen from the three posts I have written about that describes patents that use information from location history, it is possible that location history may be used in other processes used by Google.

How do you feel about mobile location history from Google?

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Social media is giving us trypophobia

January 28, 2018 No Comments

 We aren’t so much seeing through a lens darkly when we log onto Facebook or peer at personalized search results on Google, we’re being individually strapped into a custom-moulded headset that’s continuously screening a bespoke movie — in the dark, in a single-seater theatre, without any windows or doors… Read More
Social – TechCrunch

2018 Ski Gear for Sunny Days: Trew, Faction, Smith, Tecnica

January 28, 2018 No Comments

There’s nothing like skiing under blue skies. Don’t ruin it by dressing for a blizzard.
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Our Final Austin Golden Ticket Winner is…

January 28, 2018 No Comments

Thank you to everyone who participated in the Hero Conf Austin Golden Ticket Giveaway. We hope you not only had a little bit of fun, but also stumbled upon some great content here on PPC Hero.

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Google Targeted Advertising, Part 1

January 28, 2018 No Comments

Google Targeted Advertisements

One of the inventors of the newly granted patent I am writing about was behind one of the most visited Google patents I’ve written about, from Ross Koningstein, which I posted about under the title, The Google Rank-Modifying Spammers Patent It described a social engineering approach to stop site owners from using spammy tactics to raise the ranking of pages.

This new patent is about targeted advertising at Google in paid search, which I haven’t written too much about here. I did write one post about paid search, which I called, Google’s Second Most Important Algorithm? Before Google’s Panda, there was Phil I started that post with a quote from Steven Levy, the author of the book In the Plex, which goes like this:

They named the project Phil because it sounded friendly. (For those who required an acronym, they had one handy: Probabilistic Hierarchical Inferential Learner.) That was bad news for a Google Engineer named Phil who kept getting emails about the system. He begged Harik to change the name, but Phil it was.

What this showed us was that Google did not use the AdSense algorithm from the company they acquired in 2003 named Applied Semantics to build paid search. But, it’s been interesting seeing Google achieve so much based on a business model that relies upon advertising because they seemed so dead set against advertising when then first started out the search engine. For instance, there is a passage in an early paper about the search engine they developed that has an appendix about advertising.

If you read through The Anatomy of a Large-Scale Hypertextual Web Search Engine, you learn a lot about how the search engine was intended to work. But the section about advertising is really interesting. There, they tell us:

Currently, the predominant business model for commercial search engines is advertising. The goals of the advertising business model do not always correspond to providing quality search to users. For example, in our prototype search engine, one of the top results for cellular phone is “The Effect of Cellular Phone Use Upon Driver Attention”, a study which explains in great detail the distractions and risk associated with conversing on a cell phone while driving. This search result came up first because of its high importance as judged by the PageRank algorithm, an approximation of citation importance on the web [Page, 98]. It is clear that a search engine which was taking money for showing cellular phone ads would have difficulty justifying the page that our system returned to its paying advertisers. For this type of reason and historical experience with other media [Bagdikian 83], we expect that advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers.

So, when Google was granted a patent on December 26, 2017, that provides more depth on how targeted advertising might work at Google, it made interesting reading. This is a continuation patent, which means the description ideally should be approximately the same as the original patent, but the claims should be updated to reflect how the search engine might be using the processes described in a newer manner. The older version of the patent was filed on December 30, 2004, but it wasn’t granted under the earlier claims. It may be possble to dig up those earlier claims, but it is interesting looking at the description that accompanies the newest version of the patent to get a sense of how it works. Here is a link to the newest version of the patent with claims that were updated in 2015:

Associating features with entities, such as categories of web page documents, and/or weighting such features
Inventors: Ross Koningstein, Stephen Lawrence, and Valentin Spitkovsky
Assignee: Google Inc.
US Patent: 9,852,225
Granted: December 26, 2017
Filed: April 23, 2015


Features that may be used to represent relevance information (e.g., properties, characteristics, etc.) of an entity, such as a document or concept for example, may be associated with the document by accepting an identifier that identifies a document; obtaining search query information (and/or other serving parameter information) related to the document using the document identifier, determining features using the obtained query information (and/or other serving parameter information), and associating the features determined with the document. Weights of such features may be similarly determined. The weights may be determined using scores. The scores may be a function of one or more of whether the document was selected, a user dwell time on a selected document, whether or not a conversion occurred with respect to the document, etc. The document may be a Web page. The features may be n-grams. The relevance information of the document may be used to target the serving of advertisements with the document.

I will continue with details about how this patent describes how they might target advertising at Google in a part 2 of this post.

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