This is probably the most detail I've heard (head of Streams and Photos), or anyone else at Google talk about the future of Google+, confirming that not only is it not dead, but that we’re actually adapting to how the product is successful in market and doubling-down on that.
Definitely a worthwhile read, with plenty of interesting tidbits. Thanks to for getting the inside scoop on this. These were some of my favorite questions...
Is it fair to say Google is distancing itself from the original concept of Plus?
It’s fair to say you’re about to see a huge shift in what Plus is becoming. It’s a shift in response to what users are telling us. That’s a very healthy and natural thing. As opposed to sticking to strategies of years ago, we’re actually adapting to how the product is successful in market and doubling-down on that.
Looks like we also have confirmation that the Google+ might be dropping its name ideas have not seriously been considered.
Have you ever thought of dropping the name “Plus”?
I’m not sure what that would accomplish. It hasn’t seriously crossed my mind. I think there are product pivots and refinements to what that product actually is. We have been less than clear about who that product is good for and who that product is for and what it’s good for. I think you’re seeing us crisp that up and actually have a much better articulated value proposition so that that becomes very evident to users: what, when and why to use this product.
This one is interesting, in case you thought Google was no longer valuing G+ in terms of helping to understand user's identity outside of Google+ across its other platforms:
How successful was Google Plus in understanding who was using Google in general?
It’s created a huge amount of value in creating common identity for users. The Google of 10 years ago was many separate, silo-ed identity and sharing systems. I think we have been successful in unifying that experience for users. And anytime you see a name or a face on Google, our team provides the infrastructure. And it is a service that is provided to all of Google, so whether you’re on search or maps or whatever, this team helps power that service.
Announced earlier today at Google I/O, Google Photos has now become an app of its own, rather than an exclusive part of G+. I certainly agree with one of the reasons given for seperating it out like this, as was explained earlier by Google+, because it can feel kind of uneasy sharing all your personal photos into an app which is also your public social network, putting them just a click away from private to public.
As we continue to focus Google+ on helping you connect with people around the stuff you love, it’s become clear that while social networks are great for sharing images and video clips, they’re not where most people want to store all their private, personal photos and videos.
Here's a handy tip gif from Google+ today for those that haven't figured this out yet. Sharing posts to your own collections is a great way to categorize relevant content for your followers and there are other uses as well. For example, you could create your own private collections and share posts there as a kind of bookmarking, #PostsToRepost , #SaveForCaturday , #TLDR , etc.
Also, if you're starting up a collection and want to move your own relevant posts into it, better than resharing your posts into the new collection, you can just select the drop down menu from the top Right corner and choose Move post to collection, then choose the collection you want to add them to.
I think today's article from Google is a must read for businesses in sales, so you're not missing out on these critical decision making moments for your brand.
For today's constantly connected consumers, shopping never sleeps. Whether making an everyday purchase or researching a big-ticket item, we reflexively turn to mobile. These I-want-to-buy moments are important for consumers, and they're critical for brands. Are you winning these micro-moments?
Five ways brands can win these micro-moments:
1. Identify your consumers' I-want-to-buy moments.
2. Be there in these moments of need.
3. Deliver relevant messaging.
4. Make it easy for them to make a purchase.
5. Measure every moment that matters.
Before we look to make a purchase, we consult our personal shopping assistants...smartphones. Learn how people approach "I-want-to-buy" moments: goo.gl/PmAOsE
#Marketing #InternetMarketing #Ecommerce
A fascinating look at Google's attractive new photos app, with lots of screen shots. Looks like Autoawesome becomes Assisstant and there's a number of useful new features, such as snap to aspect ratios for common device displays. Hopefully we'll hear more about this from #GoogleIO later this week.
Thanks to for covering this story in and h/t .
Google is always testing new ways of organizing and presenting Search results, including Local Knowledge Panel results. Business owners must be aware of these changes, and of the benefits of claiming and optimizing their Google My Business listings.
In the following article, I discuss the three opportunities for business branding in the Local Knowledge Panel, including:
Your Business Logo
Your Map & Search Photos
Your Business Description
I've also updated the article to reflect the removal of Recent Posts on Google+ from Local Knowledge Panel results. Actively Posting to your Google+ Page might still be valuable in other ways, but is no longer of benefit for Local Knowledge Panel results.
#LocalSEO #LocalSearch #Branding
As I mentioned earlier, there's a new Google Photos app and as expected, it will be replacing the former Google+ Photos for Chrome. So just to be clear on that, we have the following announcement that the outgoing Google+ Photos app will be discontinued July 21, 2015.
Thanks to for this post.
#GooglePhotos #GooglePlusPhotos #TechNews
An interesting post the other day on machine learning from , on how recent advances in image processing technology, coupled with AI is helping to find good content algorithmically which does not have the traditional signals which could have been used to surface this content.
One of the biggest challenges in information retrieval is how to find good content which humans haven't already found.
...and quite often, good material, things people would love, simply goes unnoticed and never builds up the interaction signals which help.
The challenge is that, when new material shows up on the scene, you don't yet have any human interactions -- and quite often, good material, things people would love, simply goes unnoticed and never builds up the interaction signals which help. To detect quality in these things requires understanding the content itself, and the aspects of it which matter to people.
There are several hard aspects to this. One is simply understanding the content at the right granularity: "the color of the top-left pixel" or "the frequency of the word 'whenever'" are too fine-grained to give us a hint about whether people will like something, so we need to be able to group the content into more meaningful structures. For images, that might be "an image of a face in 3/4-profile," a certain color balance or contrast, a perspective or a cropping, and advances in image recognition in the past few years have (finally) made it possible to reliably identify such features. For text, it's much harder: there isn't yet even a clear idea of what features both could be measured about text and determine people's tastes. (How do you measure "intellectually meaty" or "hinting at scandal?")
This paper has used the recent advances in image processing, together with recent advances in AI in general, to get a sense of which pictures people will like. It started by taking several thousand images, and having them rated by humans for quality; that was used as "ground truth." Then, those thousands of images are analyzed into meaningful features, and a neural network is trained to find patterns of image features which predict human taste.
This is what neural networks, and other kinds of "supervised" machine learning systems, do in general: they take as inputs a bunch of signals, and combine them using a large number of parameters -- the "weights" -- to produce predictions of some values that you want to measure. The weights are set by taking a large number of test examples ("golden data" or "ground truth") with known values of both the signals and the test values; weights are chosen ("trained") to maximize the quality of the system's predictions for this data. To make sure that the training doesn't just teach it to recognize those specific examples, the golden data is randomly split into two groups; one is used for training, and then it's tested against the other group to make sure that the predictions with the trained weights are good. If they are, then you have a model which can predict -- given any set of measured signals -- the truth values.
In this case, the signals are these features of the image, measured by a second machine learning system; the quantity being predicted is whether people will like it. Because these are all "content-based signals" -- that is, they're based on the contents of the image, and not on people's responses to it -- the resulting model can be applied to any image.
The team then applied this model to a set of 9 million images from Flickr with fewer than five "favorites." They tested the quality of its picks by having human raters compare that result set with the set of popular images on Flickr; the result was excellent, with its "hidden gems" scoring statistically the same as the most popular images on the site.
I would expect a lot more work on related techniques over the next few years, and for this to have a significant impact on the way that content recommendation is done. The main upshot will be that more little-known works get the spotlight they deserve -- something critical, as more and more people are creating things of value that they want the world to see.
Seems to be significant movement in the local search results lately, as highlighted in the following SEMPost by .
There were increases in two types of local results. First is the local pack which saw a jump from about ~10% to ~12%.
Then there is also an increase in the local one pack results as well.
#GoogleUpdate #SEO #LocalSEO
It was bound to happen. Bored and intelligent elephants sitting at a zoo watching tourists take selfies all day and it's catching on Internationally. This first one's in Thailand and the later is in the UK.
I was wondering if the next thing we're going to see is some kind of Pinterphant, Instaphant app, but then it couldn't be more obvious... they'll probably go for the Ello-phant.
During the week of the Cannes Film Festival, we're rolling out a red carpet of our own. All app developers and app owners — welcome to Search Console!
☆ Specializes SEO, Social Media, Internet marketing.
☆ 20+ yrs computer industry, IT & computer science.
☆ Geek interested in all things science & technology.
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Written comprehensively about brands and businesses maximizing online visibility through social media (SMO) and search engine
optimization (SEO), and now with a special focus on Google Plus.
Doing business on the Internet for over 20 years & worked
extensively with every generation of computers since the first personal
computers, the Apple II & IBM PC's.
Specializing in business consulting on Internet marketing, Online Identity Management, Social Media and Search Optimization.
- G+ SEO/SMO for Brands - REALSMOConsultant, Writer, 2014