Is Your Camera the New Search Box? How Visual Intelligence is Turning Search Keyword-less

Posted by purna_v

My neighbor has the most beautiful garden ever.

Season after season, she grows the most exotic, gorgeous plants that I could never find in any local nursery. Slightly green with envy over her green thumb, I discovered a glimmer of hope.

There are apps that will identify any plant you take a photo of. Problem solved. Now the rest of the neighborhood is getting prettied up as several houses, including mine, have sprouted exotic new blooms easily ordered online.

Take a photo, get an answer. The most basic form of visual search.

Visual search addresses both convenience and curiosity. If we wanted to learn something more about what we’re looking at, we could simply upload a photo instead of trying to come up with words to describe it.

This isn’t new. Google Visual Search was demoed back in 2009. CamFind rolled out its visual search app in 2013, following similar technology that powered Google Glass.

What’s new is that a storm of visual-centric technologies are coming together to point to a future of search that makes the keyword less…key.

Artificial intelligence and machine learning are the critical new components in the visual game. Let’s focus on what this means and how it’s going to impact your marketing game.

How many kinds of reality do we actually need?

The first thing we think about with the future of visual is virtual reality or augmented reality.

There’s also a third one: mixed reality. So what’s the difference between them and how many kinds of reality can we handle?

Virtual reality (VR) is full immersion in another universe – when you have the VR headset on, you cannot see your actual reality. Virtual reality is a closed environment, meaning that you can only experience what’s been programmed into it. Oculus Rift is an example of virtual reality.

Augmented reality (AR) uses your real environment, but enhances it with the addition of a computer-generated element, like sound or graphics. Pokémon Go is a great example of this, where you still see the world around you but the Pokémon-related graphics – as well as sounds – are added to what you see.

Mixed reality (MR) is an offshoot of augmented reality, with the added element of augmented virtuality. Here, it merges your virtual world with your real world and allows you to interact with both through gestures and voice commands. HoloLens from Microsoft (my employer) is an example of mixed reality – this headset can be programmed to layer on and make interactive any kind of environment over your reality.

The difference is a big fat deal – because an open environment, like HoloLens, becomes a fantastic tool for marketers and consumers.

Let me show you what I mean.

Pretty cool, right? Just think of the commercial implications.

Retail reality

Virtual and augmented reality will reshape retail. This is because it solves a problem – for the consumer.

Online shopping has become a driving force, and we already know what its limitations are: not being able to try clothing on, feel the fabric on the couch or get a sense of the heft of a stool. All of these are obstacles to the online shopper.

According to the Harvard Business Review, augmented reality will eliminate pain points that are specific to every kind of retail shopping – not just trying on the right size, but think about envisioning how big a two-man tent actually is. With augmented reality, you can climb inside it!

If you have any doubt that augmented reality is coming, and coming fast, look no further than this recent conquering by Pokémon Go. We couldn’t get enough.

Some projections put investment in AR technology at close to $30 billion by 2020 – that’s in the next three years. HoloLens is already showing early signs for being a game-changer for advertisers.

For example, if I’m shopping for a kitchen stool I could not only look at the website, but I can see what it would look like in my home:

It’s all about being able to get a better feel for how things will look.

Fashion is one industry that has tried to find ways to solve for this and is increasingly embracing augmented reality.

Rebecca Minkoff debuted the use of augmented reality in her New York Fashion Week show this September. Women could use AR app Zeekit – live during the show – to see how the clothes would look on their own body.

Image credit: Zeekit

Why did they do this? To fix a very real problem in retail.

According to Uri Minkoff, who is a partner in his sister’s clothing company, 20 to 40 percent of purchases in retail get returned – that’s the industry standard.

If a virtual try-on can eliminate the hassle of the wrong fit, the wrong size, the wrong everything, then they will have solved a business problem while also making their customers super happy.

This trend caught on and at London Fashion Week a few weeks later there were a host of other designers following suit.

Let’s get real about reality

Let’s bring our leap into the visual back down to earth just a bit – because very few of us will be augmenting our reality today.

What’s preventing AR and VR from taking over the world just yet is going to be slow market penetration. AR and VR are relatively expensive and require entirely new hardware.

On the other hand, something like voice search – another aspect of multi-sensory search – is becoming widely adopted because it relies on a piece of hardware most of us already carry with us at all times: our mobile phone.

The future of visual intelligence relies on tying it to a platform that is already commonly used.

Imagine this. You’re reading a magazine and you like something a model is wearing.

Your phone is never more than three feet from you, so you pick it up, snap a photo of the dress, and the artificial intelligence (AI) – via your digital personal assistant – uses image search to find out where to buy it, no keywords necessary at all.

Take a look at how it could work:

Talk about a multi-sensory search experience, right?

Voice search and conversation as a platform are combined with image search to transact right within the existing platform of your digital personal assistant – which is already used by 66% of 18- to 26-year-olds and 59% of 27- to 35-year-olds, according to Forrester Research.

As personal digital assistants rise, so will the prevalence of visual intelligence.

Digital personal assistants, with their embedded artificial intelligence, are the key to the future of visual intelligence in everybody’s hands.

What’s already happening with visual intelligence?

Amazon

One of the most common uses exists right within the Amazon app. Here, the app gives you the option to find a product simply by taking a photo of something or of the bar code:

or

CamFind

The app CamFind can identify the content of pictures you’ve taken and offer links to places you could shop for it. Their website touts the fact that users can get “fast, accurate results with no typing necessary.”

For example, I took a photo of my (very dusty) mouse and it not only recognized it, but also gave me links to places I could buy it or learn more about it.

↓

↓

Pinterest

Pinterest already has a handy visual search tool for “visually similar results,” which returns results from other pins that are a mix of commerce and community posts. This is a huge benefit for retailers to take advantage of.

For example, if you were looking for pumpkin soup recipe ideas and came across a kitchen towel you liked within the Pin, you could select the part of the image you wanted to find visually similar results for.

Image credit: Pinterest

Google

Google’s purchase of Moodstocks is also very interesting to watch. Moodstocks is a startup that has developed machine learning technology to boost image recognition for the cameras on smartphones.

For example, you see something you like. Maybe it’s a pair of shoes a stranger is wearing on the subway, and you take a picture of it. The image recognition software identifies the make and model of the shoe, tells you where you can buy it and how much it costs.

Image credit: Moodstocks

Captionbot.ai

Microsoft has developed an app that describes what it sees in images. It understands thousands of objects as well as the relationship between them. That last bit is key – and is the “AI” part.

Captionbot.ai was created to showcase some of the intelligence capabilities of Microsoft Cognitive Services, such as Computer Vision, Emotion API, and Natural Language. It’s all built on machine learning, which means it will get smarter over time.

You know what else is going to make it smarter over time? It’s integrated into Skype now. This gives it a huge practice field – exactly what all machine learning technology craves.

As I said when we first started, where we are now with something like plant identification is leading us directly to the future with a way of getting your product into the hands of consumers who are dying to buy it.

What should I do?

Let’s make our marketing more visual.

We saw the signs with rich SERP results – we went from text only to images, videos and more. We’re seeing pictures everywhere in a land that used to be limited to plain text.

Images are the most important deciding factor when making a purchase, according to research by Pixel Road Designs. They also found that consumers are 80% more willing to engage with content that includes relevant images. Think about your own purchase behavior – we all do this.

This is also why all the virtual reality shenanigans are going to take root.

Up the visual appeal

Without the keyword, the image is now the star of the show. It’s almost as if the understudy suddenly got thrust into the spotlight. Are they ready? Will they succeed?

To get ready for keywordless searches, start by reviewing the images on your site. The goal here is to ensure they’re fully optimized and still recognizable without the surrounding text.

First and foremost, we want to look at the quality of the image and answer yes to as many of the following questions as possible:

• Does it clearly showcase the product?
• Is it high-resolution?
• Is the lighting natural with no distortive filters applied?
• Is it easily recognizable as being that product?

Next, we want to tell the search engines as much about the image as we can, so they can best understand it. For the same reasons that SEOs can benefit by using Schema mark-up, we want to ensure the images tell as much of a story as they can.

The wonderfully brilliant Ronell Smith touched upon this subject in his recent Moz post, and the Yoast blog offers some in-depth image SEO tips as well. To summarize a few of their key points:

• Make sure file names are descriptive
• Provide all the information: titles, captions, alt attribute, description
• Create an image XML sitemap
• Optimize file size for loading speed

Fairly simple to do, right? This primes us for the next step.

Take action now by taking advantage of existing technology:

1. Pinterest:

On Pinterest, optimize your product images for clean matches from lifestyle photos. You can reverse-engineer searches to your products via the “visually similar results” tool by posting pins of lifestyle shots (always more compelling than a white background product shot) that feature your products, in various relevant categories.

In August, Pinterest added video to its visual search machine learning functionality. This tool is still working out the kinks, but keep your eye on it so you can create relevant content with a commerce view.

For example, a crafting video about jewelry might be tagged with places to buy the tools and materials in it.

2. Slyce:

Integrate Slyce’s astounding tool, which gives your customer’s camera a “buy” button. Using image recognition technology, the Slyce tool activates visual product recognition.

Image credit: Slyce.it

Does it work? There are certainly several compelling case studies from the likes of Urban Outfitters and Neiman Marcus on their site.

3. Snapchat:

Snap your way to your customer, using Snapchat’s soon-to-come object recognition ad platform. This lets you deliver an ad to a Snapchatter by recognizing objects in the pictures they’ve just taken.

The Verge shared images from the patent Snapchat had applied for, such as:

For example, someone who snaps a pic of a woman in a cocktail dress could get an ad for cocktail dresses. Mind-blowing.

4. Blippar:

The Blippar app is practically a two-for-one in the world of visual intelligence, offering both AR as well as visual discovery options.

They’ve helped brands pave the way to AR by turning their static content into AR interactive content. A past example is Domino’s Pizza in the UK, which allowed users of the Blippar app to interact with their static posters to take actions such as download deals for their local store.

Now the company has expanded into visual discovery. When a user “Blipps” an item, the app will show a series of interrelated bubbles, each related to the original item. For example, “Blipping” a can of soda could result in information about the manufacturer, latest news, offers, and more.

Image credit: Blippar.com

Empowerment via inclusivity

Just in case you imagine all the developments are here to serve commerce, I wanted to share two examples of how visual intelligence can help with accessibility for the seeing impaired.

TapTapSee

From the creators of CamFind, TapTapSee is an app specifically designed for the blind and visually impaired.

It recognizes objects photographed and identifies them out loud for the user. All the user needs to do to take a photo is to double tap on the devices’ screen.

The Seeing AI

Created by a Microsoft engineer, the Seeing AI project combines artificial intelligence and image recognition with a pair of smart glasses to help a visually-impaired person better understand who and what is going on around them.

Take a look at them in action:

While wearing the glasses, the user simply swipes the touch panel on the eyewear to take a photo. The AI will then interpret the scene and describe it back out loud, using natural language.

It can describe what people are doing, how old they are, what emotion they’re expressing, and it can even read out text (such as a restaurant menu or newspaper) to the user.

Innovations like this are what makes search even more inclusive.

Keep Calm and Visualize On

We are visual creatures. We eat first with our eyes, we love with our eyes, we become curious with our eyes.

Cameras as the new search box is brilliant. It removes obstacles to search and helps us get answers in a more intuitive way. Our technology is adapting to us, to our very human drive to see everything.

And that is why the future of search is visual.

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The 2016 #MozCon Video Bundle Has Arrived!

Posted by Danielle_Launders

We’re excited to announce that the MozCon 2016 Video Bundle is ready! That means no more #FOMO — you can catch up on all 27 sessions and over 15 hours of online marketing goodness from some of the brightest minds in the industry. Want to earn more featured snippets? Yup, we cover that. Looking for new tools and tactics for link acquisition? You’ll learn that, too!

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A big and special thanks to everyone on the Moz team that worked hard to make these videos available (and in less than a month after the show!). It definitely takes a village. I want to send thanks (and hugs!) to the crew that worked so hard to process, edit, design, build, code, and more to make this happen. We wish you happy learning and hope to see you at MozCon 2017 in July.

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How to Use Search Analytics in Google Sheets for Better SEO Insights

Posted by mihai.aperghis

This post was originally in YouMoz, and was promoted to the main blog because it provides great value and interest to our community. The author's views are entirely his or her own and may not reflect the views of Moz, Inc.

As an SEO, whether you’re working in-house or handling many clients in an agency, you’ve likely been using this tool for a bunch of reasons. Whether it's diagnosing traffic and position changes or finding opportunities for optimizations and content ideas, Google Search Console's Search Search Analytics has been at the core of most SEOs' toolset.

The scope of this small guide is to give you a few ideas on how to use Search Analytics together with Google Sheets to help you in your SEO work. As with the guide on how to do competitive analysis in Excel, this one is also focused around a tool that I’ve built to help me get the most of Search Analytics: Search Analytics for Sheets.

The problem with the Search Analytics UI

Sorting out and managing data in the Google Search Console Search Analytics web UI in order to get meaningful insights is often difficult to do, and even the CSV downloads don't make it much easier.

The main problem with the Search Analytics UI is grouping.

If you’d like to see a list of all the keywords in Search Analytics and, at the same time, get their corresponding landing pages, you can’t do that. You instead need to filter query-by-query (to see their associated landing pages), or page-by-page (to see their associated queries). And this is just one example.

Basically, with the Search Analytics UI, you can’t do any sort of grouping on a large scale. You have to filter by each keyword, each landing page, each country etc. in order to get the data you need, which would take a LOT of time (and possible a part of your sanity as well).

In comes the API for the save

Almost one year ago (and after quite a bit of pressure from webmasters), Google launched the official API for Search Analytics.

With it, you can do pretty much anything you can do with the web UI, with the added benefit of applying any sort of grouping and/or filtering.

Excited yet?

Imagine you can now have one column filled with keywords, the next column with their corresponding landing pages, then maybe the next one with their corresponding countries or devices, and have impressions, clicks, CTR, and positions for each combination.

Everything in one API call

---

Query	Page	Country	Device	Clicks	Impressions	CTR	Position
keyword 1	http://ift.tt/2e8Wy2C	usa	DESKTOP	92	2,565	3.59%	7.3
keyword 1	http://ift.tt/2e8Wy2C	usa	MOBILE	51	1,122	4.55%	6.2
keyword 2	https://domain.com/gb/	gbr	DESKTOP	39	342	11.4%	3.8
keyword 1	http://ift.tt/2dJ1Sfa	aus	DESKTOP	21	55	38.18%	1.7
keyword 3	http://ift.tt/2e8Wy2C	usa	MOBILE	20	122	16.39%	3.6

---

Getting the data into Google Sheets

I have traditionally enjoyed using Excel but have since migrated over to Google Sheets due to its cloud nature (which means easier sharing with my co-workers) and expandability via scripts, libraries, and add-ons.

After being heavily inspired by Seer Interactive’s SEO Toolbox (an open-source Google Sheets library that offers some very nice functions for daily SEO tasks), I decided to build a Sheets script that would use the Search Analytics API.

I liked the idea of speeding up and improving my daily monitoring and diagnosing for traffic and ranking changes.

Also, using the API gave me the pretty useful feature of automatically backing up your GSC data once a month. (Before, you needed to do this manually, use a paid Sheets add-on or a Python script.)

Once things started to take shape with the script, I realized I could take this public by publishing it into an add-on.

What is Search Analytics for sheets?

Simply put, Search Analytics for Sheets is a (completely free) Google Sheets add-on that allows you to fetch data from GSC (via its API), grouped and filtered to your liking, and create automated monthly backups.

If your interest is piqued, installing the add-on is fairly simple. Either install it from the Chrome Web Store, or:

• Open a Google spreadsheet
• Go to Add-ons -> Get add-ons
• Search for Search Analytics for Sheets
• Install it (It'll ask you to authorize a bunch of stuff, but you can sleep safe: The add-on has been reviewed by Google and no data is being saved/monitored/used in any other way except grabbing it and putting it in your spreadsheets).

Once that's done, open a spreadsheet where you'd like to use the add-on and:

• Go to Add-ons -> Search Analytics for Sheets -> Open Sidebar
• Authorize it with your GSC account (make sure you’re logged in Sheets with your GSC account, then close the window once it says it was successful)

You’ll only have to do this once per user account, so once you install it, the add-on will be available for all your spreadsheets.

PS: You'll get an error if you don't have any websites verified on your logged in account.

How Search Analytics for Sheets can help you

Next, I’ll give you some examples on what you can use the add-on for, based on how I mainly use it.

Grab information on queries and their associated landing pages

Whether it is to diagnose traffic changes, find content optimization opportunities, or check for appropriate landing pages, getting data on both queries and landing pages at the same time can usually provide instant insights. Other than automated backups, this is by far the feature that I use the most, especially since it’s fairly hard to replicate the process using the standard web UI.

Best of all, it’s quite straightforward to do this and requires only a few clicks:

• Select the website
• Select your preferred date interval (by default it will grab the minimum and maximum dates available in GSC)
• In the Group field, select “Query,” then “Page”
• Click “Request Data”

That’s it.

You’ll now have a new sheet containing a list of queries, their associated landing pages, and information about impressions, clicks, CTR, and position for each query-page pair.

What you do with the data is up to you:

• Check keyword opportunities

Use a sheets filter to only show rows with positions between 10 and 21 (usually second-page results) and see whether landing pages can be further optimized to push those queries to the first page. Maybe work a bit on the title tag, content and internal linking to those pages.

• Diagnose landing page performance

Check position 20+ rows to see whether there’s a mismatch between the query and its landing page. Perhaps you should create more landing pages, or there are pages that target those queries but aren’t accessible by Google.

• Improve CTR

Look closely at position and CTR. Check low-CTR rows with associated high position values and see if there’s any way to improve titles and meta descriptions for those pages (a call-to-action might help), or maybe even add some rich snippets (they’re pretty effective in raising CTR without much work).

• Find out why your traffic dropped
  • Had significant changes in traffic? Do two requests (for example, one for the last 30 days and one for the previous 30 days) then use VLOOKUP to compare the data.
  • Positions dropped across the board? Time to check GSC for increased 4xx/5xx errors, manual actions, or faulty site or protocol migrations.
  • Positions haven’t dropped, but clicks and impressions did? Might be seasonality, time to check year-over-year analytics, Google Trends, Keyword Planner.
  • Impressions and positions haven’t dropped, but clicks/CTR did? Manually check those queries, see whether the Google UI has changed (more top ads, featured snippet, AMP carousel, “In the news” box, etc.)

I could go on, but I should probably leave this for a separate post.

Get higher granularity with further grouping and filtering options

Even though I don’t use them as much, the date, country and device groupings let you dive deep into the data, while filtering allows you to fetch specific data to one or more dimensions.

Date grouping creates a new column with the actual day when the impressions, clicks, CTR, and position were recorded. This is particularly useful together with a filter for a specific query, so you can basically have your own rank tracker.

Grouping by country and device lets you understand where your audience is.

Using country grouping will let you know how your site fares internationally, which is of course highly useful if you target users in more than one country.

However, device grouping is probably something you’ll play more with, given the rise in mobile traffic everywhere. Together with query and/or page grouping, this is useful to know how Google ranks your site on desktop and mobile, and where you might need to improve (generally speaking you’ll probably be more interested in mobile rankings here rather than desktop, since those can pinpoint problems with certain pages on your site and their mobile usability).

Filtering is exactly what it sounds like.

Choose between query, page, country and/or device to select specific information to be retrieved. You can add any number of filters; just remember that, for the time being, multiple filters are added cumulatively (all conditions must be met).

Other than the rank tracking example mentioned earlier, filtering can be useful in other situations as well.

If you’re doing a lot of content marketing, perhaps you’ll use the page filter to only retrieve URLs that contain /blog/ (or whatever subdirectory your content is under), while filtering by country is great for international sites, as you might expect.

Just remember one thing: Search Analytics offers a lot of data, but not all the data. They tend to leave out data that is too individual (as in, very few users can be aggregated in that result, such as, for example, long tail queries).

This also means that, the more you group/filter, the less aggregated the data is, and certain information will not be available. That doesn’t mean you shouldn’t use groups and filters; it’s just something to keep in mind when you’re adding up the numbers.

Saving the best for last: Automated Search Analytics backups

This is the feature that got me into building this add-on.

I use GSC data quite a bit, from client reports to comparing data from multiple time periods. Unless you’ve never used GSC/WMT in the past, it’s highly unlikely you don’t know that the data available in Search Analytics only spans about the last 90 days.

While the guys at Google have mentioned that they’re looking into expanding this window, most SEOs have had to rely on various ways of backing up data in order to access it later.

This usually requires either remembering to manually download the data each month, or using a more complicated (but automated) method such as a Python script.

The Search Analytics for Sheets add-on allows you to do this effortlessly.

Just like when requesting data, select the site and set up any grouping and filtering that you’d like to use. I highly recommend using query and page grouping, and maybe country filtering to cut some of the noise.

Then simply enable the backup.

That’s it.The current spreadsheet will host that backup from now on, until you decide to disable it.

What happens now is that once per month (typically on the 3rd day of the month) the backup will run automatically and fetch the data for the previous month into the spreadsheet (each month will have its own sheet).

In case there are delays (sometimes Search Analytics data can be delayed even up to a week), the add-on will re-attempt to run the backup every day until it succeeds.

It'll even keep a log with all backup attempts, and send you an email if you'd like.

It'll also create a separate sheet for monthly aggregated data (the total number of impressions and clicks plus CTR and position data, without any grouping or filtering), so that way you'll be sure you're 'saving' the real overview information as well.

If you’d like more than one backup (either another backup for the same site but with different grouping/filtering options or a new backup for a different site), simply open a new spreadsheet and enable the backup there. You’ll always be able to see a list with all the backups within the “About” tab.

For the moment, only monthly backups are available, though I’m thinking about including a weekly and/or daily option as well. However that might be more complicated, especially in cases where GSC data is delayed.

Going further

I hope you’ll find the tool as useful as I think it is.

There may be some bugs, even though I tried squashing them all (thanks to Russ Jones and Tori Cushing, Barry Schwartz from Search Engine Roundtable, and Cosmin Negrescu from SEOmonitor for helping me test and debug it).

If you do find anything else or have any feature requests, please let me know via the add-on feedback function in Google Sheets or via the form on the official site.

If not, I hope the tool will help you in your day-to-day SEO work as much as it helps me. Looking forward to see more use cases for it in the comments.

PS: The tool doesn't support more than 5,000 rows at the moment; working on getting that improved!

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