Mobile Product Listing Ads Improvements and Success Stories

People are increasingly turning to their mobile devices as shopping assistants at home, in stores, and on the go, and constant connectivity is helping shoppers find the products they want, whenever they want. Google Shopping connects people looking for products with the best places to buy both online and in local stores, and there are big opportunities for retailers to connect with mobile shoppers using mobile Product Listing Ads (PLA), particularly as we head into the holiday retail season.

Expanded Google Shopping results on mobile devices
As part of our efforts to help retailers promote their products to people across devices, we’re updating the look of the mobile PLA unit on Google.com to help shoppers more easily browse and discover products on their mobile device. Users can now swipe to quickly see more products in the mobile PLA unit without having to leave the search results page. The unit will also feature larger product images and product titles. This update gives more retailers the opportunity to appear on the mobile PLA unit, and early tests show that it drives more traffic to retailers.

Retailer success stories with mobile PLAs
Many advertisers have already positioned themselves for success by leveraging mobile PLAs ahead of the holidays. Below are just a couple of examples.

• REVOLVEclothing.com understands that their customers are very mobile-savvy, so they increased their focus on optimizing their mobile PLA campaign. Within a few months, they grew their mobile PLA campaign clicks by 371%, their conversions climbed 537%, and their mobile return on ad spend (ROAS) increased by 77%. Learn more.
  
  
• HalloweenCostumes.com implemented mobile PLAs for their entire product line after seeing major lifts in their online advertising efforts via desktop PLA campaigns. Mobile PLAs are now a major driver of their overall mobile growth, delivering a 31% lift in overall mobile conversions. In addition, they focused heavily on converting new mobile traffic by redesigning their mobile site to increase the speed and overall usability of product pages and navigation. Learn more.
  
  

Show your Product Listing Ads to smartphone shoppers
If your PLA campaigns aren’t already opted into mobile, there’s still time to apply mobile bid adjustments to help you capture valuable real estate on mobile shopping search results with over a month left before Thanksgiving.

If you need help getting started, please join our webinar on Wednesday, October 23 at 10 am Pacific Time to hear tips and best practices for creating and optimizing a mobile PLA campaign.

Posted by Jennifer Liu, Group Product Manager, Google Shopping

Mind the Gap: Improving Referral Information with Universal Analytics

The following is a guest post contributed by Dan Wilkerson, marketing manager at LunaMetrics, a Google Analytics Certified Partner & Digital Marketing Consultancy.

A core issue with measuring social media is that due to the way that traffic migrates around the web, there are lots of situations where we lose referrer information and those visits end up being labeled as 'Direct' inside of our analytics.

This can happen for a variety of reasons, but the most common situations where this kind of erroneous attribution occurs are:

• When a user clicks an untagged link inside an email
• When a user visits from a mobile application
• When a user clicks a link shared to them via an instant message

If a visitor has come to your site previously, Google Analytics will simply apply the same referral information it had for their previous visit, which it retrieves from the UTMZ cookie it previously saved on the visitor's browser. But, if there are no cookies, Analytics has no information, and buckets the visitor into Direct.

Obviously, this is problematic; 'Direct' is supposed to represent visitors who bookmark or directly type in our URL. These users are accessing our site through a shared link, and should be counted as referrals. Thankfully, we have some tools at our disposal to combat some of these scenarios, most notably campaign parameters. But campaign parameters only help with links that you share; what about when a visitor comes to your site and shares the link themselves?

These visits can cause serious problems when it comes time to analyze your data. For example, we offer Google Analytics & AdWords training. Most of our attendees are sponsored by their employers. This means they visit our site, scope out our training, and then email a link to a procurement officer, who clicks through and makes the purchase. Since the procurement officer comes through on the emailed link and has never visited our site, the conversion gets bucketed into 'Direct / None' and we lose all of the visit data for the employee who was interested in the first place. This can compound into a sort of feedback loop - the only data we see would be for individuals who buy their own tickets, meaning we might optimize our marketing for smaller businesses that send us less attendees. In other words, we'd be interpreting data from the wrong customers. Imagine how this kind of feedback loop might impact a B2B trying to generate enterprise-level leads - since they'd only see information on the small fry, they could wind up driving more of the wrong kind of lead to their sales team, and less of the right kind.

For a long time, this has been sort of the status quo. Now, with new features available in Universal Analytics, we have some tools we can employ to combat this problem. In this post, I want to share with you a solution that I've developed to reduce the amount of Direct traffic. We're calling it DirectMonster, and we're really excited to make it open source and available to the Google Analytics community.

What is DirectMonster?

DirectMonster is a JavaScript plug-in for Google Analytics that appends a visitor's referral information as ciphered campaign parameters as an anchor of the current URL. The result looks something like this:

When the visitor copies and shares the URL from the toolbar, they copy that stored referral information along with it. When someone without referral information lands on the site through a link with those encoded parameters, the script decodes that information as campaign parameters to pass along to Google Analytics, waits until Analytics writes a fresh UTMZ cookie, and then ciphers, encodes, and re-appends the visitors current referral information. It also appends '-slb' to the utm_content parameter. That way, those visits can be segmented from 'canonical' referrals for later analysis, if necessary. The visitor who would have had no referral information now is credited as being referred from the same source as the visitor who shared the link with them. This means that visits that normally would have been erroneously segmented as 'Direct / None' will now more accurately reflect the channel that deserves credit for the visit. 

At first, this might seem wrong - shouldn't we just let Analytics do its job and not interfere? But, the fact is that those visits aren't really Direct, at least not in its truest interpretation, and having 'assisted referrer' channel information gives you actionable insight. Plus, by weeding out those non-Direct scenarios, your Direct / None numbers will start to more accurately represent visitors who come to your site directly, which can be very important for other measurement and attribution. It's actually better all the way around. After all, if a Facebook share is what ultimately drove that visitor to your site, isn't having that information more valuable than having nothing at all? This way, you'll have last-click attribution for conversions that otherwise would have simply been bucketed as Direct. Of course, you won't have the visit history of the assisting referrer, but... well, more on that soon.

We've been fine-tuning this on our site for the past few months, and we've been able to greatly enhance our conversion attribution accuracy. In our video case study, I mentioned that we enhanced attribution by 47.5%; since that time, we've seen the accuracy of our data continue to climb; whereas before, we were seeing 'Direct / None' account for 45.5% of our conversions, it now accounts for just 20.6% - a decrease of 54.7%. Better yet, look at what it's done to all of our traffic:

We've gone from having about 20-25% of our traffic come in 'Direct / None' to just under 15%, and I anticipate that number will continue to fall.

DirectMonster and Universal Analytics

One of the coolest features that Universal Analytics has given us is Custom Dimensions. If you're not familiar with them, take a minute and read the Google Developer Resources page about what they are and how they work. Although initially designed for the asynchronous code, Universal Analytics has allowed us to put DirectMonster on steriods. 

In our Universal implementation, we store the visitors CID as a visit-level custom dimension, and we add their CID to the hashed parameters we're already storing in the anchor of their URL. 

When a visitor comes through on a link with a CID that differs from their own, we capture the stored CID as the Assisted Referrer. Then, we can open up our Custom Reports later on and view what visitors were referred to our site by whom, and what they did when they got there.

What does this mean? If a celebrity tweets a link to your product, you can discover exactly how many visitors they referred, and how much revenue those visitors generated. 

By cross-referencing the Assisted CID for single-visit 'Direct / None' purchases, you can discover the true visit history of a conversion.

Since it takes advantage of advanced Universal Analytics functionality, DirectMonster 2.0 requires some advanced implementation as well. Unlike its cousin, you'll need to adjust your Analytics tracking code to include a few functions, and you'll need to configure the Custom Dimensions you'll be storing a visitors CID and assisted referrers CID inside of. For a full reference on how to get either version of DirectMonster and configure it for your site, check out our blog post covering the topic in detail here or visit our GitHub page and get DirectMonster for yourself. 

I hope that you're as excited as I am about this development and all of the things Universal Analytics is enabling us to do. Think of a use case I didn't mention? Share it with me in the comments!

Posted by Dan Wilkerson, marketing manager at LunaMetrics

Analyze Organic Search Engine Marketing with Google Analytics & Webmaster Tools Data

There are many ways to measure the effectiveness of organic search engine marketing. We’d like to explore various techniques in a series of posts here on the Analytics blog. Today we’ll talk about understanding organic using landing pages and Webmaster Tools data. 

Today, almost all marketers are investing heavily in creating high-quality content as a way to reach users with information about their products and services. The content can take many forms - from product specific content to brand specific content. The intent is to generate traffic and conversions from a variety of sources, one of the largest of which is often search.

One way to measure the effectiveness of content is to analyze its performance as a landing page. A landing page is the first page a user sees when they land on your site. If it’s great content, and if it’s ranked highly by search engines like Google, then you should see a lot of websites ‘entrances’ via that page. Looking at landing page performance, and the traffic that flows through specific landing pages, is a great way to analyze your search engine optimization efforts.

Begin by downloading this custom report (this link will take you to your Analytics account). This report shows the landing pages that receive traffic from Google organic search and how well the traffic performs. 

Let’s start at the top. The over-time graph shows the trend of Google organic traffic for your active date range. If you are creating great content that is linked to and shared then you should see the trend increasing over time.

When you look at this data ask yourself the question: how well does the trend align with my time investment? Looking at the data below we see that the organic traffic is increasing, so this organization must be working hard to create and share good content.

Organic traffic is steadily increasing for this site. An important question to ask is, “how does this align with my search optimization efforts?”

The table, under the trend data, contains detailed data about the acquisition of users, their behavior on the site and ultimately the conversions that they generate. This includes data like Visits, % New Visits, Bounce Rate, Average Time on Site, Goal Conversion Rate, Revenue and Per Visit Value. 

Using the tabular data I can learn how search engine traffic, entering through a specific page is performing. 

Each metric provides insight about users coming from organic search and entering through certain pages. For example, % New Visits can help you understand if you’re attracting a new audience or a lot of repeat users. Bounce rate can help you understand if your content is ‘sticky’ and interesting to users. And conversion rate helps you understand if organic traffic, flowing through these landing pages, is actually converting and driving value to your business.

Again, we’re using the landing page to understand the performance of our content in search engine results.

Remember, make sure that you customize the report to include goals that are specific to your account. You can learn more about goals and conversions in our help center.  

Another very useful organic analysis technique is to group your content together by ‘theme’ and analyze the performance. For example, if you are an ecommerce company you may want to group all of your pages for a certain product category together - like cameras, laptop computers or mobile phones.

You can use the Unified Segmentation tool to bundle content together. For example, here’s a simple segment that includes two branded pages (I’m categorizing the homepage and the blog page homepage as ‘brand’ pages).

You can create other segments that include other types of pages, like specific category pages (and then view both segments together). Here is the Acquisition > Keywords > Organic report with both segments applied. This helps me get a bit more insight into the types of pages people land on when visiting from Google organic search results.

Plotting two segments, one for branded content landing pages and one for non-branded landing pages, can help you understand your specific tactics.

Regardless of the tool you use, the analysis technique is the same: look at the performance of each landing page to identify if they are generating value for your business. And don’t forget, the best context for this data is your search engine marketing plan. 

Here’s one final tip when analyzing organic traffic. Whenever you create a customization in Google Analytics, like a segment or custom report, don’t use the keyword dimension. Instead use the Source and Medium dimensions. Set the Source to ‘Google’ and Medium of ‘Organic’. It provides the most consistent data over long time periods. 

In addition to using Google Analytics, you can also use the data from Webmaster Tools to gain an understanding of your search marketing tactics. You can link your Google Analytics account and your Webmaster Tools account to access some of this data directly in Google Analytics. If you’re not familiar with Webmaster Tools, check out their help center for an overview or this awesome video.

In general the Webmaster Tools data will help you understand how well your content is crawled, indexed and ranked by Google. This is extremely tactical data that can inform many search marketing decisions, like which content to create, how to structure your content and how to design your pages. The reports are in the Acquisition > Search Engine Optimization section. 

Let’s start by viewing some data using the Acquisition > Search Engine Optimization > Landing Pages report.

Webmaster Tools data is available directly in Google Analytics. You can view the data based on landing page or search query.

Let’s review a couple of metrics that are unique to Webmaster tools: Impressions, Average Position and Click Through Rate. Impressions is the number of times pages from your site appeared in search results. If you’re continuously optimizing the content on your site you should see your content move up in the search results and thus get more impressions.

Average position is the average top position for a given page. To calculate average position, Webmaster Tools take into account the top ranking URL from your site for a particular query. For example, if Alden’s query returns your site as the #1 and #2 result, and Gary’s query returns your site in positions #2 and #7, your average top position would be 1.5 [ (1 + 2) / 2 ].

Click Through Rate (CTR) is the percentage of impressions that resulted in a click and visit to your site. Again, you can see both the impressions and the CTR for every landing page on your site. 

If we’re optimizing content then hopefully we should see our average position increase, the impressions increase and ultimately an increase in click-throughs. A very easy way to observe this behavior is by applying a date comparison to the Acquisition > Search Engine Optimization > Landing Pages report.

Use the Search Engine Optimization > Landing Pages report to understand if your content is getting ranked higher and generating clicks.

What happens if impressions and average position are increasing but you’re not getting clicks? You’re getting ranked better, but what is listed in the results may not get a response from the user. 

There are lots of ways to optimize your content and change what is listed in the search results. You could adjust your page title or meta description to improve the data that is shown to the user and thus increase the relevancy of the result and your Click Through Rate. 

We’ll be back soon with another article on measuring and optimizing organic search traffic with Analytics.

Posted by Justin Cutroni, on behalf of the Google Analytics Education team

Supporting entrepreneurs worldwide with UP Global

Startups and entrepreneurs lead the way in creating innovative products that improve lives and drive significant economic and social impact. A robust community of entrepreneurs—paired with resources, mentorship and technology—can thrive. That’s why one year ago we launched Google for Entrepreneurs, which today supports more than 70 organizations that are champions for entrepreneurship in more than 115 countries around the world.

Today we’re announcing a new partnership with UP Global which will double their impact over the next three years. UP is currently active in 500 cities globally and with our partnership aims to be in 1,000 cities by 2016. We’ll expand our existing work together to grow Startup Weekend, now powered by Google for Entrepreneurs, activating entrepreneurial communities and helping them launch companies. We’re also teaming up to power Startup Digest and NEXT to connect entrepreneurs with training and event resources—all through UP Global.

A tidal wave of startups is sweeping the globe. Connect with us on Google+ and join the movement. Here’s to the entrepreneurs!

Posted by Mary Grove, Director of Google for Entrepreneurs

New Sample Size Control and Relative Dates Features in Google Analytics APIs

Our goal is for Google Analytics APIs to be as simple to use as possible - so we just released 2 new features that make it even easier to use our APIs.

Relative dates

All Core API and MCF Reporting API queries previously required a start and end date. In the past, apps that displayed recent data - like the last 14 days - would have to manually determine today’s date, determine when 14 days ago was, and format the dates so they could be used.

To make things easier, we’ve added support for relative dates! You can now specify NdaysAgo as a value of either the start or end date. So the date range of the last 14 days from yesterday can now be expressed as:

start-date=15daysAgo&end-date=yesterday

Using these values will automatically determine the date range based on today’s date, allowing apps to always display the data for last 14 days (or whatever time period you’d like!).

Sample size control

In certain cases, data may be sampled. To simplify setting and reporting the impact of sampling, we’ve added a couple new sampling related features.

First, we added a new query parameter to set the level of sampling. Developers can now specify whether reports should be faster or be more precise.

Second, we added 2 new fields to the API response:

• sampleSize - The number of samples that were used for the sampled query.
• sampleSpace - The total sampling space size. This indicates the total available sample space size from which the samples were selected.
   

With these 2 values you can calculate the percentage of visits that were used for the query.

For example, if the sampleSize = 201,000 and sampleSpace = 220,000 then the report is based on 91.36% of visits.

Together, these values allow developers to see exactly how much data was used to calculate the sample.

Getting Started

There are two easy ways to get started: you can read our reference guide on the new relative dates feature, or check out our docs on the new sample size control query parameter and sample size API response data. As always, you can stay up to date using our change logs

.

Posted by Nick Mihailovski and Srinivasan Kannan, the Google Analytics API Team

A long way home with help from Google Earth

In 1986, a five-year-old boy named Saroo Munshi Khan accidentally fell asleep on a stationary train in India. He woke up hours later, alone and in an unfamiliar place. This fateful train ride ripped Saroo away from his home and family. For more than a quarter century, he searched for them before finding his way back home with the help of Google Earth.

This incredible true story spans decades, miles and continents. If it weren’t for hope, determination and technology, Saroo would have remained forever lost.

On that day 27 years ago, Saroo and his 14-year-old brother, Guddu, were searching a train station for change to help support their family. Guddu wandered beyond the station and Saroo fell asleep on a stationary train waiting for his brother’s return. When he woke up, the train had left the station, separating Saroo from his home and family.

The train Saroo boarded was in Berhanpur, India, and he ended up 1,500 kilometers away, in Calcutta. For weeks, he survived on the streets. Eventually, he was taken into an orphanage, where he was adopted by the Brierleys, an Australian family. He moved across an ocean to the town of Hobart in Tasmania. At six years old, Saroo had a new family, home, country and name. Though Saroo Munshi Khan couldn’t find his home, Saroo Brierley never gave up the search.

In 2011, using vague memories and Google Earth imagery, Saroo identified his home town. Using the ruler feature in Google Earth, he mapped out a search radius by making an educated guess about how far he traveled by train. After countless hours of scouring this area of Google Earth imagery, he came upon a proverbial needle in a haystack. Saroo spotted one vague landmark that led him to the next, helping him unlock a five-year-old child’s memories. He eventually spotted a neighborhood, street and tin roof that looked familiar.

In Saroo's words, "It was just like being Superman. You are able to go over and take a photo mentally and ask, 'Does this match?' And when you say, 'No,' you keep on going and going and going."

In 2012, Saroo embarked on a trip from Australia back to Khandwa, India. Once he arrived, he shared his story with locals, who helped him find his way back home to his mother and surviving brother and sister. Twenty-six years after accidentally leaving home, he finally found his way back.

The Google Earth imagery that brought Saroo home.

Maps can affect our lives in many ways, big and small—but hopefully they always help us find our way. You can now read Saroo’s book, “A Long Way Home,” for a detailed account of his journey of survival and triumph against incredible odds. It celebrates the importance of never letting go of what drives the human spirit—hope.

Posted by Peter Birch, Google Earth Product Manager

New Learn with Google Fall Webinars

We’re excited to share our fall series of Learn with Google webinars. These web events will demonstrate how to use digital marketing to build brand awareness and give you the tools you need to drive sales. This season we’re focusing on Search and Display, two fundamental building blocks for digital marketing. We will be introducing new tools, as well as providing tips for existing ones. Every webinar is led by Google product experts and includes time for live Q&A. Sign up to start becoming a smarter digital marketer today.

Upcoming webinars:

October
17 [Wallet] Maximize mobile conversions with Google Wallet Instant Buy
22 [Search] Automate your AdWords bids to achieve your Target ROAS (return on ad spend) Goal
23 [Shopping] Google Shopping - Mobile/Local
29 [Mobile] Driving More Revenue from App Installs

November
7 [Display] A Creative Approach to Engage with Your Customers Online
13 [Search] Measuring Conversions Across Devices
14 [Display] Three Strategies to Increase Customer Engagement with Your Brand
19 [Mobile] Building Multi-Screen Websites
21 [Search] Increase Relevancy with New Ad Formats and Extensions

December
3 [Search] Using Your Cross-Device Conversion Data
11 [Search] Improving Search Ad Relevance with Creative Optimizations

Webinars are held Tuesdays through Thursdays at 10am Pacific/1pm Eastern

Visit our webinar site to register for any of the live sessions and to access our large library of recorded content. You can also stay up-to-date on the schedule by adding our Learn with Google calendar to your own Google calendar to automatically see upcoming webinars.

Learn with Google is a program to help businesses succeed through winning moments that matter, enabling better decisions and constantly innovating. We hope that you’ll use these best practices and how-to’s to maximize the impact of digital and grow your business. We’re looking forward to seeing you at an upcoming session!

Posted by Matt Ludwig, Marketing Coordinator, Learn with Google

Using a permanent URL to share Custom Attribution Models & Custom Channel Groupings

The need to customize and fine-tune your marketing measurement solutions becomes a key discriminator in unlocking additional value which might have been missed when applying out-of-the-box views on your data. For this reason, the Multi-Channel Funnel Analysis within Google Analytics Attribution provides the ability to configure content based channel groupings, as well as customized attribution models. This allows you to better reflect how partial credit is assigned to the marketing efforts driving your conversions. Having the ability to develop these customized assets is great, and now you are able to easily share them with your organization, your customers, or your audience. Here is how sharing a custom channel grouping, or custom attribution model works: 

Step 1 - Build a Custom Attribution Model

Building a custom model is easy. Just go to the Model Comparison Tool report in the Attribution Section of Conversions. In the model picker you can select ‘Create new custom model’, which opens the dialog to specify rules which can better reflect the value of marketing serving your specific business model. As an example, we can develop a model to value impressions preceding a site visit higher within a 24 hour time window. We also set the relevant lookback window to 60 days, as we know our most valuable users have longer decision and decide cycles:

Click image for full-sized version

Ensure you opt-in the Impression Integration, enabling Google Display Network Impressions and Rich-Media interactions to be automatically added to your path data through the AdWords linking. Don’t forget to also check out the recorded webinar from Bill Kee, Product Management Lead for Attribution, providing more details on how to create a custom model.

Step 2 - Access the Model in Personal Tools & Assets Section

In the admin section you can now look at your personal tools & assets. The newly created model will show up in the ‘Attribution Models’ section. You can find custom channel groupings you created under Channel Groupings.

The table shows all assets available, and a drop-down allows you to ‘share’ these assets through a link.

Step 3 - Share the Link - Done!

From the drop-down Actions menu select ‘Share’, and a permanent link to the configuration of this object is generated. This link will point to the configuration of the shared asset, allowing anyone with a GA implementation and the link to make a copy of the asset config, and save it into their instance of GA. You maintain complete control over who you share your assets with. 

Include the link to your brand-new attribution model asset in an email, IM message, or even a Blog Post, such as this one.

Happy Customizing!

Posted by Stefan F. Schnabl, Product Manager, Google Analytics

Saying thank you to our Google Top Contributors

Every day, Google Top Contributors from around the world share their product expertise with people in Google’s official forums, from sharing helpful tips to answering burning user questions. Top Contributors not only help users directly, they champion user feedback, which gives our teams valuable insight on opportunities for improvement across various products. They contribute to 250 product communities in 26 different languages, and their expertise touches hundreds of millions of users each year. These Top Contributors are a critical part of the Google family and we brought many of them together at this year's Top Contributor Summit to say thank you.

Building on our first summit in 2011, we kicked off the second Top Contributor Summit last week near Google’s headquarters in Mountain View, Calif. Over three days, Top Contributors came together to discuss their favorite Google products, meet with our engineers and product managers, see demos of new products and collaborate with fellow Top Contributors.

Sebastian Miśniakiewicz, Top Contributor in the Webmasters Polish forum, talks with Program Manager Oahn Nguyen and Map Maker Program Manager Nicole Drobeck

Top Contributors met with product managers and community managers to learn the latest about some of Google’s products, and had the unique opportunity to give their feedback directly to the product team. They also sat down with designers and support team members to discuss the long and short-term vision for various products. Multi-product Top Contributor Manny Barwin (known as “The C Man” in our forums) said, “what impressed me most was the interest taken in our feedback.”

Yogi Anand, Docs Top Contributor from Michigan, tries Google Glass

Top Contributors also got a sneak peek at recently released Google products. After hearing a presentation directly from the Google Glass team, each Top Contributor was given the opportunity to try Glass for themselves. AdWords Top Contributor Adam Briggs said, “I found the best part was being able to try out Glass; it's such an awesome product and I'm really looking forward to it becoming public."

We also put on several social events where the group was able to meet Googlers, chat with their fellow Top Contributors, and have a little fun!

Top Contributors play air hockey during a social event at the San Jose Convention Center

Photograph by Paciano Triunfo

We had a great time at the summit saying thanks to our Top Contributors for all they do for our users. If you’re interested in becoming a Top Contributor, get started by becoming active in your favorite Google product’s forum or learn more about the Top Contributor Program.

Top Contributors and Googlers show their excitement on campus

Photograph by Paciano Triunfo

Posted by Sarah Claxton Deming, Top Contributor Summit Organizer

The web is working for part-time businesses

Part-time businesses play an important role in our lives and in our economy. From the gardening mom who sells her plants, to the hobbyist antiques dealer, to the weekend wedding photographer, people everywhere are earning extra money while doing what they love.

Research released yesterday by The Internet Association shows that the web is powering American part-time businesses. Nine out of 10 part-time business owners rely on the Internet to conduct their business, and the impact is significant. Internet enabled part-time businesses employ 6.6 million people and contribute $141 billion to the U.S. GDP.

We're proud to play our part to support these business owners as they grow their businesses online. Technology is at its best when it makes lives easier—and every day, our products help businesses find new customers and publishers earn money from their content while running more efficiently. With the power of the web, businesses can build better lives for their families and strengthen our economy while doing what they love.

Posted by Lisa Gevelber, VP, Americas Marketing