Tag Archives: Google Analytics

How can you get more ROI in a multi-screen world?

We live in a world of instant gratification. Wherever we are, and whatever we may be doing, when we want to know, to do, to buy we pull out our phones and search for satisfaction.

For marketers, a multi-screen world offers new opportunities for ROI. While TV accounts for 42% of all ad spending, or $78.8 billion annually,  we also know that 90% of consumers engage with a second screen* — think tablets and mobile phones — while watching TV. 

This means that in a multi-screen world, executing separate television and digital campaigns is a strategic miss. If that’s the case, why are so many of us still doing it?

The old TV measurement problem
In the past, channel-centric thinking, competing objectives, and data silos often stopped marketers from true cross-channel measurement. Even with the advent of marketing measurement best practices like marketing mix modeling, we lived with a significant blind spot around the true impact of TV advertising. 

TV airings data was hard to come by, and traditional Marketing Mix Modeling reports are often too high-level — and too slow — to offer actionable insights. So, while we’ve known for a long time that TV drives consumers online, we had no way to accurately attribute digital activity to granular TV investments.

The new TV attribution solution
Now, TV attribution makes it possible to connect the dots between TV airings data and digital activity. The resulting insights from TV attribution enable marketers to improve campaign strategies across both mass media and digital channels. 

At a high level, TV attribution carefully analyzes typical search query and site activity to establish a baseline. Then, minute-by-minute TV airings data is correlated with search and site data to detect — and accurately attribute — traffic driven by each TV ad spot. 

We’ve seen great results for marketers that have embraced this new marketing measurement best practice. For example, Nest assessed and improved cross-channel campaigning with TV attribution, achieving a 2.5x lift in search volumes and 5x increase in search and website responses by acting on resulting insights. 

For more details, read our new infographic to learn:
  • How TV attribution reveals TV-to-digital behaviors
  • How TV attribution insights help marketers quantify TV’s business value, optimize media buys, and empower creative teams
  • How deeper understanding of consumers can lead to more effective cross-channel strategies


Time to improve your ROI?
Now that TV and digital data can be analyzed to reveal cross-channel behaviors, marketers have a new opportunity to improve both mass media and digital strategies. Next week, we’ll post our top 5 tips on amplifying TV dollars with digital. If you’re ready to get going on maximizing TV ROI, stay tuned.

Posted by:  Natasha Moonka, Google Analytics team

*Source: Neal Mohan, Google, “Video Ads and Moments That Matter,” Consumer Electronics Show 2015.


Using Google Analytics to understand real-time messaging behavior

This is a guest post by Nico Miceli, a Google Developer Expert for Google Analytics, Technical Analytics Consultant on Team Demystified, quantified selfer, and all around curious guy. He blogs at nicomiceli.com and tweets from @nicomiceli.

Hello, my name is Nico, and I love data. I quantify everything, and the Google Analytics Measurement Protocol is my favorite way to do it.

With the Measurement Protocol, I can send, store, and visualize any data I want without having to build a backend collection system. I’ve even used it in my personal life to track my sleep patterns, the temperature in my house, and the number of times my brother’s cat actually uses his scratching post.

So when my team started using Slack, a real-time messaging app for teams, I wanted to get the stats. Which clients are contacting us most frequently? When are the contacting us? More importantly, who on our team is the wordiest and uses the most emojis? Out of the box, the app offered some data, but it wasn’t enough for me to answer all the questions I had.

After taking a look at the technical documentation for the messaging app, I realized that Google Analytics is the answer! With the Measurement Protocol and the Slack Real Time API, I could get SO MUCH DATA!! With help from fellow developer Joe Zeoli, Slackalytics was born.

Slackalytics (in beta) is a simple, open source bot for analyzing Slack messages. Built in node.js, it grabs messages from Slack (using the Slack Real Time Messaging API), does some textual analysis, and counts the occurrences of specific instances of words and symbols. Then, using the Measurement Protocol, it sends the data to your Google Analytics account. 



Screenshot of the report showing the custom metrics (emoji, exclamation, word, and ellipse counts) for different Slack channels.

Because the data gets stored in Google Analytics, you can visualized and analyze within the UI or use the Google Analytics Core Reporting API. I like to combine this data with other information so I have export it all into a Google sheet using the Google Analytics Spreadsheets Add-on.

In this beta version of Slackalytics, I’m using two Custom Dimensions: User ID, Channel Name... and six Custom Metrics: Word Count, Letter Count, Emoji Count :), Exclamation Count !!!, Question Count ???, Ellipse Count...

But this is just a fraction of what’s possible. Slackalytics is open source, so you can build your own version. If you’re a developer: Fork my project on GitHub.

If you’re not a developer: Fear not. You can still create your own messaging analysis bot by following my detailed walkthrough on setting this up.

Developer or not, you can build and test your own bot by using Google Analytics and any communication app that has a realtime API. Find out when your clients ask the most questions, monitor other integrations and bots, find out who talks in ☺     or build your own new Custom Dimension & Metrics combos.

- The Google Analytics Developer Relations team, on behalf of Nico Miceli

Real-Time Data Validation with Google Tag Assistant Recordings

We’ve said it before and we’ll say it again: great analytics can only happen with great data.  

That's why we've made it a priority to help our users confirm that their data is top-quality. Last year we released our automated data diagnostics feature, and now we’re proud to announce the launch of another powerful new feature: Google Tag Assistant Recordings.  

This tool helps you instantly validate your Google Analytics or Google Analytics Premium implementation. If it finds data quality issues, it helps you troubleshoot them and then recheck them on the spot.  It’s available as part of the Google Tag Assistant Chrome Extension.
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"Tag Assistant Recordings is fast becoming one of my favorite tools for debugging Google Analytics Premium installations!  I use it multiple times a day with my Premium clients to help explain odd trends in their data or debug configuration issues. Already I'm building it into my core workflow." 

- Dan Rowe, Director of Analytics at Analytics Pros

What can I use it for?

Tag Assistant Recordings works with all kinds of data events: purchases, logins, and so on. What if you sell flowers online and want to confirm that Enhanced Ecommerce is capturing the checkout flow correctly? With Tag Assistant Recordings, you can record yourself going through the checkout process as you buy a dozen red roses, and then review what Google Analytics captured.

If you find that your account isn’t set up properly — if the sale wasn't recorded or was mis-labeled — you can make adjustments and test it all over again instantly.  With Tag Assistant Recordings, you know you’re capturing all the data that’s important to you.

Tag Assistant Recordings can be particularly useful when (1) you’re in the process of implementing Google Analytics or Google Analytics Premium, (2) you’ve recently made updates to your site, or (3) you’re making changes to your Google Analytics or Google Analytics Premium configuration. It works even if your new site or your updates aren't visible to the public yet, so you can feel confident before you go live.

Tag Assistant Recordings can also help if you want to reconfigure your Google Analytics account to better reflect your business.  For example, you may want to configure multi-channel funnels to detect your AdWords channel.  Tag Assistant Recordings lets you set up this new functionality in Google Analytics and test immediately whether everything is working as you expect.  

"Tag Assistant Recordings has already been a HUGE help! Analytics Pros and About.com were working on an issue with sessions double-counting and Tag Assistant Recordings let us narrow down precisely which hits were having new sessions counted. It saved us hours of time and helped us jump right to where the problem was. So, in summary, this is awesome!"  

- Greg McDonald, Business Intelligence Analyst at About.com

How does it work?

Tag Assistant Recordings works through the Google Tag Assistant Chrome Extension, so you’ll need to download the extension if you aren’t already using it.  From there, setup is easy.  Simply open Google Tag Assistant, record the user flow you’d like to check, and then view the full report in Tag Assistant.  You’ll want to view both tabs in the report (Tag Assistant and Google Analytics) to verify that you see the intended tags.  Keep in mind that the Google Analytics data is only available if you have access to the appropriate property or view.

Tag Recordings Gif.gif

Here's a nifty bonus: If you find a problem, and you think you have fixed it by changing settings from within Google Analytics, return to the Google Analytics tab in Tag Assistant Recordings and click the “Update” button. You'll see instantly how your configuration changes would have affected this recording.

We hope that Google Tag Assistant will be a valuable new tool in your analytics toolkit.  

Why not start using it today?


Posted by:  Ajay Nainani, Frank Kieviet, and Jocelyn Whittenburg, Google Analytics team

Affiliate Attribution: Putting the Pieces Together

Originally Posted on the Adometry M2R Blog
Recently I was reminded of an article from a little while back, titled, “2013: The Year of Affiliate Attribution?” It’s an interesting take and worthwhile read for those interested in affiliate marketing and the associated measurement challenges. Given that some time has passed, I thought it would be interesting to take a look at progress to date towards realizing a more holistic and accurate view of affiliate performance as part of a comprehensive cross-channel strategy.
Most affiliate managers have a similar goal to manage affiliate holistically, meaning investing in those that predominantly drive net-new customers independent of other paid marketing investments. Ultimately, this model allows them to optimize CPA by managing commissions, coupon discounts, and brand appropriateness based on true “incremental value” provided to business. Unfortunately, due to a lack of transparency and inadequate measurement, many marketers find themselves short of this goal. The result is the ongoing nagging question, “Is my affiliate strategy working and am I overpaying for what I’m getting?”

Why ‘Affiliate Attribution’ Is Hard

Affiliate marketers’ challenges range from competing against affiliates in PPC ad programs to concerns about questionable business practices employed by some “opportunistic” affiliates offering marginal value, but still receiving credit for sales that likely would have happened regardless. Which brings us to the central question:
How do marketers determine how much credit an affiliate should receive?

As you may know, opinions about how much conversion credit affiliates deserve for any given transaction vary widely. While there are a number of factors that influence affiliate performance (e.g. where they appear in the sales funnel, industry/sector, time-to-purchase length, etc.) for most brands the attribution model that is utilized will have a significant impact on which affiliates are over- and under-valued.
For example, in a last-click world affiliates that enter the purchase path towards the bottom of the funnel often hold their own; yet, when brands begin measuring on a full-funnel basis incorporating impression data, many struggle to prove their incremental value as the consumer has many exposures to marketing long before they reach the affiliate site. Conversely, affiliates that act predominantly as top- or mid-funnel (content, loyalty, etc.) are usually undervalued using last-click but can garner more credit using a full-funnel, data-driven attribution methodology. I should also mention these are broad generalizations only meant as examples, and it’s not necessarily a zero-sum game.
Another challenge is that fractional, data-driven attribution is difficult to implement for some types of promotions. One instance of this is cash back, loyalty and reward sites that must know an exact commission amount they will receive for each transaction so that they can pass on discounts to members. Given the complexity of more sophisticated attribution models, this data isn’t readily available.
Lastly, there several organizational challenges that inhibit the use of data-driven attribution among affiliate marketers. Some industry experts have indicated that many publishers, as much as 70-80%, strip impression tracking code from affiliate URLs. Another measurement challenge we see frequently is brands managing affiliates at the channel level leaving little sub-channel categorization which is where significant optimization opportunities exist.
Affiliate Attribution and the Performance Marketing Goldmine
Of course, part of our work at Adometry is helping customers address these challenges (and more) to ensure they are measuring affiliate contributions accurately and able to take appropriate action based on fully-attributed results.
Some key advantages of using data-driven attribution to measure affiliate sales include:
  • The ability to create a unified framework to compare performance (clicks and Impressions) in which affiliates compete for budgets on equal footing,
  • Increased visibility into which publishers are truly driving net-new customers through specifying which are an integral part of a multi-touch path and which are expendable,
  • The knowledge required to implement a Publisher category taxonomy to allow more insights into how different types of publishers perform by funnel stage and areas to improve efficiency,
  • Insight into the true incremental value publishers are providing and the offering commission rates to reflect this actual value,
  • A better understanding of affiliate’s role in the overall mix, further informing marketers use of complementary tactics to maximize affiliate contributions in concert with other channels,
  • The ability to use actual performance data to counter myths and frustrations with affiliates (cookie stuffing, stealing conversions, etc.)
Taken separately, each of these represents a significant opportunity to both be more effective in how you identify and utilize affiliate attribution to drive new opportunities. Together, they represent a fundamental improvement in how you manage your overall marketing spending, strategic planning and optimization efforts.
Top-performing affiliates, particularly those at the top and middle of the funnel, also stand to benefit from more transparent, accurate and fair system for crediting conversions. In fact, several large-scale, forward-thinking affiliates are already investing in data-driven attribution to arm themselves with the data required to effectively compete and win business in the market as brands become more sophisticated and judicious with their affiliates budgets.
It’s an exciting time for performance marketing. Change is always hard, but in this case it’s absolutely change for the better.  And frankly, its time.  What are your thoughts and experiences with measuring affiliate performance and attribution?

Posted by Casey Carey, Google Analytics team

Google Analytics User Conference: G’day Australia

The Australian Google Analytics User Conference is worth clearing your diaries for, with some of the most well-known and respected international industry influencers making their way to Sydney and Melbourne to present at the conference this September.


Hosted by Google Certified Partners, Loves Data, you’ll be learning about the latest features, what’s trending and popular, best practices and uncovering ways to get the most out of Google Analytics. Topics covered include: making sure digital analytics is indispensable to your organisation; applying analytics frameworks to your whole organisation; improving your data quality and collection; data insights you can action; and presenting data to get results.

Presenting the keynote is Jim Sterne, Chairman of the Digital Analytics Association, founder of eMetrics and also known as the godfather of analytics. Joining him are two speakers from Google in the US: Krista Seiden, Google Product Manager and Analytics Advocate and Mike Kwong, Senior Staff Software Engineer.

Other leading international industry influencers presenting at the conference include Simo Ahava (Google Developer Expert; Reaktor), Chris Chapo (Enjoy), Benjamin Mangold (Loves Data), Lea Pica (Consultant, Leapica.com), Chris Samila (Optimizely), Carey Wilkins (Evolytics) and Tim Wilson (Web Analytics Demystified).  

Expect to network with other like-minded data enthusiasts, marketers, developers and strategists, plus get to know the speakers better during the Conference’s Ask Me Anything session. We’ve even covered our bases for those seeking next-level expertise with a marketing or technical masterclass available the day before the conference. Find out more information about the speakers and check out the full program.

Last year’s conference sold out way in advance and this year’s conference is heading in the same direction. Book your tickets now to avoid disappointment. 

Event details Sydney
Masterclass & Conference | 8 & 9 September 2015

Event details Melbourne
Masterclass & Conference | 10 & 11 September 2015

Posted by Will Pryor, Google Analytics team

Google Analytics Conference Nordic in Stockholm, Sweden

Join the Google Analytics Certified Partners for Google Analytics Conference Nordic in Sweden. 

The event takes place August 26 in Stockholm, Sweden, and is followed by a workshop on August 27.

Started based on an initiative by Outfox, who gathered the other Google Analytics Certified Partners, the conference is now returning for the fifth consecutive year.

Our Stockholm conference includes:

 • Case studies from businesses and other organizations, such as The Swedish Society for Nature Conservation, Viaplay, and Storebrand. In other words, Google Analytics for sales, entertainment, non-profits, insurance, and more!
 • Expert presentations by Google Analytics Certified Partners.
 • Opportunities to interact with peers and experts
 • ...much more!

The conference is being visited by two top speakers from Google, Sagnik Nandy and Daniel Waisberg.

Sagnik Nandy is technical leader and manager of several Analytics and Reporting efforts in Google. He has hands on experience in building, scaling, deploying and managing large scale systems used by millions of web sites around the world. 

Daniel Waisberg is Analytics Advocate at Google, where he is responsible for fostering Google Analytics by educating and inspiring Online Marketing professionals. Both at Google and his previous positions, Daniel has worked with some of the biggest Internet brands to measure and optimize online behavior. 

Besides meeting Google, you’ll meet several Nordic Google Analytics Certified Partners. You will also meet and learn from several end users who use Google Analytics on a daily basis.

To join us in Stockholm in August, visit the conference site and secure your ticket.


Posted by Lars Johansson, Google Analytics Certified Partner and Google Analytics Premium Authorized Reseller

[NEW VIDEO] What’s the most important growth metric for your app?

In this week’s App-etizers, Max shares what’s the most important growth metric for your app. If you’ve been following the series, we last recently discussed how the secret to rapid growth is to not focus on growth at first, but instead focus on user engagement. Likewise, the most important growth metric to focus on isn’t a traditional growth metric at all. Watch the episode to learn more:

To discover more insights about app growth, download our eBook, The No-Nonsense Guide to Growing Your Mobile App. Until next time, be sure to stay connected on all things AdMob by following our Twitter and Google+ page.














Posted by Raj Ajrawat
Product Specialist, AdMob

Source: Inside AdMob


Domino’s Increases Monthly Revenue by 6% With Google Analytics Premium and Google Tag Manager

Domino’s is one of the world’s leading pizza purveyors, having delivered 76 million pizzas only in the UK and Ireland in 2014. That’s a lot of pizza. In these markets, online sales increased 30% year over year and currently account for almost 70% of all sales; 44% of those online sales were made via mobile devices in 2014 (as opposed to only 31% in 2013).

With such a large online presence, Domino’s is always on the cutting edge of technology, enabling customers to order pizzas from virtually any device and platform. To drive success, the team knew they must break down silos, connect data sets, and gain efficient reporting to get a more holistic and actionable view of customer behavior.

Domino’s partnered with DBi, a Google Analytics Premium Authorized Reseller, in order to make the most out of their online data. They worked together to create a unified marketing measurement platform, using Google Analytics Premium, Google Tag Manager, and Google BigQuery to integrate digital data sources and CRM data in an effective and scalable way.



Domino’s deployed Google Tag Manager across apps and websites, setting customized tags for all of the company’s Ecommerce tracking and reporting needs. Despite there being a large number of unique containers, data layer consistency made it easy to duplicate tags and rules - a significant time-saver and error preventor for Domino’s. 

Domino’s used the BigQuery Export feature in Google Analytics Premium to automatically export raw data to a BigQuery project on a daily basis. They also uploaded daily CRM data into BigQuery through a secured FTP location and the BigQuery API. Following the process described above, CRM data became easily merged with Google Analytics behavioral data via transaction IDs.
“Google Analytics Premium, combined with Google Tag Manager and BigQuery, has become an integral solution that gives us the technical agility and the analytics power we need to advance our marketing strategies. DBi has been fundamental in developing our digital strategy with Google Analytics Premium.” —Nick Dutch, Head of Digital, Domino’s
Below are the main outcomes from the implementations and analyses discussed above.
  • Realized an immediate 6% increase in monthly revenue
  • Saved 80% YOY in ad serving and operations costs
  • Increased agility with streamlined tag management
  • Obtained easy access to powerful reporting and customized dashboards
Read the full case study to learn more about how DBi and Domino’s worked together to create a unified data reporting and analysis platform.

Posted by Daniel Waisberg, Analytics Advocate

L’Oréal Canada finds beauty in programmatic buying

Cross-posted on the DoubleClick Advertiser Blog

While global sales of L'Oréal Luxe makeup brand Shu Uemura were booming, reaching its target audience across North America proved challenging. By collaborating with Karl Lagerfeld (and his cat, Choupette) and using DoubleClick Bid Manager and Google Analytics Premium, the campaign delivered nearly double the anticipated revenue.
Goals
  • Re-introduce and raise awareness of the Shu Uemura cosmetics brand in North America
  • Drive North American sales of Karl Lagerfeld’s Shupette collection for Shu Uemura
  • Grow the Shu Uemura email subscriber list
    		•
    Approach
  • Organized website audiences with Google Analytics Premium
  • Used programmatic buying to lead prospects down the path to purchase
  • Leveraged a range of audience data in DoubleClick Bid Manager to buy paid media in display and social channels
    		•
    Results
  • Drove almost 2X the anticipated revenue
  • Exceeded CPA targets and achieved a 2,200% return on ad spend (ROAS)
  • Increased web traffic and email subscribers
    		•
    To learn more about Shu Uemura’s approach, check out the full case study.

    Posted by Kelly Cox, Product Marketing, DoubleClick

    How To Setup Enhanced Ecommerce Impressions Using Scroll Tracking

    A version of this post originally appeared on Google Analytics Certified Partner InfoTrust's site.
    by Nate Denlinger, Web Developer at GACP InfoTrust, LLC

    One of our specialities here at InfoTrust is helping ecommerce businesses leverage their web analytics to make better data-driven marketing decisions. This typically starts with installing Google’s Universal Analytics web analytics software and utilizing all of the functionality that is offered with Enhanced Ecommerce tracking capabilities.
    Enhanced Ecommerce provides you with a complete picture of what customers on your site are seeing, interacting with and purchasing.
    One of the ways you track what your customers are seeing is with product impressions (whenever a user sees an image or description of your products on your website).
    Normally, you track what products users see or impressions by simply adding an array of product objects to the DataLayer. These represent the products seen on the page, meaning when any page loads with product images/descriptions, data is sent to Google Analytics that a user saw those specific products. This works well.
    However, there is a major issue with this method.  Sometimes you are sending impressions for products that the user never actually sees. This can happen when your page scrolls vertically and some products are off the page or “below the fold”.
    For example, lets take a look at a page on Etsy.com:
    Sample page on Etsy.com (click for full size)
    Here are the results for the search term “Linens”. Currently, you can see sixteen products listed in the search results.  However, in the normal method of sending product impressions, a product impression would be sent for every product on the page.
    So, in reality this is what we are telling Google Analytics that the user is seeing (every single product on the page):
    Sample page of Etsy.com (click for full-size)

    Obviously, no one's screen looks like this, but by sending all products as an impression, we are effectively saying that our customer saw all 63 products. What happens if the user never scrolls past the 16 products shown in the first screenshot?
    We are greatly skewing the impressions for the products on the bottom of the page, because often times, users are not scrolling the entire length of the page (and therefore not seeing the additional products).
    This could cause you to make incorrect assumptions about how well a product is selling based off of position.
    The solution: Scroll-based impression tracking!
    Here is how it works at a high level:
    1. Instead of automatically adding all product impressions to the DataLayer, we add it to another variable just for temporary storage. Meaning, we do not send all the products loaded on a page directly to Google Analytics, but rather just identify the products that loaded on the page.
    2. When the page loads, we actually see what products are visible on the page (ones “above the fold” or where the user can actually see them) and add only those products to the DataLayer for product impressions. Now we don’t send any other product impressions unless they are actually visible to the user.
    3. Once the user starts to scroll, we start capturing all the products that haven’t been seen before. We continue to capture these products until the user stops scrolling for a certain amount of time.
    4. We then batch all of those products together and send them to the DataLayer as product impressions. 
    5. If the user starts to scroll again, we start checking again. However, we never send the same product twice on the same page. If they scroll to the bottom then back up, we don’t send the first products twice.
    Using our example on the “Linen” search results, right away we would send product impressions for the first 16 products. Then, let’s say the user scrolled halfway down the page and stopped. We would then send product impressions for products 18 through 40. The user then scrolls to the bottom of the page so we would send product impressions for 41 through 63. Finally the user scrolls back to the top of the page before clicking on the first product. No more impressions would be sent as impressions for all products have already been sent.
    The result: Product impressions are only sent as users actually navigate through the pages and can see the products. This is a much more accurate form of product impression tracking since it reflects actual user navigation. 
    Next steps: for the technical how-to guide + code samples, please see this post on the InfoTrust site.