1stdibs Luxury Marketplace Hits New Heights With Google Analytics Premium

Wednesday, October 07, 2015 | 3:40 AM

1stdibs.com is a global marketplace connecting art, design, jewelry dealers to potential buyers online. As such, the company has a complex digital ecosystem that requires an advanced analytical capability. In order to create a data-driven business, the company worked with Cardinal Path, a Google Analytics Certified Partner and Premium Authorized Reseller, in order to collect, process, store, and visualize all their data.

The companies started by discussing the most important key performance indicators (KPIs), which would be used to measure both failures and successes in their efforts. This understanding led to more customized and effective data collection, including features such as Custom Dimensions, User ID and Enhanced Ecommerce.
“You only need to look at the growth of our data and analytics team—which has quadrupled in the past year—to see what a critical role data now plays in our business. We just continue to unlock more and more value from our digital data assets.” - Adam Karp, CMO at 1stdibs
Using Google Analytics Premium to power their decisions, 1stdibs saw a 47% lift in transactions on paid media campaigns and a 10% gain in overall return on ad spend (ROAS). Plus, newly optimized email strategies led to a 34% increase in email click-through rates. 
To learn more, read the full case study.

Posted by Daniel Waisberg, Analytics Advocate

Top 5 ways to amplify the impact of TV dollars with digital

Tuesday, September 22, 2015 | 9:05 AM


Today’s consumers hop from screen to screen according to their needs-of-the-moment. They don’t give a thought to what “channel” they are using to interact with your brand — they simply expect brands to keep up. 

In last week’s post, we discussed the advent of TV Attribution and the new opportunity marketers have to drive more ROI in a multi-screen world. This week, we’ll discuss 5 key ways that TV Attribution can help you get more from mass media investments with digital insights. 

If you want more details on any of our top tips, take a look at our recent white paper or register for our upcoming webinar

1. Align creative across channels. If a friend was always chummy on the phone, but cold in person, wouldn’t you be confused? Don’t let a choppy brand presentation put off interested consumers who experience TV ads, search online, and visit your sites and apps. Use consistency between your online and offline presence for a clear message. 

2. Empower mobile search. Knowing that TV ads inspire mobile searches, make sure digital copy aligns with verbal and on-screen messages in TV ads to ensure consumers find you online. Use mobile context — include click-to-call, highlight nearby stores, show relevant hours — to move consumers from search to purchase.

3. Connect the data. Connecting TV airings data with digital signals like search query and site traffic offers a new level of granularity and immediacy of reporting. With better insights, you can fine-tune your next TV campaign and align digital strategies to capture incremental opportunity.

4. Find your best audiences. Take the guesswork out of demographic targeting with digital insights. Search and site data reveal who is really responding to TV messages by taking online actions — so you can confirm your best audiences by behavior.

5. Understand your consumer. Analyze digital signals to understand what parts of your message consumers are retaining — or not retaining. The keywords consumers search after being exposed to your TV ad offer insights that can drive faster campaign optimization, saving time and money over traditional surveys or studies.

More insight, more opportunity

TV Attribution not only offers a new, immediate, and granular view of mass media impact — it allows you to create more cross-channel synergy. Today’s consumers want immediate gratification and have high expectations for the brands they pursue. Join us for a webinar October 28th to discuss more tips and tricks for meeting new consumer expectations, and hear how top brands are leveraging minute-by-minute TV Attribution analysis to improve cross-channel marketing. If you’re ready to dive in, register here.

Programmatic helps brands make the most of micro-moments

Wednesday, September 16, 2015 | 11:50 AM

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The following was originally posted to the DoubleClick Advertiser Blog. 
Every day, your audience is filling their days with hundreds if not thousands of micro-moments—intent-rich moments when preferences are shaped and decisions are made. As consumers spread their attention across more and more screens and channels, those moments can happen almost anywhere, anytime. People search on their smartphones while in front of the TV. They watch YouTube videos on their tablets while texting their friends. They open a mobile app to shop for the perfect gift, then head to the store to buy it. With mobile devices never more than an arm’s length away, people can find and buy anything, anytime.
For marketers, this means the purchase funnel is wildly more complicated than it was just a few years ago.
“Brands can use programmatic to assemble a consumer’s micro-moments in just the right way—like joining puzzle pieces together—to see a detailed blueprint of consumer intent.”
It’s hard to plan for nonlinear purchase paths, but programmatic advertising can help, enabling brands to reach the right person with the right message in the moment of opportunity. Brands can use programmatic to assemble a consumer’s micro-moments in just the right way—like joining puzzle pieces together—to see a detailed blueprint of consumer intent. That’s a powerful proposition, and it’s why programmatic advertising spend is projected to grow by more than 77% this year.1
In this article, we share four tips for using programmatic to win these micro-moments and examples of brands that are doing it right.
Visit DoubleClick.com to read the full article.
Posted by Kelly Cox, Product Marketing Manager, DoubleClick 

1. IDC, Worldwide Programmatic Display Forecast, 2015.

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

Monday, September 14, 2015 | 4:13 PM


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

Tuesday, September 08, 2015 | 12:01 PM

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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

Thursday, August 27, 2015 | 9:00 AM

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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.

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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

Friday, August 21, 2015 | 2:30 PM


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