Today’s marketers have more opportunities than ever to drive business success. They also face increasing pressure to prove, manage, and optimize marketing performance. 

A relentless push towards accountability has driven the adoption of ever-more-sophisticated measurement tools. Many marketers use marketing mix modeling (MMM), some use data-driven attribution, while others consult a separate solution for each. 

Tools continue to evolve. Now, solutions that merge and substantively improve both of these measurement best practices promise faster, more efficient, more holistic insights. To find out more, we commissioned Forrester Consulting to survey 150 companies in order to explore how marketers are evaluating, adopting, and using these emerging tools. Key learnings will be presented in our Dec 8th webinar hosted by Google and featuring Tina Moffett, Senior Analyst at Forrester along with Dave Barney, Product Manager, Adometry at Google. Sign up here.

Why consider a merger?

While separate MMM and data-driven attribution tools offer cross-channel measurement, each has limitations:

• Speed and Granularity. Traditional MMM offers high-level analysis on a quarterly or yearly basis, which can limit more granular, or on-the-fly optimization
  
• Data Limitations. Data-driven attribution requires a wealth of granular, user-level  data, which can limit offline channel visibility

When the two measurement practices are combined, however, they improve the outputs from each. Data-driven attribution informs MMM models. MMM data feeds attribution analysis. Resulting insights allow marketers to see the impact of each marketing element in near real-time.

Pending or trending?

Today, many marketers get the optimization benefit from separate MMM and data-driven attribution tools. Will merged tools become a new marketing performance measurement standard?

While it may be too early to tell, there is a growing desire for tools that help marketers move beyond channel-based optimization to larger strategic cross-channel planning. Forrester reports that many respondents have already moved, or plan to move, on the merged measurement trend and the most common approach has been to purchase a solution from a vendor, and to make use of the vendor’s implementation support. 

“There will be a paradigm shift in understanding for the marketing channels. I think it gives them an opportunity to think holistically rather than in a silo, like, ‘this is my world, this is my budget, as long as I get this much traffic in my channel, I am ok.’ It’s no longer the case. Getting that understanding is going to be key. It gives us better understanding of how our customers navigate through different touch-points.”

— Director of Marketing And Automation Systems at a major global retailer

Benefits and challenges

Integrated MMM and data-driven attribution tools are enabling marketers to make strategic planning decisions and precisely measure individual-level interactions in near real-time. Satisfaction with integrated tools is high among those who have implemented them.

Faster access to insights has more companies looping in more stakeholders from marketing execs and analysts to customer insights or analytics, brand managers, and eCommerce professionals. 

At the same time, early adopters report challenges. Integrating tools and data sources is a big ask, learning when to make changes based on new insights takes time, and setting expectations about timelines and results is paramount. 

Ensuring that the entire organization is on board with using a merged measurement platform is critical, as is supporting stakeholders in changing business practices as a result.

Proceed with insight

As merged tools come on strong, the experiences of early adopters may be instructive to those moving to embrace a merged solution. Recommendations on best practices, processes, and supports, are examined in the full whitepaper. 

Making the right move

While companies cite common barriers to adoption, respondents suggest that a number of challenges that are stopping them today would be resolved in the near future including, skills, understanding of benefits and technology blockers.  

As merged tools mature and become more commonplace, technology concerns will abate. More marketers will know about these solutions, and about how to use them to drive marketing optimization and strategy. Staying informed is the key to making the right call on whether, when, and how to adopt merged measurement tools for your business.

To learn more, sign up for our upcoming webinar with Forrester Research on December 8th.

Posted by Natasha Moonka, Product Marketing Manager, Google Analytics team

Originally Posted on the Adometry M2R Blog

For as many dollars organizations invest in marketing, it never ceases to amaze me how many of those organizations are willing to make guesses about how effectively those dollars are being used. Even when those guesses are educated, they can be way off. We live in a world where data-driven attribution can take the guesswork out of your marketing program to gain a clear and comprehensive view into the customer journey.

It can be intimidating to get started with data-driven attribution. Many marketers are already inundated with data from marketing mix modeling, real-time bidding, website analytics, CRM and more. But the genius of data-driven attribution is that it makes all that other data better, more relevant and actionable to improve the bottom line.

With our Definitive Guide to Data-Driven Attribution, we’ve laid out just how your organization can approach marketing attribution. We’ve made it easy to understand what data-driven attribution does, how it fits in with what you’re already doing and how to get started.

What Is Attribution and What Are the Benefits?

Let’s start with the basics. There are a number of basic models such as first touch, last touch, even and custom attribution. Those models offer general answers across a basic marketing mix, but they fail to provide the true value of each marketing asset as the marketing campaigns get more complex. Today’s cross-channel marketers need a more scientific approach.

Data-driven attribution models use sophisticated algorithms to determine which touch points are the most influential. That means marketers can see the benefits of each touch point and adjust future spending to maximize results.

How Does Data-Driven Attribution Fit into my Analytics Toolset?

Odds are you’re already collecting a ton of marketing and advertising data. That’s great! Data-driven attribution doesn't replace that information. It greatly enhances it.

As an example, let’s look at marketing mix modeling. At the end of a campaign, you look back and assess performance. With data-driven attribution, you can accurately see how each tactic performed so you can plan better for the next campaign. Extending that to the next step, accurate attribution gives you insight that your real-time bidding partners can use to buy top performing ad placements.

Another example is your CRM. As you gain customers, your CRM captures transaction, contact and segment data, but CRMs tend to focus more on customer service and support, not marketing. And although CRMs track multiple channels, they look at lower-funnel activities and offer limited visibility into acquisition and cross-channel marketing in non-direct channels. CRM data is an input that can feed your data-driven attribution solution to yield a more complete picture of customer behavior.

As the graphic above shows (and details more within the guide), data-driven attribution ties all of your other marketing analytics together and improves what you’ve been getting from each one.

Getting Started

Data-driven solutions vary. To get the benefits, you’ll need to ask the right questions about your organization, solidify the right budgets and motivate the right people. In the guide we outline five key steps to getting started.

1. Define Goals: Consider your current pain points and business goals. Determine the value that all of your marketing activities must deliver for the business and take a holistic view of the data-driven changes you’ll make to meet those goals. That will help determine marketing’s impact on revenue so you can formulate budgets that will yield the highest returns.


2. Justify Budget: The right solution will pay for itself by creating cross-department efficiencies and increasing the return on each marketing investment, but change can be difficult. Check out the full Definitive Guide for a real-world budgeting exercise to help you promote the benefits of data-driven attribution to key stakeholders.


3. Be Selective: There are a number of attribution providers. Evaluate them by asking the right questions about their ease of implementation, breadth of services, methodology, capabilities and technology roadmap. Can they handle your data? How will they work with your existing partners, including your ad agency? Do they provide a consultative partnership? Is their model data-driven or rules-based? Are they media agnostic? How is their model validated? Can they measure online and offline activities? How do they account for multi-screen customer journeys? How often do they upgrade their solution?


4. Get Prepared: Picking a provider is a good start, but you also must get ready for integration. Prepare both human and data resources to hit the ground running. Evaluating data readiness and preparing stakeholders ahead of time will help you determine how much support you’ll need during implementation.


5. Evaluate Success: Your stakeholders will be more invested in driving success with data-driven attribution if they can envision what success looks like, and concretely evaluate whether goals are being achieved. Show them the way. Leverage your goals to evaluate your provider’s performance on marketing performance, enterprise ability, ease and flexibility, quality of output, total cost of ownership and an innovative roadmap.

There’s no doubt that today’s marketers need better performance measures to know whether they are producing the best results for the organization. Data-driven attribution requires investment on the front end, but it pays big rewards that will have you asking why you didn’t take the plunge sooner.

We encourage you to dive deeper to help your organization understand the true benefits and implications of data driven attribution through our definitive guide.

Originally Posted on the Adometry M2R Blog

This is a guest post by Brian Sim, Product Marketing Manager, Marin Software

Attribution is sometimes perceived as being too complex, too technical, and too cold. However, when you look beyond the advanced-level math that goes into attribution algorithms and consider consumer behavior and buying tendencies, the importance of attributing revenue across the customer purchase path becomes very apparent.

Consider your own purchase behavior. How many steps did it take to get you from awareness to purchase the last time you bought something? Chances are it took you at least a few steps to get from “I think I could use a new lawn mower” to deciding “This 200cc self-driving, side-discharge robot lawnmower is the exact one that I need, and I’m going to purchase it this weekend because there’s going to be a seasonal sale.”

With that in mind, the next logical question is, based on your customer journey, do you think it makes sense for the last advertisement you saw prior to buying to get 100% of the credit for your purchase? If your answer is “no, that doesn’t make sense,” then you’ve uncovered the problem that attribution is trying to address.

On the analysis end, attribution modeling platforms like Adometry are tackling one of the most grounded-in-reality problems marketers face: “Is my multi-channel marketing budget being spent on the right channels?” On the execution end, revenue management platforms like Marin Software enable marketers to optimize their campaigns based on their advanced attribution data and answer the question, “How can I take that attribution data and improve my future ROI?”

Three Reasons Why You Should Care About  Attribution

Reason 1: It helps you understand your customer’s path from discovery to purchase.

As a recent Google study showed, consumer purchase paths are rarely straightforward; 60% of purchases take multiple steps, and depending on the industry you’re in, up to 84% of total revenue can come from purchases that required multiple steps across several days. Advanced attribution models can quantify each step of the customer purchase pathway. Armed with this knowledge, marketers can begin to associate ROI with specific marketing channels, understand the time lag for customer decision-making, and optimize spending across different marketing channels.

Reason 2: It allows you to understand and quantify performance across channels.

The multi-step customer purchase path may not be an issue if every step occurred within a single channel, but alas that’s not the case. The path from discovery to purchase typically involves multiple disparate marketing channels, each playing a slightly different role.

In order to optimize your marketing spend, you first need to understand the interplay amongst the various channels. Data-driven attribution allows marketers to assign proper credit to each touch point along the buyer journey. This allows the marketer to understand the proper valuation of channel and budget, and bid and tailor creative more effectively.

For example, within the travel vertical, Social acts as an assistive interaction and is many steps displaced from the actual purchase decision. In contrast, Display is almost as close to the customer’s purchase decision as the paid Search channel. In this case, direct marketers may optimize their campaigns to weigh the Display and Search channels more significantly. In many other cases, Display plays much more of an assistive role, and direct marketers may optimize their campaigns towards Search or another channel that is closer to the customer’s purchase decision.

Reason 3: It gives you the insight you need to make smarter decisions.

As the saying goes, “knowing is half the battle.” But knowing is only half the battle. The value of attribution is only realized once marketers can act upon their data. Adometry’s Programmatic Connector enables marketers to seamlessly incorporate attribution data into day-to-day decision-making workflows. Additionally, this is where an open stack execution partner like Marin Software helps complete the circle. Marin’s Revenue Connect is an open, flexible platform that enables advertisers to integrate data from any of their sources, including advanced attribution data, to improve campaign performance.

Activating your attribution data can help achieve real results. MoneySuperMarket, the UK’s leading price comparison site, partnered with Marin Software and Adometry to activate their attribution data in their marketing campaigns. By marrying their search intent and first-party audience data and then applying an algorithmic multi-click attribution model, MoneySuperMarket increased CTR by 12% and reduced CPC 7% across their motor insurance campaign, and increased profit margins 14% across all insurance campaigns.

Yes, attribution can be complex. But the value in unlocking that data can provide sustainable, competitive advantages across all of your marketing decisions.

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.

Posted by Natasha Moonka, Google Analytics team

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.

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

The following is a cross post from Adometry by Google, a Marketing Analytics and Attribution product. 

Mass media drives people to interact with brands in compelling ways. When a TV or radio ad creates an I-want-to-know, I-want-to-go, or an I-want-to-buy moment in the mind of a consumer, many pursue it online. Immediately - and on whatever screen they have handy.

Last year, we announced Adometry TV Attribution, which measures the digital impact of offline channels such as television and radio. Now, we’re moving TV Attribution forward by integrating Google Search query data and Rentrak airings data to help marketers better understand the important moments their broadcast investments create.

New Search Behavior, New Search Analysis

Broadcast media doesn’t just drive consumers directly to websites — it drives searches. Now, TV attribution lets you analyze minute-by-minute aggregated Google Search query data against spot-related keywords to detect and attribute search “micro-conversions” to specific TV airings. 

With insights on the entire digital customer journey — including search behaviors — brands can better evaluate broadcast network and daypart, specific ad creative, and keyword performance. As a result, brands can:

• Assess Immediate Influence: See which messages are sticking in the minds of consumers to both maximize TV interest and choose ideal keywords for SEO and paid search strategies.
• Evaluate Awareness Goals: Optimize against a digital signal even when a site visit isn’t the primary goal, such as in brand awareness or sponsorship campaigns.
• Analyze Competitive Category: Glean which generic keywords drive category interest for the industry — a type of insights not possible through site traffic analysis alone. 

Rentrak Partnership Speeds TV Attribution Insights

Knowing when your spots aired and collecting that data for timely TV attribution analysis can be a challenge. Marketers who buy broadcast media through agencies often don’t have direct access to this data. And once data is obtained — after coordinating with multiple agencies, partners, and TV measurement companies — the time lag makes for outdated analysis. 

TV Attribution now solves these challenges a new partnership with Rentrak, the leading and trusted source for TV airings information. 

What Rentrak Integration Delivers

Integrating directly with Rentrak TV Essentials, TV Attribution now overcomes some of the biggest hurdles in TV measurement, with increases in: 

• Actionability: TV Attribution can more quickly and easily obtain TV data for analysis without time-consuming coordination from you or your agencies.  
• Accuracy: Rentrak provides a comprehensive data set with aggregated viewership information from more than 30 million televisions across the country, and from more than 230 networks.
• Frequency: A direct relationship means more frequent reporting since there is no longer a manual find-and-transfer of data required from TV buying partners.

“What makes this partnership so exciting is it removes the biggest barrier to truly measuring TV effectiveness, timely access to spot airings data including impressions,” said Tony Pecora, CMO for SelectQuote. “Rather than hunting and gathering data, we are now able to spend our time evaluating insights and optimizing our marketing investments across both TV and digital. As a CMO, this is a really big win for our business.”

Want to Get Moving?

The gap between offline and digital measurement continues to close. Learn more about how Adometry TV Attribution, now with Google Search query data and integrated Rentrak airings data, can help you gain more actionable cross-channel insights.

Posted by Dave Barney, Product Manager

Last year we introduced “The Customer Journey to Online Purchase” -- a tool that helped marketers visualize the roles played by marketing channels like paid search, email and display ads in their customers' journeys.

The goal was to help marketers learn more about the customer journeys for their industries. If social makes your customers aware, and email makes them convert -- or vice versa -- you can make sure you're in both places with the right kind of message.

Today we're happy to introduce a new improved version of the Customer Journey to Online Purchase, with a few key enhancements.  We’ve refreshed the data based on millions of consumer interactions, updated the industry classifications, and we’ve split out paid search so you can see the influence of brand and generic search terms on the purchase decision.

In each industry you can now see journeys for small, medium and large companies, which can often be quite different.

Click to enlarge image

For instance, the above image shows the journey for customers of small businesses in the shopping industry. Note that organic search is very often an "assist" interaction for these customers.

Click to enlarge image

Now here's the same journey for large shopping businesses. Note that display clicks and social are strongly assisting interactions -- while display didn’t even appear for the small businesses above. For both small and large businesses, a direct website visit is most likely to be the last interaction. Across industries, the differences from small to large businesses illustrate how different marketing strategies and customer profiles may lead to different buying behavior.

And there's more! Now you can drill down into each marketing channel for a closer look at the role it plays based on its position in the purchase path. Channels that occur more frequently in the beginning of the path are more likely to help generate awareness for your product, while the end of the path is closer to the customer’s purchase decision.

Click to enlarge image

In these charts, for example, we see the different roles that different channels play in the Shopping industry. One interesting insight is that all channels -- even those traditionally thought of as “upper funnel” or “lower funnel” -- occur throughout the purchase path, but a given channel may be more common at particular stages depending on its role (and depending on the industry).

Each marketing campaign and channel may have a different impact on customers depending on when they interact with it. Using what you learn from this tool, you can help adapt your marketing messaging to be more relevant and useful for your customers.

Try the Customer Journey to Online Purchase today. And for more helpful marketing insights, check out Measure What Matters Most: our new guide chock-full of suggestions on how to measure the impact of your marketing -- across channels -- to complement what you learn from the Customer Journey tool and take action to improve your marketing.

Happy analyzing!

The Customer Journey to Online Purchase »

Posted by Sara Jablon Moked, Product Marketing Manager, Google Analytics

"Tremendously useful."  That's what Chris Bawden of the TechSmith Corporation says about Data-Driven Attribution.

What is Data-Driven Attribution? Well, in August we launched a new leap in technology that uses algorithmic models and reports to help take the guesswork out of attribution. And it's available now to Google Analytics Premium customers around the world.

Data-Driven Attribution uses statistical probabilities and economic algorithms to analyze each customer's journey in a new way. You define the results that count — sales, sign-ups, or whatever matters to you— and the model assigns value to marketing touchpoints automatically, comparing actions and probabilities to show you which digital channels and keywords move the needle most. 

The bottom line: better returns on your marketing and ad spend. 

We checked in with companies using DDA and results have been strong:

• "Data Driven Attribution really showed us where we were driving conversions," says Will Lin, Senior Director of Global eMarketing for HomeAway. They saw a 23% increase in attributed conversions for their test keywords after making changes suggested by Data Driven Attribution. Download case study.

• TechSmith Corporation saw a 19% increase in attributed conversions under the Data Driven Attribution model. "It uncovered growth potential we would have not seen otherwise," reports Nicole Remington, their Search Marketing Manager. Download case study.

• And the digital analytics firm MaassMedia saw display leads increase 10% while costs per lead remained flat. "We now have a much more accurate measure of how display impacts our business," one of their clients told them. Download case study.

In short, the early returns for DDA users have been strong. Some of the key advantages of this model:

Algorithmic and automatic: The model distributes credit across marketing channels scientifically, based on success metrics you define. 

Transparent: Our unique Model Explorer gives you full insight into how marketing touch points are valued — no “black box” methodology.

Actionable: Detailed insights into both converting and non-converting paths offer clear guidance for your marketing decisions.

Cross-platform: DDA is deeply integrated with other Google products like AdWords, the Google Display Network, and YouTube, and you can pull in data from most any digital channel.

You'll learn much more about the benefits of Data-Driven Attribution when you download our cheat sheet. Or to learn more about Google Analytics Premium, contact your Google Account Manager or visit google.com/analytics/premium.

Posted by Bill Kee, Product Manager for Attribution, and Jody Shapiro, Product Manager for Google Analytics Premium

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

We've long known that the power of digital marketing is in its measurability. But measurability is only half the battle.  The other half is attribution — understanding how to allocate credit to your various marketing programs and appropriately recognize their impact on the customer journey.

Over the past two years, we’ve built a strong foundation in attribution with Multi-Channel Funnels and the Attribution Model Comparison Tool in Google Analytics (as well as additional tools for AdWords and Google Display Network). Today, we’re expanding our attribution capabilities with Data-Driven Attribution in Google Analytics Premium, with algorithmic models and a new set of reports designed to take the guesswork out of attribution. It’s available globally to all Google Analytics Premium customers.

Data-Driven Attribution analyzes the customer journey, whether that journey ends in a purchase (or conversion) or not. Our modeling methodology, grounded in statistics and economic principles, automatically assigns values to your marketing touchpoints. You’ll see a more complete and actionable view of which digital channels and keywords are performing best, so you can achieve a better return on your marketing and advertising investments.

Recently, a large telecommunications company used Data-Driven Attribution to help optimize media spend and placements to help capture small business leads. After using Data-Driven Attribution, they gained newfound confidence in making decisions about Display. They saw leads from Display increased 10% while cost per lead remained flat, and saw that some media placements had been undervalued by 58%. 

Why use Data-Driven Attribution:

• Algorithmic: The model automatically distributes credit across marketing channels. You define your own success metrics, like e-commerce transactions or other goals, and the model adapts and regularly refreshes using the most recent conversion path data.
• Transparent: With our unique Model Explorer, you’ll have full insight into model behavior and understand how marketing touch points are valued — no “black box” methodology.
• Actionable: Detailed insights into the individual contribution of a marketing channel (in both converting and non-converting paths) provide clear guidance, so you can make better data-driven marketing decisions
• Support: Google Analytics Premium customers can take advantage of their relationship with a dedicated services and support team.
• Cross-platform Integrations: In addition to our deep integrations with Google products such as AdWords, the Google Display Network, and YouTube, you can pull in data from virtually any digital channel.

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Applying Data-Driven Attribution to improve your results:

Google Analytics Premium customers can use the Data-Driven Attribution model to select and analyze marketing techniques, such as Display advertising or email campaigns. Easy analysis tools in the attribution reports let you compare values from the Data-Driven Attribution model to your default model, then sort and filter your data to discover where campaign changes could have the greatest impact. After identifying which channels (or campaigns, or keywords) have the greatest potential, adjust your programs and test the results. Once you’ve learned how the Data-Driven Attribution model compares to your prior model (and viewed the Model Explorer to see how the data-driven values were calculated), you can go straight to the ROI Analysis report, which lets you focus on optimization insights.

How it works:

The Data-Driven Attribution model is enabled through comparing conversion path structures and the associated likelihood of conversion given a certain order of events. The difference in path structure, and the associated difference in conversion probability, are the foundation for the algorithm which computes the channel weights. The more impact the presence of a certain marketing channel has on the conversion probability, the higher the weight of this channel in the attribution model. The underlying probability model has been shown to predict conversion significantly better than a last-click methodology. Data-Driven Attribution seeks to best represent the actual behavior of customers in the real world, but is an estimate that should be validated as much as possible using controlled experimentation.

To learn more about Google Analytics Premium, contact your Google Account Manager, or visit google.com/analytics/premium.

Posted by Bill Kee, Product Manager for Attribution, and Jody Shapiro, Product Manager for Google Analytics Premium

Webinar on Tuesday 7/16
Register for the webinar here.

When it comes to web analytics, one of the biggest complaints from marketers has long been the lack of technology to measure the ROI of social media. Thanks to our exciting integration between Google Analytics and Wildfire by Google that was first announced at thinkDoubleClick in June, those blind spots are now a thing of the past. This webinar will demonstrate how social media impacts the customer journey and then show you how Google Analytics lets you measure that influence in detail.

We’ll start by showing you the best way to set up your modeling and reporting to include all your social marketing efforts. Then we’ll give you a live demo of the Google Analytics integration with Wildfire. Now you’ll be able to see exactly how each and every social message and page published with Wildfire drives traffic and revenue to your website.

The webinar features Adam Singer, Product Marketing Manager for Google Analytics and Jessica Gilmartin, the Head of Product Marketing for Wildfire by Google. They’ll be joined by Adam Kuznia, Social Media Manager for Maryland Live! Casino, who will share the story of how he built the gaming industry’s largest East Coast social media community from scratch in just six months using Wildfire and Google Analytics, and proved to his management team the ROI of social.

This webinar is Part 1 of a three-part educational series introducing Google and Wildfire analytics integrations, so be sure not to miss it.

Date: Tuesday, July 16, 2013

Time: 10am PDT / 1pm EDT/ 5pm GMT

Duration: 1 hr

Level: 101 / Beginner

Register here.

Posted by the Wildfire Team

You invest a lot to create your marketing campaigns, and it’s important to see how your spend impacts results. In addition to comparing the conversion performance of your marketing activities, you can now view your imported AdWords cost data directly in the Google Analytics Attribution Model Comparison Tool. By evaluating your AdWords cost data under various lenses offered through Attribution, you’ll get further insight into the effectiveness of your marketing spend. We will gradually roll out this feature out to all of Google Analytics.

Extended Set of conversion data
As previously announced, to make the analysis of your conversion path data even more meaningful, we extended the lookback window within Multi-Channel Funnels to 90 days. This functionality is now available through the standard lookback window selector. Please see our help center for more details.

Explore different attribution models to see revised performance figures
Cost Per Acquisition (CPA) is one of the strongest indicators for marketers. Our Model Comparison Tool now makes this important metric available to advertisers in Google Analytics. In addition to CPA, we also allow users to look at the Return On Ad Spend (ROAS) figure, which compares the value or revenue driven by conversions under different attribution models.

As described in the Customer Journey to Online Purchase, marketing channels influence the customer at multiple touchpoints on the path to conversion. Display touchpoints, in aggregate, appear 3.1 times more often in the upper funnel (awareness, consideration, intent phase) than in the lower part of the funnel (decision phase).*


Selecting Conversion Value & ROAS from the selector in the Attribution Model Comparison Tool allows you to contrast the value driven by your spend. Comparing the performance of a channel by looking at two different attribution models can uncover hidden performance of this channel. In the above example, the Display channel drives 20% more value under a First Interaction model.

Interpret your analysis
The direction of the arrow in the % change column indicates the orientation of the shift. Please note that it matters which model is the reference model, and which model is the comparison model. A positive shift away from the valuation of the reference model will be visualized with an upwards arrow, a negative shift with a downwards arrow. The color of the arrows is used to indicate whether the alternative valuation of the comparison model has caused a favorable shift. Green indicates a significant shift in favor of the comparison model, and red indicates a significant shift in favor of the reference model. A gray dot symbol indicates that there is no relevant change between the reference and comparison model.


Get started today by linking your account to an AdWords cost data source. The more complete your cost data is for a given profile, the more stable and accurate are the insights you can gain from the analysis. Consider using the Cost Data Import service provided through the GA API to add cost data beyond AdWords.

*Source: Google Analytics, Q4 2012. N = US: 130M conversions (12K profiles)

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

As we’ve discussed in many previous posts, the customer journey is evolving — most consumers will interact with many different marketing channels before a sale or conversion. And marketers are recognizing this shift in consumer behavior. Instead of “last click” measurement, a strategy that only gives credit to the final interaction, they’re turning to full credit measurement. To help you make sense of the full customer journey, we’ve been focused on bringing you the very best full credit measurement tools in Google Analytics.

Nearly two years ago, we announced our first Google Analytics attribution product, Multi-Channel Funnels. With its ability to measure customers’ different paths to conversion, it quickly became one of our most popular reports for advertisers and publishers alike. We’ve seen great results from our users, including online travel agency On the Beach, who used data from the Multi-Channel Funnels reports and AdWords Search Funnels to explore and adjust their strategy for generic keywords. These attribution adjustments helped On the Beach to drive a 25% uplift in ROI — see the full case study here.

Beyond Multi-Channel Funnels, we also wanted to provide our users with an advanced platform for testing entirely new, more robust attribution strategies, including the ability to test alternative models or understand how metrics, such as site engagement, could impact their existing investments. So last year we released our Attribution Modeling feature — the Model Comparison Tool.

After several months of testing on a public whitelist, we're now in the process of rolling out the Attribution Model Comparison Tool to make it generally available to Google Analytics users without whitelist.  To reflect the importance of attribution, we also created a new “Attribution” section under the “Conversions” reports, so the tool will be found there.

Be sure to check out a previously recorded webinar with Product Manager Bill Kee for a complete walkthrough of the Attribution Model Comparison Tool, or view our multi-part attribution webinar series covering our entire selection of full-credit measurement tools.

Posted by Sara Jablon Moked, Product Marketing Manager, Google Analytics

Last Thursday, we held a webinar discussing how to effectively measure the customer’s journey in a multi-device world. We focused on high-level best practices and strategies, as well as how Google Analytics and other Google tools can help you measure and respond to the evolving customer journey.

Watch the webinar video here to learn more about:

• Holistic, full-credit, and active measurement
• Everyday strategies to improve your measurement and marketing performance
• Basic techniques for marketing attribution
• Google Analytics features and tools for measuring the full customer journey

During the webinar, we received dozens of great questions from viewers. Read on below for responses to some of the most common questions we received.

Questions and Answers

What other blogs would you recommend for advice on measurement best practices?

Avinash Kaushik is the author of Web Analytics 2.0 and Web Analytics: An hour a day. On his blog, he discusses how to use digital marketing and measurement to focus on the customer while maintaining your ROI.

Justin Cutroni is the author of Google Analytics, Performancing Remarketing with Google Analytics, and Google Analytics Shortcut. He uses his experience as a consultant to guide his blog topics. His blog provides readers with techniques for using Google Analytics to maximize their marketing strategies.

Where can I find the “Think Insights” website referenced during the webinar?

Visit www.google.com/think for access to all sorts of statistics and articles about the latest trends in customer behavior. To learn more about the customer journey to online purchase, view the interactive benchmarking tool here.

How does marketing attribution help with intra-channel optimization?

Marketing attribution can help you to optimize intra-channel campaigns by allowing you to see value for each of the specific moments in the customer journey that you may be addressing within that single channel. For example, if you are running a search campaign, you may think about the role that different types of keywords play at different moments to help generate awareness for your brand, move the customer to consider your product, or to help close the deal. Using tools such as AdWords Search Funnels, you can determine where in the customer path those keywords had an impact, and this can help you optimize your keyword mix.

What are first-click and last-click attribution models?

The first and last clicks are important parts of two  commonly used attribution models, the “first interaction” attribution model and the “last interaction” attribution model. Depending on which model you use, all credit for the sale (or conversion) is attributed to either the first or last click. In the “first interaction” model, the first touch point would receive 100% of the credit for the sale. In the “last interaction” model, the last touch point receives 100% of the credit. Historically, many businesses have relied on the last-click model alone, but since this model (like the first-click model) only addresses a single touch-point along the customer journey, it may miss other important marketing interactions.

There is no one specific model that will work for every business or every program within your business. Rather, you should explore different models and experiment to see which model or combination of models best fits your needs. Check out Google Analytics Multi-Channel Funnels and Attribution Modeling to get started.

What are some tips for measuring the customer journey with Universal Analytics?

Consider integrating Universal Analytics with all of your digital touchpoints (see some examples in this post). Here are a few use cases that our Certified Partners are already implementing to measure the customer journey beyond web:

• Integrated measurement and analysis of in-store POS systems along with desktop and mobile e-commerce platforms.
• Measuring offline macro and micro conversions through physical buttons or integration with CRMs.
• Measuring physical interactions -- for example at display booths at conventions or artworks at major exhibitions -- through to online engagement with associated websites.

Posted by Sara Jablon Moked & Adam Singer, Google Analytics Team