Tag Archives: Google Analytics

Using the Customer Voice to Speed Up Decision Making

Making important business decisions is often a slow process, regardless of industry or company size. In a world where innovation is increasingly important, speed is a necessity. But how does an organization streamline its decision-making process? For many companies, the answer is data. In fact, highly data-driven organizations are three times more likely than others to report significant improvement in decision-making, according to PwC research.1

When looking for meaningful insights to drive innovation and growth, market research is often a go-to data source. The problem many companies face is that market research can feel like a roadblock because it can take months to get the data.

At Lenovo, the leading PC manufacturer worldwide, constantly evolving and improving products is required to remain competitive. “We have to make decisions today for products two years from now,” says Sarah Kennedy, User Experience Researcher at Lenovo. To keep the decision-making process moving, Sarah’s team uses Google Surveys 360 for fast and accurate data. Bringing consumer insights to the table in the early stages of product development helps her team get buy-in from senior stakeholders at a faster pace. “Within seven days, we can get results that would normally take us a month,” says Sarah. 


"We put an emphasis on innovation. Collecting competitive data and industry benchmarks is critical to do this. Surveys 360 helps us get data on the current state of the market. The results are reliable and delivered at the speed we need so our teams can continue developing the best products without delay." 

– Corinna Proctor, ‎Director of User & Design Research, Lenovo 


Google Surveys 360 provides businesses with the data they need quickly, accurately, and affordably. Choose your target audience, write your survey, and get answers in as little as three days. Get started today.

Happy surveying!

1PwC's Global Data and Analytics Survey, Big Decisions™, Base: 1,135 senior executives, Global, May 2016

Posted by Kevin Fields, Product Marketing Manager, Google Surveys team

KASKUS doubles CTR and triples CPM with DoubleClick for Publishers and Google Analytics 360

Want to review a new digital camera, get gift ideas, or buy tickets to the next Morrissey concert? If you're in Indonesia, KASKUS is your place. 28 million unique users buy, sell, talk and share information on the site each month, making it the country's largest user-generated content publisher.  

With so many users, KASKUS recently faced a growing challenge: how to serve ads that are relevant to users’ age, gender and interests? 
“As KASKUS is the leading digital community and social commerce platform, our vision is to drive data-driven monetization by making our first-party audience data actionable, we want to give advertisers ways to perform better in our sites and increase the effectiveness of our impression-based ads." Ronny W. Sugiadha, Chief Marketing Officer for KASKUS
Sugiadha and his team wanted to create an audience segment that had a high demand among advertisers: users who had shown interest in mobile devices and were more likely to purchase them. 

KASKUS turned to Sparkline, a Google Analytics 360 Services and Sales Partner, who worked with them to approach the challenge to serve the most relevant ads. The process went from an advanced Google Analytics 360 implementation, to segmentation analysis and audience sharing with Doubleclick for Publishers (DFP). 

Below is a screenshot of the actual segment shared between Google Analytics 360 and DFP. To learn more about the process, read the full case study. 



How well did the new audience work compared to its old open-auction inventory in the Doubleclick Ad Exchange (AdX)? 
"Using the Google Analytics 360 Audience Segment sharing feature in DFP and AdX, we doubled our CTR and saw a 3.3X CPM uplift on this audience-targeted AdX inventory," reports Ronny Sugiadha. "We are looking forward to even more positive impact moving forward."
To learn more about how KASKUS achieved those results read the full case study. 

Posted by Catherine Candano and Daniel Waisberg, Google Analytics team

googleAnalyticsR: A new R package for the Analytics Reporting API V4

Hello, I'm Mark Edmondson and I have the honour of being a Google Developer Expert for Google Analytics, a role that looks to help developers get the most out of Google Analytics. My specialities include Google APIs and data programming, which has prompted the creation of googleAnalyticsR, a new R package to interact with the recently released Google Analytics Reporting API V4.

R is increasingly popular with web analysts due to its powerful data processing, statistics and visualisation capabilities. A large part of R’s strength in data analysis comes from its ever increasing range of open source packages. googleAnalyticsR allows you to download your Google Analytics data straight into an R session, which you could then use with other R packages to create insight and action from your data.

As well as v3 API capabilities, googleAnalyticsR also includes features unique to v4:
  •  On the fly calculated metrics 
  • Pivot reports 
  • Histogram data 
  • Multiple and more advanced segments 
  • Multi-date requests 
  • Cohorts 
  • Batched reports 
The library will also take advantage of any new aspects of the V4 API as it develops.

Getting started

To start using googleAnalyticsR, make sure you have the latest versions of R and (optionally) the R IDE, RStudio

Start up RStudio, and install the package via:

install.packages("googleAnalyticsR")

This will install the package on your computer plus any dependencies.

 After successful installation, you can load the library via library(googleAnalyticsR), and read the documentation within R via ?googleAnalyticsR, or on the package website.

An example API call — calculated metrics

Once installed, you can get your Google Analytics data similarly to the example below, which fetches an on-the-fly calculated metric:

library(googleAnalyticsR)

# authenticate with your Google Analytics login
ga_auth()

# call google analytics v4
ga4 <- google_analytics_4(viewId = 123456,
                          dateRange = c("2016-01-01",
                                       "2016-06-01"),
                          metrics = c(calc1='ga:sessions /
                                             ga:users'),
                          dimensions = 'medium')

See more examples on the v4 help page.

Segment Builder RStudio Addin

One of the powerful new features of the v4 API is enhanced segmentation, however they can be complicated to configure. To help with this, an RStudio Addin has been added which gives you a UI within RStudio to configure the segment object. To use, install the library in RStudio then select the segment builder from the Addin menu. 

Create your own Google Analytics 

Dashboards googleAnalyticsR has been built to be compatible with Shiny, a web application framework for R.  It includes functions to make Google Analytics dashboards as easy as possible, along with login functions for your end users. 

Example code for you to create your own Shiny dashboards is on the website.

BigQuery Google Analytics 360 exports 

In addition to the v4 and v3 API functions, BigQuery exports from Google Analytics 360 can also be directly queried, letting you download millions of rows of unsampled data.  

Aimed at analysts familiar with Google Analytics but not SQL, it creates the SQL for you to query common standard metrics and dimensions, using a similar interface as the API calls.  See the BigQuery section on the website for more details.

Anti-sampling 

To more easily fetch non-sampled data, googleAnalyticsR also features an anti-sampling flag which splits the API calls into self-adjusting time windows that are under the session sampling limit.  The approach used is described in more detail here.

Get involved 

If you have any suggestions, bug reports or have any ideas you would like to contribute, then you are very welcome to raise an issue or submit a pull request at the googleAnalyticsR Github repository, or ping me on Twitter at @HoloMarkeD.

Posted by Mark Edmondson, Google Developer Expert

Using Surveys to Better Understand the Customer Journey

Your organization has plenty of data about customer behavior that tells you what different customers do where and when. You can see when they visit you online, how long they search, and how much they spend.


But too often the “why" behind their actions remains elusive. With the mountains of information you collect, the insights are often difficult to find, take too much time to discern, or require additional data. All this means it takes marketers too long to get important information that could make a real difference to the customer experience — and the bottom line.


“If you want to have a major impact, you need an integrated approach to see what is happening at all customer touch points and understand how effective you are,” says Joerg Niessing, a marketing professor at INSEAD.


The number of sources of marketing and customer data that a company integrates correlates strongly to performance vis-à-vis competitors, according to a recent study published by INSEAD. The study focused on customer and marketing data, including:
  • Digital analytics, such as optimizing email campaigns, testing content, and analyzing digital pathways to optimize website use and experience.
  • Customer analytics, including lifetime value and loyalty calculations, response and purchase propensity modeling, and micro segmentation.
  • Marketing analytics, such as demand forecasting, marketing attribution models, market mix modeling, and media budget optimization.
  • Sales analytics, including pricing elasticity modeling, assortment planning, and sales territory design.
  • Consumer analytics, including surveys/questionnaires, customer experience research, and customer satisfaction/advocacy modeling.
The study found that those companies that leverage multiple sources and focus diligently on demand generation have significantly stronger business performance, especially total shareholder return.


Straight to the source
But insights uncovered from many data sources often beg the question, “Why?” To answer that, modern marketers go directly to the source: consumers.


Traditionally, companies that use surveys and field research to try to get at the “why” behind the “what” pay a lot of money for information that is often too complex to understand and too slow to arrive. When it does come in, it is sometimes no longer relevant and leaves organizations trying to solve last month’s or last year’s problem at the expense of current ones. Attempting to get speedier or less costly results risks compromising accuracy.


But innovations in market research are changing the game. Easy-to-use survey tools like Google Surveys help marketers fill out their knowledge of customer behavior much faster than traditional surveying methods.


Companies that make use of these fast, convenient survey solutions gain insight not only into what people actually do, but also what they say they will do — and in that gap there could be opportunities. “Marrying digital and marketing analytics with consumer research from surveys gives marketers deeper insights and opens up the number of hypotheses a company can test,” says Suzanne Mumford, Head of Marketing for the Google Analytics 360 Suite. “Marketing today is in near real time and your data should be, too.”

“Marrying digital and marketing analytics with consumer research from surveys gives marketers deeper insights and opens up the number of hypotheses a company can test.”
—Suzanne Mumford, Head of Marketing, Google Analytics 360 Suite

Say your website analytics reveal that one segment of your visitors are highly engaged with your site content, but their visits aren’t converting into sales. “You can ask them directly why they keep coming back but don’t end up buying. Surveys let you take your data one step further and round out the picture of the customer so you can make informed business decisions and tailor your customer experiences,” says Kevin Fields, Product Marketing Manager for Google Surveys.


Supporting business decisions with surveys
Surveys are also useful if marketers find themselves in an internal debate about two campaign concepts. Before making a large investment based on subjective opinion, marketing leaders can validate messaging by asking the target audience about their preference.


For modern marketers, surveys have become an essential element in an integrated marketing approach — they produce insights that complement those uncovered by other data sources. “I want to make sure that the customer voice is front and center but that we also surround it with other data — that we can make really good, holistic business decisions,” says Stacey Symonds, Senior Director for Consumer Insights at Orbitz.



So think about what you’d most like to ask your customers — or those who visit your site but don’t buy. Survey solutions like Google Surveys allow businesses to get sophisticated, accurate data in a matter of days, not months. Because these methods are more affordable and quick, they allow businesses to continually iterate to meet customers’ needs.


“Surveys empower organizations to get answers when they matter,” Fields says. “And getting those insights quickly helps marketing stay nimble.”


Download “Measuring Marketing Insights,” an online Insight Center Collection of articles from Harvard Business Review, to learn how organizations are using market research to gain more consumer insights.


A version of this article first appeared as sponsor content on HBR.org in August 2016.


Posted by Kevin Fields, Product Marketing Manager, Google Surveys team

Does Your Company Have a Data Science Strategy to Create Customer Value?

One of the biggest challenges for marketing leaders today is not finding or hiring analytic talent, according to new research cited in a Harvard Business Review report, but rather it is finding the right ways to move the mountains of data into insights and then into action.


The study concluded that marketing organizations need analytics professionals who understand data and the technologies that collect, house, and integrate it.1 That’s a given. But beyond that, experts say, executives need to place more emphasis on data science than on data scientists. Put another way: They should pay more attention to analyzing and acting on what they have now because analysis paralysis doesn’t create customer value.


“Data scientists are technicians who are very good at managing and manipulating data,” says Peter Fader, the Frances and Pei-Yuan Chia Professor of Marketing at the Wharton School of the University of Pennsylvania and author of Customer Centricity: Focus on the Right Customers for Strategic Advantage. “But data science is about looking for patterns, coming up with hypotheses, testing them, and acting on the results.”


Machine Learning
That’s where machine learning can speed analysis and augment your analytics team’s work — by crunching massive amounts of data to identify patterns and anomalies.


A type of artificial intelligence that uses algorithms that iteratively learn from data, machine learning can surface insights without being explicitly programmed where to look for them. It makes it more efficient to crunch massive amounts of data, calling out issues before you see them and providing answers to questions you may not have even thought to ask. This speed to insight allows marketers and analysts to do more with the data that comes in and see the whole picture of the customer journey.


Accenture Managing Partner Conor McGovern says, “If you can’t make the rubber hit the road with a disciplined approach to analytics, you will end up with customer experiences that aren’t as effective or engaging as they could be. As with any source of information, you need to embed and ingrain analytics into decision-making processes to obtain the desired results.”

“If you can’t make the rubber hit the road with a disciplined approach to analytics, you will end up with customer experiences that aren’t as effective or engaging as they could be.” —Conor McGovern, Managing Partner, Accenture

How Lenovo Harnessed Data to Create Customer Value
That targeted data science approach can give companies of any size a competitive advantage. Lenovo is a prime example of a marketing team that mastered the use of advanced technology and analytics tools, driving the company to create better value for its customers.


Ajit Sivadasan, Vice President and General Manager of Global E-commerce, realized that customer data was burgeoning and Lenovo needed to harness it. He began by establishing an analytics team in his e-commerce unit that today integrates and analyzes customer and marketing data from more than 60 sources worldwide. By integrating and analyzing Lenovo’s data, Sivadasan found that there are three main drivers of customer satisfaction that correlate to loyalty:
  1. Quality of the online experience. Sivadasan’s team tracks important variables such as how easy it is to find product information and whether Lenovo provides sufficient follow-up on the status of an order.
  2. Meeting commitments. This second driver includes how often the company misses promised ship dates.
  3. Experience with the product itself. By analyzing social media and direct customer feedback, Lenovo’s ecommerce team helps the company improve its products.
Competing on Analytics
In order to pursue an effective analytics strategy, executives have to clearly define business problems and what the questions are that analytics can answer. If executives don’t do this, they risk getting back data that sends the organization in the wrong direction.


For example, companies frequently find themselves puzzling over a dip in conversions among a desired demographic. Organizations need to be able to study the data, ask customers and potential customers the right questions, and experiment with offering different solutions to optimize the customer experience. Answers need to come in quickly so the organization can act quickly — ahead of the competition.


The speed to insight that machine learning offers can help companies act strategically on the data they have, homing in on the insights with impact, allowing executives to make informed decisions.


Says Joerg Niessing, Marketing Professor at INSEAD: “Executives still have to make the same strategic decisions that they have always made. They need to understand market dynamics and what competitors are doing — and then determine how the company should react. The only difference is that we now have a great deal more data and analytics to help make these decisions.”


Download “Measuring Marketing Insights: Turning Data Into Action,” an online Insight Center Collection of articles from Harvard Business Review, to learn more about using analytics to create customer value.


A version of this article first appeared as sponsor content on HBR.org in August 2016.


1Harvard Business Review Analytic Services, "Marketing in the Driver's Seat: Using Analytics to Create Customer Value," 2015.


Posted by Karen Budell, Content Marketing Manager, Google Analytics 360 Suite

Marketing Analytics Can Improve the Customer Experience

Almost every organization today is putting customer experience (CX) at the core of its strategy, aiming to provide products and services that meet customers at every touch point. In a crowded, multichannel marketplace, companies realize that a great customer experience — consistently delivering what customers want, when they want it — can be a powerful differentiator.


But many companies fail to deliver, according to research by Harvard Business Review Analytic Services (HBR-AS). Although half of surveyed business leaders say CX is a top-two differentiator for their business, just half of them said they perform well in it.


Although half of surveyed business leaders say CX is a top-two differentiator for their business, just half of them said they perform well in it.1


The problem isn’t access to data; most businesses said they collect mountains of information on their customers. The real obstacle to better customer experience, the research has found, is built into the way organizations share that data, analyze it, and work together.


Improving the customer experience is the end game, but getting there requires more than data. It requires the right data, from multiple channels, integrated to give a holistic picture of the customer journey. And that is where many companies struggle. HBR-AS found that fewer than a quarter of companies integrate customer data across channels to provide a single customer view.


Integrating data for customer value requires getting around organizational silos, which HBR-AS research has identified as the number one problem for companies struggling to improve their total customer experience. These silos prevent organizations from understanding the customers’ expectations at critical moments, and cultural resistance makes it tough to get the collaboration needed to solve the problem. As a result, respondents said the business doesn’t develop the right insights, get the information to the right people, or make the moves that could add real value.


Data-Driven Insight
By contrast, the study found that “best-in-class companies” — those with strong financial performance and competitive customer experiences — are more likely to have broken down those silos than are other organizations. And they use sophisticated analytics in a way that provides insights that open up the customer experience to the whole organization.


For example, at Progressive Insurance, the marketing team collected data on how mobile app users were behaving. These consumers, they discovered, wanted more than just helpful insurance quotes in the mobile app; they wanted to buy insurance on the spot. Progressive responded by giving them exactly what they wanted — the option to buy insurance — which vastly improved the customer experience and delivered a big win for the company. When a company creates customer value, the business benefits naturally follow.



Marketing Takes the Lead
But who is going to break down silos, connect the dots of the customer experience, and drive its improvement?


Today, marketing leaders need to make the case to the company that optimizing the customer experience requires breaking down silos and opening up collaboration, and shifting from a product-centric to a customer-centric approach, says Erich Joachimsthaler, author of Brand Leadership: Building Assets in an Information Economy. For example, a European beverage company assigns marketing groups to consumption moments, such as a night out, instead of brands and channels. The goal is to embed marketers deeply into a particular customer experience and focus them on each step of the customer journey.


“Marketing needs to connect the dots across all customer-facing functions of a company, including partners, in order to deliver real value instead of just communicating the brand,” says Joachimsthaler.


Robust analytics and insights have given marketing teams insight into how customers interact with brands, highlighting product preferences, purchase sequences, and so forth. And they reveal how top of the funnel marketing activities — such as an online display ad or TV commercial — tie in to in-store sales or an online website conversion. Measurement and analytics allow brand marketing and performance marketing to complement each other for the customers’ benefit.


Clearly the stakes are high, and marketing leaders and their teams are challenged to think in new ways. They don’t need more data; they need to find ways to identify and supply their organization with useful insights from that data.


Download “Measuring Marketing Insights,” a collection of Harvard Business Review Insight Center articles, to learn how companies are using data and marketing analytics to improve customer experience.


A version of this article first appeared as sponsor content on HBR.org in August 2016.

1Source: Harvard Business Review Analytic Services, "Marketing in the Driver's Seat: Using Analytics to Create Customer Value," 2015.

Posted by Karen Budell, Content Marketing Manager, Google Analytics 360 Suite

Data Studio: DoubleClick Campaign Manager Connector

Google Data Studio (beta) allows users to connect, transform, visualize, and share data no matter where it lives. Today we are happy to announce that DoubleClick Campaign Manager (DCM) customers can pull their data into Data Studio dashboards instantly!


With this new connector, DCM customers no longer need to import data into spreadsheets. Users can now quickly create dashboards with over 50 DCM metrics and dimensions. These dashboards are an effective way to track and optimize campaign performance and share reports with client and agency stakeholders.

Creating a new report with DCM data
Ready to get started? The first step is to connect to your DCM network or advertiser through the Data Sources page.



Next you can create a new report from scratch or use our DCM template. With just a few clicks, the dashboard is populated with your data.

Want to learn more? Looking for a new connector in Data Studio?

To learn more about the new DCM connector, please visit our Help Center or post your questions in the Data Studio community forums.

Is there a specific data service you wish to be able to access and visualize through Data Studio? We welcome your feedback via the connector feedback form — we read all responses and use them to prioritize new connectors.

Happy reporting!

The Data Studio team

Posted by Alon Gotesman, Google Data Studio team

Rethinking Marketing Measurement from the Ground Up

From the moment smartphones touched human hands, they began to change how people interact with brands. It happened slowly at first … but today 91 percent of smartphone users turn to their phone for ideas while doing a task.1


Consumers expect more of marketers now. They expect brands to answer their questions and deliver the exact experiences they want at the moments they need to know, go, do, or buy things. They expect this across all screens and all touch points, over hundreds of interactions on their journeys.


This means there are three questions marketers should be asking:
  1. Is our brand useful to consumers at every touch point?
  2. How can we measure our usefulness?
  3. How can we be even more useful tomorrow?
To deliver, enterprise marketers need a new approach to measurement that shows them the entire customer journey and lets them see what’s working at each step along the way. The problem is that many of our measurement tools and metrics were created for a desktop world at a time when marketing focused on channel performance.


Today we need an understanding of our audiences across devices and channels. That means taking into account the impact of mobile online and offline, quickly spotting insights, and trying new ways to provide better customer experiences.


Breaking Down the Data Silos
A car shopper today can have hundreds of digital interactions — or in this case 900-plus interactions — before buying. Each one of those moments is an opportunity for a brand to be useful. And each one leaves its own data trail.


But companies that look at data channel by channel, in a silo, can miss the forest for the trees. We need to break down measurement and strategy silos and create an integrated view of the consumer’s journey. It’s likely you have found yourself in a debate with colleagues about metrics and campaign results and thought, “It’s not about what matters to channel X — we need to zoom out to see the whole picture and do what’s best for our customers.”


The truth is that the future of enterprise measurement depends on people and departments, tools and systems, all talking to each other and sharing insights in real time about what customers want most.


From Silos to Synthesis
So if we know that one session and one click doesn’t tell the full story … and if we want to connect consumer behavior dots over time … where do we start? The best place is with the classic question “What outcomes are we trying to achieve?” But then instead of saying “How do we reach our goals?” let’s ask: “How do we measure success?”


Key performance indicators (KPIs) have to reflect the new objectives of the mobile-first world. Marketers who link their metrics to business results are three times more likely to hit revenue goals than those who don’t, according to a Forrester report.2


And while more data is always great, what marketers really need are more insights. That’s why the question “What’s working?” is so crucial. If that car buyer sees a TV commercial for a small sedan or pickup truck and searches for reviews and mileage ratings on his or her mobile phone, watches videos about special features, visits a dealer for a test-drive, and then finally buys a month later, marketers must find a way to bridge the gaps between TV airings and search lift, and display ads and video views, to see where the real influence happened.


How much credit should mobile get? How many touch points were there? Marketers need to know. And if the gaps can’t be filled perfectly, we should get comfortable with new proxies that will give us a sturdy estimate instead.


Marketers, Mobile, and Tomorrow
Evolution is a good thing, even if measuring in new ways can be awkward at first. Measurement and marketing go hand in hand — both have to keep pace with the vastly rising expectations of mobile-first consumers. Discomfort means you’re working to stay ahead.


So, take stock of what you measure and how you measure. Ask if those KPIs account for all the ways consumers may engage with your brand. If not, ask yourself why you’re measuring them in the first place. Focus on the outcomes you want and map your new metrics back to your strategy.


Smartphones have already changed how people interact with brands, and they’ll surely alter those interactions even more in years to come. We can’t predict how. But we can say that the brands that measure the results of those changes first will have a major edge over those that don’t. Measurement isn’t what happens at the end; it’s where the smarter and more successful future begins.


Download “Measuring Marketing Insights,” a collection of Harvard Business Review Insight Center articles, to read more about best practices and case studies on enterprise marketing and analytics.


A version of this article first appeared as sponsor content on HBR.org in August 2016.


1Source: Google/Ipsos, “Consumers in the Micro-Moment” study, March 2015.
2Source: Forrester, “Discover How Marketing Analytics Increases Business Performance,” March 2016


Posted by Matt Lawson, Director or Marketing, Performance Ads at Google

Falling in Love With Measurement

Why aren't more marketers measuring their campaigns? 


If Marketing and Measurement had a relationship status in today’s mobile-first world, it would be: "It's complicated." They've been sitting at the same table at lunch, there's been some small talk in the hall … but they haven't really gotten comfortable together.

Which is a shame, because these two are perfect for each other.

Connecting the dots 

Consumers often have dozens or even hundreds of digital interactions before they buy something today. The sheer amount of data created is staggering. There are more than enough dots to be connected for full visibility into the customer journey.

But, as much data as marketers collect today, the truth is many still struggle to make sense of it all. In some companies, you could say Marketing and Measurement find themselves sitting at opposite ends of the couch.

Only 5 out of 10 marketers said they think about measurement while developing campaign strategy, a recent survey of marketing decision-makers shows.1 If you don't define your measurement goals from the beginning, you may not collect the right data — and understand what's working and what isn't.

Marketing and Measurement should get cozier sooner: at the front-end of the campaign process, while developing strategy. Yet, too many marketers said they think about measurement while building materials and assets (nearly 16%), after the campaign has deployed (9%), or even after the campaign has finished (nearly 6%). What’s more, 16% of the survey respondents said they don’t measure their campaigns at all.2
Clearly, it's time for a relationship makeover. If you're ready to play matchmaker in your own organization, try starting a strategic conversation between Marketing and Measurement with these three questions:

  1. Are we measuring the consumer interactions that really matter?
  2. How quickly can we spot the key insights hidden in this data?
  3. How do we turn those insights into better customer experiences? 

When we close the gap between Measurement and Marketing, we can not only answer the question “How are we doing?” but also the more important question, “How can we do better?”

Going steady 

It doesn't have to be complicated. When Marketing and Measurement go hand-in-hand throughout the customer journey, it can lead to more useful insights, higher revenues, and better experiences for everybody.

As Matt Lawson, Google's Managing Director of Ads Marketing, says, “Measurement isn’t what happens at the end; it’s where the smarter and more successful future begins.”3


Download “Measuring Marketing Insights,” a collection of Harvard Business Review articles offering best practices and insights on measurement, analytics, and how to turn data into action. 

1-2Source: Google Surveys, "Measurement in Campaign Timeline", Base: 1,092 marketing executives, U.S., August 2016.
3Harvard Business Review, “Rethink Measurement From the Ground Up,” sponsor content from Google Analytics 360 Suite, August 2016.

Posted by Karen Budell, Content Marketing Manager, Google Analytics 360 Suite

Introducing the Firebase Demo Project

"All genuine learning comes from experience" - John Dewey
Earlier this year we introduced Firebase: a unified app platform for Android, iOS and mobile web development. It includes tools to help you develop faster, improve app quality, acquire and engage users, and monetize apps. There are many resources available to learn Firebase, from documentation, guides and free training courses (Android and iOS) we created, to advice from the Firebase community. However, there is nothing quite like learning through practical experience. To address this we’ve launched a fully functional Firebase Demo Project, available to everyone from today (get access here).

The Demo Project includes data from Flood It! (Android and iOS), a real puzzle game in which you have to flood the whole game board with one color in less than the allowed steps. Therefore, the data in the Firebase demo project is typical of what you might see for a gaming app with in-app purchases. It includes the following kinds of information:
  • Analytics: Attribution data, key events, cohorts and funnel reporting. This includes data about first opens (think of these like installs), in-app purchases, and more.
  • Remote Config: The parameters that control the app experience as well as the conditions which define which users receive which parameter values.
  • Test Lab: The automated test results from running the app on numerous device/OS combinations for quality assurance purposes.
  • Crash: Details on various crashes which have occurred in the app, including callstacks and device information.
  • Notifications: The notification campaigns that were sent to users to re-engage them. This includes data about the number of messages sent, opened and the number of conversions attributed to each campaign.

Firebase Demo Project: Analytics Dashboard
"Since the launch of Firebase we have been excited to continually build hands-on experience with its many features. Especially with the freely included and unlimited analytics solution for mobile apps, Firebase Analytics. The Firebase Demo Project has helped our team do just that and similar to what we've done with the Google Analytics Demo Account, we've incorporated the Firebase Demo Project within our training programs. This plays a critical part in helping our clients maximise their familiarity with Firebase." - Ben Gott, Analytics Director, Periscopix
Self-Learning

The Demo Project is useful for exploring Firebase features and reports. Here are a few things you can do with it:

  • View all standard Analytics reports populated with real data from the Flood It! app
  • Dissect Analytics reports by applying a variety of filters
  • See which ad campaigns are driving the most valuable users to the app
  • Track crashes and their impact on end users, and understand the steps that led to them
  • See the full notifications funnel for notifications sent from the Firebase Console
  • View Remote Config parameters to see how values are varied based on targeting parameters, especially feature flags and percent targeting for staging and Analytics audience targeting for customization
  • See test results from testing the app on real physical and virtual devices in the Firebase Test Lab

Education Programs
If you’re an educator trying to teach others to use Firebase then we encourage you to use the Demo Project within your course. For example, you can create practical exercises that students can complete using the Demo Project.

Access the Demo Project
You can get access to the Demo Project and learn more about it, from this help article. If you need some help, or have ideas to make the Demo Project more useful, please share it with the Google Analytics Community. We hope the Demo Project gives you a practical way to try new features and learn about Firebase.

Happy analyzing!
Posted by Deepak Aujla & Steve Ganem, Google Analytics team