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This space is created exclusively for users who write blogs or articles around Adobe Analytics and related technologies. You can feel free to post your genuine content around the product or related topics like Web Analytics, Mobile Analytics, Reporting, Data Analysis etc. If we like what you have written, we may well include it in our official Knowledge Base Articles and give you the due credit! If you have any questions before posting you can send me a private massage. 

Hope to see some great content here!

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The Blog Post below is from Tim Waddell, Director, Product Marketing for Media & Advertising Solutions at Adobe.


Mobile-App Analytics Are the Key to Successful App Optimization

Some estimates say that as many as 60 percent — over 400,000 — of the apps in Apple’s App Store have never been downloaded even once! And, in a study conducted on data from over 125-million mobile phones downloading apps from the Play Store, the average app loses 77 percent of its users within three days after it’s installed. On top of that, 90 percent of the users stop using the app after a month, and only five users keep using any given app.

There is a clear need for app developers and marketers to come up with better apps and better ways to monitor their uses. They need to optimize their performances and capabilities.

While mobile app analytics can tell you a lot about what is happening with your app after the download, Forrester reported that only 46 percent of companies are using a mobile app analytics solution. This means that the rest (more than half!) can’t see what their customers are doing, primarily because they aren’t even bothering to watch. These companies are missing out on valuable conversations, relationships, and income, all because they lack data desperately needed to guide their business decisions — data that is readily available.

Following is some of the mobile app analytics data available today that can give you amazing insight into how your app is being used.

App-Acquisition Metrics
The mobile app marketer embeds campaign-specific tracking links in paid, owned, and earned media that can then be tied to the app once a user has downloaded and installed it. With that, they can determine which marketing activities lead to an app download and launch event. This gives the marketer the insight necessary to make informed decisions about how to adjust future acquisition and reengagement campaigns to drive download and launch rates.

App-Lifecycle Metrics
Insights into app behavior and customer preferences are critical if you are to understand whether your app is effective. Metrics such as app launches, crashes, days since last use, and average session length are just a few of the lifecycle metrics available. This data allows you to see patterns that could reveal why someone decreased his or her use of the app. With this information, you can encourage interactions, create loyalty, and drive conversions.

App-Store Data
Just knowing how many times your app was downloaded doesn’t help much. You can also discover the region, platform, category ranks, in-app purchases, royalties, rankings, and much more.

Cohort Analysis
The next step in app analysis, cohort analysis, looks at certain groups of users and follows their behaviors over time. You can infer the expected lifespan of the app, how upgrades affect engagement, and how early- versus late-adopter behaviors differ.

Location Services
One of the most powerful pieces of information that you have access to is your customer’s location, providing an unprecedented opportunity for personalization. You can help someone find a resource he needs or do location-based targeting using GPS and beacons. Sports stadiums and retail stores are exploring the power of beacons to deliver location-specific services to users.

Developing a great app is challenging, and you need data about how people are using it to assess its success and make it better. The key is measuring engagement, and mobile app analytics allow you to understand what factors attract your audiences to your content and provide you with opportunities to discover ways to deepen involvement.

It may sound cliché, but with the right mobile app analytics data, you can turn insight into action and understand what steps you need to take to create a great app.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/mobile-app-analytics-key-successful-app-optimizat...



The Blog Post below is from Trevor Paulsen, Product Manager, Analytics Data Management at Adobe.


The Power of Segmenting and Why Specificity Matters: Introducing Segment Comparison

Segmenting is a core strategy that is crucial to any marketer’s success. As not all customers have the same characteristics or behave in the same manner, it’s increasingly important to employ different marketing tactics for each distinct group. While traditionally segmenting has been thought about quite simply in regards to age, gender and even geography – as our threshold for data-driven marketing continues to increase, the definition of segmenting has shifted as well.

Let’s use Pixar movies as an example: while one might think that their cartoons are just geared to children, in reality their movies appeal to a variety of different groups including kids, parents, couples, teenagers. The messaging and movie promotion go far beyond just simply getting a five-year old to laugh. Pixar is smart in their approach: aside from traditional advertisements on children’s programming, the movie is also positioned on shows geared towards adults such as Ellen (as they did with Finding Dory), and promoted on social channels with specific storylines that are geared toward a group.

With the massive amount of customer and behavior data at our disposal, it’s even more important to identify the key characteristics of audience segments that are most significant to a brand – to better understand the behavior that drives more positive interaction, sharing and conversions among different groups of customers.

At Adobe Summit, we introduced Segment IQ to the world – and we’re now excited to announce the first live feature within that category: Segment Comparison for Analysis Workspace. First in a series of audience analysis and discovery tools within Segment IQ, Segment Comparison intelligently discovers the differences between your sets of target audience segments through automated analysis of all your metrics and dimensions.

In speaking with customers, we saw analysts spending an incredible amount of time comparing various segments with each other in order to understand the actionable differences between them. Segments often overlap with each other or have non-obvious differences lurking deep within the data, and uncovering these insights is like picking a needle out of a haystack – sifting through these cascades of data manually to find the most significant differences is often impossible.

With Segment Comparison, marketers and analysts can gain new visibility into which segments are most important to their businesses and why, so they can acquire and convert customers much more efficiently—saving time and budget.

The best part is that all of this is done with a few clicks of a mouse. Check out this short demo to see it in action.

Segment Comparison allows brands to complete a comprehensive segment analysis within just minutes, and compare every single dimension, metric or data point between any two segments automatically discovering the most significant differences between each. In initial testing with customers, Segment IQ is one of the most popular features we’ve released, and we’re thrilled to see how it expands from here in helping to create highly targeted marketing strategies that resonate with segments based on customers’ unique behavior.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/power-segmenting-specificity-matters-introducing-...



The Blog Post below is from Nate Smith, Senior Product Marketing Manager for Adobe Analytics


Leave the Cookies in the Jar: Turn Insight Into Action With Adobe Analytics

With the Adobe Analytics Spring 2016 Release, Adobe is delivering more intelligent, automated tools that aid in the discovery and sharing of meaningful audience insights across your organization. The latest advances in Adobe Analytics feature several industry firsts, including a new people metric to understand how many actual people viewed content, purchased a product, came in via various marketing channels, or otherwise interacted with your brand.

We are all in a new online world that no one could have predicted just a few years ago. Organizations that don’t use analytics are quickly realizing that they run the risk of being left behind.

Users of the Internet come from all over the globe and have diverse interests, but nearly all of them have one thing in common: they desire seamless, amazing, relevant, personalized, and real-time experiences that connect them with your brand. If they don’t get that in the first few seconds of their visit, they are more than happy to visit your competitor.

Adobe is uniquely positioned to serve its clients’ data-analytics needs because it has a single, unified platform based on world-class security standards, and no capabilities are outsourced. The customer experience is consistent across all channels, and all digital assets are coordinated with ongoing marketing campaigns.

We All Need to Become Data Scientists

The key is, though, that our new Analytics release embraces and enables the new reality that is quickly taking shape — that we all need to become data scientists.

A powerful example of using analytics and embracing the customer journey to get the whole customer view is UniCredit Group, which is based in Italy. With more than 8,400 branches in 17 countries and 146,000+ people employed, it had a minimal online presence just a few years ago when it turned to Adobe Marketing Cloud and Analytics for the insights needed to understand its customers. After mapping the whole customer journey and making changes to the way they addressed customer needs, online users rose from 300,000 to over 3 million — with over 50,000 visitors per hour! UniCredit Group knew it needed to optimize the user experience and become a company of data scientists to understand its users.

Keep the Cookies in the Jar Where They Belong

The Web-based, user-friendly dashboards of today’s Adobe Analytics make that possible. Cookies just don’t cut it anymore. In the mid-1990s, a cookie was a reasonable approximation of a person, since most people had just one device. But today, the average person may have a desktop, laptop, work computer, tablet, smartphone, and maybe even a smart watch. Without using analytics, you have no link between the devices. The cookie crumbs you get with one cookie per browser will not get you the information you need to construct a 360º view of your customer’s journey at every touchpoint with your brand and on every channel. You have to dig deeply into the available data.

Another successful example of leaving the cookie in the jar is the leading online-investment firm, Scottrade, that uses Adobe Analytics to measure the impact of its ad spend across various channels, publishers, and social media sites. For example, through Facebook and Twitter initiatives, Scottrade has the opportunity to engage in ongoing dialogue with existing customers and with people who might not otherwise consider using an online brokerage firm. Interactive advertising analyst for Scottrade, Bill Dehlendorf, explained, “Adobe Analytics helps us measure the success of all our campaigns—Facebook, Twitter, and others. Through a single platform, we can monitor the performance levels of each channel and then adjust accordingly.”

Turning Insights Into Action

As a result, Scottrade has boosted the return on its digital strategies. Equally important, Adobe Analytics helped it boost productivity, freeing up 15 percent more time through automation and integration. “We spend a lot less time simply capturing and managing data and more time executing on campaigns that impact our bottom line,” Dehlendorf said.

Businesses that have moved toward a more automated data-analysis solution are seeing big returns on their investments. For example, let’s say you do an A/B test on a campaign, and the data shows that B is best and that it increased revenue by 20 percent. Why wait a week for the staff meeting to present the test and get approval? If you let the software — with the proper oversight — simply put version B into use, think of all that additional revenue you could make. Your marketers can always adjust the campaign based on real results. The new 2016 release of Adobe Analytics effectively puts a play button on your data.

Connect the Right Workflows to the Right Work

With the availability of highly detailed, easy-to-use data tools in the new release of Analytics — such as conversion and success metrics, real-time trend changes, the ability to see customer journeys before and after they visit your page, and instant segment comparisons — you can adjust workflows within your organization to bring the right people to the right tasks.

For example, your marketers — who know the details of your campaigns and are the best people to judge what data they need — push the buttons to collect and manage data and immediately implement the insights they gain. Revenue flows instantly.

Adobe Analytics, especially the new tools released this spring, gives you the power to make everyone in your organization responsible for the customer journey and allow an unprecedented opportunity to reorganize your talent into the roles best suited to maximize your ROI.

To provide the experiences that today’s customers expect, organizations are faced with having to gather lots of data from different sources in an attempt to get to know their audiences at every touchpoint and on every channel. Understanding the data needed to create that experience is very complicated, but because of Adobe Analytics, your entire organization can have access to the whole customer view. In an era of high-velocity change, using Analytics may be the only way to truly future-proof your organization.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/leave-cookies-jar-turn-insight-action-adobe-analy...



The Blog Post below is from Jared Lees, Senior Manager of industry strategy for financial services at Adobe.


Solve the Mystery of Your Audience With Advanced Analytics, Targeting, and Data-Management Tools!

Did you know that 69 percent of mobile users currently do some form of banking on their phones? Customers rely on you to provide them with optimized experiences that fit their preferences and contexts — but without jeopardizing their sensitive information. The good news is that customers are leaving hints everywhere regarding their needs and desires — such as why they’re calling your branch, how they use your mobile app, or what page they visit on your website.

Unfortunately, despite all of this rich information, financial institutions struggle to find meaning within their data. In fact, of the 83 percent of finance executives who say their firm’s data is their most strategic asset, 47 percent claim they don’t know how to employ it to drive value. These statistics make perfect sense. Old and siloed systems make it challenging to unify mismatched datasets that have been gathered from mobile devices, websites, branches, and call-center interactions. How can your organization thrive in this challenging environment? It all boils down to data-driven marketing. You have to transform your data into actionable intelligence and then wow your customers with rich experiences that keep them coming back for more. With new analytics and data-management capabilities, you can do just that. Where should you start? Keep reading!

Read Your Customers’ Minds.
Use data to create personalized experiences for your audiences. Customer interactions leave behind hints, creating digital fingerprints that can be analyzed for an abundance of information — right down to what your customers are doing or even how they’re feeling at that very moment. A recent survey found that 53 percent of millennial audiences claim that their banks don’t offer services that are any different from other banks. To differentiate your brand, leverage analytics to better understand your customer and context so you can offer the right product or service at the right time and personalize an experience that meets the needs of your audience.

Remember, when you create a relevant, personalized experience for your customer — one that is based on what your customers are “telling” you that they want — you build trust and brand loyalty.

Put the Pieces Together.
How do you obtain a complete picture of your customer when data is scattered into individually owned channels? You don’t — businesses must share customer information across their organizations and enable business units to collaborate with one another by creating teams of cross-functional experts from different parts of the business and enabling those experts to collaborate with one another. Sharing the same customer data across multiple channels creates a ‘platform’ where data can be aggregated into a single profile or segment ID. The goal is to unite various business units behind a single view of the customer and combine traits from customer-data sources to gain a more accurate, collective view of your customers.

Investigate the Customer Journey.
Data-driven marketing enables you to quickly communicate with your customers across all channels in a relevant manner. Now that you have access to connected, comprehensive customer information, the next step is to match the right offer or content within the context of the interaction and really impress your customer. Linking data from your online and offline channels is crucial to giving your customers the consistent, relevant experiences they desire. For example, let’s say that a customer is looking at auto-loan information on your website. Automatically sending him an email or direct-mail offer about auto loans is a great way to grab his attention and continue the conversation — and all automated based on his browsing behavior. The ability to personalize your customer experience is a vital factor standing between someone remaining loyal to your brand — or not.

Keep an Eye on Things.
Having an understanding of your customers’ behaviors and preferences is key, but it is equally important to measure performance and look for ways to optimize the customer experience. It is critical that you monitor marketing performance and your customers’ behaviors during their interactions with your brand, using analytics and data-optimization capabilities.

One capability to consider is anomaly-detection, which allows you to automatically see when an event doesn’t follow a set pattern. If your website bounce rate is increasing, the anomaly-detection feature will alert you. From there, contribution analysis can be applied to all of the factors potentially causing this particular issue so it can be fixed.

Additionally, advanced analytics, machine learning, and algorithmic attribution allow you to see how each marketing touch guides the customer’s journey toward conversion. The customer journey can be synchronized and personalized so every need is met at just the right moment. Experience metrics, solid reporting capabilities, and clean data keep you in step with your customer journey.

Act on Facts — Not Hunches.
Data-driven marketing allows you to eliminate the guesswork and act on actual facts. Respond to what you know about your customers’ preferences, interests, and behaviors. Bridge the gap between insights and actions so you can respond to your customers along any part of their journeys. Real-time analytics allow you to see how your customers are engaging with your brand in real time. This data is rich with possibilities and can be used to personalize content and optimize offers at the exact second your customer needs them. You can then analyze the data to determine what works and what doesn’t and quickly make the necessary changes. The goal is to constantly improve your marketing techniques while engaging your customers in perpetually enriched, real-time, personalized experiences.

Take Action!
You don’t have to rely on data scientists or the information technology (IT) department to decode the clues your customers are leaving you. Data-driven marketing tools are available to allow you to use your data to its fullest potential. Invest in the right products and capabilities — use advanced analytics, targeting, and data-management tools to blow your customer away with an immersive, personalized, real-time experience. It’s easier than you think.

To learn more about decoding your customer’s clues, read the white paper: Customer Clues. Solve the Mystery of Your Audience.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/solve-mystery-audience-advanced-analytics-targeti...



The Blog Post below is from Raj Sen, Group Product Marketing Manager at Adobe.


Transform Your Business Into a High-Performance Machine with Data-Driven Marketing!

Imagine your company is a car. Your performance indicators — and the goals they support — are the big, beefy engine. But, the engine doesn’t run at its top performance without data, so you must get everyone in your organization on the same page. You need to share a clear vision of how data-driven marketing will enhance performance and make this sweet machine of yours hum. Let’s look at five ways your data-driven marketing can transform your business into a high-performance machine that blows away the competition.

1. Choose and Refine Goals and Key Performance Indicators (KPIs).
Your datasets are really only useful if they’re focused on your key metrics and the business goals they support — in other words, your engine. If you want to improve the power output of your car’s engine, you have to modify the ratio of air to fuel that’s going in, get it all to burn faster, and then dispose of the waste as efficiently as possible. The same is essentially true of your KPIs.

As you decide which goals you want to pursue, you can select the appropriate data you’ll need to support each goal. Then, use that data to move toward the completion of your goal and discard the stuff that doesn’t work. Manage intake. Use it effectively. Get rid of waste.

2. Use Your Data to Make Better Decisions More Quickly.
Decisions can make or break a business. Every day, thousands of decisions are made in your organization. Some are big, some are small — but all have impact. Put simply, data gives you the ability to make better decisions more quickly.

When you modify your car’s engine to improve its performance, you have to also make similar changes to the structure of the car — the transmission, the driveshaft, the powertrain — to support the added power. In your business, your ability to make decisions is the structure that provides you with the control you need to move your key metrics and achieve the goals they represent — and data enables this ability.

3. Create Real, Compelling Customer Experiences With Data-Driven Marketing.
Historically, the requirements necessary for actually pulling off customer-centric marketing were essentially nonexistent; but today, data-driven marketing can make the promise of customer experience a reality. Storage space and processing speeds were once serious roadblocks for all industries, but technology is doing more to empower customer experiences than simply clearing away legacy roadblocks.

Today, technology is answering questions that no one would have thought to ask several years ago. For example, more remarkable than the ability to store enough data is the ability to use that data selectively and with unheard-of precision. This precision is essential for creating the experiences your customers expect — because, at its heart, customer experience is about using the power and responsiveness of your organization to race more quickly and compellingly into the hearts of individual customers.

4. Focus Not Only on Tracking Spending, but Also ROI.
One of the crucial roles that data-driven marketing can play in your organization is to make sure you’re spending your money wisely. It’s vital to not only accurately track your spending, but also to actually earn a return on that spending.

When you’ve invested in building a serious muscle car, an important part of the equation is the control that comes from visibility. Data-driven marketing works like the clear windows and projected speedometers that improve visibility in a car. It allows you to make the most of your well-oiled business machine by helping you to see where you’re going now, decide where you want to go next, and understand your potential to get there quickly.

5. Trust Data-Driven Marketing to Be a Performance Boost!
It probably comes as a relief to many marketers that humans still have roles to play in the data game. But, automation and employees — data and people — are not independent of each other. They exist together in a mutual relationship, and if you’re doing data-driven marketing right; then data, automation, attribution, and every other technology or tactic should actually make those humans better.

Today, whole businesses thrive on nothing but dashboards to make data accessible to the lay worker. At the same time, analytics software is becoming increasingly more sophisticated in its ability to offer self-service and usable data.

This kind of universal interaction with data is at the heart of what makes data-driven marketing a performance boost for your business. If everyone in your organization is making intelligent, data-driven decisions, then you’ll get the most out of your machine.

If you’re looking to improve the performance of your business using data-driven marketing, you must keep your eyes open regarding what makes your engine run well. Data-driven marketing delivers real, tangible benefits. If you’re careful and thoughtful about how you implement it, it can measurably and demonstrably improve your business.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/transform-business-high-performance-machine-data-...


Level 4

Scatter Plots in Analysis Workspace

Last week, I wrote about how to use the new Venn Diagram visualization in Analysis Workspace. Now I will discuss another new Analysis Workspace visualization – the Scatter Plot. This visualization should be familiar to those in the field and has been available in Microsoft Excel for years. The purpose of the scatter plot is to show two (or three) data points on an x/y axis so that you can visualize the differences between them. In this post, I will continue using my blog as an example of how the scatter plot can be leveraged.


The first step in creating a scatter plot visualization is to create a freeform data table. This normally means adding a dimension and a few metrics. I would recommend starting with two metrics that you want to see plotted against each other. Here you can see that I am looking at my blog posts sorted by popularity and also added Visit Time Spent:


Read the rest of this post on analyticsdemystified.com



This guest post from Jennifer Yacenda of Starwood Hotels examines how both practitioners and managers can use dynamic tag management (DTM) in different ways.


Managing analytics nowadays continues to be an evolving job, similar to the very first day I started my career in this field as a college intern. At times, it’s overwhelming, but secretly (or not so secretly), I love it. Every day is a new day with new challenges, new tools to learn, new deadlines to meet, and new questions to answer. Our world is becoming increasingly digital, and that introduces a whole new set of challenges. Since the digital world moves so quickly with such a high volume of information, our time feels compressed and much more precious. Taking some time to think about how you invest your time is critical to success, especially in analytics. As a director of analytics, this key to success is relevant for both (1) setting the right implementation strategy and (2) focusing on the right datasets that drive the most impactful insights. We own the full cycle of analytics, and it’s more important than ever before to carve out time for thinking across the spectrum of our work.

The luxury of an overnight test, as described in “A Short Lesson in Perspective,” resonates with me as a digital analyst — it’s clear that analytics and the creative process go hand in hand. This overnight test, as described from an advertising executive’s perspective, starts with (1) a raw brain dump of ideas, scribbles, and inspiration; followed by (2) ideas that stand the test of time by marinating; (3) a morning follow-up session to revisit and filter ideas, eliminate losers, and highlight winners with the end goal of delivering (4) a finalized creative concept for a campaign. With analytics and marketing-technology implementations, the decisions to use a prop or an eVar or to fire it on page load or on the next page become parts of maps that litter my office walls and whiteboards to outline our customers’ journeys in the language of variables. DTM gives us the canvas on which to develop our vision, to revisit the approach when the time is right (after experimentation), and to deliver something that makes sense for our business. DTM allows us to take that necessary step back — like an artist takes a step back from the canvas — to see the bigger picture.

Just Like Art, Implementation and Tracking Requirements Are Subjective.
When thinking through the implementation process of a new tracking project, there are a number of steps in the works before it even comes to our queue. Our business’s digital, brand, and marketing teams leads are in various phases of their own planning processes for new functionalities, new microsites (featuring that new brand program), or new landing-page redesigns. At some point — often without a ton of advanced notice — we’re asked to be the judge and juror on success. A stakeholder may come to us with the simple request, “I need tracking” but not provide any guidance. We’ll often receive a set of wireframes or a development site where we’re expected to poke around and develop a tracking strategy. As artists and implementation experts, we’re asked to think through it all on our own. Tracking can be an elusive concept, and DTM has become a tool that truly helps us start the creative process where we draw up projects and develop the right information-picture over time. It provides flexibility that we have never had before.

Implementation and tracking strategists quietly weave the intricate patterns of measurement throughout our digital ecosystem, collecting key pieces of data to derive answers about our consumers. It takes a combination of thinking, philosophy, business acumen, and curiosity to understand 1. What the business is building (what’s success mean to them), 2. What the customer will experience (what actions will they take), and 3. What questions will analysts need to answer (what’s the data structure look like). We also need to know — or at least think through — whether our strategy is within budget depending on server call implications.

Artists Need Time to Do Their Best Work
Like advertising execs — whose timeframes for developing creative concepts have shrunk in the digital world — most analytics teams have rapidly growing numbers of requests that flow through the organization since “digital” also means “more measurable.” With advances in technology, time is that much more of a luxury. Allotting time to think is a challenge as more channels develop, consumers create more data, and more executives understand the value of data. Simultaneously, small analytics teams — those at the most basic levels — are staying the same size but also being asked to (1) make sure that data is collected in a meaningful way and (2) interpret the results, drawing out valuable insights for their organizations. How is an artist to work under these conditions?

Despite being forced into this world of doing things faster, as measurers leveraging DTM, it’s clear we found a way to slow down time and employ the overnight-test approach to our tracking strategy. At the same time, DTM gives us more flexibility and agility than ever before. I remember the days of being beholden to the hard dates of the information technology (IT) release schedule. There was a certain fear that we couldn’t get anything wrong, that we had to be spot on. I remember implementing Adobe Recommendations, missing a case-sensitive “I,” and missing out on 6 months of using a product. Those days are gone. DTM has allowed us to apply fixes without the wait.

Enter the heroics of DTM to save the day and help us add and change things so quickly. We’ve been able to save the day on more than one occasion with quick DTM updates. We’ve been able to add tracking to robust internal applications with little development work or ramp-up time from our different development teams. DTM has allowed us to make friends and build bridges in unexpected places within the organization. How do you put a value on that?

The implementation of marketing technologies is a balancing act, one that will become more difficult as we go.

DTM, as a Tool, Is Powerful — Adopting Tag Management as a Culture Is Difficult!
As an analyst, as a leader, and as a human being, I have the instinct and desire to say “Yes!” to all of the requests from business owners who are looking for ways to measure their products as well as their decisions. They’re looking for data to help make more informed decisions. With everything becoming more digital, answers are now more knowable (assuming the right talent is in place). Growing parts of all organizations are turning to analytics for the first time — looking to learn something from data, to make better decisions. As a realist, I know we do not have enough resources in place.

“DTM has allowed us to make friends and build bridges in unexpected places within the organization. How do you put a value on that?”

That being said, while DTM enables us to say YES to tracking more projects with its relative ease and simplicity, it also reminds us that we must LEARN to say NO at times. We simply don’t have enough analysts or server call budget to keep up with the requested amount of data we could capture. DTM allows us to work so quickly and to do so much in new places and new channels. But, the increasing requests make it more and more difficult to take a step back and truly gain a sense of the full picture. With a limited staff, how do we document everything? How do we understand or communicate our entire implementation? How do we keep track of all the variable maps? What is our backup plan?

We gain efficiency by tracking more across more channels and driving more value. Unfortunately, our increase in efficiency and effectiveness hasn’t translated to an increase in budget to hire more folks to manage and govern this valuable asset. And, we haven’t seen budget increases in respective IT teams or quality assurance (QA) to make sure analytics is included in all parts of the release cycles. We’re now working with more teams than ever before — mobile app teams, marketing teams, and internal application teams. We have more frequent release cycles to monitor in case something breaks or something new hasn’t been tracked. We have more last-minute requests. But, because we’re moving so quickly across so many teams, things can be lost in the shuffle without the proper resources and oversight to ensure documentation is in all the right places while still moving at scale speed.

Wrapping Up
There have always been challenges facing analytics teams. DTM has fundamentally changed the way analytics teams function — more flexible, faster, more platforms, and less reliance on IT as well as allowing us to do more, think on our feet, and paint better pictures about our customers. However, it does not solve all of our problems. It gives us time and flexibility to think through things in this crazy-paced world we are now living in. But, the culture still exists in which analytics teams are small and strapped for resources. We answer more and more requests while tracking more things, which makes it challenging to ensure our time and energy is focused on the right priorities. DTM is an important step in the right direction, but it’s just a single step in the overall process.

As we elevate our implementation techniques, we must be sure we are elevating all parts of our analytics practices and business cultures — we need to develop tools and define processes that also elevate our governance, documentation, and communication to the same level.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/dynamic-tag-management-helps-analytics-teams-beat...



The Blog Post below is from Chris Wareham, Director of Product Management for Adobe Analytics


Adobe Analytics Drives Superior Video Analytics with Video Stream Measurement

Digital video isn’t the next big thing: it’s already here. As people continue to consume content where they want, when they want (TV Everywhere, or TVE, video viewing grew 107 percent between 2015 and 2016), it’s clear that optimizing and analyzing this experience is a must for any publisher or programmer. And with one of the largest broadcasting conferences, IBC, around the corner, Adobe is thrilled to announce our newest video analytics release, providing new and improved measurement for both content and ads.

Measurement is one of the major friction points when it comes to television consumption across devices. While traditional television ratings are a longstanding institution, and the foundation for major decisions for everything from programming to ad buys, the digital transformation currently happening simply wouldn’t be possible without standardized metrics. As video measurement grows increasingly important for marketers to drive major decisions, the significance of these metrics comes into focus daily. The ability to obtain data such as time spent, ad performance, device, geography, bounce rates, impressions, and more is now a crucial piece of the pie.

Adobe Analytics for Video offers brands a variety of benefits, including:

Lighter, simpler implementation: Implementations are 50–60 percent faster than before.
Greater flexibility: The SDK is much more flexible with respect to how the API calls are triggered.
Streamlined configuration: Enjoy easier control as all API calls are centralized in one place.
Error state recovery: Adobe’s Video Heartbeat Library keeps track of the current state of the playback and can automatically ignore any errors that may pop up. For example, if a buffer complete is sent without a previous buffer start, this won’t be included in the report.
Adobe has a strong footprint in the video analytics space, working with 10 out of 10 of the world’s largest media companies, 4 out of 4 of the world’s top broadcasters, 5 out of 5 of the largest cable companies, and 4 out of 5 of the top digital news sites. With more than 100 billion videos measured in 2015, this new measurement model will help marketers more clearly understand video engagement, faster.

One of the biggest challenges broadcasters and programmers face is getting detailed insights into viewing patterns. In the past, brands would pick how many server calls to send in for playback (i.e., every minute, every quarter of the video, or at the halfway mark and ending). While this approach provided insights for many brands, ultimately it presented a problem due to the lack of granularity, and the decision was often based on cost.
Adobe’s video analytics model measures engagement (time spent) with “heartbeats” pinged every 10 seconds during a video playback and/or during a live event. The initial start server call is sent directly into Adobe Analytics; however, all heartbeats are sent to a new processing layer, which aggregates those heartbeats until the viewer ends the session (i.e., completes the video, closes the browser, switches to a new video, etc.). When the viewing session is complete, a second and final server call is sent to the Adobe Analytics platform to complete the playback data set. The 10-second heartbeat measurement eliminates the blind spot and offers a much more thorough view of how content is being consumed.

By analyzing video streams, rather than just starts and stops, brands can gain a more complete picture of how content is being consumed. Moving away from simply monitoring milestones based on server calls, streams not only provide brands standardization across video performance metrics (such as video, ads, segments, and quality of experience), but also provide deeper insights into media consumption. Adobe also provides the ability to democratize video analytics—such as sharing video metrics with syndication partners for greater transparency beyond simply a brand’s properties. Additionally, stream-based measurement is the foundation data required for Adobe Certified Metrics, the standardized digital census data that Nielsen Digital Content Ratings and Federated Analytics require.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/adobe-analytics-drive-superior-video-analytics-wi...


Level 8

4 huge time-saver tips for Analysis Workspace in Adobe Analytics

Nowadays, there are lots of Workspace tips and tricks available. Probably you have seen flashes of these in some Adobe videos or discussions, but want to anyway share these again, because these are so important tips and will save hours of your time in the long run of your analysis work.

1) Duplicate freeform tables
Without a doubt the best time-saver tip forever. I thought this was impossible and I wrote to Adobe’s idea exchange about this feature. Luckily, Eric from Adobe replied, it is already possible. Whaat? Could it be possible? Impossible is nothing for workspace?  You just right-click on the top of freeform table and it will give you option to “duplicate visualization”. Just click it and boom, you have duplicated freeform table.

Read rest of the tips and full blog post in here: http://www.anttikoski.fi/4-huge-time-saver-tips-for-analysis-workspace-in-adobe-analytics/


Level 8

Basics of pages, marketing channels and tracking codes in Adobe Analytics

Sometimes it’s good to go back to basics. I’m sure for analysts using Adobe Analytics this blog post might be bit boring, but maybe you should show this to your friendly marketing manager who might not be so famialiar about the basic concepts of Adobe Analytics. I could write a book about these basic features of Adobe Analytics because there are so many possibilities to do different kind of settings for your AA variables and marketing channels etc.

Adobe Analytics is powerful tool, but with superfeatures comes some complexity. Actually, I got the idea to write this post from Adam Greco’s “different flavors of success events” posts. You can read those in here (part1) and in here (part2). Hopefully more to come. Don’t you just love Adam’s posts? Those are always just pure diamonds and useful stuff you can use yourself.

I was thinking to test all those different event allocations too with my blog and make a small post based on the results. But you know me, when I get started with a small testing, I always get so many “ah, oh” moments and want to tell more about that and this too. I did a journey on my site and let’s see what kind of reports we get when looking marketing channels, tracking codes and page reports.

Read the rest of the blog post in here: http://www.anttikoski.fi/basics-of-pages-marketing-channels-and-tracking-codes-in-adobe-analytics/



Below blog post is from Ben Gaines, Senior Product Manger for Adobe Analytics


Visual Customer Insights with Adobe Analytics Activity Map

Growing up, I often enjoyed playing flight simulators on my family’s computer. My personal favorite was one that put the player in the cockpit of a powerful fighter jet called the F/A-18 Hornet. The Hornet was sleek and fast, and I loved to kick on the afterburners until my airplane disintegrated under the stress. But, my favorite part of the game was the simulated Head-Up Display (HUD). The HUD allowed you to find information about altitude, pitch, other airplanes, points of interest, and more — right on the windshield of the digital airplane. It was so much easier to process all of this information and have a productive flight with the HUD enabled.

Data Access and Interpretation on an Adult Level
That same, almost-effortless data interpretation — gained from not having to look away from what I’m doing to access it — that I enjoyed as a child applies to customer intelligence on the web as well. In April, Adobe Analyticsgained a brand-new approach to in-context analytics that allows marketers, designers, content owners, and others to view user-click and interaction data directly on the pages of their websites: introducing Activity Map. Activity Map is a browser plug-in/add-on/extension with support for all major desktop browsers. It runs right over the top of your website and makes your data come alive in vibrant color with visual overlays that draw the eye to areas of interest for your customers. For anyone interested in how users are interacting with content, links, A/B tests, and more, Activity Map provides you with insight right on the page and makes it much easier to come to an informed decision about how to optimize. It’s like having a grownup version of the HUD for your Adobe Analytics data.


Using a very simple example, let’s say that I use Activity Map on the Adobe.com homepage. (NOTE: the screenshot above is using fake data.) By putting this data right on the page, I can see exactly where people are clicking — as well as where they are not clicking. I might discover that links to the Community Forum are really popular, while links to another area of the site are not. Having this information enables me to give better placements to the links that people are actually clicking, moving them to the main body of the page. Better yet, I may decide to run a test to see whether that placement yields better results than the incumbent experience. An insight that pops off the page in Activity Map becomes an A/B test, which becomes a better experience for my customer.

Four More Reasons to Love Activity Map
Here are just a few of the reasons we’re so excited about the new Activity Map tool:

Different Customer Segments Respond to Different Content and Links.
Activity Map allows you to apply any segments from Adobe Analytics to your data in context. Maybe your loyal customers gravitate toward one type of content, while prospects prefer something completely different. Segmentation in Activity Map makes this distinction clear and helps you to provide the right experience to the right segment at the right time.

Better Metrics in Activity Map Produce Better Insights.
Of course, Activity Map still offers clicks as a metric, but you can do so much more by using any Adobe Analytics metric to understand the downstream impact of a particular user interaction on conversion. You could have links on your homepage that are clicked a lot but don’t actually produce any leads, revenue, subscriptions, or reader loyalty.

Live Mode Presents Users With Real-Time User Interactions.
Many of you told us that you wanted to be able to see user interactions in real time, so we added a “Live Mode” to Activity Map, which streams data collected from your site straight into Activity Map. If you’re a content publisher, you can not only see, but also immediately understand the most popular articles, videos, and photos over the past 15, 30, 60, or 120 minutes — with granularity as high as minute-by-minute — and then adjust headline stacks accordingly.

Integrated Pathing Helps Users Visualize Broader Customer Journeys.
We’ve integrated pathing data directly into Activity Map so that you can see not only what engaged people when they were on a given page, but also how they arrived at that page, and where they went immediately afterward. This adds to the on-page insights by painting a picture of the broader customer journey.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/visual-customer-insights-adobe-analytics-activity...



The Blog post below is from Jeff Allen, Sr. Director of Product Marketing for Adobe Analytics


New Adobe Analytics Capabilities Make Powerful Insights More Accessible

Behind every great digital experience is robust data. Each time a consumer launches an app or puts an item in a shopping cart, a piece of data is created. Brands that are able to gather all of this and make sense of it can engage their audiences in ways that are intuitive and highly personalized. As consumers become increasingly selective about how they spend their time across digital channels, data is the secret sauce to draw in new customers and retain existing ones.

The tools we provide in Adobe Analytics bring together Adobe’s creative heritage with a robust data platform, to not only help brands analyze data and uncover insights at the click of button, but to also become better storytellers in driving action across their organizations. With Analysis Workspace, users have a visual editing platform to deliver, discover, and visualize insights and curate them to share with the organization.

Today we are announcing new capabilities in Analysis Workspace that furthers our mission to democratize analytics.

Flow Exploration and Fallout Analysis

The first step to delivering great consumer experiences is having an understanding of how people are navigating your digital channels and where they are hitting stumbling blocks. A new Flow Exploration capability within Analysis Workspace let’s brands visualize customer movement through digital experiences, showing the steps taken from entry point through to conversion or churn. Segments of users can be created based on usage patterns. A cohort of customers who are bundling related items, for instance, can be engaged with content on other suggested pairings.


The new Fallout Analysis in Analysis Workspace allows brands to easily drag, drop, and rearrange steps along the user experience to better understand at what point users are disengaging, in addition to insight around where they go after the fallout. With this data available, brands can implement the necessary adjustments to not only retain users but also improve the experience and drive more loyalty. Analysts can also create audience segments from users who stay in the funnel, or those who fall out, at any stage for remarketing and personalization.


A home improvement retailer, for example, might see that, on their mobile app, users who have to leave the shopping cart to view more items abandon purchases at higher rates and don’t return. This insight can be leveraged to re-design the cart experience as a floating interface that remains on the app screen.

Getting A Faster Start

Organizations are increasingly pushing every team member to become more data-driven in their decision-making. Given that, tools must appeal to the most novice user while still serving the needs of the most advanced analysts. We all know how difficult it is to stare at a blank page, which is why we are introducing new starter projects in Analysis Workspace. These will provide a more natural ramp-up for those trying Analysis Workspace for the first time. With out-of-the-box answers to common business questions across the web and mobile apps, as well as specific templates for vertical industries including Retail and Media & Entertainment, no one should have to be a data scientist to leverage insights to improve their work.


Seeing The Forest for the Trees

In this release, we are rolling out a series of new visualizations, as well as enhancements within Analysis Workspace that will provide users more tools to curate and present insights in ways that resonate most with stakeholders.

Time-series analysis: Within line graphs, users can now forecast expected future outcomes or automatically detect data anomalies and run contribution analysis in a single click to determine the cause of unexpected performance. A financial services company could tap into this to determine the cause of a spike in credit card applications during a typically slower month, for example.


Histograms: In a classic bar chart, each bar shows an individual value. A histogram is different in that it presents distribution. This is useful for brands as they’ll be able to pinpoint the most valuable and least valuable segments of customers, as well as the distribution of a certain behavior. A clothing retailer, for instance, can identify that most shoppers spend between $25 and $50 and turn that into a key segment for holiday promotions.


Date comparisons: At the push of a button, users can now compare data year over year, month over month, or day over day. All of this is presented in a single graphic to visualize the change, while serving up data points around percent changes as well as raw values. This can better inform everything from inventory to campaign planning.



Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/new-adobe-analytics-capabilities-make-powerful-in...


Level 6

Analysis Workspace - The Future is Here

One of the great things about Analysis Workspace is that it begs you to keep driving deeper and deeper into analysis in ways that the traditional Adobe Analytics reports do not. I have heard Ben Gaines talk about this as one of the reasons he loves Workspace so much and he is spot on. Ever since it burst onto the scenes, those who understand Adobe Analytics have realized that it represented the future of the product. The only thing holding it back was the fact that some key types of reports were unavailable, forcing users to continue to use the traditional Adobe Analytics reports.

However, this all changed yesterday. I believe that October 20th will go down in history (at least the history of Adobe Analytics geeks like me) as the day the world changed! On this day, a host of great new Analysis Workspace visualizations were released. These include:

While this may not seem like such a big deal, let me tell you why it is a huge deal. I believe that these additions represent the tipping point in which Adobe Analytics end-users give in and decide that Analysis Workspace is their primary reporting interface. While I have seen some of my clients dive head first into Analysis Workspace, I have also seen many of my clients “dip their toe in the water” with Analysis Workspace, but fall back to their comfort zone of traditional reports. It is my contention that this will no longer be possible and that Analysis Workspace will become the default going forward. Of course, this will take some time to learn the new interface, but the advantages are so compelling at this point, that those not making the shift will risk becoming Adobe Analytics dinosaurs.

Read the rest of this blog post on analyticsdemystified.com



The Blog post below is from Jeff Allen, Sr. Director of Product Marketing for Adobe Analytics


The Message is Maturity: Adobe Is a Leader in Gartner’s Magic Quadrant for Digital Marketing Analytics

“Gartner analysts call digital marketing analytics the core of successful digital experience.” And having the right tool for the job — a differentiator that helps support customer journeys across multiple channels and devices — is everything. Leaders today are imploring their analytics teams to provide more robust, cross-channel insights to allow for better understandings of strategic objectives. And they’re looking for technology that can keep pace.

In the second-ever Magic Quadrant for Marketing Analytics, Gartner placed Adobe as the overall leader in the combined categories of ability to execute and completeness of vision. With comprehensive integration, advanced segmentation capabilities, and market-leading innovation, Adobe Analytics helps make complex data simple for delivering powerful marketing insight — and then, quickly turning that insight into action. More than being informed, Adobe Analytics provides seamless integration for immediate action across verticals, and the vision is now extending to customer intelligence. Here’s a look at the strength of Adobe Analytics — a best-in-class, cost-effective, practical solution for the retail, financial services, B2B, and travel and hospitality industries.

Retail: Providing Flexibility and Functionality for Critical Reporting and Insight Discovery
For online retailers, tying metrics to products is critical — so, shouldn’t reporting be as flexible as possible? Having the ability to view any report by product (and vice versa) is smart; but placing limits on this ability decreases analytics opportunities. Adobe Analytics makes it easy for online retailers to discover revenue-generating insights while helping customers find the products they need. Through robust product-category merchandising and product-finding methods, accurate pathing analysis, and advanced cross-sell reporting, retailers can better understand their customers and put the right products in the right places at the right times.

Financial Services: Measuring and Influencing Customer Journeys for a Competitive Edge
Advancing digital maturity across financial-services institutions means being able to not only see the full lifecycle of the account application process, but also understand which marketing campaigns led to approved applications so that promotional spend is targeted and optimized to customers with higher lifetime-value propensities. Adobe Analytics is the only tool that allows financial-services organizations to see the customer lifecycle and associated product affinities by customer groups, which helps optimize revenue via its industry-leading feature set and connects online behaviors to post-website revenues and profits. Additionally, Adobe Analytics enables users to study cohorts’ share-of-wallet trends from different time periods to indicate whether website and app promotions are growing or declining with regard to relevancy, so that you can work toward increasing product adoption.

Business to Business (B2B): Connecting Highly Complex Customer Journeys
B2B and tech companies achieve digital success by associating all metrics with customer engagement over time. The customer journey typically involves an entire buying team and is highly complex — never linear and rarely contained to just one channel — and analytical insights must be based on this reality. Adobe Analytics is the leading analytics vendor for discovering high-value audiences and creating segments to fuel customer-touching engagement activities. Adobe Analytics customers can easily break down reports by any number of variables, build and customize on the fly to streamline lead-generation activities, or discover hidden opportunities with anomaly detection and contribution analysis. Since most of the success for B2B companies takes place offline, Adobe Analytics can connect online leads to downstream sales and revenue, helping to optimize campaigns and prioritize customer outreach by sales team.

Travel and Hospitality: Maximizing Customer Experience Every Step of the Way
With an intense customer-experience focus, the travel and hospitality industry faces unique challenges. Maximizing capacity and upsells to reservations requires a clear understanding of key variables such as product-search merchandising, reservation reporting, advanced booking, cross-selling, cost and margin analysis, post-purchase analysis, and booking loyalty. It is critical that reservation reporting be as flexible as possible. The ability to create an unlimited number of persona-based segments, model them, and compare them via machine-learning innovations (such as Segment IQ) is unique to Adobe Analytics. Additionally, Adobe Analytics enables travel and hospitality companies to associate all their custom metrics with the reservation process and provides the ability to break down any report by reservation/product and vice versa. With Adobe Analytics, you can build or customize any report you want on the fly.

Future Plans: What Adobe Is Doing for Customer Intelligence
Adobe was one of the pioneers of web analytics and evolved the market from web to digital analytics to marketing analytics and is now focused on customer intelligence. The recently released Gartner Magic Quadrant for Digital Marketing Analytics 2016 confirms this legacy, positioning Adobe as a leader — in both execution and vision — with a comprehensive set of marketing tools for users of all skill levels. Paired with cross-channel enablement and advancements in our user interface, our dream of democratizing the power of data is coming true for our clients across many verticals.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/the-message-is-maturity-adobe-is-a-leader-in-gart...



Below blog post is from Ben Gaines, Senior Product Manger for Adobe Analytics


Empowering Organizations With Smarter Analysis

As we talk with people — from thousands of companies all over the world — about their efforts to drive growth through smarter, data-informed decisions, a few clear trends emerge. First, analysis tools must make it easier for everyone in the organization — from top to bottom — to obtain insights. Second, and relatedly, analysis tools must assist all data consumers with achieving more relevant insights by bridging the gap between Big Data and human intuition with machine learning and other “smart” capabilities.

I’m excited to be part of the team that is realizing the vision of these two themes. The Adobe Analytics Fall 2016 release, available now, includes a handful of new features and capabilities that will directly help you drive better decisions through “smarter analysis,” aided by powerful machine learning within Adobe Analytics. In the rest of this post, I’d like to walk you through what our team has done to make your analysis tools smarter.

There’s a lot of great stuff in here, so grab a coffee, and let’s get going. By the way, everything I share in this post is available today in Adobe Analytics.

Intelligent Alerts
You can now receive an early-stage “heads-up” that something truly significant is happening in your business. We have updated our built-in email- and SMS-alerting capability to use Anomaly Detection as the default mechanism for determining whether to notify you of a spike or dip in a key metric. Anomaly Detection looks at your historical data to determine an expected range of values for a metric, and now it also takes seasonality and major holidays (including Black Friday) into account. This means that you can expect no more false-positives in your alerts — if your traffic always decreases on the weekend, you won’t be alerted every single Saturday morning. Alerts that you set up based on Anomaly Detection will be triggered only when there is a statistically significant spike or dip.

Another huge improvement here is that you can “stack” alerts. Let’s say, you have six different metrics that a certain team is interested in. You can set up a single alert that includes all six metrics and set it to be sent to everyone in the given user group. This carries several advantages. First, you will only need to manage one alert instead of six. Second, if all six alerts happen to spike simultaneously, the recipients will only receive one email instead of six, meaning it cuts down on what we might call “alert fatigue.” Third, by sending to a user group, new employees who are added to that group will automatically begin receiving these alerts; similarly, employees who move out of that group won’t need to be manually removed from the alert.


You can set up alerts a few ways. You can always go to Components > Alerts to create a new alert or manage existing alerts. But, you can also right-click just about anywhere in Analysis Workspace to set up an alert based on the data point you have highlighted. That’s my preferred way, because it preconfigures the alert for you and allows you to stay in your workflow without leaving Analysis Workspace.

Take special note of the “Alert Preview” window in the upper-right corner of the Alert Builder. This view lets you know how many times the alert you’ve configured would have been triggered based on your recent data. By observing how often the alert would have triggered, you can adjust your anomaly thresholds or other criteria so you are not alerting your users too often.

Distribution of alerts has also been improved. The email you’ll receive looks a lot nicer, and you can send alerts by both email and true SMS. International telephone numbers are supported, and you’ll want to enter the country code as well (as shown in the screenshot above).

Automated Anomalies in Analysis Workspace
If you’re like most of us, you value anything that saves you time. This effect is probably multiplied in the world of data, where separating signal from noise can be difficult for even the most polished analyst. That’s why I am so excited about improvements to Anomaly Detection, as we bring this powerful machine-learning technology into Analysis Workspace.

Adobe Analytics has featured Anomaly Detection since late 2013. But to this point, it has only been available for daily data, and it has been confined to a report in Reports & Analytics. With this release, any hourly, daily, weekly, or monthly time-series data in Analysis Workspace — whether in a table or a line graph — will automaticallyshow anomalies based on your historical data and predicted trends.


Create a freeform table, using date as my dimension, and I immediately start receiving insights into statistically significant spikes or dips in my metrics. Add a line graph, and anomalies are called out as hollow data points on the graph. Hover over any of these points in the table or on the graph to gain more details about the anomaly.

As a bonus, if you are an Adobe Analytics Premium customer, you can right-click on these data points to run a Contribution Analysis, which scans hundreds of thousands of values of dimensions to find the likely causes of an anomaly — directly in Analysis Workspace, so you can embed the why along with the what in your projects.


In keeping with the idea of automated insights, there is nothing you need to do to turn this on or to activate this feature — just start building time-series data tables and visualizations, and away you go!

Averages (means) are good for quick snapshots, but as any analyst will tell you, they often hide tremendous insights behind their single-number façade. Histograms, which we’ve added as a visualization in Analysis Workspace, tease out those insights by showing the distribution of your audience across buckets (“bins”) representing escalating tiers within any metric. This makes it much easier to identify high- and low-value segments and market to them accordingly.

To use a histogram, just drag it from the collection of visualizations onto one of your panels. It will ask you for a metric, which you can also supply from the left. The histogram will bucket your visits or visitors according to how much/many of the selected metric they had. For example, if I use revenue as my metric, by default, it will bin my visitors by how much revenue they had. Using the advanced settings, I can adjust the number of bins, the size of each bin, and the starting bin (primarily, so I can choose whether to include visitors who had zero of the selected metric).


The distribution of revenue per visitor in my dataset is mostly normal; however, there is a bit of an uptick on the right side, so the group of customers who are spending $600–700 is larger than I might have expected. If I want to analyze that segment further, I can click the dot in the upper-left corner of the visualization and choose to show the data source. In the data table that is revealed, I can find this segment and begin to explore it directly in that data table. I can also drag it to the top of the panel to apply it as an ad-hoc segment, so I can add other data tables and visualizations that will help me better understand this group of customers who are behaving interestingly.

I’m sure you can see how we are advancing our goals to empower your organization with more recommendations powered by machine-learning intelligence and advanced analytics, but these features, and the benefits they provide, are just the start for us. We’re excited to continue our journey in helping you turn your company into a truly brilliant enterprise.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/adobe-analytics-fall-2016-release-empowering-orga...



Below blog post is from Ben Gaines, Senior Product Manger for Adobe Analytics


Making Insights Easier for the Enterprise

In my last post, I covered the aspects of the Adobe Analytics Fall 2016 release that make Adobe Analytics“smarter,” aiding your analysis with machine learning and intelligence. This is one of the most pressing needs we encounter, as we speak with people from large and small organizations all over the world.

In this post, I break down the features in our most recent release that make it easier for users of all experience levels to generate insights from within Analysis Workspace. And, as with my previous post, everything discussed here is available today in Adobe Analytics.

Customer Journey Analysis
One of the first pieces of feedback we received after we released Analysis Workspace was that you wanted more insight into the customer journey. You explained that you need to understand how your app users and site visitors are progressing through their digital experiences, so that you can remove barriers from the conversion process to optimize revenue-generating opportunities. I’m happy to report that we have added two customer-journey visualizations — Fallout (which includes Funnels) and Flow — to Analysis Workspace (the alliteration was entirely unplanned):

  1. Fallout — Fallout is designed to help you analyze each user’s progress through clearly defined paths (or funnels). When you know the exact set of steps that you want to analyze, Fallout is for you.
  2. Flow — Flow, on the other hand, is more exploratory, allowing you to expand and contract your view to examine different ‘branches’ and repeatedly ask, “But, what happened next?”

You can add both Fallout and Flow to any Analysis Workspace project from the left rail where you will find them in the visualizations area. After placing them on the canvas, you can begin adding touchpoints in the forms of dimensions, values, segments, and/or metrics.

One of the great improvements to pathing with this release is the ability to use Custom Conversion (eVar) values as touchpoints in addition to more traditional dimensions like Page, Site Section, Video Name, and more. The Fallout visualization also allows you to use metrics as touchpoints. Here’s an example using a combination of eVar values, page names, and metrics to create a funnel (with the right-click menu expanded as well).


You can also mix and match dimensions in the Flow visualization. Following is an example that shows entry pages along with the internal search keywords that followed from those entry pages. Not shown is the ability to hover over any of these branches to see them highlighted, calling attention to the path you’re investigating.


Included in these customer-journey visualizations are rich segmentation functionality. As you probably expect, you can apply segments to any of these visualizations. However, you can also right-click anywhere to build a new segment based on the path you have defined, so you can use that segment elsewhere to better understand the users who follow (or don’t follow) specific paths. One of my favorite features of the Fallout visualization is the ability to add multiple segments directly to the visualization — by dragging and dropping them into the header — to compare how different segments fell out of these funnels.

Entire blog posts could (and probably should!) be written on just these two new customer-journey visualizations, so I recommend playing around with them and trying out everything they can do. Remember that right-clicking reveals access to a bunch of additional options you can use to extend the power of these new tools even further.

Starter Projects
As mentioned above, roles in the organization that may have ignored data previously are finding that they can no longer do so. But, analysis can be difficult, overwhelming even! There are so many ways to define success — and so many options for visualizing and digesting customer insights — that it is sometimes difficult to know exactly where to start. Curation in Analysis Workspace helps solve this problem by limiting thousands of dimension, metric, and segment choices down to the few that matter to a given team, but it still relies on the analyst to create these projects.

Enter stage left, “Starter Projects” in Analysis Workspace. Starter Projects are easy-to-use templates that Adobe has designed and built around specific areas businesses question such as “Content Consumption,” “Mobile App Performance,” “Acquisition,” and more.



Any user of Analysis Workspace can launch a starter project and be presented with a standardized set of visualizations that address the topic selected. And, since they are in Analysis Workspace, they come with the ability to do additional breakdowns, comparisons, curation, sharing, and more.

If I were a product manager for a mobile app, I’d be very interested in understanding app retention — in fact, this might be one of the key areas on which I am focused. But, it would be difficult for me to understand app retention by building my own project from scratch. Instead, I can now come to Analysis Workspace and launch this handy Starter Project. If I need to do some customization — add a new visualization or apply segments, for instance — I can do that. I can even save my changes as a new project and share it with the rest of my team so that they can check out the progress we’re making toward our retention goals as well.


These Starter Projects are also pre-curated to the dimensions and metrics that are most relevant to the business question at hand to prevent users from being presented with too many customization options when they first launch one of these templates.

There are 15 Starter Projects to choose from today — with many more coming soon in these and other role-based categories. This is just the beginning of our efforts to help you expand the wise and simplified use of data in your organization.

Permissions Improvements
As you may have seen in a previous announcement, this release also includes major improvements to permissions in Adobe Analytics as well as across Adobe Marketing Cloud. Within Adobe Analytics, admin-level users will notice a handful of changes.

Perhaps most excitingly, you can now set permissions on Custom Conversion (eVar) dimensions. In the past, you could grant or restrict access to any conversion metric but not to these dimensions. This change gives you greater control over data access, ensuring that only the right people can see the data in your conversion dimensions.

This change also moves access to various Adobe Analytics tools — such as Ad Hoc Analysis and Report Builder — into custom groups so that you can manage 100 percent of permissions in one place rather than be required to jump back and forth between pages in the Admin Console to control access to these tools.

Ultimately, this update sets the stage for better user management in the future — all across Adobe Marketing Cloud (as well as Creative Cloud and Document Cloud) — through the Adobe Enterprise Dashboard.

Analysis Workspace User Interface (UI) Enhancements
Last, but certainly not least, we made a number of improvements to the core of Analysis Workspace itself. You’ll notice significantly better in-browser performance. Right-click menus, the Segment Builder, and other components of Analysis Workspace load much faster, as do breakdowns you perform.

We’ve refactored the Segment Builder and the Metric Builder so that they use the same left-hand rail as Analysis Workspace — with dimensions, metrics, and segments aligned vertically; and the ability to obtain search results that include all types of components that can be added to your segments and metrics. This not only makes it faster and easier to find the components you are looking for, but also makes segment and metric creation more efficient.

We redesigned date selection in Analysis Workspace to make it more like the calendar in Reports & Analytics. You can now select both the start and end dates from the same calendar rather than have separate “start date” and “end date” calendars.

And — perhaps my personal-favorite, sneaky-cool change to Analysis Workspace in this release — when selecting dimensions, metrics, segments, and time ranges from the left-hand rail, you can now click the “Actions” menu to tag, favorite, approve, share, and/or delete what you have selected. This makes it so much easier to manage your segments and metrics (among other things) while you’re working within Analysis Workspace. You no longer need to go over to the Segment Manager or Calculated Metric Manager.


We’re excited to continue delivering on the promise of Analysis Workspace by making it even easier for organizations to drive better decisions through data-informed insights. The Adobe team will continue working to empower you and your colleagues with simpler experiences throughout Adobe Analytics.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/adobe-analytics-fall-2016-release-making-insights...



Below blog post is from Nate Smith, Product Marketing Manager for Adobe Analytics


Bridging the Divide Between Digital and Non-Digital Marketing Engagements

We all have people in our lives to whom we only speak and interact with at work. They’re nice people — we enjoy working with them. For whatever reason, we just haven’t gotten to know them outside of the office. While we know some things about these people — how they structure their emails or whether they prefer iOS or Android devices, for instance — we don’t really know who they are or what they do outside of work. As a result, it’s unlikely that we’d be able to piece together complete profiles of these people, let alone predict the choices they would make in given situations. The only people you truly know are those with whom you interact in all facets of their lives.

To a large degree, the same is true regarding your customers. If you don’t understand the full spectrums of their interactions with your brand — online, offline, and through every possible channel — you don’t have complete pictures of their behaviors, traits, and preferences. As such, you can’t really know whether they’re satisfied with your brand, much less anticipate what would make their experiences with your brand even more positive.

Marrying Digital and Non-Digital Touchpoints
Digital marketing data often comes from a number of channels — email, social, web, and more. When you add non-digital data sources to that, understanding omnichannel customer engagement and campaign attribution can start to seem overwhelming. However, if you don’t have insight into things like real-life phone conversations customers had with call center-support teams or point-of-sale interactions they had with your store managers, you don’t really have complete pictures; which could impact the customer experience in unintended, possibly even negative ways — and ultimately, the bottom line.

For instance, let’s say a first-time customer purchases a pair of adult ballet shoes from your store. You may try to entice her to make additional purchases by sending her an offer for tights. But, that offer may miss the mark if you don’t know more about what she’s doing offline. Perhaps those shoes were a gift for someone else. Perhaps those shoes were for a new hobby she had hoped to start — until it stimulated an old injury. If you know she’s recently been searching your site for ankle braces, you can send her personalized offers about ankle support and rehabilitation, so your offers are as well-received as possible. If you know what is happening offline as well as online, it’s much easier to make sure your marketing efforts actually hit the mark.

Creating an Integrated Customer Profile
So, how do you bridge the gap between digital and non-digital marketing data? To do this, you need to capture and integrate several types of event and attribute data. First, though, you must make sure your digital house is in order — is your email integrated with your web analytics so that you can combine pre-click email-campaign data with post-click site-engagement data? Do you have your survey data connected to add qualitative dimensions to your customer profile analysis? As you become adept at new types of analysis, you can then add offline datasets to enrich the customer profile. Make sure to start small — integrate one new dataset and see what new insights are revealed. Common next steps can include:

1. Customer Attributes
The base of most customer profiles can’t be ignored. This data typically comes from your customer-relationship management (CRM) system (or some type of enterprise record-management system). This can also include demographic data (e.g., gender, age) as well as psychographic data (e.g., interests, hobbies) and is a key component in building an integrated customer profile. Start with this basic information and build from there.

2. Customer Engagement Data
Once you have customer-attribute data in hand, you should integrate customer-engagement data such as event-level campaign and activity data. If you are a B2B company, you’ll want to look at this data from an account view as well as individual views. To build a truly integrated customer profile, you need to include both online and offline customer interactions with your brand.

3. Non-Marketing Data
Customers can communicate with your organization in a number of ways — with more than just marketing touches. A common next set of data revolves around the customer-service part of your business, typically post-sale. Phone calls, texts, or chat conversations with customer service are important engagement points that must be part of the customer profile. A customer who is unhappy with his current purchase won’t want to be contacted by email with an offer for a complementary service. This data can provide you with a rich understanding of what your customer’s disposition really is.

Making the Connections You Need
Bridging the gap between digital and non-digital marketing data and marketing efforts is no longer an option. In today’s marketplace, customers expect personalized messaging and seamless brand experiences. It’s critical that you start creating an integrated customer profile to marry the information you receive from all marketing platforms and customer-engagement points. Learn more now about how to integrate digital and non-digital marketing data.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/bridging-divide-digital-non-digital-marketing-eng...



Below blog post is from Emily Chu, Senior Manager for the customer reference program at Adobe


Reliable Marketing Data Helps WestJet Boost Online Bookings and Achieve Greater Savings

In an industry where higher fares coupled with fewer services is becoming the norm, WestJet quickly became Canada’s second-largest airline, building the company’s reputation upon their ability to consistently deliver outstanding services and offer amazing savings. The company aims to delight passengers by providing them with relaxing, fun experiences that exceed their expectations — in the air, on the ground, and online. Those experiences include WestJet’s digital marketing campaigns.

In recent years, WestJet’s holiday video campaigns have achieved viral success, showing WestJet that creative and meaningful campaigns — based on targeted, reliable data — can guide marketing strategies to reach more travelers, drive more website traffic, and more positively impact the bottom line. To effectively deliver and measure better digital customer experiences, WestJet relies on Adobe Marketing Cloud solutions. For instance, Adobe Analytics allows WestJet’s marketers to gauge how customer experiences across multiple channels — including email, web, app, and social media — increase online bookings.

“Because we can see how successful our campaigns are, we know which ones are best at encouraging customers to book on our website instead of other channels,” says Ahmed Elemam, senior digital analyst at WestJet. “Increasing online bookings can help us achieve greater savings that we can also pass on to customers.”

Personalized customer experiences can increase those sales and savings. Adobe Audience Manager and Analytics work in tandem to power messages — such as fare upgrades or rewards-program benefits — sent to consumers after they’ve booked flights, and Adobe Target identifies a customer’s location to deliver relevant ads.

Using Adobe Media Optimizer alongside Analytics allows WestJet to review search-campaign performances daily to maximize search-engine marketing (SEM) investments. Besides reducing SEM support time by 40 percent, overall costs of weekend campaigns dropped 14 percent.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/reliable-marketing-data-helps-westjet-boost-onlin...



Below blog post is from Jon Viray, Product Marketing for the Adobe Marketing Cloud People and Activation core services


Top Four People-Based Marketing Use Cases

A few weeks ago, I flew to Houston, Texas. It was great–a short, comfortable flight. And, there were enough vacant seats to give our youngest her own. After an hour, I picked up the shopping magazine in the pouch in front of me, despite having never bought anything throughout decades of flying. I couldn’t help it.

After flipping through page after page of seemingly useful — yet, unconventional — products, I came to the same conclusion that I’ve come to for years: Not one product had a valuable use case for me. All of them were solving problems I didn’t have.

As marketers, we sometimes slip into this trap of spamming our consumers with products that solve problems they don’t have. Oftentimes, it’s not that we don’t have the right product, but rather, we don’t have the right context. A single-device understanding of consumers doesn’t provide enough context to deliver truly personalized offers, especially considering that the average consumer owns more than three devices.

People-based marketing solves this problem. People-based marketing means that you don’t treat a device as a person. Instead, you can link devices that are used by the same person and then treat that person as an individual across all their devices.

Top Four People-Based Marketing Use Cases
With the correct components in place, people-based marketing can have a major impact on everything marketing — from reporting and analysis to attribution and experience. In this post, we’ll walk through the top four use cases for people-based marketing.

1. People-Centric Reporting and Analysis
Historically, digital-marketing measurement was built on a foundation that was intended to understand people but designed to understand devices. When a device graph is integrated with an advanced analytics solution, the device graph can transform the context of reports from being device-centric to being people-centric. This means that — for the first time — marketers can understand how many people (rather than phones and tablets) visited their site, or how many people (rather than laptops and Apple watches) interacted with their brand across multiple domains, apps, or even a brand’s offsite advertising.

internal-image-1-top-four-people-based-marketing-use-casesTo illustrate this point, let’s imagine a marketer launches two campaigns. The first campaign reports $10 of revenue per visitor. The second campaign reports $20 of revenue per visitor.

Using a device-centric metric (like revenue per visitor) creates the impression that the second campaign, costs being equal, is more effective. However, using a people-centric metric to analyze these campaigns, the same marketer might see that the first campaign touched one person across three devices, making the revenue per person $30; compared to the second campaign that touched one person on one device, giving the campaign a $10-per-person revenue. From this insight, an analyst could build a compelling case for promoting the first campaign to hit their quarterly revenue target.

People-based marketing gives marketers insights on people — not devices.

2. Seamless Cross-Device Experiences
Personalization tools strive to deliver meaningful experiences, but these tools alone can only deliver the right experiences to familiar devices. What happens with unfamiliar devices your customers use to interact with your brand? What do they see then?

For example, if an avid reader made it halfway through a riveting article on her tablet, and then visited the same website from her phone to finish reading the article, what kind of experience would she have? She’d likely fumble over the keys to enter a search, scroll down the page, and meticulously comb through each line of copy until she found exactly where she left off — far from ideal.

A people-enabled personalization tool offers a drastically different story. The reader would get halfway through the article on one device, pick up another device that she has never visited the publisher’s website from, and still be brought to the exact article she was reading on the first device.

People-based marketing makes seamless, cross-device experiences possible — experiences that are continuous, consistent, and compelling.

3. Cross-Device Efficiency for Advertising
Traditional advertising platforms, just like analytics and personalization platforms, are susceptible to the same device-centric limitations.

For the display advertiser, keeping tabs on his ROI is a relentless top priority. A common way to assure the return on ad spend (RoAS) is maximized is to apply a frequency cap on the number of times someone sees the same ad. Unfortunately, frequency caps apply to devices — not people. So, a frequency cap of five impressions per person can quickly become 20 impressions per person if each person uses an average of four devices. This leads to wasted ad dollars and perturbed customers.

People-enabled advertising platforms apply frequency caps that span the various devices used by a person. A cap of five impressions means a cap of five impressions for a person. Using the previous example, a people-enabled advertising platform could have delivered a 75-percent higher ROI than the non-people-enabled advertising platform.

4. Holistic Attribution
Attribution is constantly evolving to include more touchpoints, more-accurate weighting, and machine learning. So, it’s ironic that one of the most important factors in understanding the impact of marketing on the buying behaviors of people has always been missing.

Traditional attribution algorithms only analyze pre-login information from a single device. And, considering that the average person owns three devices, it’s likely that a big part of the attribution story is missing.

For instance, if a consumer visited company A’s website from her laptop, was retargeted with a display ad, and then finally converted on the website; a linear-attribution model would yield something like this:

internal-image-top-four-people-based-marketing-use-casesBut, in reality, this same consumer also conducted a search on her phone, read an article from company A’s site, and then went back to her laptop to make the purchase. Using the same linear-attribution model that’s being fed information from a device graph would yield this:


After a month, the marketer sees that this same article is among the top-ten, most-valuable traffic sources according to the linear-attribution model. As a result, the marketing team surfaces the article’s dialogue on all product pages (which truncates the customer journey to three touches), decreases their search spend on the keywords that point to the article, and all-in-all delivers a better experience to consumers as well as a stronger bottom line for the business.

With holistic attribution, marketers can now prove the value of all their marketing within the context of people — not devices.

A Very Real Payoff
Marketing has always been about understanding consumers to the extent that our messages and offers deliver solutions for which they are willing to pay. But, it’s only recently that nailing these key ingredients alone won’t necessarily convince consumers to make purchases. Consumers want consistent, continuous, and compelling brand experiences. Device-centric marketing can’t deliver this — but people-based marketing can!

While I haven’t personally purchased anything from in-flight magazines (like SkyMall), they’re successful business endeavors, and their magazines have been my go-to source for in-flight entertainment for many years. With a captive audience of people looking for a distraction while their devices are stowed, SkyMall may not need people-based marketing. For the rest of us, people-based marketing is like setting your tray table and seat in the upright position — you simply have to do it.


Read the original blog post at - https://blogs.adobe.com/digitalmarketing/analytics/top-four-people-based-marketing-use-cases/