5 Reasons Marketers Need Transformation


Marketing Cloud

In September 2015, I attended the Adobe Digital Marketing Symposium in Japan where digital transformation was the theme. In his presentation, Dai Tamesue, Japan’s record holder in the 400-meter hurdles, made the valid point that transformation is essential for individuals as well as businesses. I would like to introduce how his story of transformation helps marketers transform themselves in the digital world.

Here are five reasons transformation is essential for marketers.

1. Having ultimate objectives hones the right skills

One of the transformation decisions that Tamesue made was to identify his objective. In an interview with the Japan Times, Tamesue described the great opportunity the hurdles offered because there were fewer competitors than in the 100-meter sprint. He decided to transform from a 100-meter racer to a 400-meter high hurdler to achieve his goal of becoming a world champion.

In a recent MIT Sloan survey of 1,559 executives and managers on digital transformation, 78 percent of respondents predicted that transformation would become crucial to their organizations within the next two years and 63 percent said that the pace of technology change in their organization was slow. In other words, transformation was needed, but it wasn’t coming fast enough. This delay in action is usually the result of seeing transformation as too daunting of a task.

The solution is to look at what you can transform personally. Are you sticking to your marketing tactics that were successful in the past? As Tamesue’s story demonstrates, it is important to have a clear vision of your objective or you risk changing your success factor and lose confidence after all.

After identifying what the race entailed, Tamesue broke the 400-meter hurdles into segments and focused on mastering each portion of the race.

2. An incremental approach brings clear rewards

As with most change initiatives, the first step is usually the most difficult. It is hard to change at first, especially when most of the marketing activities you’ve done in the past worked fine. But when looking at transformation as equal to a form of self-adjustment, it becomes easier to make the right moves, especially when the rewards can be seen. Once success appears on the horizon, those involved in the transformation become more engaged.

When Tamesue began his transformation to the 400-meter hurdles, he realized that in this race, first you sprint 45 meters, then you clear hurdles every 35 meters until you get to the last 40-meter stretch when you sprint to the finish. He said, “There are 10 hurdles in total and I’d set out by covering the distance between each in 13 strides. But as I tired during the race, and my strides got shorter, I’d switch to 14 until, nearing the end, I’d switch to 15 as I got even more tired. Some runners would rather be consistent and stick to 15 strides, or even 13, between hurdles.”

Tamesue knew that to not only compete, but also to win, he would have to approach the race from a scientific viewpoint. Once he realized his objectives and broke them down into workable tasks, he became more engaged in the transformation. Each new and better approach led to more ideas, creating a snowball effect of discovering ways to fine-tune his performance.

3. Adaptation becomes easier

Tamesue retired from professional hurdling at age 34, authored three books, became an investor, and is currently involved in real estate. He was able to transform from a 100-meter sprinter to an Olympic 400-meter hurdler, and then from an athlete to a businessman. He savored the condition he was in at his peak, but knew he would never be in the same condition again. His journey of transformation has enabled him to better adapt to his environment on a frequent basis.

The point is that once digital marketers commit to the idea of transformation, adaptation becomes easier. Successful marketers have developed the ability to adapt purposefully and continuously to their competitive environments. But lagging marketers who fail to notice the changes going on around them and adapting to them are forced to play catch up when it is probably too late.

4. Helps you avoid the past successes trap

You may remember the “boiling frog syndrome,” that is, if you put a frog in a pot of water and slowly bring the water to a boil, the frog will stay there and die. The same is true with our successes. It is easy to allow past successes to lead us to believe that we will always hit home runs. Of course that is not true, but any of us can get caught in that trap.

Just like BF Skinner’s Operant Conditioning with rats, which concluded that reinforced behaviors tend to be repeated, for Tamesue, remaining a 100-meter sprinter was a trap. “Many athlete friends said they didn’t understand my decision, or the idea I would switch to hurdling because I wasn’t successful at 100 meters. … And to withdraw is considered evil. However, I realized my limit as a 100-meter sprinter—though I still had emotional attachments to the race.”

Think twice if you are conditioned to stick with past successes. When we keep a finger on the pulse of our strengths, skills, weaknesses, and talents, and assess our performances, we should be able to avoid the trap and embrace transformation.

Matsumoto transformation (2)

The graph above shows that waiting until performance is already declining not only increases the magnitude of the required adjustment but also puts companies in a reactive position, causing them to miss opportunities for competitive advantage. Especially in the digital world, we may wallow in our cocoon of past success following a traditional marketing style and miss the boat with greater opportunities. Transformation prevents us from sitting on our laurels, enabling us to adapt to the ever-changing digital marketing world.

5. Leads to innovation

Transformation requires innovative thinking. Tamesue went through his transformation with many trial and errors. He said the process was like Zen practice in which the Chinese character “禅” consists of the symbols for “index” and “simple.” He focused on a single objective and found many ways to approach it. This led him to be innovative and grow. For us, transformation will create a disruptor of sorts in the marketing world.

Digital marketing is a field of constant change. Transforming from how things have always been done to a new and better way releases the creative juices, opening new avenues of marketing and can catapult you to areas where you can blaze your own path.

You Can Shake Things Up

Change is difficult, but if we as marketers don’t embrace change and adapt to it, we will fail. Sticking with a marketing strategy that doesn’t work is usually the result of losing sight of our end goals.

Tamesue’s experience convinced me we should be open to adapting to an ever-changing marketing environment. We can only do this by transforming how we think about marketing and how we think about our customers, products, and services. Although you may have vast experiences in the marketing world, the lessons from Tamesue show us that we need transformation will move forward to survive.

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Hey, Data-Driven Marketing, Ready for Machine Learning? It’s Ready for You.

Marketing Cloud

When we look at the current landscape for marketing professionals, it is easy to forget that many of the tasks we currently undertake did not exist 20 years ago. Marketers were not concerned with social media or SEO. The ever-increasing role of technology in marketing has made it tough to truly understand what a marketer’s role will look like in another decade.

One thing we know for sure is that machine learning will play a big part in how marketing changes over the next several years. Machine learning is already playing a part in data-driven marketing strategies, and we will see that role continue to expand over time.

What is Machine Learning?

Machine learning seems to have many connotations and is often associated with artificial intelligence (AI). As movies have taught us through the years, AI seems to imply that machines will one day replace humans. Fortunately for all of us, that is not the goal with machine learning. Instead, we are hoping to create systems that can solve problems for marketers more quickly than they could for themselves.

To do that, though, marketers still need to determine what the problems are, what the parameters are for solving them, and which datasets will be used. Machine learning can then optimize and refine possible solutions based on the data provided and patterns within that data. However, for any of this to be possible, a marketer still needs to first create the strategy for the program.

Perhaps, it is easier to convey this idea through an example. Imagine asking your phone a question. The phone is programmed to quickly find a solution for you based on a specific set of data from which it can pull; it cannot pull information from just anywhere. In addition, it cannot decide to use inappropriate language, tell you that your question is dumb, or refuse to answer. The machine is learning based on data provided, but it is not thinking for itself; marketers set the parameters for how they want customer interactions to play out.

How Does Machine Learning Impact Digital Marketing?

Over time, we expect to see machine learning travel less of an AI path and more of an intelligent assistant (IA) route. In its current state, machine learning does not have the ability to think for itself. We do not necessarily want it to do that; we would lose the beauty of creativity that humans bring to the table. What would be a beneficial improvement is to see machine learning progress from its current state — where we ask it to complete one very specific task, as in the phone example above — to a state in which it can learn to identify and predict our needs.

Again, perhaps an example would be helpful. If every time you ask your phone to find a restaurant for you, you immediately ask it to also request an Uber, IA solutions would learn this pattern, and eventually, when you ask for a restaurant, your phone will ask if you would also like to request an Uber. This is still based on a limited set of behaviors and data points. However, like any good assistant, a phone with true intelligent assistance will be capable of learning to give you exactly what you want — even before you ask for it.

So that leads us to the question, “how does this impact digital marketing?” In the marketing world, we are inundated with data. IA technology can help us to process what is statistically relevant in that data and why it occurred. In the very near future, we can program it to do this proactively. This type of advance in IA technology will allow digital marketers to be even more proactive when acting on real-time insights.

What is the Future for Machine Learning?

When it comes right down to it, machine learning is already here. So what do we expect to see in the near future? Machine learning will begin to be less of a one-to-one interaction in which you ask one question and get one specific answer. We will first see it evolve into learning your preferences and patterns, and from there, it will become more proactive. IA technology will recognize questions that you might not be asking but should be.

In the longer term, we will see machine learning make it possible to take into account external factors, such as the weather and competitive analysis, to understand both the micro and macro factors influencing or causing an event. We will be able to understand why customers chose us over a competitor on Black Friday — whether it was because our newsletter went out before theirs, our product was better, or our pricing was more attractive (or, hopefully, all three). These types of insights that include external factors give us access to even more insights that we have never before been able to understand.

Machine learning truly has the ability to revolutionize the way we go to market and how we understand our customers. Being able to implement this technology quickly and efficiently will put many companies at the head of the pack. Getting your data and marketing strategy in line now will allow you to be on the edge of the machine-learning curve.

The post Hey, Data-Driven Marketing, Ready for Machine Learning? It’s Ready for You. appeared first on Digital Marketing Blog by Adobe.

Smart Brands Do Mobile Messaging Right – What They Do and How

Marketing Cloud

Each New Year brings a barrage of new and exciting trends destined to shake the foundation of digital marketing. This year, mobile surpassed desktop Internet use with people spending 54 percent of their digital time on mobile apps. Mobile is indeed everywhere, and brands are looking for new strategies that speak to the mobile audience while simultaneously enhancing customer experience and meeting business objectives.

Mobile messaging is emerging as an important marketing tool for reaching consumers directly in a “mobile first” economy. Luckily, there’s plenty to learn from those who are doing it right. Here’s a glimpse into what smart brands are doing with mobile messaging and a few key principles that can help drive business results.

Messaging Use Cases – Brands Who Are Doing It Well

Mobile messaging is proving valuable for brands looking for specific outcomes to use cases associated with mobile applications without having to engage a development team or roll out a new version of an app. In fact, from a marketing standpoint, many issues can be addressed simply by using analytics data as a guide. The following examples from a major insurance company and a national hotel chain highlight opportunities with mobile messaging and how innovative brands employ mobile messaging effectively.

Improving App Ratings

When we think about the opportunities with mobile messaging, one of the most obvious advantages for brands is the ability to improve certain aspects of the app experience. For example, one insurance company was concerned about a poor rating that one of its apps was generating at the app store. In talking through possible steps to resolve the problem, the conversation immediately turned to in-app messaging. How could they entice satisfied customers to leave a positive review?

They began by identifying the key activity within the app that the typical customer enjoys the most. They then used that information to target the precise moment – that point where the customer was overjoyed – to ask for a rating.

With this in mind, this insurance company rolled out a new feature that allowed users to pay their bill directly from the mobile app, eliminating time and energy logging into the website to manage an account. The idea was to give customers the chance to experience and enjoy the in-app payment feature and then ask for a review immediately after a successfully processed payment. Within a few days, they received over 40 positive reviews, increasing the app’s star rating from a 2 to a 4. The overall impact was fantastic – not only did the rating improve, but ranking within the app store also improved, resulting in increased downloads.

Resolving A Mobile Key Issue

An international hotel group was able to use mobile messaging to effectively address an issue guests were having with mobile hotel room keys not working properly. The hotel collected data on the number of failures as well as the room numbers where they occurred the most. Still, this didn’t address the specific problem of guests unable to get into their rooms.

The mobile key solution only worked with Bluetooth technology. The hotel realized that customer data could show whether the technology was turned on preceding a failed attempt to use the key. So, sending a message to guests informing them that the mobile key only worked with Bluetooth technology was extremely valuable. But then, going a step further, they could also provide information on how to turn the technology on using certain phones, enhancing customer experience in tremendous ways.

The hotel concluded that if it could detect a specific model phone, an iPhone 6 for example, it could illustrate how to hold the phone in front of the hotel door to engage the mobile app. Countless use cases were identified, from help turning on Bluetooth to describing how to hold various models of mobile phones for the best response – even contacting a help desk after multiple failed attempts.

For this company, in-app messaging offered a smart, practical way to enhance customer experience by sending a helpful personalized message. Ultimately, they realized it could avoid spending months in development creating customized detectors for each device when the entire process could be completed, literally, in one afternoon.

Key Principles for Mobile Messaging Success

Timing is everything. With mobile messaging, timing really is essential. For instance, if you’re looking to boost an app’s rating, identifying the exact moment when the customer is happiest using it is the key to asking for a positive review – and getting it. When is the right time? It could be every time the app launches, but it could also be every time a new version of the app addresses a problem an older version cannot. When users enter the “help” portion of an app, it can be assumed that there’s an issue that needs to be resolved. That’s a good time to reach out to let them know about a new version and how it may be better equipped to meet their needs.

Offer valuable information that enhances customer experience. Brands are starting to use mobile messaging to disseminate information that improves the overall experience. Alerting users to advanced functionality in an app or providing important “how-to” information can help users navigate an app easier. For example, as part of a new acquisition, the above mention hotel decided to offer free Wi-Fi to customers but were unsure how to go about communicating this new service. Since its mobile user base was most likely to use the app, the decision was made to send out a simple message: “Wi-fi is free, please enjoy.” It was a perfect example of how in-app messaging can inform a specific use case without having to engage multiple teams or take weeks to implement.

Glean insight that you may not see otherwise Some brands have used mobile messaging to identify and remedy missteps with the app design process. For example, one brand discovered issues with app functionality after raising awareness and watching use increase. In this case, messaging helped highlight the fact that functionality was buried in a menu and should be made more prominent.

Mobile consumer engagement is quickly becoming a defining feature of the “mobile first” economy. Brands looking to evolve need to embrace messaging as a preferred channel for reaching customers with information that can enhance the experience in real time.

The post Smart Brands Do Mobile Messaging Right – What They Do and How appeared first on Digital Marketing Blog by Adobe.

5 Attributes Every Cross-Channel Marketing Program Must Have

Marketing Cloud

For many, cross-channel marketing seems like an unobtainable vision or a perpetual five-year plan. But marketing across myriad channels is only as complicated as you make it. Here are my top five recommendations for creating an effective cross-channel marketing program.

1. Choose Your Channels

You don’t have to jump into every channel at once. Instead, build your way into different channels as they become useful to your campaigns. Start with what you know. What channels are you using right now? What about them works? Consider the channels you’re interested in trying and ask how they can complement the ones you are already using.

2. Stay on Message

Messaging is extremely important. The channels you deploy in your cross-channel practices are only as good as your message. Sometimes when we talk about cross-channel, we talk about it in a channel-agnostic way. Since your customers think in terms of a single brand, it shouldn’t really matter what channel you’re using as long as the message is consistent and relevant.

3. Look at the Data

Data should be at the center of any cross-channel initiative. Customer data sharpens your messaging. If you see a certain email isn’t getting opened while another is getting good engagement, you should be able to easily compare the results and use those learnings right away.

4. Start Internally

Cross-channel marketing starts with setting internal processes and objectives. There has to be an understanding of who owns certain data and what needs to happen to complete what you don’t have. Massaging out any kinks in your data management, sales, marketing and other functions before setting out on your cross-channel campaigns will save you aches down the line.

5. Take Risks

Your cross-channel marketing team has to have at least a minor appetite for exploration, particularly when trying to create a truly integrated environment. If you’re going to be cross-channel, you’ll have to be “cross-organizational” (some prefer the term “cross-functional”). Marketing will have to work with sales, sales will have to relay feedback to product development, and so on. This kind of organization won’t be without growing pains, but learn from your mistakes and keep at it.

Your cross-channel marketing practices will be considered successful if they bring in sales, yes, but you’ll find other benefits to taking on this challenge in a measured, practical way. Choose your channels wisely, look closely at the data, and be willing to take some risks. Your marketing will be better for it.

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When Your Attribution Model Becomes a Bible: How to Avoid Attribution Holy Wars

Marketing Cloud

When brands first became serious about marketing, they had no way to tie it to real results. The Don Drapers of the world convinced companies that they had to advertise to build their brands. Yet, when they were asked to prove that their advertising tactics were effective, companies had to rely on Don’s smooth charm to convince them that the ads were providing results. This ultimately meant that, when it came time to cut the budget, marketing was always one of the first areas to be slashed. As much as we like the Dons of the world, it is truly hard to believe something without any data to back it up.

Marketing attribution changed all that by allowing marketers to directly tie their efforts to referrals, website traffic, search results, leads and sales conversions. It gave marketers the insights needed to understand what worked and what did not. Best of all, it gave them the ability to prove their worth when it was time for budget cuts.

However, the problem is that the attribution models currently used by many practitioners often do not tell the whole story. To truly understand customers’ journeys, you need to understand all of their actions, including how they behaved across devices. Instead, many companies focus only on the last thing the customer touched before making a purchase. This isn’t wrong, it’s just missing the bigger picture. If a customer sees a Facebook ad and then clicks through to buy your product, it does not necessarily mean the Facebook ad was the only thing that influenced that purchase. As a result, it is important to build an attribution model that tracks all of your marketing initiatives to glean insights into how they impact the business overall. So how do you create an attribution model that takes the entire customer journey into account?

Cultural Challenges Regarding Your Attribution Model

In the current corporate culture, there are a few reasons why implementing new attribution models may cause friction. First, there are any number of methodologies to create your attribution model. Last Touch, First Click, U-Shape, Starter, Player, and Closer are a few of the more common rules-based methods marketers use. Second, many organizations have used the same model for years without giving thought to change. Lastly, the resources to implement a new model may not be available. These reasons can sometimes lead to in-fighting when someone new tries to come in and shake things up with a different way of attributing marketing effectiveness.

In fact, when your team members come to rely on one particular attribution model as their bible, it can lead to a holy war of sorts. Team members will justify all sorts of actions to defend their particular “holy model.” It is extremely difficult to bring all of your team members to believe in one true attribution model. Unity may only come after a long, drawn-out battle and many conversions.

Even if you finally establish one methodology that all team members can agree on and use consistently, you still have other teams within the firm who may be using other methodologies. What it leads to is inconsistent modeling, and ultimately, no single source of data on which to base decisions regarding marketing budgets.

Avoiding Confirmation Bias in Attribution Insights

Every person on every team wants to use the attribution model that positions them in the most favorable light. This is part of what causes the conflict discussed above. But even more than that, when choosing a model to use, many people fall prey to confirmation bias. Naturally, we want to use the model that shows us what we already believe. Even if you are not on the social team, but you believe Twitter is great for building brand recognition, chances are you will choose the attribution model that proves your existing bias to be correct.

This tendency to choose an attribution model based on confirmation bias is precisely why Gary Angel argues that data science is not a science at all. More often than not, people are naturally subjective when choosing an attribution model. Not only does this lead to significant corporate in-fighting, but also takes a tool that is meant to be objective and turns it into something inherently biased.

Is Algorithmic the Holy Grail?

So, great. Attribution models could solve all our problems, if only we could get past ourselves to let them. If only there were another way — luckily, there is.

Attribution models have now turned to algorithmic modeling to examine millions of customer touchpoints. This allows marketers to view actions across platforms as well as process the overwhelming amount of data that is included in Big Data. We can tackle these huge amounts of data while simultaneously getting rid of the bias.

Algorithmic models look at both what was successful and what was unsuccessful. However, since all journeys are different and can include things like word of mouth that are difficult to measure even in the best attribution models, some data is still left to interpretation. For example, if we are examining display, we may see that one display ad was in 50 percent of the successful journeys, while another display ad was in 30 percent of the journeys, and still another was only in 10 percent of the journeys. Suddenly, if you are just looking at display, it seems clear which seems to be the most effective.

Algorithmic models can go a step further, though, and help understand which display ads work best with which audiences. In doing so, this attribution model begins to align with customer analytics. Ultimately, this type of modeling can help to understand which things are working and with whom. As a result, we can better understand how best to utilize our marketing budget and what impact every team member’s hard work has.

There is no 100% Correct Answer, But Give Peace a Chance

To ensure that choosing an attribution model does not devolve into a holy war of sorts, it is important to remember that no single model is perfect. The goal is to look at various elements to understand how effective each one is and why. Utilizing attribution models should be an objective process rather than one that is ruled by ego and a personal belief system.

Is algorithmic attribution the one true answer? No. Algorithmic attribution is a step in the right direction, but it is not for every organization. As outlined above, there can be many factors working against you from trying to implement it. The key is to make sure that everyone has an open mind and remembers that you are working toward a common goal. Run tests and compare different models to actually spot the differences in them so that you can learn from it. No one attribution model should become your bible.

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Measuring TV Audiences with Science


Marketing Cloud

Measuring TV Programming and Commercials Across Screens

Multiscreen metrics have become increasingly important for TV programmers and distributors to examine due to the fact that consumers are increasingly dividing their attention between traditional TV and streaming video options across smartphones, PCs, tablets, and TV connected devices. This paper outlines viewing trends based on the latest research and provides a framework for companies to measure viewing of their own content across screens. The framework encourages companies to consistently gather multiscreen insights on an ongoing basis in order to have a fail-safe approach to measuring TV programming and commercials across screens.

Viewing Time is Going Up on Connected Devices 

Before Apple TV and Roku brought premium video to the living room in a big way, viewing time on connected devices was a miniscule sliver of the overall viewing pie. By 2014, this sliver grew to command a double digit percentage share of viewing time.

In 2015, the share of viewing time on connected devices increased further. For an average week in July 2015, 14.4% of viewing time among all adults (18+) happened on connected devices. In addition, an even greater 32.3% of viewing time among young adults (18-34) happened on connected devices.

Not surprisingly, viewing time on TV connected devices makes up the bulk of the digital consumption trend. 9.6% of viewing time among all adults (18+), and 22.8% of viewing time among young adults (18-34), happened on a TV connected device.

14.4% of Viewing Time Among Adults 18+ Took Place on a Connected Device in July 2015 

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Source: Data compiled from page 9 of Nielsen’s “Comparable Metrics, Q3 2015″ report, published on January 6, 2016. 

32.3% of Viewing Time Among Young Adults 18-34 Took Place on a Connected Device in July 2015

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Source: Data compiled from page 11 of Nielsen’s “Comparable Metrics, Q3 2015″ report, published on January 6, 2016. 

Indications of Continued Growth in Digital Viewing

All signs indicate that viewership on connected devices will be the engine for growth in overall television programming consumption. For example, the number of pay-TV households using TV Everywhere is up by 7% YoY to approximately 13.7 million subscribers while the number of pay-TV households not using TV Everywhere is down by 1.7% YoY to approximately 88.5 million subscribers.

7% Growth in Pay-TV Households Using TV Everywhere; 1.7% Decline in Pay-TV Households Not Using TV Everywhere 

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Source: Estimates based on data from “Digital Video Benchmark: Adobe Digital Index Q3 2015″ published by Adobe in December 2015 and from “US Pay and Non-Pay-TV Households, by Type, 2014-2019″ published by eMarketer in December 2015. 

Household data also shows a 6.7% YoY growth in households without pay TV. However, these aren’t necessarily households that won’t pay for television programming. Parks Associates reports that 8.4 million households subscribe to broadband internet and at least one over-the-top (OTT) video service, but do not subscribe to pay TV. That leaves approximately 12.4 million households that don’t pay for programming at all. This non-paying group may still consume free, ad-supported video services such as YouTube or Sony Crackle.

Impact of Digital Viewing on TV Advertising 

Video advertising that is placed in full-length, TV-quality programming and delivered over broadband is going to be huge. TDG Research calls this form of advertising “OTT TV advertising” and says it will account for approximately $40 billion in spend by 2020 in the U.S., which is just under half of 2020’s projected $85 billion in total TV ad revenue. If there’s one stat that highlights the need to measure TV programming and commercials across screens, it’s this one. For the average TV programmer or distributor, this means that half of TV ad revenue will come from viewing activity across smartphones, PCs, tablets, and TV connected devices.

A Framework for Measurement 

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TV programmers and distributors need a measurement framework that covers three key areas: video analytics, audience analytics and comparable metrics.

In the first key area, video analytics provides insights into video consumption for TV programmers and distributors. It can be used to answer very specific questions about what’s happening with video consumption, when it’s happening, why it’s happening and where it’s happening.

In the second key area, audience analytics provides insights into viewing audiences for TV programmers and distributors. It can be used to answer very specific questions about how different viewing segments are engaging with video programming.

In the third key area, comparable metrics provides a baseline for what media buyers need in order to figure out what TV advertising to buy and how much it should cost. This baseline may include reach, frequency and duration metrics. The more these metrics are universal, the better they can serve the industry. They tend to help the most when they come from an audited, accredited source such as Nielsen or Comscore.

Let’s explore each of these three key areas more closely.

Video Analytics 

Census-based video analytics can be used to measure video initiates, video time spent, completion rates, average time on video, ad starts, ad time spent, average ads per video, time spent on ads, and more. It can also measure quality of experience (QoE) data concerning bitrates, errors, buffering, and so on.

Video analytics makes this data available for deep analysis by teams across an organization, such as video ops or sales teams. For example, ad ops teams can use QoE data to optimize for high quality experiences or sales teams can add engagement metrics to their pitch.

What’s even more powerful about video analytics is the ability to combine it with other data, such as audience analytics.

Audience Analytics 

Audience analytics is another area where census-based measurement adds significant value.

It allows TV programmers and distributors to evaluate audiences from a multitude of angles, to segment audiences, and to take different actions with different segments. A TV programmer might package and sell one segment very differently than others. Or, it may communicate and market itself to one segment very differently than others. These actions all become possible when TV programmers have census-based audience analytics.

In fact, data is creating entirely new ways to sell TV advertising in ways that resonate with advertisers. All methods of data-driven targeting are becoming available in OTT TV advertising, such as retargeting, affinity targeting, behavioral targeting, location-based targeting, look-alike modeling and more. The targeting is really only limited by the data that’s used to define the target.

It’s very useful to be able to consider audience traits against other types of data. For example, companies like Rentrak and ListenFirst have innovative benchmarking data that can help media sellers tell a compelling story about the value of their inventory.

Rentrak’s Stickiness IndexTM provides a way to compare how committed an audience is to one linear program versus all other programs. It does this by comparing the average percentage viewed of each telecast to that of all telecasts. A score of 100 indicates typical levels of commitment to a program; a score above 100 indicates above average levels of commitment; and a score below 100 indicates below average levels of commitment.

ListenFirst’s Digital Audience RatingsTM for TV (DAR-TV) provides a way to compare the volume of online activity generated by one program versus the volume generated by other programs, on a daily basis. It looks across six distinct digital channels — Facebook, Instagram, Tumblr, YouTube, Google+, and Wikipedia — and aggregates the daily volume of engagements for

shows from over 80 programmers and for all OTT originals. DAR-TV rolls all this activity up into one number that represents the volume of online activity for a show.

TV programmers and distributors may find a sales sweet spot by layering audience data over benchmarks like the Stickiness IndexTM and DAR-TV. For example, an advertiser may be willing to pay a premium for a Millennial audience across shows with high stickiness (as determined by Rentrak’s Stickiness IndexTM) that also spark a lot of online engagement (as determined by ListenFirst’s DAR-TV). The pieces are rapidly falling in place for TV programmers and distributors to do this.

Clearly, the data used to analyze TV programming and commercials can come from many informative and influential sources. At least some of these sources should approach universal acceptance. That’s where the third key area of the measurement framework comes in. It calls for comparable metrics that make it easy to compare and contrast opportunities across screens.

Comparable Metrics 

Nielsen’s at the forefront of providing comparable metrics. In December 2015, it began rolling out a highly-anticipated measurement tool called Total Audience Measurement. Users of this solution will be able to compare and contrast reach, frequency and duration metrics for traditional TV, DVR, video-on-demand (VOD), connected TV, computer, and mobile devices in one place.

Total Audience Measurement will produce insights at the programmer- and distributor-level that Nielsen’s recent “Comparable Metrics: Q3 2015″ report has done at the industry level. It answers three key questions. First, how many people engage with TV programming? Second, how often do people engage with TV programming? Third, how long do people spend with TV programming?

How Many People Engage with TV Programming? 

To measure how many people engage with TV programming is to measure reach. Reach accounts for the number of people that watch in a given period of time. Figure 1 shows that 85.0% of the adult population can be reached with TV, 40.3% with TV connected devices, 39.7% with PC video, 31.7% with smartphone video, and 14.9% with tablet video. These numbers are important because they provide an upper end ballpark for how many people per week could watch any given show on each screen.

Weekly Reach by Screen Among U.S. Adults 18+

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Source: Data compiled from page 9 of Nielsen’s “Comparable Metrics, Q3 2015″ report, published on January 6, 2016. 

How Often Do People Engage with TV Programming? 

To measure how often people engage with TV programming is to measure frequency. Frequency is an important thing to measure because it’s a core component of time spent. If a TV programmer wants to increase time spent metrics among its viewers, it either needs to increase the average frequency of viewing or increase the average duration of viewing.

One specific way to measure frequency is to study the average days per week with TV or video viewing usage. The figure below shows that U.S. adults watch the TV screen more frequently than any other screen.

Average Days per Week with Usage by Screen Among U.S. Adults 18+ 

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Source: Data compiled from page 9 of Nielsen’s “Comparable Metrics, Q3 2015″ report, published on January 6, 2016.

How Long Do People Spend with TV Programming? 

To measure how long people spend with TV programming is to measure duration. Measuring duration is important for all the same reasons that it’s important to measure frequency. It’s a critical component of time spent metrics. If millions of people watch TV programming for very short durations of time, then time spent metrics will be very low. Viewers need to be engaged and sticking around to watch for hours, not just minutes.

In the U.S., time spent viewing TV is still larger than time spent with all other video viewing vehicles combined. According to Nielsen, TV delivered 458 billion minutes of viewing during an average week in July 2015 versus 77 billion minutes of viewing across all connected devices during the same period. However, growth in device ownership and viewing behaviors on connected devices may quickly change the viewing landscape.

Weekly Minutes of Viewing by Device Among Adult Users Versus Adult Population 

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Source: Data compiled from page 9 of Nielsen’s “Comparable Metrics, Q3 2015″ report, published on January 6, 2016. 

Another reason duration metrics are so important is that people only have about 16 waking hours a day to spend doing things. For the most part, audiences have maxed out the amount of time they have to spend with viewing programming. The pie for any given individual’s viewing hours is fixed. As a result, viewing duration is a highly competitive measure. When audiences have only so many hours to spend with TV programming, TV programmers and distributors must compete for attention.

Using Data to Plan for the Future 

For a fail-safe approach to measuring TV programming and commercials across screens, TV programmers and distributors need access to all the key areas of measurement insights: video analytics, audience analytics and comparable metrics. Video and audience analytics can be used to answer just about any question that a business may have about its own video consumption and viewing audiences. Then, comparable metrics helps TV programmers and distributors give media buyers what they need to make media buying decisions.

Get Video and Audience Analytics with Adobe Products 

The Adobe Marketing Cloud makes it easy for TV programmers and distributors to extract insights by automatically collecting and surfacing the data that’s essential to a successful multiscreen programming strategy. Data within the Adobe Marketing Cloud is fully actionable. If a TV programmer or distributor wants to use the data to improve its cross-channel marketing execution, optimize the viewer experience or improve its sales strategy, it can.

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Five Killer Strategies for Connecting Your DMP to Programmatic Media

Marketing Cloud

Your data is worthless.

Perhaps that is an overstatement, but there is some truth to it. Unless you connect your data management platform (DMP) to a demand-side platform (DSP) or other programmatic media, you are not realizing the full potential of your data or your programmatic media and DSPs.

Learn to Walk Before You Run

The following five strategies will help you capitalize on your DMP by connecting it to programmatic media and DSPs. However, it is worth noting that taking a crawl-walk-run approach is always ideal when it comes to integrating these tools.

The first step is to ingest and organize your data within the DMP. Many organizations stop there, but this is really just the crawling stage of the process. Once you have your data collected and organized, you must act on it.

The walking stage is where things really come to life and where you start pushing those audience profiles out to the actual targeting platforms that are the endpoints of the conversations with the consumers.

The final stage of this growth is the running stage. This is where you combine the data set from your DSP, the first-party data set from your DMP, and any other analytics you may be capturing (with tools such as Adobe Analytics) to make real-time media-buying decisions. Now, let’s move on to five strategies and examples for connecting your DMP to programmatic media.

  1. Start Simple

Back to this idea of crawl-walk-run, I think it is important to start simple and get more sophisticated with your data and data application over time. Start with building traits in your DMP that are based on your first-party site behavior. Then, you can graduate to onboarding other data and data sources — such as your customer-relationship management (CRM) into your DMP like Adobe Audience Manager — in an ongoing fashion. Keep stoking the fire of your DMP, but be sure to start with building confidence by identifying a few use cases from which you can gain some key insights. Then, for targeting purposes, you can push data out onto the spokes of your marketing wheel and ingest data back in to enhance user profiles.

  1. Your DMP Is Your Single Source of Truth

A great example of this is how Adobe approached our student audience. The student audience is very important to us, but it is also very hard to identify students digitally. Therefore, we considered users’ behavioral activities on our web properties, as well as attributes within our CRM database, to build a master list represent our student segment. Then, within our single source of truth (Audience Manager) for defining our student segment, we pushed that segment out to Adobe Target for targeting on Adobe.com. We also pushed the same segment out both to our display-marketing channel and to paid search. For a period of time, we were messaging to the same audience across all marketing channels in the same fashion and with the same message. So, after leaving the site, a user who qualified for the student segment would receive a student banner; then the user might perform a search query and receive student ad copy because Audience Manager told the search channel that person was a student; and then, upon coming back to the website, that user would experience Adobe.com’s student homepage, a student version of the homepage.

  1. Integrate Your CRM Attributes

What I mean by this is that you must tap into users you would not normally be able to with just your DMP information. For example, users who were once only reachable through email can be integrated into your DMP to give them an online ID that will allow you to not only targeting them across other channels, but also allowing you to enhance their profile with what they do behaviorally — not just their email activities. You can start saying the same thing to them in the display channel, across search, and onsite as well — all of which leads to the fourth strategy.

  1. Leverage Data Relationships

First-party data is king, but some marketers do not have enough first-party data to be impactful. With the right data partnerships and DMP, you can leverage your DMP to activate rich data and expand your reachable audiences beyond your first-party data. Marketers with a highly visted web property like Adobe have an obvious opportunity to customize their site-side targeting. To expand your addressable audience beyond your own first party data, 3rd party data can be a good solution. Doing so can help you expand the reach of who you can identify to serve them the most appropriate version of your content when they visit your site.

  1. Give Your DMP the Attention it Deserves

As with anything else in an organization, our tools are only as good as the team we put behind them. To that end, build a team around your DMP. You do not necessarily need many resources, because a DMP actually helps to reduce resources needed to do things the old way; however, at a minimum, you need someone to manage the credentials for and access to the DMP and the audience definitions as well as someone familiar with your first-party data structure and the location of that data within your organization. All of this will ensure that you are able to leverage the real-time nature and programmatic powerhouse that a DMP can really be.

Put Your Data to Work
I hope that you can use at least one of these strategies to make the most of your ad spend and get the best results possible from your DMP. Remember, your data is only as good as the results it delivers. By connecting to your DSPs and other programmatic media, you will maximize your returns  and learn more about your audience so you can continuously evolve your strategies.

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Which Type of Attribution Model is Right for My Brand: Rules-Based or Algorithmic?

Marketing Cloud

Given the massive amounts being spent in the digital space and the expectations that marketers deliver bigger, better results than they did the day before, understanding what, specifically, moves the needle is key. And it’s those attribution models that help us paint the picture: where meaningful touches are taking place, how users are being exposed to your message across all channels and, overall, the optimal budget allocations that should take place within those channels.

That’s why, now more than ever before, having a successful attribution model in place is key. In the past, we may have evaluated channel by channel with individual attribution models and experiences that were siloed from one another. However, consumers now interact with our brands across every possible platform; and to be effective, efficient and deliver relevant journeys, we need a holistic view that looks at each touchpoint through a cross-channel lens. In other words, if this consumer were influenced by multiple sources, shouldn’t every touchpoint get credit for the win? Think of it as the assist and the goal. Sure, one player scored, but without the perfect pass at the perfect time, would that ball have found its way into the net? Maybe — but maybe not.

Understanding the Models: Rules-Based vs. Algorithmic

When it comes to defining your brand’s attribution model, though, there are more considerations than just whether to have or to have not. Generally speaking, there are two categories of models: rules-based and algorithmic. A rules-based model is humanly defined, and as a result, fairly subjective. These models are based on easily understood assumptions we have such as first and last touch, equal touch and other clear-cut scenarios. For example, it was the last exposure that drove the sale or the first and last equally.

Though easy to understand and interpret, the risk here is that perception is reality in rules-based modeling. Again, this is a highly subjective process, and if your perceptions are off, your entire attribution model could go out the window. At the same time, however, if your organization is just getting started with attribution modeling or is lower on the analytics-maturity scale, this more descriptive, lower consideration process could get the job done, at least for now. As your foundation grows, it’s easy to move from single-touchpoint attribution — last interaction and first interaction — to multi-touchpoint models that assign credit to various touchpoints based on existing rules. Adobe Analytics, for example, has seven standardized multi-touchpoint attribution models available.

Moving further up the maturity scale, there’s the data-driven algorithmic approach. Here, attribution outputs are predicated based on data and the modeling of that data. Fractional attribution is assigned depending on one touch’s value relative to the others. To be successful, though, algorithmic attribution relies heavily on the richness of the incoming data. If the data is solid, you’re getting a comprehensive snapshot, but if it’s not, your results could be severely flawed. Consequently, this model requires a high degree of human interaction, despite its being rooted in machine learning. No matter how powerful your model, you’ll need context from a human analyst. This will also help curb the potential for flawed inputs, as it’s something that human marketers can likely spot quickly before they taint the outputs.

The upside to algorithmic-attribution models? While there’s a bit more resource allocation and some heavy lifting, at least in the beginning, there’s no guess and check anymore. The attributions assigned give you a comprehensive look at the impact every interaction has on the end goal, helping you better understand what’s working, what’s not, and what influences what in this big, cross-channel puzzle. And that understanding has limitless potential for marketers and brands.

Determining the Right Model for Your Organization

So which to integrate? There’s no universally right answer, but instead, a series of benefits and considerations to weigh. The simple solution, of course, is rules-based attribution — and most common within that modeling is last click or last touch. It’s straightforward and completely clear cut: the last touchpoint gets the attribution. It was the final tipping point and the moment in time that made your consumer convert. Makes sense.

Then again, there’s everything that happened upstream to consider: that initial email that got him thinking, the retargeting that reminded him repeatedly, and the exposures that happened along the way. Sure, he clicked on the display ad, but it was only after kicking around the idea for a few minutes, hours, days, or weeks — whether he realized it or not. Without those touches, would he have clicked through to buy the sneakers, book the plane ticket, or watch the video? Like the soccer goal, this one’s a maybe/maybe not. Algorithmic-attribution modeling digs into those experiences from start to finish, helping paint a better, more accurate picture while giving you a much richer view of media waste — a win/win even amidst the heavier lifting and greater resource allocation.

So Which to Choose?

I usually recommend that marketers ask themselves three simple questions to best steer the conversation:

  1. How much data do I have, and equally important, how many different marketing channels does that data extend across?
  2. Am I ingesting all of the data into the same platform?
  3. Do you have a persistent customer identifier?

If the answer to question number two is no — if you aren’t ingesting all of the data into the same platform — then it’s much easier to use a rules-based model. Having all the data sources on the same platform is a prerequisite for benefiting from a cross-channel algorithmic-attribution model.  If the answer to question three is yes — they can use it as the hub of the spokes between all marketing channel data. Imagine disparate data sources from all marketing channels, coming together as spokes on a wheel, finally connecting at the hub to be stitched together with the use of a persistent customer identifier.  Brands can more easily take action on stitching their different marketing channel data sources together to begin looking at paths to success events with consideration to user exposure across multiple channels.  

However, on the route you opt for, there’s one more critical piece of the conversation: ensuring a single source of truth (SSOT). If some people within your organization are using rules-based attribution models and assigning credit to the last touch every time while others are using an algorithmic approach, you’ll wind up with multiple sources of truth, and with them, total analysis paralysis.

Brands need to be aware that the attribution model they choose and communicate to their marketing channels will significantly influence the behavior and the tactics employed by those managing the delivery of their marketing activities. If you incentivize someone to achieve a flawed goal, you are essentially incentivizing them to behave poorly to win favorability against a poorly chosen attribution model. If brands identify a flawed model, the behavior in terms of how your marketing is managed will be flawed as a result. Think of your attribution model as the blueprint for incentivizing the behavior you want. So choosing a well-designed blueprint is paramount. While it seems daunting to stand up a rich attribution model, it’s worth the investment. It could be the difference in achieving sophisticated and data-driven marketing spend.

Whose Goal is it, Really?

Today’s consumer is far from one dimensional. She’s moving through countless experiences, platforms, extensions and influential touchpoints every day, all of which inform her journey toward your brand. Understanding attribution is a critical piece of optimizing your marketing efforts and best allocating your resources. However, given the cross-channel nature of today’s consumer, assigning attribution to the last destination will likely steer your future efforts in the wrong direction. It’s the soccer analogy: sure, sometimes you take the ball downfield and score with one swift kick. But more often than not, it’s a team effort that entails smart, strategic passing and crossing, and finally, a hard-earned goal. In that case, whose win is it, really? Being able to answer that question will catapult your marketing efforts ahead in a big way, ensuring you can best allocate resources, eliminate media waste and better architect meaningful journeys for your audience — now and in the future.

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Luxury Hotelier Transforms Digital Campaign Returns

Marketing Cloud

Customers from around the world have stayed at the five-star luxury hotels operated by Meritus Hotels & Resorts, and the hotelier would like them to come back. So Meritus is expanding its reach across countries and languages with Adobe Campaign in Adobe Marketing Cloud.

Meritus chose Campaign because it wanted a proven digital campaign management solution that would integrate with its complex backend systems and be easy for marketers to use. Integration with Meritus’ Opera booking system enabled the company to centralize customer data, which helped eliminate many of the manual steps needed for email campaigns. Processes that used to take up to two hours per campaign can now be completed in 15 minutes.

Centralized data lets dispersed teams view data from Meritus’ three hotels in Singapore and Malaysia and segment customers right down to their room preferences. Plus, responsive HTML templates let marketers send personalized messages to any device, which is vital because of growing mobile use in Asia.

“The efficiencies and insights we’ve gained through Adobe Campaign have transformed our returns,” explains Ilias Chelidonis, E-Commerce Manager, Meritus Hotels & Resorts. “With Campaign, we can track conversion rates and see which guests made bookings through our emails, including gaining a view into which touchpoints they encountered prior to booking.”

With Adobe Campaign, Meritus marketers have successfully sent almost 360,000 emails over 131 campaigns. The team is particularly proud of the “birthday” campaign in which it sends emails to customers 45 days before their birthdays. Open rates are nearly 40%. Coupled with operational efficiencies Meritus estimates its ROI on Campaign is about 1500%.

You can read the full success story here.

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Adobe Completes Acquisition of comScore Digital Analytix Business

Marketing Cloud

Today, Adobe officially closed its acquisition of the technology assets and accounts of the comScore Digital Analytix business. Adobe and comScore will work together to ensure that the business-critical workflows that Digital Analytix customers rely upon are not disrupted. We welcome all Digital Analytix customers into the Adobe family and pledge to provide you with the exceptional innovation and customer support we are known for in the industry.

Enterprise analytics is a strong and strategic focus here at Adobe. In just the last few quarters we’ve shipped hundreds of new analytics capabilities; you can check out some of the most impactful features in our last few releases here. We are delivering innovation that is fueling customer insights, driving better decisions and improving business results for organizations in every industry. We will continue to forge ahead and lead the next frontier in analytics — customer intelligence across the enterprise.

When we announced the acquisition a few months ago, I shared some thoughts on what Digital Analytix customers can expect moving forward. Now that the deal has closed, I can go into a little more detail about why Adobe acquired the Digital Analytix business, and how we plan to move forward.

The acquisition expands the Adobe Analytics footprint in the enterprise media and entertainment vertical and strengthens Adobe’s presence in the European market, all important investment areas for Adobe Analytics. Adobe also sees potential operating synergies in continuing our development of analytics capabilities for the fast-growing digital video and media space. With the growth of cross-screen content consumption, particularly through over-the-top (OTT) devices, this acquisition allows Adobe to accelerate the delivery of more robust analytics capabilities to this key growth vertical. Adobe will enable media companies to benefit from deep insights into the performance of content across screens. This will allow customers to accurately measure and monetize content across every major IP-connected device, including desktops, smartphones, tablets, game consoles and OTT boxes.

Additionally, this acquisition gives Adobe an expanded customer base in which to introduce our other Adobe Marketing Cloud solutions — the most complete set of marketing solutions available. To learn more about our current strategy and product offerings, please visit adobe.com/analytics. For additional details about this transaction, please review these updated FAQs.

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