As promised, my next five blogs will examine these focus areas further, demonstrating the enablement of practitioners to easily implement, execute and grow their optimization success at each stage of the maturity process. Focusing on these areas will also provide support for governance and broadening of optimization efforts to other practitioners within other areas of an enterprise organization.

The first critical focus area, Marketer Control and Core Governance, will help me introduce my main character in these posts, an amalgam of many of our customers who adopt and see immediate benefits from Adobe Target.

Rick Patterson works as a web analytics manager for a website that caters to expecting parents. The website offers articles with advice on pregnancy, newborns, infants and toddlers, and they also sell products. An important key performance indicator (KPI) is time spent on the site and high-value page consumption relative to ad revenue. An additional KPI relative to customer retention is completing a registration form for their “Baby Club,” which offers incentives, sales, advice and other information that is customized according to members’ preferences.

The vice president of Rick’s department has added optimization and personalization to Rick’s responsibilities. After Rick tries out a few basic a/b testing tools, he sees limitations in the depth of reporting and accuracy relative to granular audiences and profile data. He also sees limitations in his ability to optimize across channels.

He’ll need more to broadly and accurately define the right rules for adapting content to a diverse visitor population across his various touchpoints. He needs a unified optimal experience across his acquisition efforts — in emails, social campaigns, display banners and content on partner sites — and continuing to his primary web and mobile-site content. Rick also needs customized customer loyalty and service experiences, along with relevant marketing in his mobile apps and in-store digital screens. After evaluating several solutions, he realizes the new Adobe Target has everything he needs, both as a starting point and destination to where he ultimately needs to evolve his company’s optimization culture.

Marketer Control and Governance catches his eye first. He realizes how quickly he can get up and running on his web and mobile sites; all that is required is a single line of code on each page, which he can easily execute with a tag-management solution. Adobe Target includes access to the Adobe Marketing Cloud, which supplies a free tag-management solution out of the box within its broad set of core services. There’s a flexible mobile software development kit (SDK) that integrates with any type of native mobile application as well as more than 40 application programming interfaces (APIs) for implementing in offsite channels like email, display, social and the Internet of Things.

Rick sees that Adobe Target’s breadth of activities all follow a standardized three-step workflow. The single line of code allows Rick to browse and navigate to any element on his web or mobile site, regardless of whether it’s connected to a URL. He can then use a rich set of visual editing tools to make extensive basic design and content changes — such as move, resize, rearrange, hide or remove — as well as more complex changes, including rich-text and background-color editing, image swapping, layered content creation and HTML editing. A side-by-side code editor allows Rick to see his edits as he makes adjustments within the code, track his visual changes, or make more complex code changes or additions.

Rick is in control of the variations he wishes to test and can easily share these variations for approval with auto-generated experience URLs, which makes quality assurance (QA) easy. He can select an audience to target the test to and either set his own success metrics and conversion goals for tracking or leverage the audience segments and success metrics natively from Adobe Analytics to filter and mine test results.

Rick can use this same three-step workflow for experience (rules-based) targeting, automated recommendations (and testing different algorithms), or even executing automated personalization (machine-learning algorithms). Rick can easily run these independently with built-in safeguards for prioritization of concurrent activities and activity collision, or ideally, he can use them in concert for refining strategy. For example, he can run an automated-personalization algorithm on his homepage hero banner to uncover which audiences react to different creative options. While targeting these variations to each individual in real time for maximum return, he can a/b test each banner to refine it further or even use a multivariate test to see which element is causing this banner to resonate with specific audiences.

With all of these activities within a single interface, it’s easy for Rick to manage and govern his program and decide how and when he launches separate or connected activities as well as how he monitors and takes action upon receiving his results. Practitioners from different areas of the organization who have test ideas that they can assist with building can easily be given access rights, and he can govern when and how these activities are pushed live, results are reported, and what recommendations are made on next steps. He can post these test results to the Adobe Marketing Cloud dashboard to share with any team member he wishes, so he can socialize the positive results he’s generating through optimization and the personalization of content based on performance. Both optimization (testing of different variations) and personalization (choosing the best content to target to an audience) are within Adobe Target, and all data and results are in real-time, which makes his action and reaction immediate and profitable while reducing the opportunity cost of introducing a suboptimal experience for a substantial period of time.

Rick can hit those high-value locations where he knows that personalization will be important and impactful based on his key KPIs and show value right out of the gate without any heavy lifting by development or other resources. He can also have confidence in the statistical rigor and analysis he’s receiving from Adobe Target due to its well-proven statistical methodologies in terms of manual a/b testing and machine-learning automation. Rick has a depth of analysis and qualification at his fingertips based on an easily augmented, unified visitor profile that begins at first touch and grows with online and offline interactions over time.

In my next posts, we’ll see how Rick has access to sophisticated profiles and audiences to fuel his testing and automated activities as well as how he can expand his program to customer-journey testing across channels efficiently over time.

The post Advantages of New Adobe Target: Marketer Control and Governance appeared first on Digital Marketing Blog by Adobe.

This post is part of a series derived from two recent Adobe Summits and my conversations with many of the attendees. A number of people told me that they are already using Adobe Audience Manager, and I am proud to note that Forrester reported that “Adobe Audience Manager leads the pack” among DMPs (Data Management Platforms).

So let’s now look at how to make coherent sense of oceans of data from multiple streams by developing custom segmented audiences through which data can be converted into actionable insights. To clarify the major use case for each solution, I have found it helpful to assign a one-word label to each as follows:

• Audience Manager: DMP (Data Management Platform)
• Analytics: Measurement
• Media Optimizer: Advertising

Of course, each solution goes far beyond this one-word description. For instance, Audience Manager provides features to integrate online and offline data and deliver a unified view of all your audiences; Analytics provides tools for predictive and real-time analysis; and Media Optimizer provides tools that manage, forecast, and optimize media solutions. Core Services provides the link to support and integrate all of the above-described solutions. Importantly, core services are intuitive, permitting campaigns to be designed and defined by marketers without Information Technology (IT) intervention or assistance. Moreover, insights from Analytics can be picked up automatically by other components within the Marketing Cloud to create personalized experiences for a specific audience. Particularly relevant to today’s topic is the Adobe Audience Profile Core Service. It provides free access to a solution that allows audience segments to turn into activated marketing for those who have one license for one tool within the Marketing Cloud. Considering the extensive scope of that which is provided by a DMP and its commensurate contract, the free access provided by the Audience Profile Core Service provides a value that can be seen in ROAS lift.

To understand segment creation within Analytics, let’s look at the image below, which I describe as the magic checkbox.

I recognize that, even with today’s cross-functional teams, there are still divisions of labor; however, if you’ve never seen this screen within Analytics, I highly recommend that you begin your audience-segmentation journey here. Working with the segment builder within Analytics is not just something that will make your reports more interesting at meetings. When shared across the Marketing Cloud, audience segments don’t need to be built from scratch. Data from Analytics that is integrated with Media Optimizer permits you to access the display, search, and social audience targeting section and build upon factors that you have already identified as profitable.

One of the questions you may have now — considering that there is a dedicated solution in Audience Manager as a DMP — is, “Why does Adobe offer a free Core Service that deals with audiences?” In brief, the free audience core service is valuable when sharing 1^st party analytics audiences for display targeting in Media Optimizer. However, you will want to use the full Audience Manager DMP for situations in which you have 3^rd party data sources and lookalike modeling activated in ad platforms outside of Media Optimizer. So let’s apply that to the main principle that is the focus of this series: RLSAs.

Linking Your Search Activity With a DMP

If you’ve had experience with RLSAs, you know that Google requires all the cookies to be populated by the Google remarketing pixel. Without the right tools, getting the Google remarketing pixel on your site might involve pleading with your IT team to stop what they’re doing to implement another pixel directly on your landing pages. However, if you’ve already implemented Dynamic Tag Manager or Audience Manager, you can put that Google remarketing pixel inside those container tags.

Not only does that simplify your setup, but also allows you to perform magic — conditionally firing the remarketing pixel — by which I mean looking at other sets of data that you might have access but Google does not. This method allows you to have a new type of value in addition to the basic functionality that the platforms present.

Even though Audience Manager can combine 1^st party and 3^rd party traits for display targeting, Google policy only allows 1^st party traits to be used in RLSA targeting. Nevertheless, the application of 1^st party web analytics and CRM data still makes RLSA audiences much easier to control.

Volume: The Achilles’ Heel of Remarketing

We know that the Achilles’ heel of any form of remarketing is volume. Therefore, start off by identifying what your largest, most-valuable audiences might be. Next, identify (through Analytics) what their behaviors are — but don’t let that report just sit in your boss’s inbox. Activate that data through the Audience Core Service and take advantage of bid modifiers to make sure that you have invested in those audiences before you revert to the more general targeting.

Remarketing Is More Than a Tactic When it Is Combined With Advertising Channels

I must admit that I love the nuts and bolts of advertising, and having come from a consulting background, I love tactics. Astute tactics can provide a much-needed, mid-course correction when the goals of a strategic plan are going astray.

However, I understand that many of you reading this post may be managers and supervisors and that you have teams or agencies helping you with execution. Therefore, one of the things you can do to go beyond the technical details of remarketing to create strategy is to break down the silos of those who are engaged in this function.

Remarketing can be made strategic by sharing audiences across search, social, and display. For example, in my entire career, I’ve looked at remarketing as that one thing that the email

specialists do, but we can’t have that attitude anymore. The coordinated efforts of those in search, social, and display will truly make the definitions of those audiences strategic across all the channels with which we are dealing.

If you have missed any of the previous posts in this series, you’ll want to be sure you check out all of the information-packed posts:

• Part I: The Five Most Expensive Search Marketing Mistakes
• Part II: The Five Most Expensive Search-Marketing Mistakes: Query Mining
• Part III: Finding the Right Balance for Your Investments in SEM
• Part IV: Overcoming Stale Messages in Creative Testing
• Part V: Overcoming Search Engine Marketing (SEM) Mistakes: Vague Targeting

Next time, we’ll expand on this topic and discuss evolving your audience as well as what search, social, and display retargeting can mean for your business.

In the interim, feel free to leave a comment or ask a question.

The post Advanced Remarketing Lists for Search Ads Techniques for Success appeared first on Digital Marketing Blog by Adobe.

Later that night, I came back to that analysis and did something I should have done the first time. I compared Safari traffic to Android traffic. Odd. Android traffic was following the same downward trend as the Safari traffic! Oh boy. This wasn’t an iOS problem, it was something else. False alarm. Yes, there was a downward trend in traffic for one report suite, but it had nothing to do with the release of iOS 9.

So, to help you avoid making my mistake, let me share some information I’ve learned about iOS 9 traffic trends.

First, there is no measureable drop in Safari traffic across Adobe’s customer base. Second, there are some fool-proof (ahem, Bret-proof) ways to measure the ad-blocker impact on your data. (This is indirect measurement because there’s no way to send hits to Analytics when content blockers are active because those hits are, well, blocked.) Third, your apps are not affected, and there’s an easy way to prevent them from being affected in the future.

Global Impact

What is the impact of ad blocking apps across Adobe’s customer base? So far, it’s not measurable. Honestly, I’m a little surprised. This year’s edition of scare-analysts-into-thinking-their-job-is-over seems more plausible than previous years because it’s fueled by real market problems: limited battery life, costly bandwidth plans, and mediocre network speed. If these problems are so convincing, then why aren’t iOS ad blockers having a visible impact?

Apple does not publish the number of ad-blocking apps downloaded, however, Adobe’s Digital Index team has access to traffic data across hundreds of sites, and they analyzed the Safari traffic trend over the last couple months. How much has Safari traffic dropped since the release of iOS 9? Nada. Notice in the graph below that there is no noticeable drop in the share of Safari traffic in the last 13 days.

We also took another approach to include standard deviations. In order to do this accurately, we had to sum traffic across customers, which tends to give higher weighting to our largest volume customers. It’s interesting that at a global level, the number of Safari visits has been increasing over the last few weeks. This could be a result of college football or other factors, which is why this view should be taken with a grain of salt. The point is, there doesn’t appear to be a drop.

Adobe will be watching this trend carefully over the coming months. If it changes, we’ll be sure to notify you.

Local Impact

You’re probably not satisfied looking at global trends, or more importantly, you know your boss won’t be satisfied with a global trend. You want a concrete way to see iOS traffic trends in your report suites. I thought you’d never ask. My colleague, Justin Grover, did some wicked cool, and surprisingly simple, analysis in the Analysis Workspace feature we released last week. That analysis is described in this blog post and video. Here is the chart you’ll be able to create by following his instructions:

Justin has run this analysis on a handful of report suites. In each case there has been no statistically significant drop in iOS Safari hits.

What Else Do We Know?

Here are some relevant pieces of information you can use to help calm concerns in your organization:

1. iOS apps are not affected by iOS 9 ad blocking, even if you have a hybrid app that renders web pages. There is one exception to this rule. With the release of iOS 9, a new library called SFSafariViewController can be used to render content. If this library is used, then content blockers apply within the app. If you want to avoid content blockers, make sure your developers don’t use this library.
2. Apple is planning to release ad blocking support in OSX with the release of El Capitan. Is that the next ad-Armageddon? Probably not. Desktop ad blockers have always had low adoption because battery, bandwidth and speed concerns are not an issue, and while ads can get annoying on the desktop too, there’s plenty of screen real-estate for them.
3. All of the Adobe Marketing Cloud products appear to be affected by ad blockers, including Dynamic Tag Manager. Even if your collection domain is whitelisted in an app, the DTM container JavaScript file may fail to load. Just watch for this if you do any testing.
4. Adobe Analytics data is blocked by the largest iOS ad blockers. This makes it impossible (or at least really hard) to create a report in Adobe Analytics that shows how many ad blockers are installed for your visitors. Instead, you’ll need to compare the Safari traffic to the expected Safari traffic as Justin explains here.
5. As my colleague, Travis Sabin, pointed out, ad blockers require users to opt-in, which significantly lowers adoption numbers. I know that’s a repeat of information you may have, but it’s worth reminding folks you work with. You could also try asking your coworkers how many of their friends have installed an ad blocking app on their phone. Most people just don’t know or care enough to install that kind of app.

Action Plans

In summary, you now have a lot more information at your disposal. You can avoid making analysis mistakes like the one I made. You know what Adobe’s global trends are. This should help calm fears and direct efforts. But iOS ad blocking apps are a big enough concern that you may want to draft a plan in case trends change.

Here are some things we recommend considering. Some you could do now, others you may just add to the contingency plan you’re compiling in case ad blocking grows to measurable levels:

1. Contact the app developer. If your site is rendered useless by an ad blocker (as we’ve seen in some news articles), or if the customer experience suffers, contact the app developer to inform them of this. The app developer wants positive app reviews. If their app breaks your site, tell them. This approach worked for at least one Adobe Analytics customer already, at least with one app developer. To contact an app developer, go to the app store and find the developer web site for the top ad blocking apps. Currently, only two apps are in the top 100 paid apps: Purify (#3) and Crystal (#42). Adobe is trying to contact these developers as well, but sometimes you have more influence than “the vendor.”
2. Consider measuring something server-side. (Warning, this could be a big-ish resource investment.) A lot of companies already measure some metrics server-side, such as purchase revenue. If the difference between a server-side metric and a client-side metric is growing over time, you may want to collect some user agent information server-side to see if the iOS 9 discrepancy is growing faster than other browsers.
3. Stay tuned in. Adobe is actively monitoring the situation and we have a few ideas about how to ensure you have accurate metrics by which you run your business. As we see changes, we’ll keep you informed about the options. As you know, we both have a pretty strong incentive to create reliable and valuable metrics.

The post iOS 9 Content Blocking Follow-up: Are Your Metrics Affected? appeared first on Digital Marketing Blog by Adobe.

Quick History

On Sept. 16, 2015, Apple released iOS 9 for general use. One part of that release was support for content blockers in the mobile version of Safari (native apps are un-affected). Ad blockers have been around for quite some time and represent a very small percentage of web traffic. However, on mobile devices with slower connections and more limited processors there is a greater incentive for users to adopt content blockers. Several of our customers have expressed concerns that large portions of their traffic will suddenly go dark. We are monitoring how this is affecting our customers, and to-date, we’ve seen very little impact on mobile traffic. We do, however, want to provide you with some strategies for doing the same types of analyses that we are doing. Below are two techniques you can use to determine if you are losing a meaningful amount of traffic to ad blocking.

The Easy Way – Anomaly Detection

The easiest way to look for any sudden changes in iOS traffic from Safari is to use Anomaly Detection in Adobe Analytics. Anomaly Detection uses a Holt-Winters algorithm to predict what metrics should be and what is within the expected bands. Here is how you do it:

1. Create a segment of iOS Safari Users.
   
2. Open up Anomaly Detection (View All Reports > Site Metrics > Anomaly Detection)
3. Select the Occurrences (essentially hits), Page Views, Visits, and Visitors Metric.
4. Change the training window and view period to 90 days
   
5. Look for any negative anomalies in these metrics after September 16th.
   
   In this data set things look good there is no statistically significant drop in traffic.

As you do this analysis there are a couple of things to keep in mind:

• There are normal variations in traffic. You will notice in the chart above that iOS Safari traffic is down a bit the week after 16th. However, if you look at previous weeks you see a very similar trend.
• A negative anomaly after the 16th means that there ‘could’ be some traffic that is blocked. However, it could be unrelated, so be sure to investigate if there are other possible causes as well. (e.g. bugs in your web site that affect iOS 9 Safari, campaigns that ended, etc.)
• If you run Contribution Analysis on any of these anomalies be aware that iOS and Safari will likely show up in the results set because we are filtering down to just those users. I would only attribute this to ad blocking if Contribution Analysis shows no other significant contributing factors.
• Finally, to be thorough, be sure nothing could be inflating traffic (like a new campaign) that would hide a drop in traffic.

The Manual Way

With this method, Analysis Workspace in Adobe Analytics can help you find a predictor of iOS Safari traffic. You’ll then use that predictor to estimate iOS Safari traffic and compare that estimate to the actual values. Finally, you will see whether the difference is statistically significant.

Finding a Predictor

You want to find a segment that is closely related to iOS traffic but would not be affected by people upgrading to iOS 9 so that you can impute what iOS traffic should be on the site. Here are a few things you might try:

• Traffic from browsers other than Safari
  
• Traffic from iOS devices with a non-Safari browser
  
• Android traffic
  

The best way to do this would be to run a correlation between several metrics and then find the one that most closely correlates to iOS traffic. You can, however, use a trick in Analysis Workspace to quickly pick out which metrics follow the trend most closely—here’s how:

1. Open Analysis Workspace (> Analysis Workspace)
2. Create a new project
3. Change the dates to March 1 – August 30 (roughly six months or the maximum number of days you can display). We want to look at traffic before there were any concerns about ad blocking to find the best predictor.
4. Add in the Day Dimension and drop the segment we created above for iOS Safari on the occurrences metric (occurrences is equivalent to hits or server calls).
   
5. Add in the additional metrics (Non-Safari, Android and iOS Non-Safari)
   
6. Add a line visualization and select the normalize option
   
   Normalizing puts all the metrics on the same scale. It will allow you to quickly see visually how closely the metrics tie into each other.
7. Deselect all but the first metric. Then view the other metrics one at a time to find the one that most closely follows the traffic line.
   
   
   
   In this case I would say Android traffic is probably the best predictor, however, feel free to try others or to run a correlation analysis.

Estimating iOS Traffic

The next step is to estimate what iOS traffic should be and compare it against what it was to see if there is a significant difference. To do this you are going to need a few calculated metrics, such as:

• Predicted iOS Safari Traffic from Android
  
  This metric uses the Linear Regression Predicted Y Function. It is one of the advanced functions so be sure to check the advanced checkbox when looking for it. Use the metric you selected above as x to predict iOS safari traffic as y.
• Mean iOS Safari Hits
  
• Upper Bound (2 Standard Deviations)
  
• Lower Bound (2 Standard Deviations). The only thing different is the subtraction operator.
  

Once you have these created you can put them on a chart to get a better indication of changes in iOS 9 traffic. Here’s how:

1. Add a Freeform Table to Analysis Workspace.
2. Add the following metrics:
   • Occurrences for Safari iOS users
   • Predicted iOS Safari Traffic from Android
   • Mean iOS Safari Hits
   • Upper Bound (2 Standard Deviations)
   • Lower Bound (2 Standard Deviations)
     

3. Set your date range for something relatively long (at least 200 days) so that the mean and predicted values are affected by a drop in iOS traffic (if one exists).
4. Change the row count on the table to 200 and descending (so you see the most recent dates first).
   
5. Now add an Area Chart visualization. It should look something like this:
   The straight lines are the mean, the upper and lower bound (at 2 standard deviations). If you go outside of those bands then there is a 95-percent chance that the traffic pattern is not normal. Generally speaking if your traffic is inside of those bands then you shouldn’t be concerned. You can also see that the predicted traffic matches pretty closely the actual traffic, which gives further reassurance that for this data set, there is no measurable effect resulting from iOS 9 ad blocking. If you did have a lot of traffic being blocked you would see the actuals fall well below the predicted iOS traffic and it would fall below your lower bound.
   

6. Save the project. After putting something like this together, you’ll want to hold onto it so you can show your grandkids. You will also want to come back to this view every couple weeks to see if things are changing.

Conclusion

These two methods should give you a good way to assess how impactful the ad blocking is for your business and your audiences. We will continue to share new information as we learn more about how this is behaving in the real world.

The post Assessing the Impact of iOS 9 Content Blocking with Adobe Analytics appeared first on Digital Marketing Blog by Adobe.

Looking back on how the industry has evolved, offline direct marketing tactics and principles have been applied to online ad technology and have now come full circle connecting the offline and online worlds. Because of these changes advertisers can now track and target audiences, and measure performance results across channels on and offline.

How did we get here? Looking back 15 years.

For today’s internet based businesses, online advertising channels like search and display are top channels used to acquire new customers. However, that wasn’t the case for us back in 2000. During the dot-com boom and crash, I was managing direct marketing programs for new customer acquisition at the “first online grocer,” HomeGrocer.com (Amazon was an investor, and it was later acquired by Webvan). We tested various direct marketing channels at HomeGrocer.com. Interestingly, we found that direct mail was the most efficient channel for us to profitably acquire new customers at scale (~$20M annual spend).

A lot has changed since the peak of the internet bubble in 2000 for advertisers. Here is a snap shot of today’s common direct marketing channels as they were used 15 years ago.

• Display – The first click-based banners were sold on HotWired.com in 1994. In 2000, display buying was done primarily through direct placements. Yahoo, AOL and MSN were the big portals around. Advertising.com, the ad network, came around in 2000 and was acquired by AOL in 2004.
• Search – Overture and Google were just getting started in 2000 with the auction-based, pay per click (PPC) model. Google launched AdWords in 2000, began offering PPC in 2002 and went public in 2004.
• Social – Facebook didn’t exist in 2000 and was founded in 2004 the same year they ran their first ads.
• Email – The perception on email marketing was low due to poor performance and offshore spammers clogging up inboxes with non-relevant content. The Can SPAM Act of 2003 helped to legitimize email marketing with clear opt-in and opt-out requirements.
• Direct Mail – Direct mail was a scalable and effective direct marketing channel. From magazine subscriber files to those compiled from warranty registration cards and phone books, advertisers had access to hundreds of marketing lists to test for performance (and all appended with demographic data). Database marketing was popular and allowed an advertiser to efficiently manage their prospect lists and direct mail communications.

With the growth of the internet and access to more audience data, the industry has progressed. Search, social and display channels have had tremendous growth and are now primary direct marketing channels for many businesses. Today we have a lot more options for online advertising.

Advertising 15 years later

Advertisers now have access to massive amounts of audience data, everything from online site activity and web analytics data to offline customer data, and 3^rd party data. Offline CRM data and email marketing data can now be linked to programmatic ad buying platforms. Audience targeting can be done more efficiently now than ever before.

Today advertisers have access to advanced advertising technology platforms to help them manage and optimize their online advertising campaigns. Everything from search bid optimization, display ad buying and audience management, to site optimization and marketing automation tools.

All channels have shifted to programmatic (or automated) ad buying with a focus on audience-based buying. In display, audiences can be reached at the impression level through real-time bidding. Facebook enables social advertisers to reach custom, website and search retargeting audiences. In search, advertiser can now target audiences for their campaigns via RLSAs (Retargeting Lists for Search Ads).

Display Advertising 15 Years Later

For display advertising channels specifically, the industry has changed completely.

Ad exchanges revolutionized the industry. The ad exchange allows a publisher to sell their ad inventory in an auction environment, and an advertiser to bid on ad inventory in real time. The auction determines the fair price for the user impression. This process is similar to the stock market with buyers and sellers, but with display ads instead of stock.

Demand side platforms (DSPs) permit advertisers to plug into the ad exchanges to bid in real time on their key audiences at the user impression level. The DSP optimizes for the advertisers performance objective. The advertiser can bid what they believe to be the right price for each impression based on the available data they have about that user.

Data management platforms (DMPs) enable advertisers to consolidate audience data from various sources, identify and segment high value audiences, and activate those audiences for cross channel marketing on external ad targeting platforms like DSPs and site optimization platforms.

Dynamic creative optimization (DCO) allow advertisers to reach granular audiences with flexible ad creative that is personalized in real time to drive user engagement, conversions and integrated experiences.

Everything is in real time (or close to it). The entire real-time bidding process takes less than 100 milliseconds. When a user visits a website, before the ad appears, an ad call goes to an ad exchange which sends bid requests to participating demand-side platforms (DSPs). The DSPs in play then evaluate available data on that impression (audience, category, time of day, geo device, etc.) and send back bids based on what they know about the user and the likelihood they’ll convert for the advertiser. The ad exchange holds the auction and the highest bid wins. The ad served on the website was the highest bidder of the real time advertising auction. All this takes place in less than 100 milliseconds.

Real-Time Bidding Process

Are we coming full circle between the offline and online advertising worlds?

Many traditional offline direct marketing tactics and principles are still in practice and have been applied to ad tech and the online world.

In 2000, marketing databases were popular with offline direct marketers. These databases allowed for consolidation of various audiences down to individual user profiles, and expanding of audiences using 3^rd party demographic, business attribute data and look-alike models. It gave the advertiser deeper insights into their audiences, enabling them to identify high value audience segments and deliver them to vendors for direct marketing channels. Does that sound like a Data Management Platform in the online world?

Direct mail letters were laser personalized with creative content (offer, message) based on the audience segment. Sound like Dynamic Creative in the online world?

We have come full circle and connected the offline and online advertising worlds by allowing advertisers to take their offline data (CRM, call center, POS), consolidate it with their online data (site visitors, web analytics) and use it for online targeting of ads.

Today marketers can meet their objectives more efficiently with Programmatic

Manual processes have given way to automated buying. CPM based selling models have moved to transparent pricing models. The massive amounts of online and offline data that is available to an advertiser can now be easily harnessed for ad targeting and optimization. Programmatic enables advertisers to buy more efficiently, reach more granular high value audiences, and as a result drive better performance.

Here is how Adobe views programmatic and some key characteristics of programmatic today.

Automated buying of ads across channels and devices

Many think of display advertising and real-time bidding (RTB) when they think of programmatic but it includes the automated buying of ads on any digital channel including Search and Social.

Data driven

Data powers programmatic ad buying. To gain the most from programmatic, it’s all about using and applying data. This includes online and offline data, and 1^st, 2^nd and 3^rd party data. Data powers the following benefits of programmatic:

• Enables an advertiser to build and reach high value audiences
• Fuels performance optimizations
• Powers personalized dynamic experiences
• Drives automated ad buying decisions across channels and devices

Transparency

Advertisers have financial transparency into media costs by ad inventory source, and visibility into where their ads are running.

Advertising has come a long way

Today’s advertisers have access to integrated buy side tools – from audience management (DMP) to media buying (DSP) to dynamic creative optimization (DCO) – to help them most efficiently meet their online advertising objectives.

Programmatic ad spend continues to grow at a fast pace. According to IDC, global programmatic display spend will grow from $10B in 2014 to $70B in 2019. 60% of display transactions will be programmatic by 2019.

For advertisers that are embracing programmatic ad buying and leveraging their businesses audience data, the result is more efficient ad buying, better audience targeting across channels, and better performance results.

The post 15 Years of Defining Programmatic appeared first on Digital Marketing Blog by Adobe.

The majority of consumers exploring new products or services turn to the Internet as their primary research tool, according to Forrester Research. During this discovery process, customers are naturally going to visit your website to explore and learn about your product. This could happen organically or as a result of being pulled in by engaging marketing content and other campaigns. Think of your website like Rome — just as all roads radiated out from Rome during the Roman Empire, so do your campaigns and customers from your website. It is the hub to which your social media, display advertising and email campaigns ultimately drive.

Ideally, exploration leads to purchase. The owned experience remains crucial even for in-person sales, as 42 percent of offline retail sales will be web-influenced by 2020. But when online, conversion on your website is key. It is such an important channel, that CMOs today rank their website as the most critical online channel to leverage during the buying stage of the customer experience, according to The CMO Club.

What this all boils down to is that your owned properties and website remain critical touch points. Your site is both a channel to deliver content as part of your content marketing strategy and a critical hub to which other campaigns are directed.

A core advantage of your owned experiences is that, unlike social sites, you control the experience. That means you have the ability to deliver the optimal customer experience. The question then, is how do you best engage with your customers on your site and deliver this optimal experience? It’s about sourcing compelling content and fostering interactions. One side of this is your own branded content — content your enterprise creates to tell your brand’s story. But there is another side. You can leverage content and conversations coming directly from your customers (i.e., user-generated content or UGC) to spark interactivity and engagement.

One way to leverage conversations and UGC is to foster an online community on your owned properties. This gives customers and prospects a way to engage using forums, groups and comments. It could be a dedicated customer community (learn more here) or integrated across your website alongside normal branded content.

Alternatively, there is an often-overlooked way to leverage conversation and UGC. That is to source it from the social web (e.g., Facebook, Twitter, Instagram, etc.) and weave this user content directly back into the fabric of your site, creating an interactive experience that combines your branded content with original content coming directly from social networks. Employing UGC this way adds enormous leverage to your resource-constrained marketing teams by opening the door to an untapped source of compelling content. The authenticity of this content about your products can also favorably influence buying behavior and conversions.

Our recently announced partnership with Livefyre, for the first time ever gives marketers the easiest, most powerful tools to interweave these two streams of content: brand- and user-created. Now brands can marry content from Adobe Creative Cloud with social content discovered, and curated by Livefyre, and then easily deliver these experiences using Adobe Marketing Cloud and Adobe Experience Manager. If you are looking for this kind of edge in the battle to win your customers, please contact us to learn more about how Adobe and Livefyre can help.

The post Why Your Website is Crucial to Your Content Marketing Strategy appeared first on Digital Marketing Blog by Adobe.

Calculating Quality Score

Quality Score is calculated on a scale of 1 – 10, with 1 meaning that the search engine considers the ad irrelevant and 10 meaning it is considered very relevant. Ideally, search marketers want to optimize their ads so they are given the highest possible Quality Score.

Three main factors are weighed to determine Quality Score: expected click-through rate (CTR), keyword relevance, and landing-page experience. Each of these receives a rating from Google of either “above average,” “average,” or “below average.” When all three factors are rated “average” or “above average,” you get the benefits of a high Quality Score — higher ad rank and lower cost per click, for instance. If one or more of them are ”below average,” your ad may have a low Quality Score, which can cause it to be placed lower on the SRP and limit how often it is shown.

Expected CTR

Your expected CTR is the number of clicks Google expects your ad will likely receive compared to how many times it is seen. The more your ad is clicked, the higher the CTR and better indication that your ad is relevant to a person’s search.

Keyword Relevance

The keywords you use for your ad group determine when your ad is seen on the SRP. The more targeted the keywords are, the more relevant your ad is as a search result and the better the Quality Score.

Landing Page Experience

When your ad is clicked from the SRP, the ability and immediacy of people finding what they are looking for improves their landing page experience and can help raise your Quality Score.

Ad Rank vs. Quality Score

Quality Score is one of the factors used to determine ad rank. A higher Quality Score will improve your ad rank, which will then improve your ad position and can help lower your costs.

Check Your Quality Score and Then Optimize It

If you have never checked on your Quality Score before, you can find your keyword rankings from your AdWords Keywords tab. Complete instructions are on the Google support page. Look at which keywords and campaigns have been assigned a Quality Score of seven or higher. These campaigns are ranking well and will likely rank well on the SRP.

If you have keywords and campaigns that are ranked below a 4, they need immediate attention. Check the keyword and ad copy for relevance and update the ad if necessary. Try adding new keywords as well and breaking up large Ad Groups into smaller, more specific themed Ad Groups.

New keywords are given a Quality Score of 6, which is then adjusted as your campaign runs. Google calculations are dependent on the amount of traffic that an ad receives, but you can typically see a change within a day or two. Adding negative keywords can also help your CTR and ad relevance, which can indirectly improve your Quality Score.

Double check that the landing page linked to your ad is where searchers will find the information they are expecting. There should be no broken links, and the keyword used to find your ad and your site should be represented on the page. Ensuring that all of these factors are in place will help improve both your customers’ experiences and your Quality Score. Testing variations is the best way to see improvements in your Quality Score.

The post Defining and Optimizing Your Quality Score appeared first on Digital Marketing Blog by Adobe.

The next major plateau for our industry was the shift to an expanded paradigm of digital analytics. Companies continued to measure the effectiveness of the web, but also started to analyze email, advertising, video, social and offline marketing channels. Sophisticated nurturing and content marketing strategies emerged as the norm, and enterprises pushed to understand their “digital” customers holistically. This shift was a natural evolution, but it left out several key leaders around the CMO’s table: social, PR and of course, offline brand interactions.

Today, most organizations are thinking about marketing analytics — a CMO-wide lens on marketing interactions with customers and prospective customers. This broader lens has had to make sense of big data, which led marketers into machine learning, predictive algorithms and the beginnings of a lights-out marketing automation movement. The analysis is beyond intelligent — incorporating artificial intelligence and data science that sometimes sounds almost Jetson-esque.

In a marketing analytics worldview, companies must have an accounting of and insight into all of their marketing programs in all of their channels, including web/browser, mobile apps, TV/video, social, paid media, field, print, outdoor and others. If your organization is still stuck in the web analytics mindset, it is time to reinvent your analytics practice, leveraging all of the available marketing analytics tools to make faster and better decisions and drive your business.

Just recently, Gartner published their first ever Digital Marketing Analytics Magic Quadrant. The report covered 12 different analytics vendors, positioning them in the four quadrants of their legendary grid: leaders, challengers, visionaries and niche players. As usual, vendors were evaluated against two dimensions: ability to execute (the vertical dimension) and completeness of vision (the horizontal dimension). We were pleased — although not surprised — by the results of Gartner’s analysis: Adobe Analytics was positioned as a leader (view the full report). Gartner’s rankings were very similar to the findings of Forrester Research in 2014 (read the full Forrester Wave Report).

While analyst reports and rankings are important when selecting a vendor, it is much more important for data-driven companies to understand which analytics vendor(s) are best suited to help solve their specific problems and drive results. It turns out that sometimes, the criteria the analyst firm used for their assessment needs some modification in order to apply to the needs of brands. To help with that, I thought I would share a few key questions and information I would recommend considering when you are evaluating which analytics solution is best to help solve your business challenges, keeping in mind that once you have your criteria, Forrester and Gartner both have the ability to help you re-cast their research to help you select a vendor.

Throughout my career, I have had the opportunity to lead marketing organizations at multiple market-leading companies. I have seen, first-hand, the power of effective marketing analytics. I have, unfortunately, also seen the limitations that can come from leveraging less than stellar analytics systems. If I were searching for a best-in-class, comprehensive analytics solution today, these are the key questions I would challenge my team to answer:

1. How does our analytics practice compare to others in our industry, and where are we specifically trying to improve (this assessment tool might help them with that analysis)?
2. Can my current analytics solution help the team I currently have in place identify high-value segments and then expand our market reach into those audiences?
3. Will the solution I am considering, help my company to measure all of the channels that we need to (silos are going to kill my ability to do important jobs like prediction and attribution modeling)?
4. Does my analytics vendor’s technology enable employees with forward-looking intelligence to make better data-driven decisions?
5. Does my analytics vendor have integrations with other key marketing technologies?
6. How much is the vendor investing in incremental innovation (I don’t want to buy a product that is undergoing time-consuming and expensive platform renovations – I want powerful functionality)?
7. Is the company willing to really listen to my needs and let me play a role in the future of the solution?
8. Does the solution have the rich functionality to help me solve business problems? Are they investing in things that will make it easier for me to hire normal people — not just Ph.D data science ninjas?
9. Is the vendor thinking about everyone in my organization who needs access to data? Can they help me deliver those analyses in a way that un-trained users can get immediate value from the insights?
10. Does the solution tie into the systems where insights are applied, such as my CMS, testing and targeting, personalization, email, advertising and other systems?

A vendor who adequately answers these questions will help ensure that your organization most effectively maximizes your marketing investments.

As we move beyond marketing analytics, Adobe believes the next frontier is customer intelligence. This moves the focus beyond the CMO’s organization to analytics for the entire enterprise — and even on to business partners. In the customer intelligence paradigm, sales, product development, customer care and other customer-facing teams will begin to leverage the marketing analytics insights and tools to gain insights to shape their job functions. This will require vendors to make it even easier to find insights that can be acted upon. Organizations must have a 360-degree view of their customers to effectively deliver rich experiences to them.

At Adobe, we are happy to be positioned by Gartner as a leader in the new Digital Marketing Analytics Magic Quadrant. However, we are even more excited with the groundbreaking capabilities our team will continue to deliver as we pursue the opportunities and challenges customer intelligence will present.

The post Web Analytics Led Us to Marketing Analytics — Next Stop: Customer Intelligence appeared first on Digital Marketing Blog by Adobe.

By now, you must be curious about what RichSMS is all about and what it can do for you, so I asked Jean-Philippe Gallet, cofounder of MyElefant to help me present it to you.

MB: What is RichSMS and why should Campaign customers use it?

JPG: RichSMS is all about mobile engagement. With 90 percent of the global population expected to own a mobile phone by 2020, it’s obvious that mobile is now crucial to building efficient customer relationships.

Even though mobile is part of the digital mix, it has its own characteristics that differentiate it from the well-known “PC” digital marketing. It’s intimate, instantaneous, and in-context, which are key success factors that drive new uses and new expectations.

That’s why we created RichSMS, a mobile messaging ad format tailored to these needs. It mixes the reach of SMS media with the complex functional scenarios available on smartphones. The format is highly personalized. It dives the end user into your brand universe, integrating pre-packaged functionalities such as coupons, drive-to-store, product catalogs, click-and-collect, add-to-calendar, data collection modules, and more.

MB: Is it complicated to set up a RichSMS Campaign?

JPG: Not at all. Our offer is a one-stop-shop for mobile messaging marketing. Basically, RichSMS is a module of our mobile messaging ad-management platform, called EnRich. It works as an add-on to Adobe Campaign, making it super-easy for marketers to do what they need:

• Create rich and personalized mobile ads without needing any technical skills.
• Broadcast those ads over the best SMS routes worldwide, thanks to close partnerships with established SMS operators such as Mblox.
• Monitor campaign success with built-in real-time analytics.

The best part for Campaign users is that we fully integrated EnRich with Campaign workflows. A web API and dedicated connector help you pilot the EnRich platform through Adobe Campaign and then automatically import customer data generated by RichSMS campaigns back into Campaign.

MB: Can you share some specific client benefits and success stories?

JPG: Of course. There are many benefits, but I’ll concentrate on three of the most important ones. First is efficiency. It should be no surprise that when you add media and functionality to the classic SMS experience, you enhance campaign ROI. The improved efficiency takes many forms, depending on your campaign goals.

For example, one of our clients, a clothing brand, saw 50 percent higher increase in sales compared to their experience with a regular SMS campaign. Another client, a retailer, saw a 100 percent higher increase in store visits. A third, who runs surveys for the drug industry, saw a 60 percent higher response rate to their surveys.

The second benefit is how easy it is to track results user-by-user with our built-in analytics. During a recent chat with a clothing-brand marketing manager, she told us that RichSMS campaigns dramatically increased her mobile site turnover. What’s more, using our analytics, she could track the comprehensive click-stream by each individual user from SMS delivery to payment, including clicks on mobile ads and product catalog items, as well as customer location and handset characteristics.

The third main benefit, and one our clients really appreciate, is agility. Setting up mobile projects can be a long and complicated process. With RichSMS, they can go live with rich mobile scenarios in just a few days, including an iterative test-and-improve process. That was the experience of one of our clients in the travel industry. Last summer, before the vacation rush, they launched an automated personal assistant in just three weeks. This let them send each guest a daily RichSMS message containing highly customized information on activities available in the resort. Use rate was tremendous, so our client decided to deploy this on a large scale, tweaking and improving it before each vacation season.

Conclusion

RichSMS is a powerful key to unlocking the power of SMS marketing whatever your business goals and industry. Integrated with Adobe Campaign, it makes it easy to seamlessly implement highly personalized cross-channel marketing. The built-in analytics lets you dig deep into data showing each customer’s journey. This makes it simple to identify problems, turning them into opportunities for improved customer experience. It also lets you see what is working and build on it even more.

If you use SMS marketing, what do you think would become possible by using RichSMS? If you already use RichSMS, what’s your experience?

The post Pump Up Your Results With RichSMS appeared first on Digital Marketing Blog by Adobe.

The product just got delivered to the warehouse, the photo and video shoots are done, but now you’re looking at a long lead time while your web commerce team prepares the multimedia experiences for the new products on the mobile and desktop sites. Meanwhile, your competitor could launch a competing product at any time. Your great idea for monogramming? It’s going to have to wait because otherwise you’ll never get this product out the door. And even when you do launch, the experience may not work well on mobile. You can’t get funds to solve any of these problems because no one knows if it will really improve sales.

This example highlights some of the challenges we often hear about from brand and retail marketers and merchandizers when they try to deliver engaging and innovative experiences to all screens. Let’s look at these more closely and then see how Tumi, a leading manufacturer of travel bags, met these challenges.

Provide Visual Detail Without Impacting Product Lead Times

Providing visual detail to online shoppers assures that the product meets their needs. Brands that we work with are reporting that their customers are more likely to buy after looking at a zoom view. You should provide views of the interior of a product, 360-degree views, high-quality zoom views, and even videos of a product to help them see better what they will be getting ─ and you need to do this for every color or style variation of the product. The views have to be photo-realistic to convey the quality of the product and boost customer satisfaction rates.

You will need to shoot all of the angles and views of each product. However, with a good dynamic media solution, you don’t need to shoot dozens of images for different screen sizes. You shoot one master image and the dynamic media server will create a version with the correct sizing, pixel density, sharpening, and cropping on demand and deliver it to the device.

The same is true for alternate views and zooms. Only one master has to be shot for each zoom. You can present these views using dynamic media in a responsive in-line viewer that presents the right size to the right screen with the right quality.

Dynamic media also lets you deliver the right message. Check out this demo page to see how text overlays can be dynamically changed to personalize or customize any image.

These benefits work with video as well. The responsive media players will automatically detect the device type that is being used and choose Flash, Apple, or HTML5 progressive streaming as appropriate for the best video experience. The server will also monitor network conditions and seamlessly switch streaming versions if the bandwidth changes.

Product Customization Does Not Need to Delay Your Product Launch

A modern dynamic media solution will let you show your products in any color offered and even show customizations such as monogramming without shooting every possible combination. You can use the same master image that was already shot and add color/fabric swatches. The dynamic media server will create a high-resolution preview for each product-color combination on demand, perfectly sized, and rendered in photo-realistic detail for any viewing screen.

You can offer monogramming and the server will also deliver previews on demand that show details such as stitching and curvature with picture-perfect accuracy. This starts with the same master image and color/fabric samples.

Dynamic media can be fully integrated with a cloud-based digital asset management system, allowing assets to be viewed immediately by designers, customers, and manufacturing, slashing more development time.

What About Mobile?

When you use dynamic media technology, mobile is taken care of using the same master image. You simply add information on cropping and other quality features for each screen-size breakpoint and the server does the rest. Adaptive streaming and responsive viewers will use your master asset and deliver an optimized experience to any mobile device.

Inform Your Business Decisions With Relevant Data

How do you get C-level buy-in for this great technology? Dynamic media can be integrated with analytics to provide specific, detailed data on how each experience impacts your business results. This data will remove any debate on whether the experiences are worth it. Furthermore, the ability to A/B test any asset or experience will give you the agility to make improvements to any campaign in real time.

Turn Nightmares Into Dreams and Dreams Into Reality

With the right technology, engaging experiences on any screen with fast time-to-market can be a reality instead of a dream. Tumi did just that. They brought their image production in-house and adopted solutions from the Adobe Marketing Cloud. They were able to launch products 40 percent faster, accelerating sales and business results while delivering an improved experience showing multiple photos of each product, including various angles, inside views, and zooms, as well as engaging videos that show features such as telescoping handles in motion.

“With the Adobe Experience Manager assets capability for dynamic media, we only need to create assets once,” says Naveen Gunti, Senior Director of e-Commerce Technology and Operations at Tumi. “The Adobe solution automatically resizes images and videos to any size or format we need, so we can deliver mobile-friendly displays on any device without spending additional time creating assets.”

In addition, Tumi offers personalized monograms, an exclusive feature of Adobe dynamic media, on their bags and luggage. Customers can choose font style and color for a three-letter monogram and see how it looks on their bag or luggage tag in real time. Coupled with the improved visual experience, this resulted in increased engagement on mobile and a 40 percent increase in time on-site.

Tumi measures performance on all of this activity with Adobe Analytics to see how customers interact with the monogramming and dynamic images. They see data about page views and clicks that show how these experiences are affecting the customer experience and sales. Forbes recently reported that they gained unexpected insights into customer behavior. This improves their knowledge of return on investment, helping to drive smarter decisions going forward. “By looking at what people are monogramming, we can rethink how we position personalization services in the future,” says Gunti.

Moving to the Adobe Marketing Cloud has allowed Tumi to cut production time and cost while still delivering a quality, engaging customer experience and at the same time gaining insight into how these experiences affect their online sales. You can read the entire case study here.

The post Tumi’s Travel Bags: Impressive On Any Screen appeared first on Digital Marketing Blog by Adobe.

XLCubed provides the XL3SetProperty() formula, which enables manipulation of many of the core objects such as Grids, Slicers and Small Multiples. It means report users can have simple slicer selections to change the structure of a report, what’s being displayed in a chart, or to vary the chart type. It gives flexibility within the report, but requires no product knowledge from the end user which can be crucial when delivering web reports on a widespread basis.

One common example of usage is where the hierarchy to be viewed in a grid needs to change based on the measure a user selects (depending on the structure of the cube some measures may not be applicable for all hierarchies). Typically that would need to be handled in two Grids, but we can use XL3SetProperty to bring this together, and also to give user choice on the associated Small Multiple Chart view.

The final published report is shown below:

If the user selects an “Internet” measure, we show Customer Geography on rows, whereas a “Reseller” measure should show Reseller Type on rows. The same logic applies to the Small Multiple chart. In the screenshot below, the user has selected Reseller Gross Profit as the measure, and ‘Stacked Column’ as the chart type. You can see that the hierarchy on rows has been switched, as has the split within the individual charts, allowing the user to easily vary their view of the data with simple button selectors.

This is implemented through the following key points:

• A lookup table in Excel to determine what hierarchy is applicable for each measure
• An Excel list showing the available chart types – this is used in the Chart Type slicer
  • The chart slicer outputs its selection into cell $AG$10
• The measure slicer is linked directly to the grid and the small multiple, but also outputs its selection to an Excel cell ($A$B4)
• A vlookup determines which hierarchy to use based on the selected measure
• Three XL3SetProperty() formulae now control what is displayed based on user selections:
  • $AB$7 – sets the grid rows
  • $AB$8 – sets the small multiple columns
  • $AB$7 – sets the chart type

The approach gives a deep level of access to the key XLCubed reporting objects, and enables controlled flexibility within web and mobile-delivered reports. No programming is needed, just a mid-level understanding of Excel itself, and XLCubed.

This is just one example of what the approach can achieve – it’s really limited only by imagination. See XL3SetProperty() for more detail, or contact us if you’d like the example workbook.