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Blog and Article Sharing Corner

TanmayMathur

Community Manager

02-08-2016

Hey Community Folks!

This space is created exclusively for users who write blogs or articles around Adobe Advertising Cloud/Adobe Media Optimizer/Tubemogul and related technologies. You can feel free to post your genuine content around topics like Search/Display/Social Marketing, programmatic ad buying etc. If we like what you have written, we may well include it in our official Knowledge Base Articles and give you the due credit! If you have any questions before posting you can send me a private message.

Hope to see some great content here!

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TanmayMathur

Community Manager

02-08-2016

The Blog Post below is from Monica Lay, Sr. Product Marketing Manager for Adobe Media Optimizer. 

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The Growth of Programmatic Advertising on Social

Seventy-two percent of the US display market will be programmatic by 2017. Staggering? Absolutely. It may seem unusual for some to talk about social networks like Facebook® and Instagram when programmatic advertising is typically associated with demand-side platforms or open exchanges. Well, the way Adobe looks at programmatic advertising centers around three key attributes. It’s automated, transparent, and data-driven. Looking at programmatic from this lens, it’s easy to see why Facebook is a huge force in the digital-advertising ecosystem.

So, where does social fit into the programmatic-advertising world? Well, social-media advertising is a “must-do.” With automated buying, selling, and the ability to reach a precise audience with highly relevant ads, programmatic advertising on social helps marketers run more impactful campaigns. The growth of programmatic advertising is attributed to two factors: efficiency in ad buying and relevancy in ad targeting.

Facebook: A Force Behind Programmatic Advertising
US digital display-ad spend is estimated to top $27 billion in 2017, with 72 percent coming from programmatic. That’s astronomical. That means nearly three out of every four display-ad dollars is spent programmatically.

Programmatic advertising can be complex; it is easier to understand when broken down into two components: Real Time Bidding (RTB) and Programmatic Direct. Real Time Bidding consists of auction-based ads that are transacted in real time at the impression level, mainly comprising of the open marketplace and private marketplaces. Programmatic Direct is the purchase of display ads via an application-program interface (API), whether it’s publisher-owned (like Facebook and Twitter) or facilitated using preexisting RTB technology like a demand-side platform (DSP). Here, buyers typically agree to a set pricing model (CPM) and may or may not agree to a fixed amount of inventory.

Next year, more than half of all programmatic display will be purchased by Programmatic Direct. Even more interesting is that a majority of Programmatic Direct will be driven by the likes of Facebook. Facebook is positioned to represent almost 30 percent of the US digital-display market by 2017.

Why Are Social-Media Marketers Apprehensive About Instagram?
Facebook wasted no time expanding into advertising. By the end of 2014, Facebook branched out to create the Facebook Audience Network, giving marketers access to third-party inventory as well as mobile web. Last year, it launched the Instagram ads API. Obviously, Instagram is hot right now, and marketers are excited, but many are scrambling to learn how to use it as an ad platform, particularly for direct response.

Despite that excitement, there’s still some apprehension. Advertisers often wonder why, if they’re running ads on Facebook, they need to buy on Instagram to reach potentially the same audience. There is also the perception that the content strategy across the two platforms should be vastly different — marketers often assume that they need to spend more time and money creating different assets.

Future Challenges and Opportunities
Digital advertising moves quickly, and the rapid adoption of Facebook in programmatic makes it clear that social advertising is maturing. But, at the same time, it continues to evolve, and better measurement will be the key to proving business value. As gaps in measurement are identified, accessing more robust data and more advanced bidding and optimization across different platforms will likely be a major theme into the future.

In fact, Facebook has been making some progress already on the measurement side with the recent announcement that advertisers are able to run the same ad set across Facebook, Instagram, and the Audience Network. This new feature allows advertisers to reach their target audiences and optimize performances in real time, improve performances for a number of campaign objectives, and provide incremental and efficient reach. Early results to date show positive signs to this new approach to buying and optimization, and I’m looking forward to future whitepapers and case studies from the Facebook and Instagram measurement team.

Today, Instagram and Facebook are destinations where marketers can reach huge audiences on mobile and elsewhere. They’re actually leading the pack in the programmatic space, turning digital advertising on its head. With the continual growth of programmatic on social, social advertising really is a “must-do!” If you’re not currently playing in the social-advertising realm, consider its value and growth as you take a hard look at how it could help your advertising.

Facebook® is a registered trademark of Facebook, Inc.

Read the Original blog post here - https://blogs.adobe.com/digitalmarketing/social-media/growth-programmatic-advertising-social/

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TanmayMathur

Community Manager

05-08-2016

The Blog Post below is from Monica Lay, Sr. Product Marketing Manager for Adobe Media Optimizer.

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To Pay or Not to Pay: Ad Strategies for Facebook

Whether you spend any amount of time using it, Facebook® has become a valuable platform through which businesses can advertise and build brand awareness and loyalty. One recent example I experienced was a business that wanted to do a promotion around the holidays. They were in the process of releasing a new app, so they ran some promotions and gave new users a 50-percent-off coupon for signing up. They created a huge amount of organic content from the promotion and were very successful with it. Now, we all understand that it’s easier to achieve this type of success around holidays or other big events, but how do you stay successful during the rest of the year?

To answer that question, you have to look at two things: creative and targeting. In a basic sense, with better creative and better targeting, you have better performance. What is less obvious is that, because you have better creative and better ad targeting, you will also have a higher click-through rate (CTR) and conversion rate. Directly, that means a lower cost per click (CPC) and CTR, implying you’ll achieve 10 – 30 percent more clicks for the same budget, which translates to better performance in the end.

Creative
When it comes to creative, we always recommend jumping on new things that Facebook releases.

As I agree, when Facebook releases new features, it seems that they give those features extra exposure on the platform because they want them to perform well. This is similar to lead ads; they’re simple and effective. A user will see an ad and click on it. They select what information they want to share, and then the email address is sent to the advertiser. When paired with local incentives or opportunities, it becomes a really nice method for collecting email addresses for businesses in which past advertising performances weren’t that great.

So, it’s good to hop on new Facebook features as they’re released because, if you’re first to adopt — and, thus, get the leverage effect of being the first mover — you will likely have higher performance. What’s nice about this is that an audience network will grow out of this mobile space and into the desktop environment, where you can buy more targeted ads across different devices to continue with your social campaigns.

This is a huge step forward — and a direct response to those who say Facebook hasn’t worked well in the past for advertising — as they continue to evolve and figure things out just as the industry does. Dynamic product ads (DPAs) were once considered only for retail or ecommerce; but lately, we’ve seen other businesses leveraging the dynamic retargeting functionality — even those that are not retail clients such as the travel vertical.

Targeting
Another important concept with regard to targeting is that you shouldn’t overlap. Overlap tends to waste delivery opportunities due to a frequency cap of two per day. In fact, separating your audiences is key to targeting specific users with specific content and not overexposing your content to uninterested people. When it comes to customer audience, one interesting strategy is to use lookalike audiences based on your initial custom audience.

Lookalikes involve choosing an email list that’s derived from your first-party data, and then Facebook figures out groups of people — or audiences — that look similar to the ones that you have. This is also called a tiered lookalike strategy. With this strategy, you start with, let’s say, 1 percent of a target audience, and you bid to the right amount. From those results, you select a second customer audience based on the same criteria, or you select the same customer audience and build a second lookalike tier, excluding the first one. This results in specific tiers containing the 1 percent, the 2 percent, the 3 percent, and so on.

Bottom Line
In the end, when it comes to Facebook advertising, you have to ask yourself, “What do I want to optimize toward?” This could be conversions, clicks, video views, or any other metric that your business is focused on. The next question becomes, “What am I willing to pay?” In this case, you want to ensure that you are receiving true value out of your investment. Here, true value means that you shouldn’t underbid — invest too little and you won’t get the delivery in the ads — but, you also shouldn’t overpay. Make sure you’ve done your research on what is an acceptable price for the intended return on investment.

 

Read the Original Blog Post here - https://blogs.adobe.com/digitalmarketing/advertising/pay-not-pay-ad-strategies-facebook-s204/

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TanmayMathur

Community Manager

05-08-2016

As the digital landscape evolves, the channels that advertisers use become more complex and multifaceted. Increasingly intricate and complementary cross-channel advertising campaigns will become crucial components of an advertiser’s online presence in coming years. In this paper, you’ll learn why understanding the user journey and integrating data and technology is critical for your cross-channel advertising success.

Learn more about:
•    Where search marketing stands in 2015 across the US and UK
•    The recent shift of consumer transactions on their mobile devices
•    What we believe you’ll find is the next big trend in ad spend

Download the Adobe Media Optimizer Whitepaper here - http://www.adobeeventsonline.com/AMO/2016/DigAdvertising/invite.html

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Manu_Malhotra

Employee

08-08-2016

By Manu Malhotra, AMO-Consultant, Adobe

[SEM] Keyword Coverage and Match Type Split: Key to Great Customer Experience

Consumer googles!

You are one shrewd marketer – in your Search Engine Marketing account, the keyword coverage is strong.

Exact keywords get triggered as much as 80% to 90% of the times. And, relevant ad copy is served taking consumer to the correct landing page. Much to your delight, consumer converts, Bingo!

Everything falling in place, that’s too fairy, right?

For us, the Digital Marketers, that is exactly what we want. Everything should beautifully fall in place. Practically, that is far from reality and when accounts go big, spend levels increase, structures become humongous as well as complex it is a job easier said than done.

In such a time, taking control of keyword match type split – making sure it is healthy makes a huge difference.

What is Match Type Split?

Match Type Split is the Revenue/cost share of ‘Exact’, ‘Broad’ and Phrase (and other) match type in your account. This analysis helps the user evaluate the impact of recent account work on account performance by reporting spend and revenue share of each match type over time.

Why does it matter?

A healthy match type split with strong Revenue/Cost share of ‘Exact’ is desired. It ensures most relevant Keywords get triggered and hence customer will be served with most relevant ad copy resulting in a higher CTR. In my personal experience, I have seen significant improvements in CTR by making sure search terms are being analyzed regularly. Frequency and exhaustiveness of implementation of search term reports depends upon various factors such as lifecycle stage (introductory, growth, maturity or decline) of the branded products –such as iPhone and other electronic devices and overall maturity of account. Initially, when these products are about to be launched – customers tend to search a lot about product specifications and features. That is the time to take control of match type split more rigorously.

How Adobe Media Optimizer makes life easy?

Adobe Media Optimizer, a flagship offering by Adobe as part of Adobe Digital Marketing Cloud which works on portfolio theory makes life easy in many ways. As an adjunct functionality to Adobe Media Optimizer, frontier tool provides a simple yet very impactful tool in ‘Match Type’ “[M]”. It provides spend and revenue distribution across match types over the last 30 days. By choosing portfolios we can easily identify which portfolios need more attention – which portfolios (congregation of campaigns with same objectives) have a poor Revenue/Cost share of ‘Exact’ Match type? Whether broad keywords drive irrelevant traffic is also evaluated.

In addition, it also provides Daily RPC, Daily CPC AND Daily RoI for each match type for last 30 days.

What do you achieve?

Aiming at having a high exact match type share by cost as well as revenue along with a healthy quality score results in

  • lower CPC
  • higher CTR
  • higher RPC
  • higher ROI
  • And to say the least, A Great Customer Experience.

Do share your own stories about how you are creating great customer experience in Search, Display or Social Media marketing. I will be happy to hear and engage.

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This article may be reproduced without my permission as long as it attributes in full and includes a link to this piece. (https://manumalhotra.in/2016/06/06/sem-keyword-coverage-and-match-type-split-key-to-great-customer-e...)

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TanmayMathur

Community Manager

08-08-2016

The Blog Post below is from Sid Shah, Director of Business Analytics for Adobe Digital Marketing Business.

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Capturing Real-Time Digital Behavior for Programmatic Advertising Buys

There’s a lot of talk about Big Data and who is using it best. Everyone claims that they’re going to tackle data in a new way. But, at the end of the day, behavioral data is only useful if you have a tangible way to make it actionable. One way to make digital-behavior data actionable is to use it to drive your programmatic-advertising campaigns. We have seen a number of brands implement this strategy with very strong results.

At its core, analytics is used to track users’ digital behaviors — where your traffic comes from, which pages a user visited on your site, which page was the last a user visited, and so forth. Data science allows you to understand your customer behavior very well. Data science can apply algorithms to this digital-behavior data that allow you to segment your users into various personas. You can create an almost unlimited number of segments with these algorithms, allowing you to target each of your campaigns to a specific subset of users.

Why Programmatic Advertising?
The purpose of programmatic advertising is to increase the likelihood that someone will purchase from your brand. Programmatic advertising can be improved by using digital-behavior data to optimize your advertising campaigns. With access to behavioral data, you can more effectively personalize your online advertising campaigns to increase sales.

For example, if a user on your site browses for luxury bags around a $200 price point but does not make a purchase, you can use behavioral data to understand what types of campaigns that user may be most likely to respond to. You can look at her demographic information and behavior to understand which segment of users she most closely represents. You can get answers to questions like:

Which other users have been looking at luxury bags at $200 price points?
What did they end up purchasing, if anything?
Which ad were they last shown before finally converting?
What platform were they on when they viewed the ad that prompted their conversions?
Were they likely to shop via mobile?
Did they prefer making purchases online or via brick-and-mortar stores?
From there, you can better understand what types of ads users in this segment respond to, allowing you to carefully personalize your online advertising campaign. This can include specially crafted in-app messaging, discounts for related products, targeted remarketing campaigns, and more.

Programmatic-Advertising Benefits
Ultimately, when digital behavior is used to improve programmatic advertising, organizations see an average additional lift of 18 percent over ads where behavior has not been utilized. You can actually mold and transform the data to display more relevant ads and then display those ads to appropriate audiences in a very customized way. The more data you have access to, the greater your organization’s ability to parse data and get down to a more granular level of understanding. Simply put, the more behavioral data you have, the greater your ability to send the right ad to the right audience at the same time.

The beauty of the technologies at the center of digital-behavior and programmatic-advertising integration is that they get marketers out of the business of manually parsing data. This allows them to focus their energies on more strategic marketing decisions. They have opportunities to reach broader audiences with increasingly more personalized messages. Being able to have a wider reach, while also increasing personalization, essentially means you are reaching more people more effectively and at a lower cost than ever before. With all of that on the table, who wouldn’t want to be able to integrate behavioral data into their programmatic-advertising decisions?

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Read the original blog post at - https://blogs.adobe.com/digitalmarketing/advertising/capturing-real-time-digital-behavior-programmat...

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TanmayMathur

Community Manager

10-08-2016

The Blog Post below is from Pete Kluge, Group Product Marketing Manager for Media & Advertising Solutions at Adobe

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Programmatic Display: Make the Most of Your Data With Today’s Technology

By 2019, 50 percent of display ads are forecasted to be transacted programmatically. Access to data and technology is driving this growth. Advertisers have access to increasing amounts of data, including site-visitor and partner information, demographic- and business-attribute data, and even offline info such as customer-relationship management (CRM) data — and all that can be used for targeting. Advertisers also have access to advanced technologies — like demand-side platforms (DSPs), ad exchanges, and data-management platforms (DMPs) — that they can use to target users in real time across channels and devices.

In North America, $11 billion is projected to be spent on programmatic advertising this year, with forecasts of $30 billion by 2019. Yet, at the end of last year, when marketers were asked their levels of understanding regarding programmatic, only 23 percent used programmatic actively, 36 percent were aware of programmatic but were not using it regularly, and 41 percent weren’t using programmatic at all. Programmatic is in its early stages, and its level of awareness is still growing.

During a presentation I put together for Adobe Summit 2016, we explored the programmatic world and how it helped Redbox, Chegg, and eHealth meet their advertising objectives. Here are some of the insights we gained:

Redbox: Time Savings and Better Performance Through Site-Analytics Integration
Redbox deploys audience targeting via Adobe Analytics, resulting in time savings and improved ad performance.

Redbox is a movie- and game-rental business that is focused, like many businesses, on driving a greater return on ad spend (ROAS) from its digital marketing programs. They target a number of different audiences — from online users who visited and interacted with the website to entertainment junkies to families — for display retargeting and prospecting programs. This often requires that their information technology (IT) department put relevant, trackable content on their site to build audience segments.

Redbox already used Adobe Analytics, which tracks, organizes, and reports on what different segments of customers do. Instead of deploying new tags for tracking, Redbox leveraged the integration between Analytics and the Adobe Media Optimizer DSP and used the existing Analytics audience segments for retargeting.

This integration saved Redbox time when creating, launching, and testing new segments; and it drove better performance due to access to granular audiences — all while reducing reliance on their busy IT department. Using Adobe Media Optimizer and Analytics segment targeting, Redbox achieved:

A 30 percent lift in return on ad spend (ROAS) for retargeting campaigns,
A 3x lift on ROAS for a specific campaign, and
More than 8 – 10 hours/week of time saved.


Chegg: Using Multiple Data Sources to Effectively Reach High-Value Audiences
Insight from the programmatic process helped Chegg infer and expand its audience segments to improve returns and lower acquisition costs.

Understanding your audience segments is a key factor in programmatic advertising. In the ‘old days’, Chegg — an online business that supplies textbooks, online tutors, internship opportunities, and one-on-one help for students — bought advertisements from specific websites where they assumed students would be. They didn’t have insight into the return on investment they would receive, and they didn’t know what they were getting from their advertising buys. Insight from their programmatic ad-buying solution helped Chegg infer the data they needed.

Chegg wanted to figure out the type of content they had on the site and where students were actually going. For example, Chegg was interested in knowing whether students were searching for and viewing a particular science, technology, engineering, and math (STEM) book as well as other relevant information about the students.

Next, Chegg looked at STEM students and student grade levels. They identified both of these characteristics as being worth a certain amount of investment. However, it wasn’t until they merged the information that they actually started finding real value. They found that, while a freshman is worth about 1.5 times the lifetime value of a student, and a STEM student is worth about 25 percent more than that, the group that holds both freshmen and STEM students was found to be worth two times as much — greater than the sum of its parts.

They can now put their different groups into a ROAS curve to determine who they should be spending money on first. Overall, they saw a 22 percent drop in acquisition costs (and when you’re spending millions of dollars in this space, that’s a huge savings). Even better, the students they are targeting have the potential to give them twice the amount of returns or loan-to-value (LTV).

eHealth: Data Transparency and Integrated Ad Stack Drives Smarter Decision Making
In early 2015, eHealth — the nation’s first and largest private health-insurance exchange — overhauled its remarketing program. Since only a small percentage of people actually convert on their first visits, remarketing is very important to them. To achieve this, eHealth needed to reengage with its customers and rekindle the health-insurance conversation.

Prior to working with Adobe, eHealth was using five different platforms for display advertising. This resulted in reporting challenges such as duplicate counting of conversions, network competition in bidding, and discrepancies on audience and performance reporting. By using a combination of Adobe Analytics, Adobe Media Optimizer, and Adobe Audience Manager, eHealth has a more unified view of its audience and reporting across its media. All of the pixeling throughout the site is done uniformly, so activity is being recorded equally, allowing eHealth to drill down into its audience and form a more granular definition of who they are.

As an example, they’ve taken a look at their “quoter” audience. A quoter is someone who has visited the site and looked at various health-insurance plans but hasn’t actually purchased a plan. By knowing their customers, they’ve been able to segregate quoters who are moms versus dads. This allows them to serve up messages and creative that would appeal more to each audience segment, resulting in higher conversions.

In the end, this newfound transparency into audiences and media buying resulted in efficiencies and cost savings, whereby, eHealth reduced media-buying costs (CPMs) by $.22 during their last open-enrollment period and beat their cost-per-acquisition (COA) target by 19 percent.

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Read the original blog post at - https://blogs.adobe.com/digitalmarketing/advertising/programmatic-display-make-data-todays-technolog...

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nidhik

10-08-2016

By Nidhi Kapoor, AMO- Senior Consultant, Adobe

Edit: This post has been published as a Knowledge Base Article for Adobe Media Optimer and can we viewed here

Crucial Role of Cost Models: Managing Frequently Changing Budget

Managing a budget is no less than an art. It is a mixture of skill, patience and little creativity with an effective technology.

As managing budget is just not a one shot deal and requires an active monitoring and managing skills as well, technologically strong platform with its guidance can help drive the management in right direction. This is exactly where Adobe Media Optimizer (AMO) and its technology comes into picture and helps in doing the job at its best. Beauty of AMO’s features is that it guides the management of budget in such a way that the conversions/objective would not get disturbed and actually maximized. 

AMO provides various options that can be used in managing day to day spend targets at portfolio level. Such as:

  1. Daily spend target
  2. Spend strategy
  3. Campaign caps and multiplier
  4. Auto adjust campaign budget limits
  5. Mobile bid adjustments
  6. Constraints
  7. Learning budgets
  8. Models – Cost Model and Revenue Models and their half life’s
  9. Campaign bids

In an account where the budget amount varies frequently every month and the history of traffic and conversions inflow has been strong, the ‘Cost Model’ feature of AMO helps a lot in effective budget management. Particularly the case when the budget has been reduced suddenly by big percentage and the history of previous month has been strong. The unexpected pulling interferes with AMO’s gathered information and takes time to get under control. This is where cost models provide the detail information at bid unit level. To recap, the bid unit in AMO at search level is actually a ‘keyword + match type’.

Brief about modelling

Modeling can be understood as forecasting (i.e. predicting performance). *

Based on gathered information, the AMO comes up with forecasts of how the bid unit can be expected to perform and based on the objective, it will optimize the bid for each bid unit, such that the objective is maximized. *

Steps of data collection to produce models

  • Advertisers run their campaigns on search engine.
  • Campaign’s click and cost data info. gets collected.
  • Conversions data is also captured from client website in AMO.
  • AMO then processes the data captured to come up with analysis, forecast in the form of models.
  • To create models, AMO gathers information at various data points (bid vs cost, bid vs CPC, bid vs. clicks, bid vs impressions, bid vs. position etc.) and predictive modelling technique is used to analyze and generate data.

There are two types of models a) Cost Models b) Revenue Models. These reports and models can be seen on dashboard. Let’s discuss the role of cost model and its contribution below.

Role of Cost Model

Cost Model Accuracy guides how the bid unit is performing as per its predicted performance*.

The model can be accessed through ‘Portfolio cards’ tab and then selecting ‘Model accuracy’ option. It can be studied at following level:

a) Click volume level

b) Bid unit level

c) Device level

d) Mobile by ad group level

Close to 100% cost accuracy is the best figure to have in any account. If it is higher than +10 to +20% depending upon the use case basis, cost model should be studied in detail at all levels to understand from where the major cost is coming from. ‘Bid unit level’ is an important section to focus on in depth. It actually provides a detail on list of bid units where how much cost and click is incurred in actual performance than predicted value. This performance data then can further be analyzed where cost and clicks are higher than the predicted values. Once the list of bid units is shortlisted which are spending more than required then the next step is to study those bid unit’s search terms in detail. To do so, search terms at search engine or AMO level can be evaluated to ensure keywords are matching to right terms particularly of those keywords where our models are showing discrepancies. By setting those terms as ‘exact negatives’ and ‘cross positives exact’ at right ad group level help control the following metrics:

  • Inaccurate impressions
  • Irrelevant clicks
  • Exact match types ratio
  • CPC’s

The above action helps in immediate control of spend on irrelevant search terms. This helps not only in controlling the day to day spend but also further metrics such as CTR as it controls the unwanted impressions. CPC’s also get improved as exact match type increases and also the appearance of ads on relevant keywords only. As relevancy increases, it helps in improving the quality score further which in turn improves the CPC’s and avg. position. As a result of these basic metrics improvement, CR also gets better as the cost and the overall relevancy factor are getting controlled.

Therefore, the cost model accuracy information plays an important role in understanding the spend level path and helps taking the optimization action in right direction.

Hope this information helps in analyzing the cost model data, feel free to share your views and engage here.

                                                                                                                                                                                                                                                                                     *Definition taken from AMO training material from Gauri Bhat.

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TanmayMathur

Community Manager

16-08-2016

The Blog Post below is from Pete Kluge, Group Product Marketing Manager for Media & Advertising Solutions at Adobe

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Taking Programmatic Advertising to the Next Level with Adobe Media Optimizer

Consumers are engaging with brands across multiple devices and digital channels and they’re expecting a personalized, consistent and compelling experience whenever and wherever they’re accessing content. Advertisers have access to deeper data insights about their audience than ever before to deliver relevant content at scale in real-time. The disruption of our computing and business landscape is forcing companies to rethink everything – including the ways they engage customers and prospects through digital advertising. Advertisers need technology that enables them to harness their data to deliver integrated and customized experiences that consumers have come to expect to drive an “Experience Business.”

Adobe Marketing Cloud Audiences Inform Dynamic Ad Experiences

Advertisers can use audience segments built in Adobe Analytics, Adobe Audience Manager or Adobe Media Optimizer (AMO) to enable a unique creative ad layout or frame of reference. This allows advertisers to alter an ad’s experience for each audience while maintaining automated granular creative decisioning based on customer intent. AMO Dynamic Creative Optimization (DCO) automatically assembles creative elements (product, price, image, promotional copy, colors) based on the user and audience.

For example, a traveler may visit a hotel website, and search for a hotel and travel destination. Later as they surf the web, the traveler would see an ad that contains the hotel that they viewed, city, discount price offer and promotional copy – thanks to AMO DCO.

Adobe takes this consumer experience further by personalizing at an even deeper level. Marketing Cloud audience segments built using CRM, site visitor, partner and third-party data can inform more personalized ad experiences. For example, airline or hotel loyalty program status could trigger specials or a rewards-related ad layout. Third-party data may inform luxury hotel content (based on income range), family vacation specials (households with children) or business travel promotion (business attribute data). Also, a marketer could create a segment of high value users (i.e. business travelers) in Adobe Campaign for e-mail/cross-channel marketing and then deliver an engaging ad experience to that audience using AMO DCO.

Through integration with Adobe Marketing Cloud, Adobe Media Optimizer allows advertisers to deliver unparalleled digital ad experiences, with relevant and personalized content, for their customers and prospects. AMO’s integration with Adobe Marketing Cloud offers companies greater cookie coverage and reach into high value audiences due to its cross-channel engagement with consumers.

Complete View of Advertising Activities in Adobe Analytics

We also announced a new integration with Adobe Analytics for display advertising. Advertisers are already enjoying the benefits of the bi-directional integration for search and the ability to reach Analytics audience segments in display. Now the integration is being expanded to be bi-directional for display, giving advertisers insights into display campaign activity (impressions, clicks, cost, conversions) for view and click-based converters from AMO display campaigns in Analytics. With these enhancements, we’re providing reporting insights at the ad strategy level — even for DCO and video campaigns. Adobe Analytics’ engagement metrics for view/click-based converters are available in AMO for optimization and campaign reporting.

AMO display campaign activity for view and click based converters in Analytics reporting

AMO display and search campaign activity in Analytics reporting

Cross Channel Attribution and Path to Conversion Insights

Do you wonder what the impact is of your dynamic creative or video campaigns in relation to your search or Facebook campaigns – or which channel(s) are generating the most revenue? Well, Adobe Media Optimizer helps answers those questions.

AMO uses a common conversion tracking pixel across all channels to give advertisers an accurate view into attribution, as well as insights into the path to conversion – for display (DCO, video, banner ads), search, and social (Facebook, Instagram) channels.

Adobe Transforms Programmatic Advertising

As businesses reconsider their approach to engaging customers in the rapidly changing digital environment, and consumers expect more compelling, personal experiences, we’re continuing to innovate our programmatic ad buying platform (Adobe Media Optimizer) with industry-leading dynamic creative optimization capabilities and integrations with Adobe Marketing Cloud. By connecting Marketing Cloud audiences to programmatic advertising, Adobe Media Optimizer is redefining digital marketing and advertising to drive consistent, compelling and customized experiences across digital touch points.

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Read the original blog post at - https://blogs.adobe.com/digitalmarketing/advertising/amo-update-emea-summit/

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TanmayMathur

Community Manager

19-08-2016

The Blog Post below is from Giselle Abramovich, Senoir & Strategic Editor, CMO.com

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5 Things you won't believe about Programmatic Advertising 

Brands and agencies alike certainly appreciate the efficiency that programmatic brings from a pricing standpoint. They also like its scalability, the way it brings in immediate returns, and that it clearly shows how messaging is optimized.

But the truth of the matter is, programmatic isn’t all rainbows and unicorns, and much is often left unsaid. Until now. CMO.com reached out to the industry to get perspective on some of the unspoken truths behind programmatic. Here’s what they told us.

1. Brands Are Starting To Bring Programmatic In-House
An AOL study from October found that 68% of brand advertisers plan to bring programmatic in-house in the next 12 months. 

“That’s a massive number and a massive undertaking,” said JoAnna Foyle, AOL’s SVP of client services and operations. “It’s a massive undertaking because you need to be rethinking how you structure your advertising spend, your organization, and how you set up your technology stack and manage your data.”

According to Tim Waddell, ‎director of product marketing at Adobe (CMO.com’s parent company) and the company's resident programmatic expert, the lack of transparency in pricing is one of the reasons why brands are now bringing programmatic in-house.

“We are seeing a lot of interest from customers to bring advertising management in-house and running it on their own," he told CMO.com. "This is because they want more insight into the costs of technology and media vs a black box scenario. Agencies are recognizing this trend and realizing that transparency is a critical topic in the market."

2. Setting Up A Private Marketplace Is No Small Feat
Some companies jump into programmatic, and especially private marketplaces, thinking it’s easy. Many companies think the efficiencies abound and costs plummet after the initial setup. This is not the case, according to Joe Laszlo, VP of industry initiatives at the Interactive Advertising Bureau (IAB). Many companies think that private marketplaces, in particular, are turnkey solutions, he said. However, a lot of fine-tuning needs to happen.

“In real life, private marketplaces can be incredibly beneficial for buyers and sellers alike, but there’s a lot of work that goes into setting up a private marketplace correctly so that you get those efficiencies,” Laszlo told CMO.com. “The IAB actually put out a checklist last year that goes through all of the things that buyers and sellers alike need to do in the process of setting up data, a private marketplace, just to remind everybody that it’s not like you flip a switch and the cost savings start rolling in. It takes a lot of planning and a lot of effort and a lot of thought to use them successfully.”

3. Programmatic Isn’t Transparent
Programmatic, while it does aim to make things a lot more efficient, doesn’t necessarily make them more transparent and, in some ways, makes the process of transacting, buying, and selling ad inventory more opaque. That’s a challenge, IAB’s Laszlo said.

“It shouldn’t dissuade anybody, but I think it’s something that people need to keep in mind when they think about maximizing the value of programmatic,” he told CMO.com.

This “opaqueness” comes up the most when it relates to location data. When a company is transacting programmatically and a piece of ad inventory with location data is appended to it, there’s an automatic mistrust of that location data because you don’t really know where it’s coming from, Laszlo said. It could be accurate, generated by the GPS on a device and routed with the end user’s knowledge, or it could be somebody knows they can get a slightly higher bid by making up a latitude and longitude and sticking it into the RTB description of the piece of ad inventory, he said.

“At the end of the day, automation is bringing all sorts of new data streams online that can make a buyer much more confident that they’re reaching somebody who’s going to be likely interested in their message,” Laszlo said. “But you don’t always know where that data is coming from. There’s a level of trust that’s still needed between an ad buyer and the DSP that they’re working with, between the DSP and the various DMPs that it’s working with, and all the different acronyms up and down the chain.”

4. Programmatic Is Complex
Many brands view programmatic as a “magical solution” that solves all of marketing’s challenges, Adobe’s Waddell said. But programmatic requires a lot of technology, data, and audience building.

“The No. 1 issue is making your programmatic technology work together with existing technology,” Waddell told CMO.com. “Operations, IT–everyone’s got to be involved to some degree. And for those companies that are bringing programmatic in-house, it’s even tougher. I’ve seen discussions out there on the market where it’s hard to find the right people. There’s not a huge mass of them just sitting out there waiting to be hired.”

And the trouble doesn’t end once the technology is all set up and ready to go. There’s also the issue of data. With programmatic, data is key, but marketers are still struggling with harnessing the right data. According to Waddell, marketers need to be using the data to build audience profiles they’re looking to target.

“Once I’ve got the audiences defined, I’ll need a DMP and someone who knows how to run programmatic for the business, whether it’s someone in-house or at an agency. And I’ll also need the analytics system as well,” Waddell said. “Now that I know the people I want to reach, I actually need to reach them. Should I  use search, display, social, email, TV? Or maybe video and mobile? Maybe I’ll decide to dabble in each. Well, guess what? Now I need technology that connects to all those different data sources.”

5. Programmatic Won’t Ever Replace The Human
Media buying will never completely become a machine-only function, according to Warren Zenna, EVP, managing director at Mobext.

“It's always going to be easier and more effective if you go at it from a direct-buying approach, particularly if you are really interested in not only the message, but in the environment in which the message resides,” Zenna told CMO.com. “So even though we have these programmatic PMPs that do, in fact, close out a lot of inventory that you distribute across–let’s call them nondesirable publishers–it’s still not as good as directly tying right into the publishing networks in a way we know is successful and consistent.”

That’s also why programmatic, while it works well for direct response campaigns, is not as effective for branding, he added. “We’re finding for many of our clients who are more interested in branding than direct response, automated buying doesn’t really work for them to get the kinds of outcomes they want,” Zenna said.

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Read the original blog post at - http://www.cmo.com/features/articles/2016/2/10/5-facts-no-one-wants-to-tell-you-about-programmatic-a...