CONCLUSIONS PAPER

Maximizing Digital Advertising in a Customer-Centric Marketing StrategyHow to Get the Most Revenue from Online Properties

Featuring:

Wilson Raj, Global Customer Intelligence Director, SAS

Jeff Wood, Senior Director of Intelligent Advertising, SAS

Insights from a webinar sponsored by the Association of National Advertisers (ANA) and SAS

SAS Conclusions Paper

Table of Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Legacy Systems Can’t Keep Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

The Development Path to the Present . . . . . . . . . . . . . . . . . . . . . . . . . 4

Four Great Ideas for Digital Publishers . . . . . . . . . . . . . . . . . . . . . . . . . 4

One Solution for All Digital Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Simulation-Based Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Business Intelligence with Data Visualization . . . . . . . . . . . . . . . . . . . . 6

Analytic Insights to Allocate Inventory Intelligently . . . . . . . . . . . . . . . . 7

Intelligent digital advertising in action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

For More Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

About the Presenters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

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Maximizing Digital Advertising in a Customer-Centric Marketing Strategy

IntroductionTwo decades ago, it was just an idea . Today, it’s a fast-growing, multibillion-dollar industry . Online advertising continues to evolve and transform, from the humble beginnings of the website banner ad, to the later advent of pay-per-click models such as Google’s AdWords, to more recent innovations in demand-side platforms (DSPs) and mobile and video ads – and their use in location-aware mobile devices, such as smartphones and iPads® .

According to a June 2012 report from the Interactive Advertising Bureau (IAB) and PricewaterhouseCoopers (PwC), online advertising revenues grew about 15 percent in the first quarter of 2012 compared to the first quarter of 2011 – after having jumped 24 percent for that period the previous year .1 In the US, online advertising brought in $8 .4 billion over the first three months of 2012, the best first quarter the industry has recorded to date .

Mobile, social and video are responsible for most of this growth . Analysts estimate the mobile advertising market to be $5 billion worldwide and predict that advertising revenue on social media channels will reach $10 billion in 2013 .

Even if some of this revenue has been cannibalized from traditional advertising properties, digital advertising still represents quite a bonanza . However, owners of online properties are struggling to manage and sell advertising inventory in a way that creates a rich customer experience and maximizes the benefit to ad buyers and sellers alike . There several reasons this has been difficult:

• Proliferation of channels. Consumers use multiple digital channels and devices that use different protocols, specifications and interfaces – and they interact with those devices in different ways and for different purposes . “There are just so many more digital media channels to reach,” said Jeff Wood, Senior Director of Intelligent Advertising at SAS . “You have mobile, video and display, and when you span that out, you’re delivering to notebooks, tablets, set-top boxes for TV, game consoles, smartphones and more .”

It can be a challenge to deliver the right content, especially when consumers expect their interactions to have a greater immediacy and personalization than ever . And they expect advertisers to remember them, respect their preferences and be relevant . The digital publishers that have a handle on their audiences across channels are going to be able to make the most persuasive case to ad buyers .

1 “Online Advertising Revenue Sets Record High in First Quarter”, June 11, 2012, Lauren Indvik on Mashable.com.

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SAS Conclusions Paper

• Increasingly complex inventory management. In the early days of online advertising, inventory was simple: A publisher had a place for a banner ad on a homepage, made an educated assumption about likely visitors to that page and sold the online real estate that fit best . Servers didn’t recognize visitors as members of specific demographics, so there was no expectation to serve tailored ads . Inventory management is infinitely more complex now . Behavioral and contextual targeting enables publishers to tailor the ad experience for specific demographics, so inventory can be sold hundreds of different ways, with different results . Furthermore, there are more types of ad content being produced, such as floating and interstitial ads, streaming video and mobile – making inventory management a much more complicated task than in the past .

• Intensifying competition. Unlike the print media industry, where the cost of entry is high, websites are comparatively easy to establish . With new properties launching every day, competition for advertising dollars is increasing . In addition, online advertising is highly skewed, with the top 10 ad-carrying Web properties earning as much as one-third of all Internet page views, which leaves smaller properties fighting for their share . And traditional publishers are no longer the only players; popular social media sites and mobile applications are offering lucrative advertising opportunities as well .

• Mobile challenges. “Digital publishers are just now feeling the shift from the PC to mobile devices,” said Wood . As 10 to 20 percent of their traffic moves to mobile devices, new questions and challenges arise . For one, there’s the imperative for device detection, to understand how to best serve the content . With mobile devices, you can’t just drop a cookie and redirect users to a third party for inventory . There’s potentially the need to have software development kits (SDKs) to handle the mediation layer and appropriately deliver content within apps . And how do you get customers to register or subscribe so you gain valuable information about them?

• Exploding data volumes. “In the wake of using these channels, consumers leave behind a whole trail of data,” said Wilson Raj, Global Customer Intelligence Director for SAS . “That data can be harnessed and reused to create a richer and more relevant customer experience . It is about managing the explosion of data, finding the right data from big data, and using it in a way that enables you to monetize those channels and make the right decisions .” As a result, marketers must be just as strong in analytical thinking and marketing science as they are on the creative side .

Amid new constraints and complexities, how can digital publishers maximize revenues from their online properties? Faced with an ever-growing number of media channels, devices and selling models, how do you identify the strategies and actions that will deliver the best revenue returns?

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Maximizing Digital Advertising in a Customer-Centric Marketing Strategy

Legacy Systems Can’t Keep UpSystems and processes that were sufficient for managing digital advertising 10 years ago – or even five – aren’t keeping pace with the needs of the industry today . Third-party vendors have been creating tools that address specific pain points in the digital ad delivery process, but these technologies have limited effectiveness because most of them address only one issue .

Publishers are then forced to use multiple tools – with some using six, seven or more . In an informal poll during the webcast, 87 percent of respondents said they use more than one system to manage digital advertising; 37 percent used more than three . When multiple systems with different taxonomies are used, efforts are duplicated, training becomes more difficult, and problem resolution becomes infinitely more complex .

This multisystem approach is not surprising in an evolving industry, said Wood . “Each new digital environment brings different challenges – different formats, sizes, targeting attributes and data that can be collected – and you have to have a solution that’s ready for that .” For example, as advertising advanced into animations and video, publishers gained the opportunity to collect many more data points than with static ads . As advertising expanded to mobile devices, many publishers needed SDKs to interwork with the various types of smartphones or tablets . Each channel presents unique aspects to overcome .

“So-called ‘best-of-breed’ players have popped up . They are good at solving the problems of getting the advertisements to these devices, but the challenge now is how to use these business systems across channels,” said Wood .

Integration is essential, not only across channels but across process areas . With older technologies, there’s little or no communication among the applications used in the ad sales and delivery process – which, in turn, prevents publishers from seeing the big picture . For instance, an ad server can report impressions delivered, but if it doesn’t integrate with analysis tools, a publisher has difficulty knowing if those impressions were sold at the right price, or how inventory could be better packaged to maximize value and performance .

When asked how they would rate their current data and/or analytics capabilities for digital advertising, our webinar audience gave themselves a B+ . About 40 percent said their analytics were only “moderately effective,” with 50 percent saying “somewhat effective .”

“So-called ‘best-of-breed’

players have popped up.

They are good at solving

the problems of getting

the advertisements to

these devices, but the

challenge now is how to

use these business

systems across channels.”

Jeff WoodSenior Director of Intelligent Advertising, SAS

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SAS Conclusions Paper

The Development Path to the PresentWood is the former CEO of aiMatch – creator of a digital advertising server, now part of the SAS Intelligent Advertising division . “Our team has been building ad serving technology and the publisher advertising stack for about 15 years,” said Wood . “We built the Accipiter ad server, which was later acquired by Atlas and then Microsoft . The majority of the aiMatch team had worked at Microsoft, building out its publisher advertising stack, so we’ve had a great learning experience and gained a lot of insight into how to do better .

“There has been so much evolution in the industry that, at Microsoft, we eventually got to the point where we were evaluating buying products from six or seven different vendors and integrating those platforms together . Conceptually we did the right thing; we did a great job putting together the right pieces of the publisher stack . But since the solutions used different taxonomies, had evolved at different times and had been integrated to the solutions before them, we spent a lot of time integrating those technologies . We were getting bottlenecked on the innovation side, and it was getting a little bit harder to move forward .

“We decided to start over, asking, ‘If we were to build the publisher technology stack today, how would we build it?’ That led to very interesting conversations, because of the wealth of new data available for targeting and servicing customers . Things such as social networking, social media, location-based devices, apps and more have transformed our industry in the last three to five years . We had done a good job building this platform 10 years ago, but it wasn’t designed for all the new data available today . In the past, a publisher might have known five or six things about its audience . Today there’s the opportunity to know 35 or 50 things about the audience .”

Wood’s team wanted to build a system that could harness all that data – first-party and third-party data – and give publishers control over it to make better decisions . The first goal was to help the publisher know more about the audience . When an advertiser asks, “Do you have this audience?” the publisher needs to be able to say, “Yes,” and have the data to back it up .

The second goal in developing the new system was to empower the publisher for self-service business analysis – to assemble order data, financial data and ad serving data all in one place to analyze performance and identify revenue opportunities in businesswide context .

Four Great Ideas for Digital PublishersWood and Raj discussed several ways digital publishers can better manage their advertising inventory and address the challenges and complexities of emerging technologies .

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Maximizing Digital Advertising in a Customer-Centric Marketing Strategy

One Solution for All Digital Devices

“If the goal is to optimize customer engagement across devices so you can improve the relationship between [ad space] buyers and sellers, then you need to focus on being able to treat the customer the same across devices,” said Wood . “You don’t want to treat that customer as a brand new, unique visitor across each digital channel . You want to know what you already know about them from the first channel, and treat them with that respect .

“Today there is fragmentation . Some companies have a solution for mobile ad serving, another for video ad serving, another for display, and so on . That approach probably isn’t sustainable over the long haul, because of the way digital is evolving so quickly . Publishers need an approach that allows core business systems to work across mobile devices and platforms .”

Visitor profiles, frequency management, forecasting, attribution and analysis should all work across devices . Advertisers want to be able to frequency-cap a multichannel campaign, or perhaps tell a story that builds in sequential steps across contacts . Digital publishers need to be able to forecast inventory, handle workflow, manage attribution and understand performance in a holistic way rather than channel by channel .

Simulation-Based Forecasting

“The biggest pain point publishers have expressed to us over the last 15 years is forecasting,” said Wood . “Forecasting is a really tough thing . When you’re trying to forecast available inventory, you are trying to forecast decisions that are going to be made by your ad server . That ad server is pretty dynamic, always changing as you put in new features and change the way you’re buying and selling .

“In the past, our approach to forecasting was to build a second system that can mimic what the ad server might do, and tell us how much inventory is available . That was a good idea, but it wasn’t as effective and efficient as it could be . When we looked at it this time around, we came up with a new approach that reflected the availability of new data sources, cheap data storage and processing power – and the cloud .”

Cloud computing makes it possible to run simulations on a copy of the ad server, using whatever sample set of data the analyst wants to use . “We actually store all the data from all visitor sessions – how they interacted with the site, what pages they saw, what ads they saw, what ads were frequency-capped, and so on . We can then build a future data set based on that historical data and run that through the ad server in a simulation format .”

Wood’s developers were just trying to answer questions about available inventory, but in the process they also equipped publishers to model and make a host of decisions based on past data projected into the future . Publishers can forecast the outcomes of an endless number of scenarios simply by tweaking pricing, placement of ad inventory, changing the policy for frequency capping and so on .

“Simulation-based forecasting

is unique in that it uses all

historical information, including

distinct visitor session and

page-level information. This

information is modeled to create

a ‘future impression request’

data set that we run against our

actual delivery engine, rather

than an algorithm written to

mimic a delivery engine.”

Jeff WoodSenior Director of Intelligent Advertising, SAS

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SAS Conclusions Paper

“By incorporating pricing data, order data, historical site traffic and usage patterns into our forecasting, we are able to provide a holistic view of how inventory will be consumed by the site visitors, how certain campaigns will compete for this inventory, how audience and contextual segments will overlap, and how the inventory is valued by the publisher against rate card,” said Wood . “This view enables a publisher to uncover high-value segments and products that would best utilize the inventory available .”

Business Intelligence with Data Visualization

“When you’re collecting a lot of data and you want to get meaning out of it, you have to have the right tools,” said Wood . “When we introduced the concepts of business intelligence and data visualization to our customers, it was surprising to get their reactions . We gave them the ability to ask any questions they wanted and get the answers from their data . That’s really good for the analytical-minded people, but there is also a subset of users who need to have certain answers every day, pushed to them through a data visualization .”

Data visualization uses imagery to enable rapid analysis of large amounts of data . When you need to know facts regarding campaign delivery, inventory availability or placement, historical performance, or products that need to be sold, you should be able to get these insights in a simple and elegant way .

Suppose the publisher is running 10,000 or 20,000 simultaneous campaigns . Traditionally, it might have analyzed those activities by printing out a spreadsheet and trying to figure out which campaigns are on schedule, which ones aren’t, and how to deal with it . A data visualization makes it much easier to see where to focus your efforts .

Wood showed a visualization where all the campaigns are represented by circles, with the size of the circle indicating the amount of revenue the campaign produces . Whether the circle is on the bottom of the screen or the top indicates where the campaign is in its life cycle, from just getting started to almost finished . Campaigns on the left of the screen are ahead of schedule; those on the right are behind schedule . “Because we’ve done simulation-based forecasting, you can push a button and watch the dots move through the future for the next 10 days and see if your campaigns are going to finish on time,” said Wood . “If a campaign is in trouble, it is automatically highlighted, so an operational person can take action right away .”

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Maximizing Digital Advertising in a Customer-Centric Marketing Strategy

Figure 1: A data visualization gives users at-a-glance insights about multiple campaigns.

For another example, consider a chief revenue officer who wants to know every day what to sell and to whom – and wants this information pushed to him every day . A data visualization can present this information in a clear way, showing the top 20 products available, by amount of inventory or by amount of revenue . Click on a product to see additional details about the customers that bought that inventory in the past, when they bought it and how much they paid for it . Instant access to this information helps both buyers and sellers be far more efficient .

Analytic Insights to Allocate Inventory Intelligently

“We’ve been helping publishers understand the best way to participate with exchanges,” said Wood . “That means giving them a mechanism where they can say, ‘I’ve got lots of different kinds of inventory that I sell lots of different ways . This type of inventory here I might sell with a full rate of this price; this other inventory I would never sell programmatically unless the full rate was above this price .’ So we have focused on giving them the tools to classify their inventory, apply different floor prices to different pieces of the inventory, and then make the connections with the exchanges with which they want to connect . They can get real-time bids from all those exchanges, use analytics to assess performance over time, and then adjust their inventory allocation decisions to maximize the results .”

A selling platform that communicates directly with other platforms, server to server, gives publishers a lot more control and confidence in those decisions, said Wood . “A server-to-server approach creates a situation that encourages more premium publishers to put in more of their premium inventory . With the right tool, the publisher should be able to work with bids from multiple platforms and apply those bids to the different products and floor prices they have . This integrated approach is very helpful .

Data from each channel is

united in a single reporting

interface. Data visualization

tools enable you to see and

comprehend your data in an

illustrative, easy-to-understand

way, so you can focus on what’s

important and make decisions

that maximize revenues.

An intelligent exchange

management solution gives

publishers insight and control

when using indirect sales

channels. The exchange

manager module is not an ad

exchange itself, but a better

way to manage relationships

with real-time bidding (RTB),

exchange and network partners

– all within the publisher’s ad

delivery platform.

8

SAS Conclusions Paper

“Analytics is key to this integration . If you have the right analytics on the back side, you can get to that goal of optimizing inventory allocation . You want to provide the audience to the channel that can monetize it best . Whether that means selling it directly or putting it through an exchange, let’s give the audience to the channel that can perform best with it .”

Intelligent digital advertising in action

With more than 100 million registered users and 45 million unique visitors per month, a leading photo- and video-sharing service takes pride in offering its advertising partners creative solutions that reach an engaged audience .

“Through a combination of analytics and forecasting, we helped this online leader analyze its entire product catalog and find products that overlapped with each other, yet didn’t have the same price point,” said Wood . “This insight enabled the company to reconfigure its product catalog to avoid cases where it was essentially selling the same inventory at different rates . Once the company built that new product catalog, it ran a simulation to make sure the changes had a positive effect on revenue . It found that in many areas where it sells the premium inventory directly, it was able to get as much as a 500 percent uplift . That’s pretty powerful .”

“Having business intelligence, sales performance management, forecasting and delivery insights together in one easy-to-use system has significantly improved our ability to manage and maximize the value of our inventory,” said the company’s chief revenue officer .

Closing Thoughts

Despite its size, the digital ad market is fragmented and lacks purpose-built analytical applications, making it difficult for publishers to effectively manage ad inventory and optimize profitability . There is huge potential for digital advertising to benefit from applying integrated tools and advanced analytics to manage the challenges of inventory forecasting, competition for ad dollars, technology complexity and sales performance management .

“SAS has been helping marketers and advertisers in the offline world for a long time, helping them figure out such things as segmentation, targeting, marketing mix and attribution,” said Wood . “With its customer intelligence offering, SAS is a leader in the buy side of digital marketing, so SAS’ legacy strengths fit very nicely into what we were creating on the sell side .”

SAS® Intelligent Advertising for Publishers provides a unified platform for sales order management, simulation-based forecasting, optimized ad serving, reporting and dashboards, and managing ad exchange with partners . This cloud-based offering:

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Maximizing Digital Advertising in a Customer-Centric Marketing Strategy

• Reducesthetimeittakestodiscoverhigh-valueinventoryandbringthoseproducts to market .

• Enablesrapidresponsetorequestsforproposals(RFPs).

• Pinpointsrevenueleakageduetointernalsaleorganizations’discounts and bonuses .

• Supportsanonlinearuserexperienceforadservingandfastsearchfunctionality.

• Providessmartadservingalgorithmstoensuredeliverygoalsaremet.

“The changes in our industry in the last three to five years have created the opportunity to use analytics and big data tools to create a better customer experience for all sides of the equation,” said Wood . “The ultimate goal is to maximize revenue for the publisher by instantly seeing the value of every product in inventory, making informed decisions and creating opportunities that deliver the best value to its agency and advertiser customers .”

For More InformationFor more about SAS® Intelligent Advertising:

sas.com/software/customer-intelligence/intelligent-advertising.html

Download the white paper Moving Beyond the Ad Server: A Comprehensive Approach That Delivers Advertising Intelligence

go.sas.com/e3135b

Read more thought leader views at the Customer Intelligence Knowledge Exchange:

sas.com/knowledge-exchange/customer-intelligence

Get fresh perspectives from marketing practitioners on the Customer Analytics blog: blogs.sas.com/content/customeranalytics/

About the PresentersWilson Raj is the Global Customer Intelligence Director in the Worldwide Alliances and Product Marketing Division at SAS . His responsibilities include collaborating with industry leaders, customers, alliances, sales, marketing and product teams to establish and evangelize SAS’ customer intelligence growth strategy in the market .

With 18 years of experience in diverse industries, Raj has built data-driven brand value, engagement and loyalty through expertise in integrating traditional and digital marketing, social media, multichannel relationship marketing, and public relations . He has held global lead marketing and strategy positions for leading Fortune Global 500® companies and digital agencies such as Microsoft, Medtronic, Philips, Novell, Ameritech (now AT&T Midwest), Publicis and WPP (Y&R) .

Raj holds a bachelor’s degree in English and an MBA from Brigham Young University; he also has a certificate in education from the Institute of Education in Singapore .

SAS® Intelligent Advertising for Publishers An ad serving platform for all digital devices that includes sales order management, simulation-based forecasting, yield management and exchange management – all wrapped by a comprehensive business intelligence and data visualization suite that gives digital publishers control over their own data and analysis .

•Manageinventoryin real time .

•Serveupadsinan automated fashion .

•Targetaudiences with precision .

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SAS Conclusions Paper

Jeff Wood is Senior Director of Intelligent Advertising at SAS . An online advertising industry veteran, Wood co-founded aiMatch in March 2010, which was acquired in 2011 by SAS . Before co-founding aiMatch, he was Vice President of Publisher Sales at Microsoft Advertising, after its $6 billion acquisition of aQuantive . He was also a founding partner and Vice President of Sales at Accipiter, where he was responsible for significant revenue growth and international expansion in the US, Europe, Australia and Japan as the company became a leader in online advertising technology . Today, Wood is regularly called upon to share his online advertising expertise at industry events such as IAB, Mediapost, AdMonsters and ABM .

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Maximizing Digital Advertising in a Customer-Centric Marketing Strategy

About SASSAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market . Through innovative solutions, SAS helps customers at more than 60,000 sites improve performance and deliver value by making better decisions faster . Since 1976 SAS has been giving customers around the world THE POWER TO KNOW® . For more information on SAS® Business Analytics software and services, visit sas.com .

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