Watch to explore the possibilities of using mobile location and beacon data to better segment, target, and analyze consumers. Featuring FreckleIOT, Ticketmaster/Live Nation, and PlaceIQ.
Today, eConsultancy launches Best Practices in Digital Display Advertising, a comprehensive look at how to efficiently manage online advertising. We asked the author, Chris O’Hara, about the report and work that went into it.
Why did you write Best Practices in Digital Display Media?
In my last job, a good part of my assignment was traveling around the country visiting with about 500 regional advertising agencies and marketers, large and small, over three years. I was selling ad technology. Most advertisers seemed extremely engaged and interested to find out about new tools and technology that could help them bring efficiency to their business and, more importantly, results to their clients. The problem was that they didn’t have time to evaluate the 250+ vendors in the space, and certainly didn’t have the resources (financial or time) to really evaluate their options and get a sense of what’s working and what isn’t.
First and foremost, I wanted the report to be a good, comprehensive primer to what’s out there for digital marketers including digital ad agencies. That way, someone looking at engaging with data vendors, say, could get an idea of whether they needed one big relationship (with an aggregator), no data relationships, or needed very specific deals with key data providers. The guide can help set the basis for those evaluations. Marketers have been basically forced to license their own “technology stack” to be proficient at buying banner ads. I hope the Guide will be a map through that process.
What was the methodology you used to put it together?
I essentially looked at the digital display ecosystem through the lens of a marketer trying to take a campaign from initial concept through to billing, and making sure I covered the keys parts of the workflow chain. What technologies do you employ to find the right media, to buy it, and ultimately to measure it? Are all of these technologies leading to the promised land of efficiency and performance? Will they eventually? I used those questions as the basis of my approach, and leveraged the many vendor relationships and available data to try and answer some of those questions.
What’s the biggest thing to take away from the report?
I think the one thing that really runs through the entire report is the importance of data. I think the World Economic Forum originally said the “data is the new oil” [actually, the earliest citation we can find is from Michael Palmer in 2006, quoting Clive Humby] and many others have since parroted that sentiment. If you think about the 250-odd technology companies that populate the “ecosystem,” most are part of the trend towards audience buying, which is another way of saying “data-driven marketing.” Data runs through everything the digital marketer does, from research through to performance reporting and attribution. In a sense, the Guide is about the various technologies and methodologies for getting a grip on marketing data—and leveraging it to maximum effect.
There’s an explosion of three letter acronyms these days (DSP, DMP, SSP, AMP, etc) that marketers are still trying to sort out. Do we need all of them? Is there another one around the corner?
I am not really sure what the next big acronym will be, but you can be certain there will be several more categories to come, as technology changes (along with many updates to Guides such as these). That being said, I think the meta-trends you will see involve a certain “compression” by both ends of the spectrum, where the demand side and supply side players look to build more of their own data-driven capabilities. Publishers obviously want to use more of their own data to layer targeting on top of site traffic and get incremental CPM lift on every marketable impression. By the same token, advertisers are finding the costs of storing data remarkably cheap, and want to leverage that data for targeting, so they are building their own capabilities to do that. That means the whole space thrives on disintermediation. Whereas before, the tech companies were able to eat away at the margins, you will see the real players in the space build, license, or buy technology that puts them back in the driver’s seat. TheBest Practices in Digital Display Advertising Guide is kind of the “program” for this interesting game.
To learn more about the Best Practices in Digital Display Advertising Guide, download the report here.
Technology may still capture the most advertising value, but what if publishers own it?
A few years ago, ad technology banker Terence Kawaja gave a groundbreaking IAB presentation entitled, “Parsing the Mayhem: Developments in the Advertising Technology Landscape.” Ever since then, his famed logo vomit slide featuring (then) 290 different tech companies has been passed around more than a Derek Jeter rookie card.
While the eye chart continues to change, the really important slide in that deck essentially remains the same. The “Carving up the stack” slide (see above), which depicts how little revenue publishers see at the end of the ad technology chain, has changed little since May 2010. In fact you could argue that it has gotten worse. The original slide described the path of an advertiser’s $5 as it made it’s way past the agency, through ad networks and exchanges, and finally into the publisher’s pocket.
The agency took about $0.50 (10%), the ad networks grabbed the biggest portion at $2.00 (40%), the data provider took two bits (5%), the ad exchange sucked out $0.35 (7%), and the ad server grabbed a small sliver worth $0.10 (2%), for a grand total of 64%. The publisher was left with a measly $1.80. The story hasn’t changed, and neither have the players, but the amounts have altered slightly.
While Kawaja correctly argued that DSPs provided some value back to both advertisers and publishers through efficiency, let’s look ahead through the lens of the original slide. Here’s what has happened to the players over the last 2 years:
- Advertiser: The advertiser continues to be in the cat bird seat, enjoying the fact that more and more technology is coming to his aid to make buying directly a fact of life. Yes, the agency is still a necessary (and welcomed) evil, but with Facebook, Google, Pandora, and all of the big publishers willing to provide robust customer service for the biggest spenders, he’s not giving up much. Plus, agency margins continue to shrink, meaning more of their $5.00 ends up as display, video, and rich media units on popular sites.
- Agency: It’s been a tough ride for agencies lately. Let’s face it: more and more spending is going to social networks, and you don’t need to pay 10%-15% to find audiences with Facebook. You simply plug in audience attributes and buy. With average CPMs in the $0.50 range (as opposed to $2.50 for the Web as a whole), advertisers have more and more reason to find targeted reach by themselves, or with Facebook’s help. Google nascent search-keyword powered display network isn’t exactly helping matters. Agencies are trying to adapt and become technology enablers, but that’s a long putt for an industry that has long depended on underpaying 22 year olds to manage multi-million dollar ad budgets, rather than overpaying 22 year old engineers to build products.
- Networks: Everyone’s talking about the demise of the ad network, but they really haven’t disappeared. Yesterday’s ad networks (Turn, Lotame) are today’s “data management platforms.” Instead of packaging the inventory, they are letting publishers do it themselves. This is the right instinct, but legacy networks may well be overestimating the extent to which the bulk of publishers are willing (and able) to do this work. Networks (and especially vertical networks) thrived because they were convenient—and they worked. Horizontal networks are dying, and the money is simply leaking into the data-powered exchange space…
- Data Providers: There’s data, and then there’s data. With ubiquitous access to Experian, IXI, and other popular data types through 3rd party providers, the value of 3rd party segments has declined dramatically. Great exchanges like eXelate give marketers a one-stop shop for almost every off-the-shelf segment worth purchasing, so you don’t need to strike 20 different license deals. Yet, data is still the lifeblood of the ecosystem. Unfortunately for pure-play segment providers, the real value is in helping advertisers unlock the value of their first party data. The value of 3rd party data will continue to decline, especially as more and more marketers use less of it to create “seeds” from which lookalike models are created.
- Exchanges: Exchanges have been the biggest beneficiary of the move away from ad networks. Data + Exchange = Ad Network. Now that there are so many plug and play technologies giving advertisers access to the world of exchanges, the money had flowed away from the networks and into the pockets of Google AdX, Microsoft, Rubicon. PubMatic, and RMX.
- Ad Serving: Ad serving will always be a tax on digital advertising but, as providers in the video and rich media space provide more value, their chunk of the advertiser pie has increased. Yes, serving is a $0.03 commodity, but there is still money to be made in dynamic allocation technology, reporting, and tag management. As an industry, we like to solve the problems we create, and make our solutions expensive. As the technology moves away from standardized display, new “ad enablement” technologies will add value, and be able to capture more share.
- Publisher: Agencies, networks, and technologists have bamboozled big publishers for years, but now smart publishers are starting to strike back. With smart data management, they are now able to realize the value of their own audiences—without the networks and exchanges getting the lion’s share of the budget. This has everything to do with leveraging today’s new data management technology to unlock the value of first party data—and more quickly aggregate all available data types to do rapid audience discovery and segmentation.
The slide we are going to be seeing in 2012, 2013 and beyond will show publishers with a much larger share, as they take control of their own data. Data management technology is not just the sole province of the “Big Five” publishers anymore. Now, even mid-sized publishers can leverage data management technology to discover their audiences, segment them, and create reach extension through lookalike modeling. Instead of going to a network and getting $0.65 for “in-market auto intenders” they are creating their own—and getting $15.00.
Now, that’s a much bigger slice of the advertising pie.
[This post originally appeared in ClickZ on 2/1/12]
Today’s digital media agency has access to enormous amounts of data, but using it effectively is what is going to make the difference between the shops of the future and the also-rans. Delivering data-driven insights is the key to being a 21st century agency. Here are three ways you should be working with data to secure your future:
How much time are you and your colleagues spending collating data, building reports, and formatting spreadsheets and PowerPoint decks for your clients? Most of the agencies I have worked with over the years admit to dedicating an embarrassingly large amount of (highly expensive) time towards these menial tasks. It’s not that getting your clients the data they need is not worth the time, it’s simply that there are now so many automated ways to deliver the data without burning salary.
To paraphrase former agency head and Akamai leader David Kenny, if you are doing things with people that you can be doing with computers, you have already lost. Why spend time formatting Excel spreadsheets and populating PowerPoint report templates with data, when you can be spending salaried employee time selling more services, optimizing campaigns, and delivering great strategy and creative? Today’s automated ad management solutions and DMPs offer powerful ways to port both audience and ad serving reporting data into a single interface, to get instant access to key metrics such as frequency to conversion, churn rate, and channel attribution.
Ask yourself if the cost of such a system is more than the cost of the time your employees you have been spending building reports—and, ultimately, more than the cost of your eventual demise, should you ignore the changes afoot in your business.
Aggregate and Activate it
Think of all the data you have access to from a digital media standpoint. If you are helping clients execute a digital media campaign, you have traditional serving data from your demand side server, such as DFA. You probably also have engagement data from your rich media ad server. If you have access to your clients’ website pages (or at least tags there), you have site-side data, including conversion event data. If you are using an audience measurement tool, or are doing audience-specific buying through a demand side platform, you also have audience measurement data. Great. What are you doing with all of it? Moreover, what kind of data does your client have that you can help them add to activate the common advertising data types I have just described?
Let’s take the example of an agency using an audience measurement reporting tool, alongside an ad server report. In this case, it is possible that the analyst knows that the highest frequency converters for his travel campaign belong to a popular PRIZM segment, and he may also know that visitors to a popular travel site are three times as likely to engage with his rich media ad creative. Now what? Obviously, the right move is to buy more of the audience segment and double up with guaranteed advertising on the travel site. But what about audience overlap?
How can the advertiser reduce ad waste by ensuring that members of his audience segment that he is securing for as little as $2.00 CPM on exchanges are not overrepresented on the premium site for which he is paying $18.00 CPM? Plus, how many members of that audience are also already registered as customers? If you are not deploying a DMP to aggregate your clients’ CRM (first-party) data alongside the site-side and ad serving (2nd party) data and the purchased (3rd party) data segments, then there is going to lots of duplicated uniques in your audience. Smart data aggregation creates ad activation through waste reduction, lifting conversion rates, while lowering cost per conversion. Getting an effective universal frequency cap across digital channels is very difficult, but every dollar not wasted on duplicate impressions is another dollar that may be spent finding a new audience member. Reducing waste adds reach—and performance, which every client likes.
As a digital media agency, you’ve run hundreds, perhaps even thousands of campaigns, producing thousands of data-rich reports for your clients. How much of that knowledge are you leveraging? Although you might know the top travel sites and audience segments to reach “moms of school-age children in-market for a beach vacation,” how readily available is that knowledge? Is it sitting inside your Media Director’s head, or hidden in various documents that don’t talk to one another? How about access to normative campaign data? How quickly can you find out how certain sites performed against similar KPIs without doing hours of research?
Like or not, advertisers want to know how their campaigns are performing against known standards, and it’s gotten a lot more complicated than beating a 0.1% click-through rate lately. Knowing how your last 10 travel campaigns performed—from which guaranteed site buys succeeded, to which audience segments performed, to which creatives elicited the highest CTR—is just step one. Having that data available for quick reference means that every new campaign can start from an advanced performance level, and your media people don’t have to recreate the wheel every time you receive an RFP.
Today’s smart DMPs also feature the ability to leverage your data to an even greater extent, especially for audience buying. Why limit yourself to pre-packaged audience segments that do not include your client’s first-party data? Today’s more advanced DMPs give marketers the ability to create audience segments on the fly, building discrete segments from data that includes available third-party data—but also first-party data, such as registration details, transactional records, and signals from hosted social media listening solutions. It’s the difference between buying from an ad network and creating your own.
Buying into portals’ site sections was the first phase in the effort to bring contextual and audience relevance to ad buying. Networks followed, offering packaged audiences at scale. Then bidded exchange buying came, offering pre-packaged audience segments at the individual cookie level. Today’s best practices include marrying all available data types to give marketers the ability to create their own targeted buys, and modern data management platforms are helping the largest advertisers automate what they have been doing since the first direct mail piece went out: finding targeted audiences. Leveraging today’s DMP technology can not only help you find those audiences more easily, but help you understand who they are, why they respond, and help you find them again.
Chris O’Hara is head of strategic partnerships for nPario, a DMP with clients that include Yahoo! and Electronic Arts, among others. A frequent contributor to industry publications, this is his first column for The Agency Post. He can be reached through his blog on www.chrisohara.com
[This article originally appeared in The Agency Post on 1/25/12]
These days, advertising and data platforms are giving marketers a wealth of information that can be used to validate their strategies, and optimize their digital campaigns for better performance. There is a lot of data to sort through—some more useful than others. Sometimes, good campaign optimization comes down to the basics: Understanding who your audience is, and why they are doing what they are doing.
Let’s look at a real life example of a digital display campaign, run through the digital ad agency of a popular mattress retailer. The agency wanted to test new inventory sources for the campaign by running broadly on general interest sites, evaluating the demography of audiences that showed purchase intent, and optimize over the course of the campaign to maximize impact.
A theory being tested was that older audiences, who report more difficulty sleeping than younger demographic groups, would respond more favorably to the retailer’s online display ads. Campaigns were initially skewed to sites that over-indexed against audience composed of 50 and older.
As Figure 1 shows, a bulk of impressions during the discovery portion of the campaign were delivered to visitors aged 46-65 years of age, which was the desired demographic. After analysis of those who viewed or clicked on a display ad, and then went on to purchase, the audience composition was remarkably different. As shown in Figure 2, the bulk of conversions came from those aged 18-45.
The agency adjusted the ad buy to heavy up on sites that over-indexed for a younger audience, and opted out of buys tailored to the older demographic. As wasted impressions were trimmed down in the overall plan, conversion rates increased dramatically. Testing and validating your instincts with data on an ongoing basis is the key to success in digital display advertising. The mattress retailer, who experienced better sales from older store visitors (offline), found a more responsive younger audience online. Although it seems obvious, having the initial data means being able to smartly allocate marketing capital, and having access to ongoing data means not having to rely on old insights in a changing marketplace.
Another offline theory the mattress retailer sought to validate was the mattress life cycle. After collecting brick and mortar sales data for years, the retailer knew that the average life of a mattress was approximately 7 years, and that the single greatest life event influencing the purchase of a new mattress was moving. Therefore, it made sense to target audiences based on length of residence (>7 years), and target content around buying or renting a new home.
Inventory was bought from a wide range of home-specific and moving sites, and measured using Aperture audience measurement populated with data sets from Experian, IXI financial, V12 demographic, and Nielsen PRIZM data.
As Figures 3 and 4 amply demonstrate, the mattress retailer was targeting the bulk of impressions towards individuals reporting over seven years residence in a single location, and clicks among that group indexed the highest in aggregate. That data validated the approach of buying into sites with a strong audience of self-reported homeowners. However, a deeper look into audience data revealed a strong distinction between renters and buyers.
As noted in Figure 5, although the bulk of impressions in the campaign were served to homeowners, renters were the ones buying the most mattresses. This learning did more than any other data point to drive campaign optimization.
Naturally, the next step in the campaign optimization process was to focus inventory delivery to sites that promised a concentrated audience of home renters. Sites such as ForRent.com, ApartmentGuide.com, and Renters.com were added to the optimization plan.
More insights came as the Aperture data was collected. Despite purporting to have a heavy concentration of renters, two of the more popular sites actually index much higher among homeowners, as shown in Figure 6. It looked as though homeowners that were looking into renting made up the majority audience—a fact that helped the retailer tailor specific messaging to them.
Figure 6: In this example, a media site aimed at renters, over-indexes against current homeowners.
For this particular campaign, the ability for the retailer to validate certain audience assumptions using real demographic data was critical, as well as the ability to leverage the distinction between two types of potential customers: home owners, and renters. Additionally, getting real audience metrics beyond a publisher’s media kit or self-declared audience information enabled the retailer to craft its creative and messaging in a highly specific way that increased conversions.
When it comes to audience validation and campaign optimization, here are three keys:
- Know Your Data: In today’s technology-driven marketing world, knowing how to leverage the data available to you is critical to both understanding and targeting your audience. Make sure your marketing investment decisions are driven through the analysis and usage of 1st party data, including registration data for demographic modeling; 2nd party data, such as ad server and search data for behavioral modeling; and 3rd party data, such as available audience segments from providers like Nielsen and Datalogix, for audience validation, matching, and lookalike modeling. Data is not just about buying audience segments for targeting; it’s about trying to get a 360-degree view of your ideal customer.
- Choose the Right DMP: There are DMPs for every marketer, so be careful to choose the right one. Big Data needs call for pure play DMPs that can stitch together highly disparate data sets that include all data types, and make both insights, audience segments, and lookalike modeling available in real-time. Marketers looking to buy from a variety of 3rd party audience segment providers should choose a data marketplace such as Exelate, or be willing to access a more limited number of data sources inside a DSP such as AppNexus.
- Leverage Audience Measurement: Finally, there is a lot that audience segments can bring to the table in terms of audience insights. Understanding the audience composition of who saw, clicked on, and converted after seeing your campaign gives you the ability to learn about your target customers, their online behaviors, and (most importantly) find more of them. Your DMP should have the ability to marry audience and campaign data to give you a highly granular level view of your best (and worst) performing audience types—down to the creative level.
Learnings from this case study, and other valuable information, can be found in my upcoming “Best Practices in Digital Display Media,” coming in January 2012 from eConsultancy.com.
[This article originally appeared in ClickZ on 1/4/2012]
The other day, I was updating my Spotify app on my Android device. When it finally loaded, I was asked to log in again. I immediately loaded up a new playlist that I had been building—a real deep dive into the 1980s hardcore music I loved back in my early youth. I’m not sure if you are familiar with the type of music that was happening on New York City’s lower east side between 1977 and 1986, but it was some pretty raw stuff…bands like the Beastie Boys (before they went rap), False Prophets, the Dead Boys, Minor Threat, the Bad Brains, etc. They had some very aggressive songs, with the lyrics and titles to match.
Well, I put my headphones in, and started walking from my office on 6th Avenue and 36th street across to Penn Station to catch the 6:30 train home to Long Island…all the while broadcasting every single song I was listening to on Facebook. Among the least offensive tunes that showed up within my Facebook stream was a Dead Kennedys song with the F-word featured prominently in the song title. A classic, to be sure, but probably not something all of my wife’s friends wanted to know about.
As you can imagine, my wife (online at the time), was frantically e-mailing me, trying to tell me to stop the offensive social media madness that was seemingly putting a lie to my carefully cultivated, clean, preppy, suburban image.
So why, as a digital marketer, would you care about my Spotify Facebook horror story?
Because my listening habits (and everything else you and I do online, for that matter) are considered invaluable social data “signals” that you are mining to discover my demographic profile, buying habits, shoe size, and (ultimately) what banner ad to serve me in real time. The only problem is that, although I love hardcore music, it doesn’t really define who I am, what I buy, or anything else about me. It is just a sliver of time, captured digitally, sitting alongside billions of pieces of atomic level data, captured somewhere in a massive columnar database.
Here are some other examples of data that are commonly available to marketers, and why they may not offer the insights we think they might:
— Zip Code: Generally, zip codes are considered a decent proxy for income, especially in areas like Alpine, New Jersey, which is small and exclusive. But how about Huntington, Long Island, with an average home value of $516,000? That zip code contains the village of Lloyd Harbor (average home value of $1,300,000) and waterside areas in Huntington Bay like Wincoma, where people with lots of disposable income live).
— Income: In the same vein, income is certainly important and there are a variety of reliable sources that can get close to a consumer’s income profile, but isn’t disposable income a better metric? If you earn $250,000 per year, and your expenses are $260,000, then you are not exactly Nordstrom’s choicest customer. In fact, you are what we call “broke.” Maybe that was okay back in the good old days of government-style deficit spending but, these days, luxury marketers need a sharper scalpel to separate the truly wealthy from the paper tigers.
— Self-Declared Data: We all like to put a lot of emphasis on the answers real consumers give us on surveys, but who hasn’t told a little fib from time to time? If I am “considering a new car” is my price range “$19,000 – $25,500” or “35,000 – $50,000?” This type of social desirability bias is so common that reaearchers have sought other ways of inferring income and purchase behavior. When people lie about themselves, to themselves (in private, no less) you must take a good deal of self-declared data with a hearty grain of salt.
— Automobile Ownership: Want to know how much dough a person has? Don’t bother looking at his home or zip code. Look at his car. A person who has $1,800 a month to burn on a Land Rover is probably the same person liable to blow $120 on mail order steaks, or book that Easter condo at Steamboat. Auto ownership, among other things, is a great proxy for disposable income.
It would be overly didactic to rehearse all of the possible iterations of false data signals that are being used by marketers right now to make real-time bidding decisions in digital media. There are literally thousands—and social “listening” is starting to make traditional segmentation errors look tame. Take a recent Wall Street Journal article that reported that the three most widely socially-touted television shows fared worse than those than shows which received far less social media attention.
Sorry, but maybe that hot social “meme” you are trying to connect with just isn’t that valuable as a “signal.” We all hear the fire truck going by on 7th Avenue. The problem is that the only people who turn to look at it are the tourists. So what is the savvy marketer to do?
Remember that all data signals are just that: Signals. Small pieces of a very complicated data puzzle that you must weave together to create a profile. Unless you are leveraging reliable first-party data, second-party data, and third party data, and stitching that data together, you cannot get a true view of the consumer.
In my next column, we’ll look at how stitching together disparate data sources can reveal new, more reliable, “signals” of consumer interest and intent.
[This article was originally published in ClickZ on 12/2/2011]
A “platform” is a system that can be programmed and therefore customized by outside developers — users — and in that way, adapted to countless needs and niches that the platform’s original developers could not have possibly contemplated, much less had time to accommodate. – Marc Andreessen, 2007
Last week’s news of the merger between DDS and MediaBank was certainly exciting. In digital media management terms, it’s kind of akin to rooting for the Yankees; only their fans want to see them grow more powerful, because it sure ain’t good for baseball. These two behemoths have been fighting over agency budgets for the last four years, and have managed to steal a bit of market share from one another, while advancing the cross-media efficiency agenda slightly. The stated hope for this merger is that the corporate combination will give them enough firepower to finish the golf swing and solve the insanely complicated digital media puzzle, making cross media management possible in a real way.
Is this merger good for the digital media ecosystem? Maybe. Here are the three factors that will determine whether MediaOcean will become the digital media industry’s defacto system:
Standards are good: First off, it helps when everybody is reading from the same sheet of music, and there isn’t an industry that hasn’t benefitted from a common, accepted set of standards. The IAB has done a great job in terms of helping standardize ad sizes and out clauses, and some of the systems and procedures that help oil digital business transactions. An argument could be made that having 80% of agency dollar volume running through the same system brings efficiencies to the entire media buying landscape, but I’m not sure anyone in the industry would say that this was the case when DDS had larger market share.
For digital marketers, a significant hassle has been bill/pay and reconciliation, and that has been an area of focus for DDS and MediaBank across digital and traditional media. There is no doubt they can help standardize the process by which advertisers and publishers reconcile delivery even just by being the largest player – they can bring a de facto standard to bear, but how quickly can they really react to a rapidly evolving space with myriad nuances in ideal workflows for almost every customer? If they can change their DNA, they will be a force to be contended with.
— Platforms are good: Secondly (and most importantly), the right approach to solving this problem is an open platform approach. But none of the leaders in this space have shown any predisposition for opening things up. This is in large part because the technology landscape has evolved so fast that the legacy companies haven’t been able to adapt their systems to keep up. The market needs an open, extensible platform approach to solve its numerous problems, the question is can any of the existing leaders in the space, including MediaOcean, provide that?
My colleague, Eric Picard, learned about the power of platform effects while working at Microsoft over the last several years. He recently educated me on the varieties of platform approaches that could be taken in our space, and has offered to let me publish that here:
* Systems vs. Platforms: The first thing to discuss is that most companies in our space have built systems – not platforms (despite everyone using the word platform for everything.) A system simply exists on its own, is proprietary and closed – it doesn’t allow third parties to build on top of it. This describes almost all the offerings in our industry today.
* Simple Platforms – or Mashups: Most of us have experienced a ‘mashup’ in one shape or another by now. This is where a tool or web site is built that calls to numerous remote services (APIs or Web Services) to build one cohesive interface. In this case, the platform is really all the multiple different systems used ‘behind the scenes’ to create one simple application that you could use. Many web sites use this technique, using various content management systems, ad servers, etc… A lot of the SEMs and DSPs use this approach, building their own interface that hits each of the Paid Search providers or Ad Exchanges via API.
* Consumable Back-End Platforms: Lots of companies now offer API access to their systems. This kind of ‘back-end’ access is then used by third parties to ‘mash-up’ the functionality with either their own or other third party functionality. AppNexus, Right Media Exchange, Atlas, DoubleClick, and numerous others provided this kind of back-end access by API. Some of the more sophisticated providers, like AppNexus and RMX even enable third parties to extend their functionality to some degree – but they don’t make that extension generically consumable.
* Ecosystem-like Platforms: A great example of this is Salesforce.com – which has built out a platform that really begins to live up to the market opportunity that the industry should be looking for. Salesforce enables numerous services that can be consumed, like the platforms and mashups we discussed above. But they also let third party vendors come in and extend the functionality of the core Salesforce platform. They even provide an App marketplace, similar to iTunes, that allows third party vendors to distribute their applications to existing Salesforce customers. This is a powerful approach, but requires a whole new set of skills that most companies in the ad technology space are not quite able to pull off.
Within this overall context of platforms verses systems, you can see the variety of approaches being taken by the various parties in the ad ecosystem:
Google offers third parties APIs to write against, but keeps the vendors playing in the search ecosystem on their toes by frequently changing the APIs, and it’s fairly clear that their goal is to be both the platform and the applications that run the advertising ecosystem. They support third parties, but only as it furthers their end-game.
The ad servers understand that their value is in the engine, much more-so than their workflow. And they’ve opened up APIs to let other workflows plug in and become mashups that ultimately are powered by the smarts of the ad servers behind the scenes.
Donovan Data Systems has brought one mashup workflow to market, their iDesk product. It interfaces with DDS’s other applications fairly well, and can integrate with the dominant ad servers. MediaBank has done somewhat similar things with their application suites, but has taken a more “Google-like” approach when it comes to their business – investing in their own DSP and automated media buying systems. This investment in products that compete directly with the very vendors that would need to integrate into the combined system causes me to pause a bit.
At the end of the day – it’s hard to understand who might have the right DNA among these constituents to actually roll out the right platform to solve the industry’s needs.
–Creativity is good: Finally, I think a development like this is excellent, if it actually creates an environment that transforms where digital media people spend their time. Right now, digital agencies spend most of their time and effort trying to wrangle an “ecosystem” of nearly 300 technology, data, and media providers. They spend the bulk of their time trying to execute media plans, rather than coming up with creative strategies to engage consumers. The mess of systems, lack of standards, multiple log-ins, and unmanageable hoards of data that each system throws off has created the ultimate irony: digital media is becoming the least creative, least profitable, and least measurable channel for marketers. If the merger brings us one step closer to making the digital execution piece easier, and gets the conversation back to creative, than I think it’s a step in the right direction.
After being out in the field, and talking to over 400 agencies about their digital media needs, I know that a standardized platform is what everybody wants. Whether or not MediaOcean is going to be nimble and creative enough to deliver a system that meets the needs of our growing ecosystem is very much in question. Technology has always thrived on choice, flexibility, and open standards. I believe that the company that can deliver on all three will end up winning.
[This commentary appeared in Adotas on 9/29/11]