Punching the Monkey

BannerAdI recently tried to explain what I do for a living to my 14-year-old son. I found myself telling him about ad tech.

“Basically, we make technology that helps marketers buy different kinds of banner ads,” I told him.

“You mean the kind of annoying pop-up ads that everyone hates?” he asked.

His look of profound disappointment said it all. I explained that the kind of work we do wasn’t just about populating the Internet with the “Lose five pounds with one stupid trick” type of banner. But even though we are getting a lot right, my explanations eventually started sounding pretty weak.

I have been working in this business since 1995. Aside from doing some ad implementation testing, I have probably clicked on about a dozen banner ads in as many years. Today’s robust, real-time ad tech “stack” has been purpose-built to optimize the delivery of the kind of banner ads most people already hate: standardized IAB units, retargeted ads, auto-play video pre-roll units and even the dreaded pop-up and pop-under.

Publishers without robust direct sales options depend on networks and exchanges to monetize the endless streams of traffic they create, and they happily collect their $1.10 eCPM (cost per mille) payments. Advertisers looking for cheap reach and performance plumb the depths of such inventory to find the rare conversion, and hope they are getting what they pay for rather than a shady “last view” attributed banner.

Today, the highest and best use of the standardized banner has enabled marketers to leverage their first-party data to bombard site visitors with retargeted ads – an effective tactic, since they are essentially paying to accelerate a conversion that has a great chance of happening on its own.

As an industry, it seems pretty clear that we will look back on this era in digital ad technology and see how primitive it was. Have we built a trillion-dollar real-time ad serving machine for punch-the-monkey ads, or have we really innovated and created disruption?

RTB Is Dead, Long Live RTB

The recent acquisition of [X+1] by Rocket Fuel is a great sign for our industry. It basically validates the idea that, for programmatic RTB to be effective, real data science must inform targeting. [X+1] is one of the best at cross-channel targeting, and they have already started to figure out the cross-channel attribution puzzle. An everlasting always-on stream of RTB banners for branding and retargeting might prove to be a hugely important part of unlocking a broader multi-channel strategy – if the data can dictate it. If data management platform technology can be leveraged to truly optimize addressable marketing, then RTB will survive and thrive. With consumers always on the move, and every form of media starting to be addressable, real-time programmatic will be something marketers have permanently switched on, and we’ll see the true value of the pipes we have created.

Programmatic Direct

How about inventory that is relatively standard, but a bit nicer than that found within the exchange environment? Transacting on this tier of inventory works quite nicely with all kinds of one-to-one connections within RTB, and buyers and sellers are quickly leveraging the pipes to make private marketplace deals.

If I am a quality financial publisher, why wouldn’t I sell within RTB for $8 CPMs, rather than pay a $200,000 salesperson to sell at $12 CPMs? The math just makes sense. Delivering higher tiers of inventory at scale to private buyers is a great use of RTB, but not a panacea for overall inefficiency in media procurement. But, we have seen those RTB pipes service entire new classes of inventory, and start to appeal to brand marketers.

Workflow Automation

The problem with getting really good inventory has always been the difficulty understanding rates and availability. That’s why the RFP exists today, and isn’t going away anytime soon. Publishers will always want full control over the really good stuff. Because they know their inventory better than any algorithm, there will always be a need for human control and creativity. Big, custom sponsorships and custom-curated native executions will only increase over time, as more television and print budgets shift into addressable digital. You just can’t automate those deals. Marketers and agencies will demand programmatic efficiency to compress an expensive, 42-step process for securing guaranteed inventory. This is one area that programmatic RTB has not been built to handle (these deals are neither “real time” nor “bidded”), but we are seeing real innovation from a number of companies trying to bring programmatic efficiency to guaranteed deals.

It’s hard to explain everything that we are getting right to a 14-year-old who spends more time on mobile apps than in an Internet browser. His assessment, in surfing the desktop Internet, is probably right – it looks like a lot of weight loss ads and sneaker retargeting. But, it’s still early days nearly 20 years after the first banner ad was served.

Does Driving Efficiency Drive Profit (A Contrarian View of RTB)

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Online display would be like this, if branding metrics took profit into account.

I’ve always loved the notion of programmatic RTB. As a data hound and an early adopter of Appnexus , the notion that advertisers can achieve highly granular levels of targeting and utilize algorithms to impact performance is right in my wheelhouse. Today’s ad tech, replete with 300 companies that enable data-driven audience segmentation, targeting, and analytics is testament to the efficiency of buying ads one impression at a time.

But what if driving efficiency in display actually does more harm than good?

Today’s RTB practitioners have become extremely relentless in pursuit of the perfect audience. It starts with retargeting, which uses first party data to serve ads only to people who are already deeply within the customer funnel. No waste there. The next tactic is to target behavioral “intenders” who, according to their cookies, have done everything BUT purchase something. Guess what? If I have searched 4 times in the last three hours for a flight from JFK to SFO, eventually you will get last view attribution for my ticket purchase if you serve me enough ads. Next? Finding “lookalike” audiences that closely resemble past purchasers. Data companies, each of whom sell a variety of segments that can be mixed to create a 35 year-old suburban woman, do a great job of delivering audiences with a predilection to purchase.

But what if we are serving ads to people that are already going to buy? Is efficiency really driving new sales, or are we just helping marketers save money on marketing?

It seems like online display wants to be more and more like television. Television is simple to buy, it works, and it drives tons of top funnel awareness that leads to bottom funnel results. We know branding works, and even those who didn’t necessarily believe in online branding need look no further than Facebook for proof. With their Datalogix offline data partnership, Facebook conclusively proved that people exposed to lots of Facebook ads tended to grab more items off of store shelves. It just makes sense. So why are we frequency capping audiences at 3—or even 10? I can’t remember the last time I watched network television and didn’t see the same car ad about 20 times.

The other thing that RTB misses out on is profit. RTB drives advertising towards lowering the overall cost of media needed to drive a sale. Even if today’s attribution models were capable of taking into account all of the top-funnel activity that eventually creates an online shopping cart purchase (a ludicrous notion), we are still just measuring those things that are measureable. TV ads, billboard ads, and word of mouth never get online credit—yet I believe they drive most of the online sales. Sorry, but I believe the RTB industry creates attribution models that favor RTB buying. Shocking, I know.

So, what is true performance and what really drives it? For most businesses, performance is more profit. In other words, the notion that a sales territory that has 100 sales a day can generate 120 sales a day. That’s called profit optimization. If I can use advertising to create those additional 20 sales, and still make a profit after expenses, than that’s a winner. RTB makes it cheaper to get the 100 sales you already have, but doesn’t necessarily get the next twenty. Getting the next batch of customers requires spending more on media, and driving more top-funnel activity.

The other thing RTB tends to fumble is how real life sales actually happen. Sure, audience buying knows what type of audience tends to buy, and where to find them online, but misses with frequency capping and a lack of contextual relevance. Let me explain. In real life, people live in neighborhoods. The houses in those neighborhoods are roughly the same price, the kids go to the same school district, the people have similar jobs, and their kids do similar activities and play the same sports. The Smiths drive similar cars to the Joneses, they eat at the same restaurants, and shop at the same stores. If the Smiths get a new BMW, then it’s likely the Joneses will keep up with a new Audi or Lexus in the near future. When neighbors get together, they ask each other what they did on February Break, and they get their vacation ideas from each other. That’s how life works.

What media most closely supports this real-life model, where we are influenced most by our neighbors?  Is it serving the Jones family a few carefully selected banners on cheap exchange inventory, which is highly targeted and cost effective? Or is it jamming the Smiths and Joneses with top-funnel brand impressions across the web? The latter not only gets Smith, the BMW owner, to keep his car top-of-mind and be more likely to recommend it—but also predisposes Jones to regard his neighbor’s vehicle in a more desirable light. That takes a lot of impressions of various types of media. You can’t do that and remain efficient. The thing is—you can do that and create incremental profit.

Isn’t that what marketers really want?

[This post originally appeared in AdExchanger on 5/20/13]

Stealing Some Of Microsoft’s 76% Ad Tech Market Share

downloadWhen you think of advertising technology in the display space, the first names you’re likely to think of are Google, PubMatic, Adobe, and AppNexus. But Microsoft? Not really top of mind, unless you are thinking of its disastrous aQuantive acquisition in 2007. Sure, every now and then MSFT will pick up the odd Rapt or Yammer, but is it really having a huge impact in the ad tech space? Even if you’re a regular AdExchanger reader, you’d be justified in thinking it’s not.

But you’d be 100% wrong.

Microsoft has been quietly running the inner ad-technology workings of digital display since the first banner ad was purchased in 1995. According to some recent research, the company’s ad-planning software boasts an amazing 76% market share among agency media planners. MediaVisor ranks a distant second with a measly 9.7 Almost nine in 10 planners who use Excel spend more than an hour a day using its software, while almost 35% use it for more than four hours per day[CO1] . [l2]

That software is called Microsoft Excel.

Released in 1985 (originally for Macintosh), Excel is nearly three decades old and has been powering digital-media planning since its inception. Combined with Outlook, Word, and PowerPoint in the Office suite of products, Microsoft tools have been central to the digital-media planning process for a long time. Planners plan in Excel, publishers pitch in Excel and PowerPoint, contracts are made in Word, and everything is communicated via Outlook. And then there are the billing and reconciliation tasks that occur inside spreadsheets. Nobody ever seems to wonder why more than $6 billion in digital display media transactions (representing nearly 70% of all ads sold) use Microsoft tools and the occasional fax machine.

While innovative companies have challenged the dominance of these systems in the past, early efforts fizzled. The complexities of modern digital-media planning, combined with the reluctance of agency planners to change their behavior, have hindered innovation. Looking at past and current “systems of record” for media buying, it’s no wonder planners are scared of change. If you have ever seen legacy agency operating systems, you wonder if a single dollar was ever spent on user experience or user interface design.

Why Programmatic-Direct Planners Use Excel

As an ad technology “evangelist” of sorts, it is my job to show agencies the future of digital-media planning. This is starting to be called programmatic buying, a term which encompasses both “programmatic direct” buying, which targets the transactional RFP business that accounts for the bulk – 70% – of digital display ads, and “programmatic RTB,” which accounts for the impression-by-impression purchases that represent another $2.4 billion, or 25[CO3] % of the pie.

Companies like MediaMath and AppNexus have made the latter category wildly efficient. Buyers don’t use Excel to create an audience-buying campaign across exchange inventory. Instead, they log into a web-based RTB platform.

For automating guaranteed display buys, though, Excel has become the default for media planners, even though if it doesn’t have the features of many web-based systems available. For example, Excel doesn’t track your changes. When planners change something, multiple files are created, and it’s easy for two people to work on a plan at the same time, duplicating work and botching it up. Excel isn’t Sarbanes-Oxley compliant, either. Agencies end up with thousands of Excel sheets on hard drives and servers, and a complicated file versioning and access system that makes replicating and tracking plans really difficult. Excel doesn’t integrate easily with other systems. At the file level, Excel is great. You can import and export Excel files into almost anything. But Excel can’t send out an RFP, or accept an order. Excel can’t automatically set an ad placement inside an ad server like DFA or MediaMind, or get Comscore updates. Excel is amazingly flexible, but it wasn’t built for media planning.

Today, the average digital-media plan costs nearly $40,000 to produce and takes as many as 42 steps to complete. That’s why, according to a recent Digiday survey, more than two thirds of agency employees will leave their jobs within the next two years. Digital-media planning should be fun and innovative, and young, smart people should want to be spending their time influencing how major brands leverage new technologies and media outlets to sell their products.

The reality is that young media planners are finding their days are filled with reconciling monthly invoices and ad delivery numbers. Have you noticed media planners’ eyes glazing over during your latest “lunch and learn?” That’s today’s young agency employees’ way of calling bullshit on ad tech. Our technology has been making their lives harder and their hours longer, rather than ushering in a new era of efficiency and performance.

How We Can Finally Beat Excel

I believe that dynamic is rapidly changing now. Buy-side technologies from innovative software companies, combined with offerings from sell-side players that are plugging into publisher ad servers are creating a programmatic future by building web-based, easy to use, and extensible platforms.Here are a few reasons these types of systems will start to get adoption:

  • Pushback on agency pricing models: Big agencies have been getting paid by the hour for years, but their clients are starting to push back on cost-plus pricing schemes. After exposure to self-service platforms and programmatic buying, they are getting used to seeing a larger percentage of their money applied to the media, and that trend is only likely to continue. Brand advertisers are demanding more efficiency in direct-to-publisher buys, and that means agencies must start to embrace programmatic direct technologies.
  • User interfaces and user experiences are improving: Young people plan media. They are used to really cool web-based technologies, such as Snapchat and Twitter. Today’s platforms not only centralize workflow and data, but increasingly come with something even more critical to gaining user adoption: a nice interface. When we start building tools that people want to use and a user experience that maps to the tasks being performed online, adoption will quickly increase.
  • Prevalence of APIs: Today’s platforms are being built in an open, extensible way that enables linkage with other systems. Since there are so many phases in modern digital media planning (research, planning, buying, ad serving, reporting, billing) it makes sense for platforms to be able to talk to one another. While some legacy APIs are not the best, they are getting better. Servers-to-server integrations make a lot more sense than 23-year-old planners updating spreadsheets. As David Kenny, CEO of The Weather Company, once remarked, “If you are using people to do the work of machines, you are already irrelevant .”

Because of these factors, I expect 2013 will be the year that programmatic direct buying changes from a fun concept for a planners’ “lunch and learn” to a reality. It’s time for us to finally get cracking on stealing some of Microsoft’s ad technology market share.

[This post was originally pushed in AdExchanger on 4-23-13]