September 06, 2017

Is Digital Marketing having its ‘Deep Blue’ moment?


Garry Kasparov will forever be remembered as perhaps the greatest chess player of all time, dominating the game for almost twenty years until his retirement in 2005. But ironically he may be best remembered for the match he failed to win twenty years ago in 1997 against IBM’s Deep Blue chess computer. That watershed moment – marking the point at which computers effectively surpassed humans in chess-playing ability – prompted much speculation and hand-wringing about the coming obsolescence of the human brain, now that a mere computer had been able to beat the best chess grandmaster in the world.

Since then, computers and chess software have only grown more powerful, to the point that a $50 commercial chess program (or even a mobile app) can beat most grandmasters easily. Faced with this, you might expect Kasparov and other top-flight players to have grown disillusioned with the game, or defensive about the encroachment of computers on their intellectual territory; but in fact the reverse is true.

Today’s chess grandmasters make extensive use of computers to practice, try out new strategies, and prepare for tournaments, in the process becoming a little more like the machines that outpaced them in 1997. Kasparov himself was instrumental in pioneering a  new type of chess game, Advanced Chess, in which humans are allowed to consult with chess software as they play. In his new book, “Deep Thinking: Where Machine Intelligence Ends and Human Intelligence Begins”, Kasparov writes about an Advanced Chess match he played in 1998 against Veselin Topalov:

“Having a computer partner also meant never having to worry about making a tactical blunder. The computer could project the consequences of each move we considered, pointing out possible outcomes and countermoves we might otherwise have missed. With that taken care of for us, we could concentrate on strategic planning instead of spending so much time on calculations. Human creativity was even more paramount under these conditions.”

What Kasparov and his successors in the competitive chess-playing world have discovered was that, when it comes to chess, the strongest player is not man or machine, but man and machine. In fact, a new kind of chess tournament has sprung up, Freestyle Chess, in which teams of humans and computers compete against one another, each bringing their respective strengths to the game: creativity, strategy and intuition from the humans, and tactical outcome prediction from the computers.

And your point is?

You may be asking what relevance this has to digital marketing. In fact, there are strong similarities between chess and marketing (particularly digital marketing):  they are both highly quantifiable pursuits with clear outcomes which have historically relied solely on human intuition and creativity for success.

As in chess, digital marketing relies upon a continuous reassessment of the ‘board’ (customer behaviors and history) in order to decide upon the next ‘move’ (a particular campaign communication aimed at a particular group of customers). Once the move has been made, the board needs to be reassessed before taking the next move.

Today’s digital marketer is much like the chess grandmaster of the early 1990s – they rely on their intuitive understanding of their audience’s makeup and preferences to decide what offers and messages they want to deliver, to which users, and in which channels. Of course, digital marketers understand that measuring campaign outcomes and audience response (using techniques like control groups and attribution analysis) is very important, but most still operate in a world where the humans make the decisions, and the computers merely provide the numbers to support the decision-making.

Luddites 2.0

When Kasparov was asked in 1990 if a computer could beat a grandmaster before the year 2000, he quipped:

“No way - and if any grandmaster has difficulties playing computers, I would be happy to provide my advice.”

Today’s digital marketers can be forgiven for exhibiting some of the same skepticism. Ask them how they came up with a new idea for an ad, or how they know that a particular product will be just right for a particular audience, and they may not be able to answer – they will just know that their intuition is sound. As a result it can seem incredible that a computer can pick the right audience for a campaign, and match the appropriate offer and creative to that audience. 

But the computers are coming. As I mentioned in my earlier post on bandit experimentation, companies like Amplero, Kahuna and Cerebri AI are pitching intelligent systems that claim to take a lot of this decision-making about creative choice, audience, channel and other campaign variables out of the hands of humans. But where does that leave the digital marketer?

We welcome our robot colleagues

The clue lies in the insights that Kasparov ultimately drew from his defeat. He realized that the strengths he brought were different and complementary to the strengths of the computer. The same holds true for digital marketing. Coming up with product value propositions, campaign messaging and creative are activities which computers are nowhere close to being good at, especially in the context of broader intangible brand attributes. On the other hand, audience selection and targeting, as well as creative optimization, are highly suited to automation, to the extent that computers can be expected to perform significantly better than their human counterparts, much as chess software outperforms human players.

Clearly humans and machines need to work together to create and execute the best performing campaigns, but exactly how this model will work is still being figured out.

Today, most digital marketers build campaign audiences by hand, identifying specific audience attributes (such as demographics or behavioral history) and applying filters to to those attributes to build segments. The more sophisticated the marketer attempts to be in selecting audience attributes for campaign segments, the more cost they incur in the setup of those campaigns, making the ROI equation harder to balance.

The emerging alternative approach is to provide an ML/AI system with a set of audience (and campaign) attributes, and let it figure out which combinations of audience and offer/creative deliver the best results by experimenting with different combinations of these attributes in outbound communications. But this raises some important questions:

  • How to choose the attributes in the first place
  • How to understand which attributes make a difference
  • How to fit ML/AI-driven campaigns into a broader communications cadence & strategy
  • How to use learnings from ML/AI-driven campaigns to develop new value propositions and creative executions

In other words, ML/AI-driven marketing systems cannot simply be ‘black boxes’ into which campaign objectives and creative are dumped, and then left to deliver clicks or conversions on the resulting campaign delivery. They need to inform and involve marketers as they do their work, so that the marketers can make their uniquely human contribution to the process of designing effective campaigns. The black box needs some knobs and dials, in other words.

The world of chess offers a further useful parallel here. Chess grandmasters make extensive use of specialized chess software like Fritz 15 or Shredder, which not only provide a comprehensive database of chess moves, but also training and analysis capabilities to help human players improve their chess and plan their games. These programs don’t simply play chess – they explain how they are making their recommendations, to enable their human counterparts to make their own decisions more effectively.

These are the kinds of systems that digital marketers need to transform their marketing with AI. In turn, marketers need to adjust the way they plan and define campaigns in the same way that chess grandmasters have dramatically changed the way they study, plan and play games of chess in the last twenty years, working alongside the computers before, during and after campaigns are run.

In 1997, it was far from clear how chess, and the people who played it , would react to the arrival of computers. Digital Marketing stands on a similar threshold today. Twenty years from now it will seem obvious how marketers’ roles would evolve, and how technology would adapt to support them. We’re in the fortunate position of getting to figure this out as it all unfolds, much as Kasparov did. diggDigg RedditReddit StumbleUponStumbleUpon

August 26, 2015

Got a DMP coming in? Pick up your underwear

mr-messy-nr-8If you’re like me, and have succumbed to the unpardonably bourgeois luxury of hiring a cleaner, then you may also have found yourself running around your house before the cleaner comes, picking up stray items of laundry and frantically doing the dishes. Much of this is motivated by “cleaner guilt”, but there is a more practical purpose – if our house is a mess when the cleaner comes, all she spends her time doing is tidying up (often in ways that turn out to be infuriating, as she piles stuff up in unlikely places) rather than actually cleaning (exhibit one: my daughter’s bedroom floor).

This analogy occurred to me as I was thinking about the experience of working with a Data Management Platform (DMP) provider. DMPs spend a lot of time coming in and “cleaning house” for their customers, tying together messy datasets and connecting them to digital marketing platforms. But if your data systems and processes are covered with the metaphorical equivalent of three layers of discarded underwear, the DMP will have to spend a lot of time picking that up (or working around it) before they can add any serious value.

So what can you do ahead of time to get the best value out of bringing in a DMP? That’s what this post is about.

What is a DMP, anyway?

That is a excellent question. DMPs have evolved and matured considerably since they emerged onto the scene a few years ago. It’s also become harder to clearly identify the boundaries of a DMP’s services because many of the leading solutions have been integrated into broader “marketing cloud” offerings (such as those from Adobe, Oracle or Salesforce). But most DMPs worth their salt provide the following three core services:

Data ingestion & integration: The starting place for DMPs, this is about bringing a marketer’s disparate audience data together in a coherent data warehouse that can then be used for analytics and audience segment building. Central to this warehouse is a master user profile  – a joined set of ID-linked data which provides the backbone of a customer’s profile, together with attributes drawn from first-party sources (such as product telemetry, historical purchase data or website usage data) and third-party sources (such as aggregated behavioral data the DMP has collected or brokered).

Analytics & segment building: DMPs typically offer their own tools for analyzing audience data and building segments, often as part of a broader campaign management workflow. These capabilities can vary in sophistication, and sometimes include lookalike modeling, where the DMP uses the attributes of an existing segment (for example, existing customers) to identify other prospects in the audience pool who have similar attributes, and conversion attribution - identifying which components of a multi-channel campaign actually influenced the desired outcomes (e.g. a sale).

Delivery system integration: The whole point of hiring a DMP to integrate data and enable segment building is to support targeted digital marketing. So DMPs now provide integration points to marketing delivery systems across email, display (via DSP and Exchange integration), in-app and other channels. This integration is typically patchy and influenced by other components of the DMP provider’s portfolio, but is steadily improving.

Making the best of your DMP relationship

The whole reason that DMPs exist in the first place is because achieving the above three things is hard – unless your organization in a position to build out and manage its own data infrastructure and put some serious investment behind data integration and development, you are unlikely to be able to replicate the services of a DMP (especially when it comes to integration with third-party data and delivery systems). But there are a number of things you can do to make sure you get the best value out of your DMP relationship.


1. Clean up your data

dirty-dishesThis is the area where you can make the most difference ahead of time. Bringing signals about your audience/customers together will benefit your business across the board, not just in a marketing context. You should set your sights on integrating (or at least cataloging and understanding) all data that represents customer/prospect interaction with your organization, such as:

  • Website visits
  • Purchases
  • Product usage (if you have a product that you can track the usage of)
  • Mobile app usage
  • Social media interaction (e.g. tweets)
  • Marketing campaign response (e.g. email clicks)
  • Customer support interactions
  • Survey/feedback response

You should also integrate any datasets you have that describe what you already know about your customers or users, such as previous purchases or demographic data.

The goal here is, for a given user/customer, to be able to identify all of their interactions with your organization, so that you can cross-reference that data to build interesting and useful segments that you can use to communicate with your audience. So for user XYZ123, for example, you want to know that:

  • They visited your website 3 times in the past month, focusing mainly on information about your Widget3000 product
  • They have downloaded your free WidgetFinder app, and run it 7 times
  • They previously purchased a Widget2000, but haven’t used it for four months
  • They are male, and live in Sioux Falls, South Dakota
  • Last week they tweeted:

Unless you’re some kind of data saint (or delusional), reading the two preceding paragraphs probably filled you with exhaustion. Because all of the above kinds of data have different schemas (if they have schemas at all), and more importantly (or depressingly), they all use different (or at least independent) ways of identifying who the user/customer actually is. How are you supposed to join all this data if you don’t have a common key?

DSPs solve these problems in a couple of ways:

  • They provide a unified ID system (usually via a third-party tag/cookie) for all online interaction points (such as web, display ads, some social)
  • They will map/aggregate key behavioral signals onto a common schema to create a single user profile (or online user profile, at any rate), typically hosted in the DMP’s cloud

The upside of this approach is that you can achieve some degree of data integration via the (relatively) painless means of inserting another bit of JavaScript into all of your web pages and ad templates, and also that you can access other companies’ audiences who are tagged with the same cookie – so-called audience extension.

However, there are some downsides, also. Key amongst these are:

Yet another ID: If you already have multiple ways of IDing your users, adding another “master ID” to the mix may just increase complexity. And it may be difficult to link key behaviors (such as mobile app purchases) or offline data (such as purchase history) to this ID.

Your data in someone else’s cloud: Most marketing cloud/DMP solutions assume that the master audience profile dataset will be stored in the cloud. That necessarily limits the amount and detail of information you can include in the profile – for example, credit card information.

It doesn’t help your data: Just taking a post-facto approach with a DMP (i.e. fixing all your data issues downstream of the source, in the DMP’s profile store) doesn’t do anything to improve the core quality of the source data.

So what should you do? My recommendation is to catalog, clean up and join your most important datasets before you start working with a DMP, and (if possible) identify an ID that you already own that you can use as a master ID. The more you can achieve here, the less time your DMP will spend picking up your metaphorical underwear, and the more time they’ll spend providing value-added services such as audience extension and building integrations into your online marketing systems.


2. Think about your marketing goals and segments

cpc_01You should actually think about your marketing goals before you even think about bringing in a DMP or indeed make any other investments in your digital marketing capabilities. But if your DMP is already coming in, make sure you can answer questions about what you want to achieve with your audience (for example, conversions vs engagement) and how you segment them (or would like to segment them).

Once you have an idea of the segments you want to use to target your audience, then you can see whether you have the data already in-house to build these segments. Any work you can do here up-front will save your DMP a lot of digging around to find this data themselves. It will also equip you well for conversations with the DMP about how you can go about acquiring or generating that data, and may save you from accidentally paying the DMP for third-party data that you actually don’t need.


3. Do your own due diligence on delivery systems and DSPs

catapultYour DMP will come with their own set of opinions and partnerships around Demand-side Platforms (DSPs) and delivery systems (e.g. email or display ad platforms). Before you talk with the DMP on this, make sure you understand your own needs well, and ideally, do some due diligence with the solutions in the marketplace (not just the tools you’re already using) as a fit to your needs. Questions to ask here include:

  • Do you need realtime (or near-realtime) targeting capabilities, and under what conditions? For example, if someone activates your product, do you want to be able to send them an email with hints and tips within a few hours?
  • What kinds of customer journeys do you want to enable? If you have complex customer journeys (with several stages of consideration, multiple channels, etc) then you will need a more capable ‘journey builder’ function in your marketing workflow tools, and your DMP will need to integrate with this.
  • Do you have any unusual places you want to serve digital messaging, such as in-product/in-app, via partners, or offline? Places where you can’t serve (or read) a cookie will be harder to reach with your DMP and may require custom integration.

The answers to these questions are important: on the one hand there may be a great third-party system with functionality that you really like, but which will need custom integration with your DMP; on the other hand, the solutions that the DMP can integrate with easily may get you started quickly and painlessly, but may not meet your needs over time.


If you can successfully perform the above housekeeping activities before your DMP arrives and starts gasping at the mountain of dishes piled up in your kitchen sink, you’ll be in pretty good shape. diggDigg RedditReddit StumbleUponStumbleUpon

May 17, 2015

The rise of the Chief Data Officer

mad-men-monolithAs the final season of Mad Men came to a close this weekend, one of my favorite memories from Season 7 is the appearance of the IBM 360 mainframe in the Sterling Cooper & Partners offices, much to the chagrin of the creative team (whose lounge was removed to make space for the beast), especially poor old Ginsberg, who became convinced the “monolith” was turning him gay (and took radical steps to address the issue).

My affection for the 360 is partly driven by the fact that I started my career at IBM, closer in time to Man Men Series 7 (set in 1969) than the present day (and now I feel tremendously old having just written that sentence). The other reason I feel an affinity for the Big Blue Box is because my day job consists of thinking of ways to use data to make marketing more effective, and of course that is what the computer at SC&P was for. It was brought in at the urging of the nerdish (and universally unloved) Harry Crane, to enable him to crunch the audience numbers coming from Nielsen’s TV audience measurement service to make TV media buying decisions. This was a major milestone in the evolution of data-driven marketing, because it linked advertising spend to actual advertising delivery, something that we now take for granted.

The whole point of Mad Men introducing the IBM computer into the SC&P offices was to make a point about the changing nature of advertising in the early 1970s – in particular that Don Draper and his “three martini lunch” tribe’s days were numbered. Since then, the rise of the Harry Cranes, and the use of data in marketing and advertising, has been relentless. Today, many agencies have a Chief Data Officer, an individual charged with the task of helping the agency and its clients to get the best out of data.

But what does, or should, a Chief Data Officer (or CDO) do? At an advertising & marketing agency, it involves the following areas:

Enabling clients to maximize the value they get from data. Many agency clients have significant data assets locked up inside their organization, such as sales history, product telemetry, or web data, and need help to join this data together and link it to their marketing efforts, in order to deliver more targeted messaging and drive loyalty and ROI. Additionally, the CDO should advise clients on how they can use their existing data to deliver direct value, for example by licensing it.

Advising clients on how to gather more data, safely. A good CDO offers advice to clients on strategies for collecting more useful data (e.g. through additional telemetry), or working with third-party data and data service providers, while respecting the client’s customers’ privacy needs.

Managing in-house data assets & services. Some agencies maintain their own in-house data assets and services, from proprietary datasets to analytics services. The CDO needs to manage and evolve these services to ensure they meet the needs of clients. In particular, the CDO should nurture leading-edge marketing science techniques, such as predictive modeling, to help clients become even more data-driven in their approach.

Managing data partnerships. Since data is such an important part of a modern agency’s value proposition, most agencies maintain ongoing relationships with key third-party data providers, such as BlueKai or Lotame.The CDO needs to manage these relationships so that they complement the in-house capabilities of the agency, and so the agency (and its clients) don’t end up letting valuable data “walk out of the door”.

Driving standards. As agencies increasingly look to data as a differentiating ingredient across multiple channels, using data and measurement consistently becomes ever more important. The CDO needs to drive consistent standards for campaign measurement and attribution across the agency so that as a client works with different teams, their measurement framework stays the same.

Engaging with the industry & championing privacy. Using data for marketing & advertising is not without controversy, so the DCO needs to be a champion for data privacy and actively engaged with the industry on this and other key topics.

As you can see, that’s plenty for the ambitious CDO to do, and in particular plenty that is not covered by other traditional C-level roles in an ad agency. I think we’ll be seeing plenty more CDOs appointed in the months and years to come. diggDigg RedditReddit StumbleUponStumbleUpon

November 09, 2011

Building the Perfect Display Ad Performance Dashboard, Part I – creating a measurement framework

dashboard-warning-lightsThere is no shortage of pontification available about how to measure your online marketing campaigns: how to integrate social media measurement, landing page optimization, ensuring your site has the right feng shui to deliver optimal conversions, etc. But there is very little writing about the other side of the coin: if you’re the one selling the advertising, on your site, or blog, or whatever, how do you understand and then maximize the revenue that your site earns?

As I’ve covered previously in my Online Advertising 101 series, publishers have a number of tools and techniques available to manage the price that their online ad inventory is sold for. But the use of those tools is guided by data and metrics. And it’s the generation and analysis of this data that is the focus of this series of posts.

In this series, I’ll unpack the key data components that you will need to pull together to create a dashboard that will give you meaningful, actionable information about how your site is generating money – or monetizing, to use the jargon.

We’ll start by taking a high-level look at a framework for analyzing a site’s (or network’s) monetization performance. In subsequent posts, we’ll drill into the topics that we touch on briefly here.


Getting the measure of the business

Ultimately, for any business, revenue (or strictly speaking, income or profit) is king. If you’re not generating revenue, you can’t pay the bills (despite what trendy start-ups will tell you). But anyone running a business needs a bit more detail to make decisions that will drive increased revenue.

In the ad-supported publishing business, these decisions fall into a couple of broad buckets:

  • How to create more (or more appealing) supply of sellable advertising inventory
  • How to monetize the supply more effectively – either by selling more of it, or selling it for a better price, or both

Another way of thinking about these decisions is in a supply/demand framework that is common to almost all businesses: If your product is selling like hot cakes and you can’t mint enough to meet demand, you have a supply problem, and you need to focus on creating more supply. If, on the other hand, you have a lot of unsold stock sitting around in warehouses (real or virtual), you have a demand problem, and you need to think about how to make your products more compelling, or your sales force more effective, or both.

Online publishers usually suffer from both problems at the same time: Part of their inventory supply will be in high demand, and the business will be supply-constrained (it is not easy to mint new ad impressions the way a widget manufacturer can stamp out new widgets). Other parts of the inventory, on the other hand, will be hard to shift, and the business will be demand-constrained – and unlike widgets, unsold ad inventory goes poof! when the clock strikes midnight.

So analysis of an online ad business needs to be based on the following key measures:

  • How much inventory was available to sell (the Supply)
  • How much inventory was actually sold (the Volume Sold)
  • How much the inventory was actually sold for (the Rate)

It’s ultimately these measures (and a few others that can be derived from them) that will tell you whether you’re succeeding or failing in your efforts to monetize your site. But like any reasonably complex business (and online advertising is, at the very least, unreasonably complex), it’s really how you segment the analysis that counts in terms of making decisions.


What did we sell, and how did we sell it?

Most businesses would be doing a pretty poor job of analysis if they couldn’t look at business performance broken out by the products they sell. A grocery chain that didn’t know if it was selling more grapes or grape-nuts would not last very long. Online advertising is no exception – in fact, quite the opposite. Because online ad inventory can be packaged so flexibly, it’s essential to answer the question “What did we sell?” in a variety of ways, such as:

  • What site areas (or sub-areas) were sold
  • What audience/targeting segments were sold
  • What day-parts were sold
  • What ad unit sizes were sold
  • What rich media types were sold

The online ad sales business also has the unusual property that the same supply can (and is) sold through multiple channels at different price points. So it is very important to segment the business based on how the supply was sold, such as:

  • Direct vs indirect (e.g. via a network or exchange)
  • Reserved vs remnant/discretionary

Depending on the kind of site or network you’re analyzing, different aspects of these what and how dimensions will be more important. For example, if you’re running a site with lots of high-quality editorial content, analyzing sales by content area/topic will be very important; on the other hand, if the site is a community site with lots of undifferentiated content but a loyal user base, audience segments will be more relevant.


Bringing it together – the framework

I don’t know about you, but since I am a visual person to start with, and have spent most of the last ten years looking at spreadsheets or data tables of one sort or another, when I think of combining the components that I’ve described above, I think of a table that looks a bit like the following:


This table is really just a visual way of remembering the differences between the measures that we’re interested in (volume, rate etc) and the dimensions that we want to break things out by (the “what” and “how” detail). If you don’t spend as much of your time talking to people about data cubes as I do, these terms may be a little unfamiliar to you, which is why I’m formally introducing them here. (As an aside, I have found that if you authoritatively bandy about terms like “dimensionality” when talking about data, you come across as very wise-sounding.)

In the next posts in this series, I shall dig into these measures and dimensions (and others) in more detail, to allow us to populate the framework above with real numbers. We’ll also be looking at how you can tune the scope of your analysis to ensure that

For now, here’s an example of the kinds of questions that you would be able to answer if you looked at premium vs non-premium ad units as the “what” dimension, and direct vs indirect as the “how” dimension:



As this series progresses, I’d love to know what you think of it, as well as topics that you would like me to focus on. So please make use of the comments box below. diggDigg RedditReddit StumbleUponStumbleUpon

October 17, 2011

Nicely executed retargeting opt-out (for a change)

Retargeting (sometimes called remessaging or remarketing) has taken off in a big way, recently – Google introduced the feature into AdWords earlier this year, and a host of other players are in the game. Consequently, the interwebs now abound with commentary on the rather spooky nature of the technology, with people being “followed around” the Internet by ads for things they were either searching for, or were looking at on e-commerce websites.

It is true that most retargeting implementations are a bit clunky, and I have been on the receiving end of plenty of them myself. Their most irritating aspect seems to be that the time window for perceived relevance of the retargeted ads seem to be ridiculously long. It’s somehow almost more irritating to be deluged by ads for that miscellaneous widget site that you once visited a few weeks ago (even though you have since satisfied your need for widgets elsewhere) than it is to be served non-targeted (or more broadly targeted) ads.

Such ads are made more bearable by a robust opt-out capability; many ad networks have adopted the IAB’s self-regulatory program, which calls for the advertiser to make it possible to opt out of these kinds of ads, which is to say, stop receiving them; stopping the data collection is a more difficult matter.

So today I want to give a little love to TellApart, not because their retargeting implementation is especially subtle or innovative, but simply because they provide a nice opt-out implementation. Last week I spent a little time looking for a desk for my daughter (who currently occupies our dining table with her homework). So since then I have been served retargeted ads on behalf of the site I visited ( on various sites. Here’s one from Business Insider:


The nice thing about the ad is it has a little “x” icon in the top right (which actually makes a little more sense than the IAB’s suggested “Advertising Option Icon”, which is a bit cryptic). Clicking it gives me this:


The ability to opt out right in the ad unit is nice, and makes me feel more well-disposed to the advertiser and the site that the ad is running on. Clicking through the “Learn More About These Ads” link at the bottom takes me to TellApart’s website with a little more information and the same option to opt out – though no option to opt out of certain categories of ads, or groups of advertisers.If more retargeting networks provided simpler opt-out capabilities like these, it might help to make these ads seem like less of a scary proposition. diggDigg RedditReddit StumbleUponStumbleUpon

May 13, 2009

Does Display help Search? Or does Search help Display?

One of the topics that we didn’t get quite enough time to cover in detail in my face-off with Avinash Kaushik at last week’s eMetrics Summit (of which more in another post) was the thorny issue of conversion attribution. When I asked Avinash about it, he made the sensible point that trying to correctly “attribute” a conversion to a mix of the interactions that preceded it ends up being a very subjective process, and that adopting a more experimental approach – tweaking aspects of a campaign and seeing which tweaks result in higher conversion rates – is more sound.

I asked the question in part because conversion attribution is conspicuously absent from Google Analytics – a fact which raises an interesting question about whether it’s in Google’s interest to include a feature like this, since it may stand to lose more than it gains by doing so (since the effective ROI of search will almost certainly go down when other channels are mixed into an attribution model).

Our own Atlas Institute is quite vocal on this topic, and has published a number of white papers such as this one [PDF] about the consideration/conversion funnel, and this one [PDF], on which channels are winners and losers in the new world of Engagement Mapping (our term for multi-channel conversion attribution).

The Atlas Institute has also opined about how adding display to a search campaign can raise the effectiveness of that campaign by 22% compared to search alone – in other words, how display helps search to be better.

However, a recent study from iProspect throws some new light on this discussion. The study – a survey of 1,575 web consumers – attempted to discover how people respond to display advertising. And one of the most interesting findings from the study is that, whilst 31% of users claim to have clicked on a display ad in the last 6 months, almost as many – 27% – claimed that they responded to the ad by searching for that product or brand:


This raises the interesting idea that search can actually help display be better, by providing a response mechanism that differs from the traditional ad click behavior that we expect. Of course, this still doesn’t mean that search should get 100% of the credit for a conversion in this kind of scenario – in fact, it makes a stronger case for “view-through” attribution of display campaigns – something that ad networks (like, er, our own Microsoft Media Network) are keen to encourage people to do, to make performance-based campaigns look better.

All this really means that, of course, it’s not a case of display vs. search, but display and search (and a whole lot of other ways of reaching consumers). Whether you take the view that it’s your display campaign that helps your search to be more effective, or your search keywords that help your display campaign to drive more response, multi-channel online marketing – and the complexity that goes with measuring it – looks set for the big time. And by “big time”, I mean the army of small advertisers currently using systems like Google’s AdWords, or our own adCenter. So maybe we’ll see multi-channel conversion attribution in Google Analytics before long. diggDigg RedditReddit StumbleUponStumbleUpon

January 14, 2009

PubMatic kicks us when we’re down (but gently)

image As if we weren’t all feeling gloomy enough already, PubMatic has just released its Q4 2008 Ad Price Index report, which makes for sobering reading. For those of you not familiar with PubMatic, they provide “multi-network optimization” for publishers who are looking to maximize the yield on their remnant ad inventory (i.e. the inventory the publisher can’t sell themselves).

Rather than manually dealing with a handful of networks directly, the publisher hands their inventory over to PubMatic who ensures that the most profitable ad is shown, whether it comes from Google Adsense, BlueLithium, AdBrite, or another network. Since a key part of what PubMatic does is measure the CPMs for online ads, they have access to lots of ad price data, and every quarter they roll this data up into a report (available here as a PDF).

PubMatic has been doing this for 15 months now, and so far, they’ve yet to deliver any good news:


Given the economic Armageddon that overtook the world, PubMatic’s report that average prices only softened by $0.01 during Q4 actually seems like pretty good news. But then again, Q4 was the holiday season; and compared to Q4 2007, Q4 2008’s numbers look pretty horrendous.

The detail of the report contains some more interesting tidbits: for example, the average CPMs for small sites (fewer than 1 million PV/mo) are the highest, at around $0.60, whilst the average CPMs for large sites languish at around the $0.17 mark.

Before you start predicting the doom of the mainstream media, however, it should be pointed out (as Mike Nolet has done) that there is a sample bias in the PubMatic numbers – whereas a small publisher is wholly dependent on ad networks for all their revenue (lacking the resources to sell their inventory themselves), and so is likely sending all its inventory (including the juiciest stuff on the home page) to PubMatic, a large site will only be sending the inventory that they couldn’t sell themselves – i.e. the bottom-of-the-barrel stuff.

It also turns out that average prices for the largest and smallest publishers have slumped by around 50% in the past year, whilst prices for medium-sized sites have remained more solid:


I’m at something of a loss to explain why this might be – at the high end, it may be because large sites are becoming more efficient at selling their inventory themselves, so it’s only the really cheap stuff that is being passed on to PubMatic; whilst at the bottom end, small publishers are becoming increasingly crowded out by new sites.

What would be immensely useful would be for PubMatic to provide some kind of indication of the proportion of inventory from sites that is being served through them; this would make it easier to understand if changes in average prices through PubMatic are the result of a change in the mix of inventory that is being passed to the company. However, I would be very surprised if PubMatic had access to this kind of data.

One more thing…

image Once you’re done reading the Ad Price Report, stick around on the PubMatic site a little longer and download their White Paper entitled “Death to the Ad Network Daisy Chain”. This little document does a nice job of explaining how an impression is passed from one ad network to another, and highlights the surprisingly high proportion of ad calls that are returned ‘unsold’ by networks. The document then goes on to talk about how ad operations folk have to manually set up ‘daisy chains’ of ad networks to try to ensure that the maximum amount of inventory is sold. As the title of the document implies, this is held to be a bad thing.

Because of the nature of the business that PubMatic is in, the recommendation in the document is that publishers use ‘dynamic daisy-chaining’ (which is essentially what PubMatic does, choosing the order of daisy-chaining based on expectations about which network will be likely to monetize an impression most effectively) to solve this problem. At one point the document states (my emphasis):

Due to the volatility of online ad pricing … creating a dynamic “chain” of ad networks, rather than a static one, is the only way for a publisher to ensure that they can get the best price possible for their ad space.

I would respectfully disagree with this statement; another way of achieving this is to use an ad network that is a member of an ad exchange, and which can therefore draw on a larger pool of advertisers than just those with whom it has a direct relationship.

But I don’t disagree with the main sentiment of the PubMatic paper, which is that publishers still struggle with significant inefficiencies in the way they monetize inventory; and I believe we’ll see the kind of multi-network optimization solution that PubMatic offers (also available from Rubicon and AdMeld) become increasingly important as the year wears on. diggDigg RedditReddit StumbleUponStumbleUpon

September 25, 2008

Yahoo! APT (formerly AMP!) emerges blinking into the sunlight

apt_logo_150x76 Well, they said it would launch in Q3, and it has – yesterday Yahoo! unveiled its new ad management platform, called APT, at a razzamatazz-filled event in New York introduced by John Hamm, star of Mad Men (currently the topic of much debate in our house as to its merits).

APT started life as “Project APEX”, which Jerry Yang started to talk about around a year ago as a successor/complement to Panama (Yahoo’s search ad platform, properly known as Yahoo Search Marketing). Yahoo then pre-announced something called AMP! (Ad Management Platform) in March, saying that it would revolutionize the way that ad media was bought and sold, drastically simplifying the selling process for publishers in particular. And yesterday AMP!’s name had changed again, to APT. APT does not appear to stand for anything, but at least they have dropped the exclamation point.

So what is APT? Well, according to Yahoo, it’s “designed to simplify the process of buying and selling ads online while connecting all the market players -- publishers, advertisers, agencies, networks, partners and developers -- from a unified platform to do business more efficiently and effectively”.

However, in its first incarnation, APT is principally a tool for publishers, aiming to make it easier for them to respond to advertiser/agency RFPs, and allowing them to build ad hoc private networks in order to be able to re-sell other publishers’ inventory. This latter capability is strongly linked to the initial user base for AMP, which is Yahoo’s Newspaper Consortium [PDF] members. The Newspaper Consortium is a sort of hybrid advertising and content network. Two members of this network – the San Francisco Chronicle and San Jose Mercury News – are founding customers of APT.


APT for publishers

According to the blurb on the APT site, APT will bring the following benefits for publishers:

  • Simplified workflow (creative management, campaign management)
  • Analytics & yield optimization (inventory management & prediction)
  • Increased inventory liquidity through cross-selling abilities and integration with Right Media Exchange
  • Extensibility via web services & an API
  • Access to Yahoo!’s expertise (creative development, targeting, etc)

The only screenshot available is of the APT dashboard, which looks fairly nice, but doesn’t reveal that much about the functionality of the system (click the image to view it full-size):


There are also some more tidbits in the video which Yahoo release in April during the original AMP announcement.

Of the announced functionality, the ‘cross-selling’ capabilities in APT are some of the most interesting, representing a twist on the network model. Rather than running a traditional network where the publisher members give a certain portion of their inventory to a centrally managed hub, with APT, publishers can buy and sell inventory directly to and from one another. This will likely mean that publishers can get a better price for that inventory than if they sold it at remnant prices. If APT does a good job of this, it will make publishers’ lives much easier, and deliver a much-needed fillip to their revenues.

SImilarly, APT – by integrating with Yahoo’s Right Media Exchange (RMX) – could attract networks to work with Yahoo/RMX by offering a new pool of inventory that those networks could resell.


What about advertisers and agencies?

imageWhere APT’s value is less clear to me is as a tool for advertisers and agencies. APT will provide a one-stop-shop for buying inventory on Yahoo’s properties and those of its publisher partners (i.e. the Newspaper Consortium folks) – and, given Yahoo’s strength in behavioral targeting, should be able to offer innovative inventory packages and highly targeted buying. But do advertisers and agencies need another interface for buying ads? These organizations would prefer to buy their media through the third-party ad server solutions (DoubleClick and Atlas, mostly) that they already use. Already they face fragmentation in their buying systems for search, contextual, display and rich media advertising – another tool may add to the pain, not ease it.

This may seem like a biased comment (since Atlas is now part of Microsoft), but in fact Microsoft faces the same kinds of challenge as we evolve our advertising platform. Our adCenter product offers a web UI for buying advertising (mostly search and contextual, with display coming), but larger advertisers prefer to interact with it indirectly through its APIs. And this – rather than a ‘one-stop-shop’ buying UI – is how I think APT will end up interacting with advertisers and agencies. Sensibly, Yahoo seems to have appreciated this, since a big part of its APT story is its extensibility through third-party integration.

in conclusion, if APT can create a more transparent network buying experience, and at the same time enable publishers to gain more control over how their inventory is sold, then it will definitely be a Good Thing for the industry – and for Yahoo, as it looks to position itself against Google and Microsoft. It’s going to be interesting to see how APT develops. diggDigg RedditReddit StumbleUponStumbleUpon

July 18, 2008

Online Ad Business 101, Part III - Ad Networks

So far in my nascent Online Ad Business 101 series, I've covered the overall advertising value chain, and looked at a superficial level at how an ad 'call' is actually handled. This installment brings together themes from those two first posts, by taking a look at ad networks.

As I have mentioned before, ad networks are in the media representation business. Even the biggest publishers don't typically have the resources to sell every last scrap of their available inventory day in, day out, so they hand over a portion of their inventory - the remnant inventory - to ad networks. Small publishers, on the other hand, have no resources of their own to sell their inventory, so they have to go to the market via networks. The networks aggregate all the inventory that they have available and then sell this inventory to advertisers.

Ad networks make money by selling the inventory for a higher price than they buy it. They can achieve this in a number of ways, which I shall list in broad order of sophistication/difficulty (with the easiest first):

  • Simple arbitrage: The network buys from the publisher at a rock-bottom price (because the publisher would literally make nothing from the inventory otherwise) and sells the inventory on in larger aggregated blocks at a slightly higher price. The "value add" is small - the network is simply allowing the advertiser to soak up some remaining part of their budget without having to go to lots of individual publishers.
  • Vertical aggregation: The network buys lots of small parcels of inventory in specific verticals (e.g. travel). It then aggregates the inventory for sale according to these segments, enabling it to charge a bit more. The advertiser is able to extend the reach of their campaign in a target audience without having to deal with lots of publishers.
  • Price model arbitrage: The network buys inventory on a CPM (cost-per-thousand impressions) basis, providing the publishers with a nice, reliable revenue stream. But it sells the inventory on a CPC (cost-per-click) or CPA (cost-per-acquisition) basis, reducing the risk of the inventory for advertisers (who are only paying for success), and absorbing the associated risk itself. The network makes money on the difference between the CPM it pays publishers and the "effective CPM" (eCPM) it charges advertisers.
  • Platform specialization: Advertising on emerging-media platforms such as video and mobile still requires quite a lot of specialized technology, forcing Rich Media vendors to build close relationships with the publishers that they deal with. Over time, many of the vendors in this space have gone the extra mile for their advertiser customers and turned themselves into networks, making it easier for advertisers to buy ads in these new formats across a range of publishers.
  • Behavioral targeting: The network buys inventory from publishers, and when the ad call is passed over to the network, it drops a third-party cookie. By doing this across all its publisher clients, the network can build up a profile of users by cookie ID - knowing, for example, that cookie ID XYZ123 has visited ten sites about watersports in the past week. The network can then use this information to add value to the inventory it's reselling, enabling advertisers to buy "active surfer dudes" and the like.
Can you give me some examples?

Sure. Here are some examples of ad networks which (roughly) map to the types above. In practice, of course, most ad networks employ a combination of the above techniques to maximize the margin on the media they represent.

Simple arbitrage:
adcom No doubt my description of as a "simple arbitrage" network will generate howls of protest from AOL ('s parent company). But one of's main value propositions is the breadth of sites and audience it can deliver. Because deals with so many publishers, advertisers can almost always find some inventory that maps onto the audience they're looking for, and are happy to pay a (relatively) modest fee for the privilege.

Simple arbitrage 2: Google Content Network (AdSense)
adsense_logo_main No discussion of networks would be complete without a mention of Google AdSense. AdSense provides a way for lots of small publishers to make inventory available to the pool of advertisers that use Google Adwords - in addition to their ads appearing next to Google's search results, these ads can also appear on the small publishers' sites; the ads are matched with the sites on a contextual basis (the content of the site is crawled to extract keywords which then stand in for the keywords that advertisers normally bid against for paid search results).

A crucial feature of this system is that the publisher is paid on a cost-per-click basis, so assumes a big chunk of the risk - if no one clicks, the publisher doesn't get paid. Google makes its money on the margin between the cost-per-click they pay the publisher, and the cost-per-click they charge the advertiser. The value proposition lies in connecting lots of small (and large) advertisers to lots of small publishers who are running sites which have a really good content match to the advertiser's offering. In other words, if you manufacture Mongolian nose-flutes, AdSense allows you to get your ads onto all the Mongolian nose-flute fansites out there, with very little effort.

Vertical aggregation: Martha's Circle
marthascircle Martha's Circle is the (rather winsome) name for the ad network run by Martha Stewart Omnimedia. It's a classic example of a publisher/media owner extending their brand (and saleable audience) by signing up sites in the same sector (in this case, lifestyle) and creating a niche network. For an advertiser wanting to reach thirty-something women with an interest in the home, this kind of network is a no-brainer when building a media plan. is another good example, as is Fox Interactive Media.

Price Model Arbitrage: DRIVEpm
drivepm_logo DRIVEpm is Microsoft's own advertising network, acquired with the acquisition of aQuantive last year. DRIVEpm styles itself as a "performance" network, meaning that it uses a variety of techniques (amongst them, price model arbitrage) to enable advertisers to buy inventory on a cost-per-performance basis, whilst still paying publishers on a cost-per-thousand basis. Scott Howe, former GM for DRIVEpm and now VP for the Microsoft Advertising business unit, wrote a great article back in 2005 about some of the dynamics in a performance network from the perspective of a media buyer looking to get the best ROI. Well worth a read.

Platform specialization: VideoEgg
image VideoEgg is a video advertising network (the clue's in the name, I guess). Its offering is a classic mix of innovative ad unit technology (their latest offering is something called "AdFrames") with a network attached. Another feature of VideoEgg is that it offers advertisers a CPE (cost-per-engagement) model for buying video advertising, performing the same kind of price model arbitrage that DRIVEpm is doing. Their publisher audience is widget & app developers for social media environments such as Facebook and MySpace, ensuring that their value proposition to advertisers is further differentiated (essential as the online video market becomes more crowded).

Behavioral targeting: Tacoda
image Tacoda is also part of AOL's Platform A unit, and markets itself as the world's "first" behaviorally-targeted ad network (a hard claim to substantiate, but equally hard to refute). Tacoda tracks behaviors of the visitors to its network of over 4,000 sites and uses this information to associate behavioral profiles with those users. It then sells inventory on these sites on a user-target-group basis, rather than by group of site or content area. These "audience segments" have names like "Family Chef" and "Photo Bug".


How does it actually work?

Understanding how ad networks actually serve their ads is essential in understanding how some of the above business models (especially targeting) work. I'll cover two scenarios - a small publisher/small advertiser scenario, and a large publisher/large advertiser scenario.

Small publisher/small advertiser
A small publisher will insert their ad network's ad code directly onto their site - in many cases, this is the only ad code the publisher is using, and is serving 100% of that publisher's ads. On the other side of the fence, the ad network may provide a web UI to enable advertisers to create or upload ads, and (possibly) allow the advertiser to choose which sites (or groups of site) those ads will appear on. The diagram below summarizes this (thanks to Right Media for the advertiser & publisher people icons):


Examples of this kind of system are Google AdSense and the Yahoo Publisher Network (these are often called "self-service" ad networks). The actual ad delivery model is pretty simple - the same ad server (the network ad server) functions as both publisher and advertiser ad server (the ad call path is on the left side of the diagram above).

Large publisher/large advertiser
When it comes to large publishers using ad networks to deliver inventory to large advertisers, things get more complicated. In this scenario, both the publisher and the advertiser will likely have their own ad servers. The publisher will configure its ad server to "hand off" a certain block of inventory to the network, whilst on the advertiser side, the advertiser ad server will be configured to buy a certain portion of a campaign from a network (or networks). So the ad call has to be passed from the publisher to the advertiser via the network:



It's the point at which the ad call passes through the network ad server when the network is able to drop a cookie on the user's machine, enabling behavioral tracking and targeting (assuming, of course, that the users don't delete their cookies in the meantime).

Of course, hybrids of the two models above also exist: large publishers will sometimes hand over some of their inventory to a self-serve network, in particular, in which case the publisher's ad server calls the network ad server, which serves the ad itself.

This picture also becomes more complicated when you consider that many ad networks will pass the ad request on to another ad network if they themselves can't fulfil it (or fulfil it economically). So, for example, a targeted ad network may receive an ad call from a user it has no information about. Rather than serve an ad for that user at a low cost (and thereby preventing that ad impression from being served to another user at a higher cost), the ad network passes the ad call on to a "value" (read: cheap) network. So in the picture above you can have two, or even three or four, ad networks passing the ad call around like a hot potato.

This game of pass-the-parcel isn't really very good for the user, who has to wait a long time to see the ad (which really hurts the advertiser most, since a slow-loading ad might as well not render at all); and it's also not great from a security point of view, because the publisher is ceding control of a portion of their site's screen real-estate to an unknown network and an even more unknown advertiser. Which is why ad exchanges are emerging which provide a centralized clearing-house for inventory, thus dispensing with the round-robin approach described above.

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June 02, 2008

Online Advertising Business 101, Part I - The Online Advertising Value Chain

When you spend as much time as I do examining the workings of the online ad industry, it's easy to forget that, to many people, it really is pretty opaque. Not only is it characterized by some of the most complex and scalable technology in the world, but it also has its own, pretty unique, economic model to boot.

I've lost track of the number of times I've been asked by people, even super-smart colleagues from within Microsoft, "so, how does the online ad industry actually work?" So I thought I would attempt to provide a bit of a primer through the medium of this blog. Who knows, maybe someone will read it and offer me a book deal ;-)

In this first installment, I'm going to take a look at what I call the online advertising value chain:


This is a simplistic view of the industry, but it does enable us to understand where the key players sit; on the demand side of the value chain, there are advertisers, and their agencies; and on the supply side, publishers, and ad networks (and/or ad exchanges).


What's the product?

Before I get onto the content of the boxes in the above diagram, though, we should be clear about what's in the arrows; that is, what's traded in this market? What's the actual product, here?

The answer is advertising inventory. There are no very good definitions of advertising inventory out their on the Internet (Dave Chaffey offers one of the better ones), so I offer my own definition:

Advertising inventory is the supply of opportunities to display advertising in a particular medium.

Most people would use the term "ad impression" instead of "opportunity to display" - the reason I haven't is because I don't like to offer a definition of a term which contains another term that you may need to go and look up. The most common definition of ad impression is this:

An ad impression is a single viewing of a single ad by a single individual.

(Another reason I didn't use it is because it fails to capture the increasing complexity in ad inventory as online advertising evolves. For example, if you're serving video ads, and the user watched half of your 30-second pre-roll ad, was that an ad impression?)

In our value chain above, it's the Publishers who are the creators of advertising inventory. By building websites or software apps or video games or e-mails which are seen by lots of people, and inserting ads into these environments, publishers create a constant stream of ad inventory which, of course, they are looking to sell to advertisers. Agencies and Networks merely help the process along.

Online ad inventory is a very interesting type of good (to use the economics term). It has an incredibly short shelf life (measured in milliseconds as a page loads), but its supply is only indirectly under the control of publishers; external factors (such as a very newsworthy event) can dramatically impact the amount of inventory that a publisher has to offer. As a result, inventory prediction is a major task for publishers; I'll be returning to this topic in a future installment.

Calculating ad inventory

Another useful way of understanding ad inventory is to look at a simple example of how it's calculated. Imagine a pretty straightforward website (this blog, for example), showing pretty simple ads, with no fancy auto-refresh stuff going on (i.e. once a page is loaded, the ads don't change, so for each page impression, you get one batch of ads). How much ad inventory is created?

The answer to this is dependent on two variables - the number of page impressions on the site, and the average number of ads per page. So, for example, if my blog generated a million page impressions per month (I wish), and had an average of 5 ads per page, then the total ad inventory (if you're just using a simple ad impression model) is 5 x 1m = 5m ad impressions per month.


The Players

Now that we understand what's being traded, let's take a brief look at the major players in the value chain, and then I'll let you get back to whatever it was you were doing before you started reading this post.


The Publisher

02_first_book We've already covered this guy. He's the one with the site, or the game, or the mobile portal, who is creating ad inventory and wants to sell it to advertisers to provide income for his business. Publishers are interested in maximizing revenues, but also at minimizing risk - they hate to have unsold inventory (that is, ad space with no ads in it) so they employ a number of tactics to ensure that at least something gets shown in an ad unit that they can get a little money for.

Larger publishers have their own sales teams who maintain direct relationships with advertisers and their agencies, cutting deals for big blocks of advertising inventory over expensive lunches in chic Greenwich Village restaurants. But this model only works for big publishers selling to big advertisers. Small publishers can't afford to maintain their own sales force, and even if they did, they'd never get through the doors of Ford, or CapitalOne, because they don't have enough inventory to be of interest on their own account. So these guys sell their ad inventory through Ad Networks.

One other kind of publisher it's worth calling out here is the search engine - i.e. Google, Yahoo and Microsoft. These search engines are the creators of huge amounts of ad inventory that is sold directly to advertisers and agencies, as well as running significant ad networks (see below).


The Ad Network

salesman Ad Networks are essentially outsourced sales houses for publisher inventory. An ad network strikes deals with lots of publishers for their inventory and then aggregates this inventory and sells it on to advertisers and agencies. There are over 300 ad networks in existence today - a breathtakingly large number which is sure to fall soon.

An ad network's value proposition to publishers is that it can sell inventory that the publisher can't sell itself - either because the publisher is small (and so doesn't have its own sales force), or, in the case of larger publishers, the inventory is of too low-value to merit direct selling. This kind of inventory is called remnant inventory.

The network's value to an advertiser is that the advertiser can appear on lots of sites across the Internet (potentially thousands) without having to establish direct relationships with those publishers individually.

At bottom, the Ad Network business model is to buy inventory cheaply and sell it on at a higher price. There are a variety of ways of doing this, some of which I've covered before. One of the most promising is to add value to the ad inventory by adding targeting data (so that the impression can be sold for a higher price). I'll cover this in a future installment.

Networks come in all shapes and sizes. There are 'premium' networks which work with remnant inventory for large publishers; there are vertical networks which focus on a particular industry or technology (such as video); and, at the bottom end, there are contextual networks which provide an auction-based marketplace for selling keyword-based ads on small sites. You may have heard of the #1 network in this space - it's called Google AdSense.


The Advertiser

coke_ad_1 Advertisers also come in all shapes and sizes, of course. The big name advertisers - the folk we've all heard of - will have significant internal marketing departments, and will also likely retain the services of an agency to help them manage their marketing. Their marketing objectives will likely be a mix of brand marketing (raising general awareness) and direct response marketing (getting someone to actually buy something online now).

Smaller online advertisers are almost always focused on direct-response - getting someone to click and buy, or possibly call up. By and large, these folk can't afford to retain an agency to do their marketing for them, so they tend to go straight to certain ad networks or publishers to buy their ads. Again, the #1 in this space is our friend Google, with AdWords (the advertiser-facing side of the AdSense network).

Advertisers are motivated by getting the best ROI on their ad investment; but amongst larger advertisers some other curious motivations creep in, like wanting to make sure that a committed ad budget for a quarter actually gets spent (so that budget isn't cut the following quarter). This drives the behavior of ad agencies, to an extent.


The Agency

1 Last but by no means least, the media agency is an essential intermediary in the advertising value chain. Ad agencies usually do one of two things (or both, such as is the case with our own Avenue A|Razorfish): they create ads (anything from designing an animated banner to filming a 30-second TV ad) - known as the creative business - and they buy the media (i.e. the ad inventory) to display the ads (known as the media business). Whilst the creative side is cooler, the part of ad agencies that is relevant here is the media business.

A media agency, then, is one that buys media on behalf of its advertiser client. The advertiser typically says "I have x million dollars this quarter for online, and this campaign I want to run. Buy me the best media to reach my target audience". It's then the media agency's job to plan a media buy that will deliver the best return for the advertiser.

At the small-business end of the spectrum, the 'media agency' morphs into small SEM (Search Engine Marketing) shops who are good at buying Google AdWords, and maybe have some SEO (Search Engine Optimization) skills to boot to boost a company's natural search rankings.

Media agencies' motivation is driven by getting as much media under their control as possible, since they're paid (particularly at the high-end) with a cut (usually something like 15%) of the advertiser's media budget. They also don't want to under-spend on the budget they've been given, as this can annoy their client (see above).

Media buying is a manual, labor-intensive process right now, and one I'll come back to. Improvements to technology will mean that agencies (especially larger ones) will have to do some pretty fancy footwork to continue to add value for their advertiser clients.


That's it for now. in future installments, I shall be looking at the key players in a bit more detail, and looking at some of the interesting economics which underpin the industry. In the meantime, if you have a comment, or something you'd like me to cover, leave a comment.

[Update 6/3/08: A little more info on Ad Networks added]

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