Silos Brimming with Data

Since the dawning of the information age, Brand Managers have been inundated with reporting from a myriad of different agencies and independent data vendors.  Intuitively, one might think an organization with more data (or more raw materials) is inherently better off than an organization with less.  But the best marketers know it makes no difference if their silos are brimming with data or with grain – the success of any enterprise depends on its ability to efficiently process materials and distribute a salient product.

Data, much like grain, cannot be consumed from inside a silo.  So in the case of the Brand Manager, it begs the question how can a company’s data supply be intelligently synthesized and made actionable in a business context?

The Connection Index

The Marketing Intelligence practice at Organic found the answer to the dilemma of the data-inundated Brand Manager…but it wasn’t simple.  The solution – known as the “Connection Index” – is a proprietary application of tried and true advanced mathematical tools (like statistical data reduction, multi-variate factor analysis, and other quantitative voodoo) in the digital space.  In essence, it’s a custom-modeled encapsulation of several KPIs baked into a singular measure of significance.

At its most basic level, the algorithm evaluates interactions between a vast set of fast-moving digital data sources, and determines which channels drive the most intense consumer connection with the brand.  Ultimately, the algorithm produces an easily-tracked score that can be used to map connection to a brand throughout marketing campaign cycles.

The Proof is in The Pudding

Although the product was never positioned as a full-blown forecasting tool or marketing mix model, clients wanted to understand how it could be used to inform budget allocation decisions and future optimization initiatives.  At first, the team looked for correlations between brand sales and digital engagement.  But it didn’t stop there.  Organics’s Advanced Analytics team decided to undertake a Granger Causality study, and were able to demonstrate a causal relationship between the Connection Index and fluctuations in future brand sales.

This causal relationships now serves as the foundation for the CI’s forecasting capabilities, something the team plans to continually recalibrate over time.

The Holy Grail of Digital Marketing

In the words of a Senior Brand Manager for a $20B CPG company that recently purchased the product, “this is the Holy Grail”.  For the first time, the company had been able to directly attribute the impact of its online marketing initiatives upon offline sales.  Additionally, the brand team was able to leverage the model outside the digital realm to inform its CRM strategy and reprioritize consumer segmentations for outbound email campaigns.

Looking ahead, as brands invest more and more resources in diversifying their digital ecosystems, they’ll have increased demand for tools that help make sense of the millions of interactions occurring between paid/earned/owned channels.  And in turn, solutions like the Connection Index will continue to gain traction due to their real-time predictive properties and consumer insight.

For more information and a detailed analysis of the Connection Index, please visit Organic SVP of Strategy, Media & Analytics Steve Kerho’s Fast Company column here.

Ben Kaufman is an Analytics Manager at Organic

You’ve had an outstanding career with some really impressive accomplishments. What has your experience taught you about sponsorships, and where do you think the real value is?

I think sponsorships are a really important touch point within the marketing mix. There are so many different communication channels we can go through, such as broadcast, print and digital, but at the end, they’re all about trying to drive engagement. Nothing drives more engagement than when you’re actually at an event or a sponsorship. Being there allows customers to touch and feel the product and  get the full sensory experience. I think sponsorship is the ultimate driver in terms of engagement, which is an important part of the mix that’s becoming harder to get.

Tell me about the Heisman Trophy sponsorship you did with Sports Illustrated and ESPN when you were at Nissan. What was your thinking behind that?

The Heisman Trophy is one of the most recognized awards in college sports. It’s an incredible event that people get very passionate about. We were attracted to it because of the passion in the discussions surrounding it.

It’s also an event that has a very long lead time over the course of the whole year. Even before football season starts, people talk about potential Heisman candidates for the year. The list evolves and changes throughout the season, and there’s a huge focus on it at the end of the year. We saw a lot of people that were very passionate about that, so we wanted to be part of that passion in an authentic and respectful way.

The campaign had many touch points along the way, and digital played a huge part in that. The Heisman Trust is a first-class organization and a great group to work with. One of the things we did in the digital space to let people actually vote, and those votes would count toward the final winner at the end of the year. This created a lot of great engagement around our digital efforts. Further into the season, everybody starts talking about who’s on the Heisman list, so Nissan’s name got associated with those conversations.

This digital strategy was implemented in conjunction with a slew of more traditional marketing channels. Part of the sponsorship included a lot of broadcast media during halftime shows, and we showed up at games and let people take pictures with a real Heisman trophy. It was a 360-degree opportunity to connect with consumers across all channels including print, broadcast, digital and events.

What story was Nissan telling about itself by doing this sponsorship?

When thinking about Heisman, it was easy for us to decide that we wanted to share some equity with them. The trophy is awarded to the most talented college football player of the year and is voted on by all of the previous Heisman winners as well as major sports journalists. We loved the focus on attributes around being really good at what you do, being a good team player and being able to perform well under pressure – we wanted Nissan to be associated with all of these character attributes.

People often try to classify digital sponsorships as media buys. Do you see a difference between digital sponsorships and media buys, and if so, what is that difference?

There’s a lot of confusion around this question, but I think there is a big difference. A digital media buy usually happens through a demand-side platform (DSP), so I would talk to someone at a media agency. As the media agency, I would call Yahoo, MSN or SI.com and talk about the elements of that particular media buy – how many impressions, placement on the page, CPM – but I can do an awful lot of that now without talking on the phone.

A sponsorship would never be handled this way, because your goal is much bigger than it would be with a traditional media buy. Your goal is to associate yourself with a particular event or brand by bringing an advertiser together with someone with great content. There needs to be authenticity, so the ads you show need to feel like they belongs there. You’re also trying to create a bar of interest between the two groups, so there’s a lot of planning around that.

Sponsorships are more difficult to do, they take longer to plan and you often have to create content that’s custom for them, but when they’re done right, you get to see 1 + 1 = 4. I think there’s a huge upside when you do good sponsorships, even in the digital space.

Some people have a hard time understanding why and when to use a sponsorship and what to expect from it. To them, I would say that to get the best returns from sponsorship, you need to invest a lot of time with partners and advertisers to bring them together in an authentic way.

How would you explain the value of sponsorships both in terms of metrics and big picture marketing? How does a company know if a sponsorship or a partnership is right or wrong when the ROI isn’t immediately clear?

This question comes up around sponsorships a lot, and the difficult economic situation we’ve had has put an enormous amount of pressure on marketing activities such as sponsorships. In the traditional sense, they may appear more difficult to measure, but in the end, they do lend themselves to a great deal of not only qualitative but quantitative measurement.

I think the qualitative measurements are pretty straightforward and everyone kind of understands that part of the equation. Do the people who are exposed to your sponsorship think more or less of your brand as a result of the sponsorship? What brand attributes are you measuring, and what brand equity do you have? Those measurements are very important but fairly obvious.

But what we’re finding is that the amount and quality of overall digital activity that a brand gets is a good measure of how much demand you’re generating around it. These metrics include total traffic that comes to your website, the kinds of activities they perform on the website, the brand’s search terms, how many news and blog mentions and how many people are talking on social media. When you roll all of these up and look at the whole digital ecosystem, it’s a very sensitive measure of demand. It’s sensitive to marketing activities, media spend, things competitors do, changes in the macroeconomy, etc. Within this context, we see significant peaks in the overall digital activity consistent with well-executed sponsorships.

The ROI model is more complicated, and there need to be common measures that cross different touch points. For instance, I have measures to help me analyze social media and the paid-earned distinction. Some more work is needed to figure out how all these things are connected, so we use a variety of different tools. One of them is a principal component factor analysis that looks at how each point inside this digital ecosystem is contributing to our overall goals and objectives. A lot of times, you’ll want to separate an upper-funnel ROI model from a lower-funnel ROI model. If you have the same model trying to measure both, you’re invariably punishing one unfairly.

But having taken all this into account, post-sponsorship we see increases in brand search activity, site visits and social activity. Putting all these things together, we’ve seen upticks in brands’ digital ecosystems connected to sponsorships they’ve done.

The ROI measurement world used to be about following the dollars within media, but it’s now more about following behaviors. With that in mind, we have seen that sponsorships can cause a noticeable impact in a fairly short period of time.

These results are encouraging because we don’t want to see sponsorships get unfairly punished. Just because people weren’t creative in trying to measure the impact doesn’t mean they shouldn’t be part of the mix. It just means that the measurement wasn’t sophisticated enough yet.

What do you think is the secret sauce for amazing partnerships and sponsorships?

First, you need to come to the table with a really good understanding of what your brand is about and who you’re partnering with. Ask yourself, “Is there a good mash-up between our respective qualities?”

The second piece is to be very clear with what you’re trying to achieve with the sponsorship. I’ve seen many cases where a campaign will start off being about driving brand awareness or familiarity, and then right before launch, the brand decides that it’s actually about consideration or sales, which is totally different. It seems relatively straightforward to be clear on what your objectives are and not to change them, but I see sponsorships fall prey to this all too often. They try to achieve too many things, priorities are not clear and they often end up with something watered down that doesn’t do a good job of achieving any objectives.

The last piece is that you need to take time and effort to fairly measure the impact on a quantitative basis. There are many opportunities to do this now that didn’t exist before. It’s very important to fair and accurate measurements. In fact, one of the reasons why many promotions don’t make it through the marketing approval process is not having a good measurement plan.

This interview was originally published on SponsorsWin here.



It’s no secret, tablets are taking the world by storm, and are quickly gaining followers in the business world, in schools, and in the homes of many technology lovers.   They are the popular trend that gives you the capability to be mobile and at the same time, connect to the internet, check your email, have a video chat as well as capture images and video.  These interactive devices are intended to be portable, with small proportions and a lightweight design.   This surge in tablet popularity, has defined the tablet as an advertising medium that should not be ignored.

According to Juan Anotonio Giner in his article from the The Nieman Foundation for Journalism at Harvard:

In my opinion, tablets, like the Internet in the past, are fantastic opportunities, not just devices on which to perform the same old tricks.

As media consultant Daniel Ambrose wrote on his Online Publishing Insider blog:

It should be clear…that if reaching the maximum number of readers and customers-and customers for advertisers-remains a key strategy for media companies, they’ll be doing that on a wider and wider range of devices and platforms…Companies that do so will thrive.  New opportunities are emerging every day that their staffs will recognize and exploit.  Companies that don’t will see the future pass them by.

My work as a Paid Search Marketing analyst has uncovered data to support these theories.  When looking at the advertising campaign for a recent client, the mobile budgets were split between overall Mobile, Android, iPad, iPhone, Palm, and WAP devices.  24% of the spend was assigned to iPad devices and these campaigns returned almost 100% of the mobile revenue, resulting in a ROI that was 300% better compared to the mobile categories average.



Along the course of the of the six month timeframe (January 2011-June 2011), as budgets were increased for the iPad campaign, the revenue gain followed a similar steep incline.  ROI peaked during mid-term, lowest month being in May.

The data in this example infers that people are more likely to convert from an iPad than a typical mobile phone, maybe because it has usability closer to a desktop or laptop computer.  Tablets have the functionality that resembles standard computers and thus allow for easy comparison, different screens, larger surface, etc.   Other mobile devices should not be overlooked when developing a marketing plan.  Consumers might tend to use mobile phones as a conversion vehicle when they are closer to being ready to buy, meaning that they that are further down the purchase funnel.

Tablets should be regarded as a major player in the ever-changing advertising landscape.   I have only skimmed the surface of this marketing opportunity and more testing is needed to fully discover tablets’ full potential.   Advanced analytics models would be the perfect tools to allow for real-time testing – shifting dollars to see what kind of maximum ROI tablets have the ability to produce.

For the latest from Organic on its modeling and advanced analytics capabilities, be sure to check out these other pieces from the Organic Marketing Intelligence team:

“5 Steps To Measure The ROI Of Digital Media Channels”
“Winning the Consumer’s Share of Mind – How to Tell if Marketing is Working”
“If It Can Save Lives, Why Can’t It Sell Salt?“

Monica Stromberg is in Marketing Intelligence at Organic.

More often than not, when two people or groups are trying to compare metrics, they do not match because the comparisons are not “apples to apples.”  Without consistent definitions for data points across an organization, this struggle will be far too common of an occurrence.

This can be quite apparent when reviewing performance with your CMO or CFO.  Imagine that you are in a performance review with multiple internal groups and when it comes time to talk numbers, the tracked revenue from your side does not match your finance department’s figure.  Usually in this situation, the marketing folks will lose out on what the “official” number is because the finance team is more likely to serve as the official source of revenue data.  What is really happening here is that the teams are not generating a true “apples to apples” comparison.  There could be differences such as: is sales tax included, currency conversion issues if your company is multinational, different opinions of what constitutes the web, or even both systems measuring the same exact thing but in two different ways.  Any of these things can create differences in measuring your KPI’s.   This doesn’t necessarily mean that either data set is wrong, per se, it is just that they are each unique ways of interpreting and translating the raw information.

Besides revenue, another area where companies struggle in solidifying data definitions is the standardization of the visit metric.  While most web tracking software defines a visit the same, the devil can be in the details of latency.  The industry standard is considered to be a 30-minute window but should take into consideration the type of site you are responsible for (ecommerce vs. informational), as well as site architecture and user behavior.   If your online advertising team feels that a 45-minute window is appropriate but Web Analytics wants a 30-minute time-frame, this most basic of KPI’s will never align.

These are just two relatively common examples of KPIs not aligning between groups.  If your company or group has to align to different internal reporting systems, there are plenty more that are abound.

When it comes time to develop the rules by which these KPI’s are measured it is important to include the correct groups, partners, clients and agencies in the fold.  Not all KPI definitions will be determined by the Web Analytics or any one group.  Some will come from your Demand Generation team, Finance, Operations, IT or your leadership.  With effective, detailed communication between groups, you can begin to align definitions of the KPI’s to match your unique business and objectives.

Because of the different business needs between groups (and different goals), this sometimes can be difficult to overcome.  There are many tools that are quite useful at helping to create a starting point or getting your organization through the last push to consistent reporting and analysis.  Start by laying out a road map of where you are when where you need to be.  Ask around for recommendations or investigate a current reference guide  (http://amzn.to/tTZK5x) on your industry’s standards.  Most importantly, consult with other groups within your organization to review best practices and stick with consistent definitions to help ease the confusion of whether or not the goals of your project are being met.

Eric Westen is a Senior Analyst, MI at Organic

Economically, 2011 is shaping up to be a challenging year. Key measures are all trending down, and global equity markets are in retreat. But this doesn’t mean that we stop marketing. Instead, it underscores the need to ensure that our direct-response efforts are as effective as possible.

In support of greater effectiveness, I have listed five key best practices to consider for online direct response marketing:

1.  Create a multi-touchpoint, lower funnel, digital ecosystem.

The Internet is the single most important research tool for many goods and services. Whether researching a new automobile, shopping for a mortgage or looking to book a dream vacation, consumers conduct most of their research online. We are even seeing significant online activity as a precursor to offline sales for consumer packaged goods products, making the digital channel essential in delivering your message to target consumers.

Marketers have been creating ever-larger digital ecosystems for their brands for some time now. Too often, the ecosystem is built to accommodate multiple messaging strategies, such as driving brand awareness and shopping. A best practice is to build ecosystems that have congruent messaging objectives. In this case, we are talking about an ecosystem solely dedicated to shopping and sales. Ideally, this ecosystem would contain a mix of media elements such as paid, earned and owned. Appropriate touch points may include:

* Brand.com with a strong focus on shopping activities
* Brand.com shopping that is mobile friendly
* Dynamic landing pages for each product or service
* Dynamic banner ads
* Distributed shopping tools
* Native apps focused on shopping
* Facebook
* Flash sale partnerships
* Twitter
* Community/loyalist management
* Location-based targeting

Additionally, you should consider the use of offline channels, such as broadcast, print, and radio to increase the overall reach and frequency of direct-response messaging.

2.  Devise an effective single-search strategy.

Search is still the single most important tool to drive traffic to your ecosystem. There is increasing competition for a high page rank in all types of search. Most marketers realize that the most effective SEO (natural search) and PPC (pay-per-click) or SEM (paid search) strategies are holistic and interdependent.

In addition to natural search including more maps, images, video and real-time social elements, the criteria for site ranking is becoming more complex. To drive relevance and remove superfluous results from natural search, Google has made significant changes to the calculation of its quality score. The quality and volume of inbound site links is increasingly related to content quality.

There is also more competition in pay per click (PPC) rankings. In many verticals, marketers compete hard for generic terms with research sites, lead aggregators, affiliates, and of course, other brand marketers. Marketers are becoming better practitioners at using paid search to acquire new customers while driving loyalty, and this practice raises the bar for everyone.

Hence, it is critical to ensure effective keyword coverage for your brand and generic terms and to ensure that natural search and PPC programs are integrated and working together. You can’t afford not to have a high page rank for your key terms.

3.  Purchase display media with the right tools.

It is a buyer’s market right now because of an over-abundance of display ad inventory. Advertisers can benefit from this situation with the right technology. Specifically, direct-response advertisers can utilize demand-side platforms or DSPs to exploit this market condition. A DSP enables auction-based bidding in real time to reflect the true market price one impression at a time. In short, a DSP enables search like buying of display impressions. This is a highly effective strategy that yields less waste and greater price efficiency.

Another benefit of using a DSP is the ability to buy an audience that has exhibited specific purchase-intent behaviors, such as visiting a third-party research site. There is virtually unlimited intent data available to enhance targeting accuracy as technology now allows the separation of content from audience. Such data can also greatly improve ROI–improved targeting should lead to more conversions. Responsible advertisers make all the content providers actively compete, within a transparent market place, for our clients’ limited advertising dollars.

If your organization is new to using DSPs it is a best practice to use one at a time and add in data components to learn which data lifts performance. Alternatively, marketers with more extensive experience can simultaneously test multiple DSPs as long as they have the tools in place to effectively manage frequency caps and avoid “double counting.”

4.  Dynamic customers require dynamic content.

Not all customers are alike, nor should the content or offers that are delivered to them be. One of the great advantages of digital is that it can create different experiences, in terms of offers and messaging, in almost real-time for different consumers.

The best performing direct-response ecosystems make effective use of dynamic landing pages and dynamic banner ads. You want a very tight integration between your paid-search ad copy and the landing page delivered to the customer. Multivariate testing between these key elements should be extensive and ongoing. A “test early and test often” mantra always improves the results of any campaign.

If you are going to deliver custom offers such as special purchase incentives through your digital ecosystem, then maintaining an up-to-date and accurate online CRM database is key. Most sophisticated marketers have long since integrated their online and offline CRM databases. A holistic view of customer lifetime value allows marketers to determine the right offer to the right consumer at the right time.

5.  Optimize, optimize, optimize.

Optimization should be at the center of every direct-response ecosystem. Custom ROI models that measure that value of each interaction and touch point should guide decision-making. For non-ecommerce sites, the ROI model will need to account for how online activity drives offline behaviors, such as sales. All ROI measures must share or attribute the value of each and every conversion activity. The monetary credit of submitting a lead, requesting a quote, or making an online purchase must be shared among all digital touch points from the consumer’s unique journey.

Each campaign should be optimized prior to launch and while it is still live. Prior to launch, scenario planning should be used to determine the optimal media spend and mix with a deep understanding of the interplay between search and display. In this case, you will need an ROI model that is as much a forecasting tool as an evaluator of past performance.

Once the campaign is launched the second phase of optimization begins. Targets are set for each touch point within the ecosystem and evaluated immediately after launch and throughout the duration of the campaign. Because the media plan is being updated weekly, underperforming media placements should be dropped immediately. Paid search should be monitored daily with frequent adjustments to bid strategies, keyword lists and daypart schedules. As mentioned earlier, multivariate testing should be used to determine the ideal search copy, landing page content and banner-ad creative.

Post-launch optimization that follows this schedule can improve ROI results significantly, by as much as four to six times. However, this approach requires a highly integrated team and a consolidated data environment. Your display media, search, creative, account and technology teams need to operate as one, using a single source for marketing intelligence. Clearly established processes and a battle-tested toolset are the cost of entry. Finally, you will need a highly effective work processes between agency and client that are built on trust and a mutual understanding of success.

Driving Home Results

Let us all make the most of this challenging marketplace. Use it as a reason to hone and perfect our direct-response marketing efforts. Manage this complex ecosystem with a comprehensive strategy that includes:

* Single-search strategy
* Powerful display media placement tools
* Dynamic content
* Pre- and post-launch optimization

An effective combination of these elements offers new opportunities to connect with consumers like never before and significantly improve the ROI of your digital direct-response efforts.

Steve Kerho, Senior Vice President of Strategy, Analytics, Media and Marketing Optimization at Organic

This post was also published on Steve Kerho’s Fast Company Expert blog found here.

[Image: Flickr user Helen K]

The term “gamer” used to conjure images of guys locked up in basements playing video games all night. But over the past few years gaming has increasingly become part of our everyday life. According to Forrester research, 60% of consumers play video games online in a typical week –  largely driven by casual games amplified on social platforms such as Facebook. With games requiring customers to be online, this provides marketers with an abundance of data, and a huge opportunity to reach engaged audiences.

Nowadays, almost nothing is private in the gaming industry once you connect your console to the Internet. Gaming companies are able to track every click you’ve made, what levels you’ve been having difficulties in, how many hours and which titles you play. They even track what kind of personal downloads you use to enhance your character’s looks (long blonde ponytail, muscular build, board shorts). All of this is happening regardless of whether or not you opt in. With all of this data, it is a constant balance between knowing one’s customers to provide them with products they’ll love and being considered creepy by invading their privacy.

A big part of marketing in the gaming industry is focused on collecting both customer and gameplay data. Marketers then use this data to figure out what kind of programs will have the biggest impact from cross-selling products the game players are interested in to helping with tips and tricks to beat the level they’ve been stuck on.

Past campaigns have looked at what consumers play and their motivations for playing each type of game. When looking at the collection of games an owner has, sometimes odd titles emerge right next to each other – Hasbro Family Game Night (a virtual board game) with Dead Space 2 (a survival horror game).

However, when looking at who the consumers are and who they are playing with, it all starts to come together. Often times behaviors vary depending on who is playing the game, going beyond just the login. There is the dad who plays family games in the evening with his kids, then switches to violent horror games at bedtime, or the frat boy who plays Madden football with all of his friends and then later plays Sims with his girlfriend.

Processes to measure these activities are continually becoming more sophisticated. They work to identify the top customers – metrics include a combination of who logs the most hours playing with friends, who is buying all of the hit titles, and who is active in the online gamer community – and treating them differently. This could include giving them early access to new releases or even free titles hoping that they will be community advocates for new products.

Whether it’s a tip helping you beat a level so you don’t get frustrated and quit the game or giving you a weapon to enhance your game play, marketing can make a big difference in overall enjoyment and your likelihood to be a happy customer. As with all marketing, it’s all about hitting consumers at the right time with the right message.

Michi Arthur is a Director, Marketing Intelligence at Organic

As part of Omnicom’s strategic partnership with the University of Pennsylvania’s Wharton School, Organic engaged with the Wharton Customer Analytics Initiative to dive into the great online debate of how to properly attribute conversion credit to online media.

Over 18 months, Organic worked with the Wharton team along with researchers around the country to investigate this problem, culminating in a day long symposium where the research findings were presented and discussed with Organic’s Marketing Intelligence team along with other top analytics minds from Omnicom sister agencies.

Through this partnership five research teams from top universities were awarded access to a data set from Organic following a review of their proposals. Each team set out to create new analytical approaches that build on and augment Organic’s current models for measuring how individual online ad exposure patterns (featuring different creative executions and timing patterns) impact individual viewers, uniquely.

One of the research teams, Professors Michael Braun (MIT) and Wendy Moe (University of Maryland), developed a model that studied the interaction of three well-established advertising effects:

• Goodwill: Advertising affects a person’s long-term sentiment toward a company, and that effect is at its peak when the ad is shown but then fades with time.
• Wear-Out: Repetition of a single advertising creative concept makes it less effective. If you keep seeing the same ad, it becomes less effective each time you see it.
• Restoration: Taking a break from redundant ad exposure reverses wear-out. If you don’t see an ad for a long time, when you finally see it again, it is more effective.

They used their method to analyze an example data set provided by Organic that tracked the advertising exposures and subsequent conversions for individual Web browsers. This analysis uncovered the impact of the complex interrelation of these effects in a manner that had not been explored in previous research. The report’s complete findings will be unveiled in Q4 2011.

The findings also build upon the insights of Organic’s proprietary modeling efforts in the past and corroborate previous internal studies around creative impact and wear-out.

For more on the partnership and the full news release.

Image credit: Wharton Customer Analytics Initiative Picasa profile

With many predicting the death of the ad network, 2011 was supposed to be a watershed moment in the world of display advertising. Much of what was trumpeted was that greater transparency could be had by using a Demand Side Platform (DSP)–no more black box–driving greater insights into your audience for smarter buying strategies. Well, 2011 is more than half over, and as far as I can tell, the ad network is still alive so I’m not sure if the promise of transparency has fully arrived.

Transparency can be defined in many ways. Greater transparency would at least mean full disclosure about inventory sourcing, where an advertiser’s message ran, a basic understanding of the algorithms used to identify targets, and identification of the behavioral data that enhanced performance. This information is largely available now thanks to Demand Side Platforms so the question remains: If I have so much transparency, why am I not smarter?

First, let’s remind ourselves of how display advertising has evolved and why ad networks emerged in the first place. In the beginning, display advertising was bought and sold no differently from traditional media — fixed placement for a specific duration of time and for a guaranteed number of impressions. This model is still very much in use and is referred to as contextual advertising. The principal idea is that certain segments of audiences are more likely to be reached on sites that have a higher composition of a desired audience than the overall population. For example, if I’m trying to reach men aged 35-54 with a household income of $100K, I’m more likely to reach my target audience on, say, a business news site than a grocery-coupon site. While this type of buying strategy does in fact help me reach of a lot of the desired audience, inherently there is also a lot of waste; not everyone on a business news site is male, aged 35-54 with a household income of $100K.

As the web began to grow and expand into more niche sites, and advertisers demanded greater efficiencies, the idea of reaching the right audience independent of site or context began to emerge. Performance hungry advertisers needed to deliver audiences in ways that would reach a specific performance objective, typically conversion, in the most cost-efficient manner. Thus, the original ad networks arose to address this challenge. Ad networks’ first clients only cared about conversion, little attention was paid to the factors driving it. Cost-per-acquisition became the ultimate measure of success, and transparency remained in the background.

While ad networks quickly became entrenched with direct-response advertisers early on, agencies with more traditional brand-oriented clients were dealing with a different battle, keeping up with the number of sites and numerous buying options available. Digital planning tools, which evolved from traditional media needs, simply failed to truly serve the needs of the digital media planner. Thus, ad networks began to catch on with these agencies as they were attracted to the planning solution they provided — reaching the audience efficiently–and networks became standard fare in digital media plans. Unfortunately, the success metrics associated with buying via networks didn’t adjust to the needs of non-direct response clients and the “black box” of ad networks continued on.

However, in 2005, the first ad exchange, Right Media, emerged and it appeared that the black box might finally come to an end. Ad exchanges proposed that the more timely the demand could correspond to supply, the greater the value derived, bringing prices in alignment with their true market value. The exchange concept soon incorporated not only media inventory but behavioral data as well, and the promise to eradicate the black box seemed within reach. As we entered 2009, not only had Google’s ad exchange brought significantly more inventory to market, causing advertisers to pay attention, but all the major agency holding companies had formed their own business units built around the idea of forming specialized buying units. Once holding companies came on the scene, the era of the DSP had fully arrived, and again, the promise of transparency seemed inevitable and closer than ever.

It’s mid-2011, and one has to ask if the promise of transparency has been fulfilled. There is definitely more transparency via a DSP. Not only do we have insight into which sites are most effectively achieving our campaign goals, but I can now drill down to which pages on those sites contributed most. Many companies such as Accuen are doing a great job of creating robust user profiles to understand and cluster behaviors to help create deeper customer segmentation. Data providers are also getting better at providing transparency into the recency or frequency of intent data, data “freshness,” and other cookie-level audience attributes. So, with all this transparency, why am I not getting smarter or making better decisions? Has technology perhaps gotten ahead of application? Or, are we even asking the right questions?

Insights derived from the DSPs simply aren’t as actionable as they could be because there is still a fundamental disconnect in the algorithms used by a DSP to find an audience versus the ability to use audience insights to enhance performance. DSP’s were built to deliver efficiency, and until there are better measures of upper-funnel performance that the can be optimized, DSPs will remain stuck in the lower funnel doing what they do best, delivering performance very efficiently. Until there are agreed-upon metrics of success beyond conversion, audience insights derived from DSPs should be used the old-fashioned way by people, and not machines. Additionally, because DSPs focus solely on display advertising and cannot track other types of conversions, the technology only gives marketers a limited snapshot to make critical buying decisions. If the DSP is to evolve and grab more market share, it must begin to address attribution. Until DSPs start pulling in all touch points into the conversion funnel to provide complete transparency into how all media channels interact, the black box will continue.

Steve Kerho, Senior Vice President of Strategy, Analytics, Media and Marketing Optimization at Organic

This post was also published on Steve Kerho’s Fast Company Expert blog found here.

[Image: Flickr user shannonkingren]