Of course, it’s worth remembering that:

you don’t have to have either to advertise—even if an advertiser doesn’t have an app or a mobile website, they can still have a mobile landing page.”

See our previous takes on the “mobilization of music” and mobile analytics standards.

So what are the differences between these types of tests and how do you know which is right for you? Technologists and serious analytics pros will debate the precise definitions, but at the most basic level, A/B tests evaluate the differences between entire pages, banner ads or emails. Meanwhile, multivariate testing focuses on elements within individual pages, ads or emails.

As a general approach, companies will often utilize A/B tests in the earlier phases of digital marketing or Web analytics programs to determine the best overall layout or design for their site.  Typically, these tests require less traffic for valid results so action can be taken quickly to create the right experience for your audience.

Generally speaking, once they have established the overall layout they want, advanced marketers with more extensive and complex sites will turn to multivariate testing to optimize the many elements within their pages.  For instance, they will seek to understand how different individual (and combinations of) offers, headlines, images, copy, color schemes, etc. impact the goals for a specific page. They will use the insights they generate to make small but significant tweaks to their content or campaigns. It’s important to note that multivariate tests usually require a higher volume of page views and user sessions to generate meaningful results.

In the past, conventional wisdom held that testing was expensive and complex. Thus, it was only for big companies with big analytics budgets, huge software platforms, and teams of statisticians. But today, digital marketers can learn a lot for a relatively small investment. In fact, free tools like Google Analytics have pretty strong testing capabilities.

As companies evolve their testing culture and capabilities, they’re able to tap into even more advanced features of their testing platforms. They can use segmentation and personalization to understand what site designs and elements work best for different types of users – current vs. new customers, visitors from different states, visitors who come to the site from social media vs. search, etc. – and automatically deliver the right experiences to different types of visitors. That’s when things get really exciting.

We are great believers in the concept of “test and learn.” In fact, we believe it is a central pillar of effective Web analytics and digital marketing. That’s why we encourage our clients to “always be testing” and to experiment with different types of tests depending on their needs and goals. Testing and learning are relatively easy and cost-effective, and can produce real value. For example, basic A/B comparisons of different home page treatments can give quantifiable evidence about which is more compelling, “sticky” or effective in driving desirable behaviors. The same goes for offers in banner ads on social media sites, alternative formats for email newsletters, or different check-out flows for retail sites.

Thinking about new types of content to offer your customers? Testing is a great way to find out what users may be looking for or would be interested in hearing about from you. Of course, the best tests will come out of a strong process. For example, have your team generate a few new content ideas, shape a hypothesis about which consumers might like it, test it with different executions and measure the results against a few clear metrics. Maybe you’ll hit on a new way to connect with target audiences and meet your business goals – or maybe you’ll find out your current offerings are already attractive to users.

Testing is only growing more important as channels and devices continue to proliferate. Remember testing can be applied almost anywhere – with existing sites and popular search engines, on Facebook and Twitter, even with iPhone apps and mobile campaigns. You could test a small community outreach campaign on social media sites or a few different keywords. The results will give you some basic insight into the impact of different variables, but may also uncover hidden and valuable opportunities.

Because digital marketing is still a new and rapidly evolving discipline, improvement requires that organizations are able to understand what works and doesn’t work, and where the improvement opportunities lie. And that’s what testing and learning is all about.

(Bonus: Check out a bit more about how testing helps analytics and digital marketing pros go back to school and further their Web analytics education here.)

The numbers are almost jaw-dropping:

•  242 million apps were downloaded on Christmas Day 2011. That’s up from a 85 million on December 25, 2010.
• 108 million apps were downloaded per day during the first three weeks of December.
• 6.8 million iOS and Android devices were activated on Christmas Day 2011.
• 1.5 million devices were activated daily during the first three weeks of December.

The problem is, many businesses launch into online marketing programs without considering how they will measure the success of these efforts. These are often the same people who would never ever consider going into Thanksgiving without an elaborate plan.

Think I’m overstating the case? Just consider all the variables you have to manage, especially if you have a large family with many different tastes in turkey preparation (baked vs. grilled vs. deep-fried), the overall menu (Tofurkey or Turducken anyone?), types of stuffing, wine preferences, etc

Then there are the weird side dishes, which every family seems to have. I’m thankful that creamed onions, certain jello salads and mincemeat pies are foods I’m expected to taste only once a year. And of course, since it’s family, there may be a bit of culinary competition – you know, “I will not dry out the bird like my knuckle-head brother-in-law did last year!” or “We will get the meal on the table, no more than two hours late and at least one or two dishes will be hot!”

Ah, the pleasures of holiday hosting. The best, most effective Turkey Day host I have ever seen is my neighbor, Bob, whose ace planning and project management skills would make him an exceptional analytics or IT professional. A few years ago, Bob and his wife Sylvia hosted a big group of friends and family. My wife and I offered to help out and came over a few hours early to pitch in and offer moral support (like calling out the score of the football game) as Bob and Sylvia slaved away.

What we saw astounded us. Bob had created an extensive, multi-tab, color-coded spreadsheet to stage, sequence and manage the full output of foods. It was amazing to behold. You could sort by cooking area or device – range, oven, microwave – or course (appetizers, sides, desserts). Drill-down detail was available for individual dishes (e.g., Aunt Jenny’s sweet potato soufflé).

The turkey tab was broken down in 15-minute sequences, with approximate basting volumes, rotation angles and temperature adjustments – e.g., “pour 1 cup over turkey, (2 cups if skin is already browning), turn 45% and reduce oven temp to 350.” Sylvia thought her husband was half-nuts for overdoing it, but I noticed she also had a detailed seating chart.

You could also sort the “project plan” by person to see who was responsible for each activity. I’m pleased to report that we achieved our objective by delivering a pumpkin pie on time and getting it into the right oven, with fresh whipping cream ready to go.

Halftime of the football game was shaded out to show it was a “red zone” of activity for ensuring everything was cooked properly and kept warm before serving. He even had a simpler tab for the beverages – beer, white wine and soft drinks were outside in a cooler; red wine was inside on the bar.

I joked with Bob that I was disappointed that he hadn’t exported the whole thing Microsoft Project to create swim lanes. “Good idea for next year,” he replied. “And if I have enough time, I could do some scenario modeling in case the turkey cooks more slowly than planned or people show up late.”

Besides being a warm holiday memory that I still tease my neighbor about, this approach is an excellent template for effective analytics programs and projects. Think about it: Bob’s objective was to feed a big crew of hungry folks; he designed and integrated a set of processes to make it happen; he built the right organization (with clearly defined roles and responsibilities) and gave them the tools, technology and data to succeed. Given the scope of work involved, Bob stayed amazingly calm throughout the day. I think he even enjoyed himself, which isn’t always the case for head chefs on Thanksgiving days.

Happy Thanksgiving!

Once upon a time, AOL’s Network Operations Center (NOC) had a rapid notification system; if there was a sudden drop of modem connections, technicians in the NOC received immediate alerts (via pager, of course, the major wireless technology of the day) and began assessing if there were server outages or other network issues.

Before long, the technicians came to expect a flurry of pages every Thursday night, at 8:00 p.m. Eastern. Like clockwork, hundreds of thousands of modem connections would drop, with no apparent network issue. Turns out people were logging off to watch Friends, the wildly popular new NBC show that was the basis for “appointment TV.” Thus, our NOC team began using TV Guide as a diagnostic tool to predict when signoffs would peak. Way back when, that was a way we tracked audience behavior across channels. Sounds primitive, right?

Many of our top Internet executives came from networks or other large media properties, which explains why they thought in “day parts,” as TV and radio programmers do. It made sense, because, in those days, users really didn’t or couldn’t be online and watch TV at the same time. There were particular times of days when TV audiences would spike, and other times when Internet usage would rise. “Always on” and ubiquitous connectivity simply didn’t exist yet.

Now, jumping ahead to Fall 2011 in New York, our friends and colleagues gathered at the Infinitive Analytics Roundtable were all abuzz about the proliferation of smart phones and tablets, and how users can be engaged simultaneously with live events, print or television programming and their mobile devices in all kinds of entertaining and beneficial ways. All the spikes and curves are flattening out. “Day parts” that were once associated with low online traffic are now picking up. Users routinely interact with multiple screens at once.

The “always-on” vision has become a reality, as has “connect everywhere.” Today, “appointment TV” means downloading a TV episode from three weeks ago onto a tablet to watch on a plane ride. Or watching an entire season of a classic TV show streaming from Amazon or Netflix through the Web to your TV. Or keeping up with a live sporting event on your smartphone while on the sidelines at your kids’ soccer games. And users can more or less program their own radio stations today. There are huge benefits for companies who can gain visibility into the behaviors and motivations as users move through digital space as friends, fans, influencers, buyers, commenters, etc.

All this is revolutionary. But, from an analytics perspective, it remains very complicated to track users and, therefore, put a clear value on content and other “programming.” Audiences are shifting and expanding to fill all the old dead spots. They are consuming more and richer content – and a ton of video – on a broader range of devices. They are interacting with and sharing that content across their networks of friends (which may include friends of Friends) or co-workers. And they are multi-tasking, Tweeting about the TV shows they’re watching as they watch them, posting to Facebook about the YouTube videos that make them LOL or haha. It’s this last level of engagement that should be of most interest to all types of online businesses.

Yes, companies can track all this behavior in the individual channels, but following users across channels and through complex, multi-dimensional engagement models remains very difficult. Obviously, Web analytics has come a long way, but  in some cases it’s just as challenging as it was back when Jennifer Aniston and David Schwimmer were newcomers.

Today, publishers must understand how usage patterns are changing  and what it means to their business. How different are your target audiences (customers, prospects and partners) in terms of the devices and operating systems they use? Is your content optimized and distributed properly for all of these channels? What do users want when they are on a smartphone versus a tablet versus a laptop? Are there any temporal patterns worth noting in how users consume or share your content? Is it easy to share your content?

As is often the case with analytics, there are more questions than answers here, but that’s just the reality of our fast moving and constantly changing industry. The larger content management systems have begun to address these issues, with tools that allow for quick modifications to fit various screen sizes and OS types. This is critical as we are on the cusp of a major boom in mobile advertising. The messaging, content and offers simply must be optimized to fit the reality of what users see on their small screens (whether they are Androids or iPhones or Blackberries). Companies that can track users from the mobile world across social media to the traditional Web are rare and way ahead of the game, though this should be the long-term goal for many types of companies.

Our colleagues in NYC reflected about how difficult it is to enable this advanced level of tracking, given all the fragmentation and lack of standards. However, they agreed that, for now, they must capture the best metrics they can get and use broad trending analysis to identify big-picture traffic flows and engagement patterns. Then, as the technology matures, they can begin to methodically trace the impact of these patterns on desired business outcomes (be it brand and product awareness, direct sales and/or stronger engagement with content to support advertising rates).

This is one reason why we try to keep our clients focused on balancing expectations with reality. We emphasize understanding what can be measured and how various metrics connect to key business outcomes. If the capability to measure is limited in the early going, then expectations should be managed accordingly.

The point is, in the fragmented, post-Friends era, companies that can figure out the right metrics to track online audiences and target users accurately and consistently stand to enjoy major benefits.

To earn a larger share of television ad dollars … YouTube has to recruit new kinds of advertisers, beyond the music, entertainment and technology companies that have flocked to the site … And Google, which got its start and still makes the vast majority of its money from search ads … has to learn how to work with advertisers who want to sear their brands into the minds of Internet users.

Further, YouTube’s edge in targeting and sharing  capabilities may not last much longer:

Unlike television, YouTube incorporates social elements by inviting viewers to choose whether they watch, share or create their own videos about advertisers’ products …YouTube has a small window of time to capitalize on that ability, because as Internet-connected televisions, including Google TV, become more popular, television will also be able to show personalized ads.

And then there’s interesting announcement from YouTube’s new leadership:

all ads on YouTube would eventually be video ads for brands.

Here’s our latest take on the state of online video.

Facebook … is where you go to see what your friends are up to. Now it wants to be a force that shapes what you watch, hear, read and buy.

The plan is to orient users to start searching through Facebook, based on the idea that “friends will direct other friends to content.” Said one Forrester analyst:

“It changes the game for what social networks have been doing. What Facebook is saying is, we are your life online, and also how you discover and share.”

This analysis claims that Facebook is on the verge of replacing Google as the “fabric of the Web.”

Interestingly, Facebook won’t get directly into the business of providing media (like music or movies) for users to consume, but rather to become a channel for its partners (including Netflix, Hulu, Spotify, The Washington Post, Yahoo and others) to make the connection.

Everywhere on the site, users will be able to more precisely signal what they are reading, watching, hearing or eating. This will let Facebook reap even more valuable data than it does now about its users’ habits and desires, which in turn can be used to sell more fine-tuned advertising.

In other words, it’s all about the Web analytics – tracking user behavior in terms of how they follow recommendations and convert “likes” across into actual purchases. If you’re going to connect with customers and prospects via the “new Facebook,” you’ll need robust Web analytics capabilities.

Competitively, becoming “a hub for content discovery—places [Facebook] more firmly in opposition to Google,” as this analysis put it. And its partnership with Spotify ups the ante against Apple. From the partners’ point of view, they are banking on more likes and more sharing from Facebook leading to more traffic to their own sites, where they monetize their own content (through ads or subscriptions).

This sort of centralized hub approach does have risks. Some pundits suggest it might lead to more privacy concerns among consumer advocates and drive some frustrated users to Google+. It also raises the dangerous “walled garden” factor. But, it’s interesting to see Facebook continue its strategic evolution – and to do so aggressively. These are bold moves, and an interesting spin on the typical content monetization strategy.

Digital media companies are battling over content ownership (and driving up the price in the meantime), while consumers just want easy and reasonably affordable access to content (no matter who owns it). Facebook may have just made itself a link that no one knew was missing.

A recent post over at Web Analytics Demystified about the “myth of data-driven organization” generated a good bit of buzz in the field. Indeed, the comments section of the post is very interesting, and suggests that analytics practitioners are coming to terms of what it really means to be “data-driven.”

The debate boils down to the question of how dependent companies should be on raw data to make decisions. In the online marketing space, that might mean looking at performance data to find the right ad network or set of online properties for an ad campaign, tweaking promotional offers (e.g., buy one, get one free vs. blanket 30% off) and choosing new keywords. Within digital media, publishers are more likely to address questions around content placement and monetization strategies.

Some data purists seem to believe that data should dictate – and even automate – much business decision making. You choose offers, ad formats or content placement based purely on performance. Whatever attracts the most eyeballs wins – period and end of story. The only problem with that theory is that Lady Gaga, the cast of Jersey Shore and NFL scores would have to feature prominently in every page of a site. And if you’re not in the business of sharing celebrity news or providing sports content, well … you get the point.  (Actually, some decidedly non-celebrity media sites are quite comfortable using Lady Gaga for headlines.)

We don’t agree with this “bait and switch” practice, but sadly we see it all too often. The point is, Lady Gaga should not be in the headline of this post, but we expect at least a few clickthroughs based on our little trick! Particularly sad is the fact that some people seem committed to prove that this sort of trickery is productive use the “data-driven” concept, which sounds so worthwhile, as justification. “As a data-driven organization,” they seem to say, “we have to put Lady Gaga in our headlines.”

Obviously, this is not the way to go. Raw data is more prevalent than ever. But that doesn’t mean organizations should be driven by any data or seek to capture the most data. It’s about finding out which data is most important to overall goals or specific challenges, and then leveraging that particular set of data in the proper way.

Sure, top businesses and admired non-profits are strong quantitatively in how they measure their sales and operations. Yes, they have lots of great data, but they are also exceptional in qualitative terms. They have strong leadership to set the mission and inspire the entire organization. They use management and operational infrastructure to execute against core strategies. They establish cultures that enable their people and teams can perform at a high level. In fact, a “strategy-driven” or “culture-empowered” organization would beat a purely “data-driven” company any day of the week.

It takes a lot more than data to succeed, in other words. Companies must also have the skills, processes and resources to leverage it properly if they are to gain the potentially huge “data-driven” advantage over their peers.

It’s important to remember that even the most admired “data-driven” companies, like Amazon and Capital One, are far from perfect. If you’ve ever bought a baby gift for a friend or music for your teenage kids through Amazon, you know that many of the follow-on product recommendations are not exactly on the mark. That’s what happens when you business gets turned over to the algorithms.

Such “best practices” reflect the fact that there are still too many limitations when it comes to data collection and real-world analytics today. For one thing, data sets are never really complete. The next Lady Gaga-sized sensation, who will generate unprecedented numbers of tweets and likes sometime in the very near future, is on very few radar screens today.  (We’ll talk about predictive analytics another day.)

Similarly, web analytics today, with many different tracking and measurement methodologies, creates gaps and grey areas that must be analyzed and understood. As much data as we have, analytics pros recognize significant variations in how even relatively simple metrics (like page views) are defined. So if you’re going to be data-driven in our fragmented analytics environment, you have to ask yourself which data set you want to do the driving.

Strategic context may be an even more important issue, as several commenters to the original post mentioned. One argued for measurement concepts like “popularity quality” that would balance raw traffic measures with notions of strategic alignment. As with complex metrics like customer engagement, the analytics community has some work to do to nail down robust and statistically valid means to capture such concepts. In the meantime, companies must understand even simpler metrics – like raw traffic and impressions – within the context of their strategies. Different companies need different metrics because they have different strategies.

Lastly, the decision making and reporting culture have a role to play here. As data increases in value, it’s important that senior executives understand where it comes from and its various limitations. Reporting tools, formats and tempos must be structured for effective use and clear information sharing in ways that make sense for the business. In our experience with high-performing organizations, data analysts have a role to play in educating and counseling business stakeholders and decisions makers on the data itself.

In the end, those commenters who suggest that this is largely a semantics question have a point. Whether it’s more about being “data-informed” or “data-enabled” as opposed to “data-driven” may not matter that much. What matters is that online marketing, digital media and analytics teams must validate that they have the right data based on their unique business goals and objectives, and then use that data in the best ways to enable the achievement of those goals and objectives.

Not as exciting as Lady Gaga posts perhaps, but then again we’re more likely to click through on headlines about conversion rates and page tagging than on Lady Gaga!

But while data governance is one of the least visible aspects of Web analytics, it’s easily one of the most impactful. Companies that get it right generate “Ferrari” benefits – significant savings of time and money, plus greatly improved confidence in their data. In other words, data governance is actually a “must have.” Without it, an organization’s entire data strategy and online marketing approach may rest on a shaky foundation.

First, a quick definition. While data governance continues to evolve as a discipline, we view it as the process by which online marketing and Web analytics organizations define and manage different types and categories of data related to behavior tracking, audience measurement, ecommerce and other aspects of online business. Synonymous with “quality control,” data governance strives to ensure companies have reliable and consistent data sets to assess performance and make management decisions.

It’s critical to remember that effective governance is not a one-time exercise, but rather a fully developed effort and repeatable process. That’s the only way to ensure ongoing compliance with corporate standards and requirements, data integrity in light of future changes (like evolving business challenges, emerging technologies and new data flows).

There is a fairly common perception that governance is all about policies, and having them captured in a full set of documents that live on the company servers somewhere. But the most effect governance programs are active and ongoing, consisting of regularly scheduled audits, reconciliations, compliance reviews and quality control activities.

In our experience, data governance matters to company in five essential ways:

1. You can’t get consistent, reliable and repeatable data without it. A central role of data governance is to ensure that metrics are defined consistently across the organization. So when managers or analysts talk about “conversion rates” or “unique visitors,” everyone else knows precisely what they’re talking about. Without clearly documented standards around metrics, decisions may be made around false assumptions. Obviously, communication and reporting suffer in such situations, an especially important consideration in environments with multiple analytics tools.

 2.     Analysis and reporting issues are most often data governance problems, not technology problems. Many organizations are quick to blame their tools or technology when there is confusion about the meaning of Web analytics data or lack of clarity in reports. Typically, the tools and reports have not been configured to clarify what various metrics mean, how they align to specific goals, or where they may vary from data provided by different systems. Companies end up ripping and replacing perfectly good systems before doing the necessary governance work to ensure they work properly.

3. Data governance guides all other analytics activities. Just as contractors would never build a house without clear blueprints, analytics teams need data governance to guide and structure their most important activities. In this sense, data governance informs everything from analytics software implementation to page tagging to report design. Specifically, it ensures that your data capture mechanisms are set up to capture what you need to capture. It may also outline who is responsible for which Web analytics tasks or data. Perhaps most importantly, data governance can help ensure that there is clear alignment between Web analytics tactics and big-picture strategic goals.

4. It helps you (and your boss) sleep at night. Too many Web analytics veterans know the nightmares that come with trying to explain or reconcile conflicting data sets to skeptical executives, and the headaches and second-guessing that occurs when there is less than 100% confidence in analytics data. Truly effective data governance helps eliminate those headaches by clarifying what metrics mean, which are the most important, how internal numbers relate to outside ratings and why there may be gaps. More clarity means more confidence in decisions and more peaceful nights for everyone. There is also a compliance angle here. With more legal and financial reporting ramifications for online businesses, effective governance models can ensure all of your online operations follow relevant privacy policies, data security guidelines and consumer information regulations, especially around cookie usage.

5.  It saves you money. Having a firm grip on how you define “page views” or other core metrics can help you when you’re negotiating to buy or sell advertising. You won’t be low-balled because an external organization (like an ad network) claims your traffic is too low or theirs is too high. And you’ll be able to probe the metrics used by others to price their own inventory and determine if their information is credible and accurate. Even if you’re not in the advertising market, data governance can save you money in other ways. For example, it may help you avoid the premature and costly “flipping” of systems (see point #2).

Next time someone seems bored or disinterested at the mention of data governance, you can remind them why data governance is important. And be on the lookout for a future post on how you “do” data governance.

the rapid evolution of online video has been one of the most fascinating – and fastest-growing – competitive spaces on the Web. … That’s why so many content providers and media companies are moving aggressively to integrate digital channels into their content supply chains and build strong video advertising platforms and products.

Sometimes it seems like online video is a war of all against all. Consider the players involved in various forms of online video: Netflix, Overstock, Amazon, Wal-Mart, Google, Apple, the major cable providers, television networks and movie studios. Who isn’t in the online video business may be the simpler question to ask.

In some cases, these players are partnering; in other cases they are directly competing. Most everybody still falls into the “Content is King” camp. From a business perspective, the jury is sill out on the optimum models – paid subscriptions, ad-supported or freemium – to build a business on video content. Media and entertainment companies for instance are trying to figure when to make programming available online – that is, how long after it premiers on cable or “over the air.” The timing and mix of delivery methods is critical for them to most effectively monetize their content.

Businesses in other sectors – like consumer packaged goods or financial services firms – may be looking at videos to drive traffic to their Web sites or improve conversions. Increasingly, retail sites are featuring videos on their own sites or sites like YouTube and Facebook to expose their brands and products. As of Q4, 2010, almost three-quarters of U.S. retail sites featured included videos, according to this eMarketer report. Similarly, a recent Forrester report highlighted how some retailers are using video effectively.

At a high level, we see a “trifurcating market” when it comes to online video. Companies are using it in three primary ways:

• Promotional or product information to boost brand awareness, drive traffic to online properties and generate online sales
• Programming content – like DIY Videos, TV shows, Web-only programs or movie streaming (subscriptions or rental)
• General non-paid content – like customer support videos or CEO messaging.

From an analytics standpoint, companies with substantial online operations need to know how and why their customers and prospects are interacting with this critical category of content and how it is contributing to their business goals. What are the most compelling ways to use video in the sales cycle or for ecommerce purposes? Do customers who view a product video buy more? Are they less likely to contact customer support if they watch a demo? What are the key ingredients or elements that attract visitors and keep them most engaged? These are the types of questions effective analytics can answer.

The video tracking landscape is particularly complex. It is not uncommon for companies to leverage multiple video platforms and players, several video formats, or use various partners to distribute videos. Think about companies having a dedicated channel on YouTube or a video tab on their Facebook page. Each of these variables makes it harder for companies to collect and coordinate full and consistent data sets about their customers’ video consumption. This is where following a disciplined governance process and adhering to data management standards is especially important.

Most enterprise analytics platforms, like Adobe Omniture, IBM Coremetrics, and WebTrends can embed code into players to track video activity. In addition to standard traffic metrics, these packages can collect data to determine how much of a video is viewed, if it was auto-played or started by the user, shared with a friend, and attributed to downstream conversions. When it comes to understanding the competitive landscape, that data still often comes from third-party syndicated sources, like comScore or Nielsen. Pulling all this information together and integrating it to gain a clear and comprehensive picture of current usage and future opportunities remains the core analytics challenge around online video.

On the monetization front, many companies are still attempting to master the back-end. Technology requirements vary based on the business model. Many original content creators employ a premium service model where subscription, rental or purchase fees are charged for access to video. Others utilize a media model where videos are monetized through advertising. In both cases, there are information capture, validation and tracking requirements to manage.

Speaking of video ads, eMarketer predicts the domestic online video advertising market will reach almost $6B by 2014, up from $1.4B in 2010. Video ads typically take the form of in-stream ads (pre-roll, mid-roll, post-roll), banner ads packaged with the video, or branded promotional video content. Consistent with the display market, advertisers and publishers require detailed reporting of ad performance metrics to maximize yield and revenue. They must find the best ad-serving and traffic management toolsets and develop efficient sales processes. With comScore reporting that “Video ads accounted for 13.6 percent of all videos viewed and 1.3 percent of all minutes spent viewing video online” as of June 2011, these are increasingly important questions.

It’s fair to say that whatever type of business you’re trying to build online, video is likely to play an increasingly important role in it. So you must find the strategic linkage between your goals and video usage. Analytics and tracking, along with disciplined data strategy, can provide the necessary insights to manage and reinforce that linkage and – even more importantly – make sure you’re getting the most bang for your video buck.