Vox Media recently announced a plan to launch a new unnamed site, with Ezra Klein from The Washington Post as chief editor and Matthew Yglesias from Slate as a founding contributor. This event follows the recent re-launch of AllThingsD as Re/code as well as what seems like a flurry of activity from publishers that have recently risen to prominence. Is there an audience for another ad-supported digital publisher of first-party content? Our data suggests there is. The share of ad-supported digital publishers comprising the top 250 Quantified sites, or sites directly measured by Quantcast, grew 68% from 2009 to 2013. Not only are there significantly more ad-supported digital publishers today, they’ve grown their audiences nearly 50 percentage points faster than the remainder of the top 250 sites. In this report, we examine the dramatic rise of ad-supported digital publishers.
2013: The year of digital native publishers
The incredible success of ad-supported digital publishers in the past few years has been well reported, from BuzzFeed to Upworthy to Gawker Media. However, in the past year, the trend has intensified, with seemingly more interesting new publishers emerging every month. To understand the data behind this trend, we examined the top 250 Quantified sites from 2009 to 2013 and identified ad-supported digital native sites that primarily publish first-party content, which excluded both pure content aggregators and the digital presences of offline media properties.
We found that while these sites comprised less than a quarter of the top 250 Quantified sites from 2009 to 2011, they grew dramatically to 38% of the top 250 sites in 2013. The average audience for these sites grew significantly as well, increasing 96% over the same time period, compared to a 46% increase for other sites in the top 250. In four years, ad-supported digital native publishers have swelled in number and audience reach.
Growth by category: Niche content finds a large audience
To learn what types of content are driving this growth of ad-supported digital publishers, we first examined their categories.
News was the largest category, including sites dedicated to general news, local news and opinion. Entertainment was the next largest, including sites covering celebrity culture and gossip and those with reviews of television shows, movies, music and games. The remaining large category was Lifestyle, which includes sites focused on food, home, health and men’s and women’s interests.
Next we identified the categories of the ad-supported publishers with the most significant growth from 2009 to 2013.
Entertainment, Lifestyle and News categories grew the most in number of sites, and Sports grew the most in percentage. The number of Entertainment sites grew 75%, led by general entertainment and gossip sites. Notably new in the category since 2009 are sites covering African-American culture, such as Bossip and Global Grind. The number of top Lifestyle sites grew 90%, driven by the addition of women’s interest sites such as Jezebel and health sites such as MindBodyGreen. The largest category, News, saw large growth in general news sites and the addition of sites focused on niche political content. Notable in the Sports category, Rant Sports reinforces the theme of more specialized content—in this case, for specific sports and teams.
The takeaway: digital publishing is healthy—and growing
The digital publishing landscape has evolved rapidly since 2009. Today Facebook and Pinterest are huge sources of traffic, and on the advertising side more dollars are being directed to digital via native ads and performance display. Are these linked to what we observe in the increased success of more specialized content publishers? Whatever the cause, it is clear that more ad-supported content publishers are reaching larger audiences, and by that measure, digital publishing has never been so healthy. We look forward to seeing the trend continue.
Posted by Art Prateepvanich, Head of Product Marketing, Publisher Solutions and Samuel Lo, Data Anthropologist
The shopping is mostly done, but the cooking has just begun. Because so many of us are looking forward to big holiday meals, BigOven is looking forward to a big holiday audience. To learn how it prepared for the holiday season, we spoke to BigOven’s Director of Product Kate Handel.
What is BigOven?
KH: Our free apps help home cooks get inspired and organized, in the kitchen and on the go. We offer 500,000+ recipes and menus, a menu planning calendar, a grocery list and more.
How did you get started?
KH: Our founder, Steve Murch, loves to cook and started BigOven in 2004 to store his favorite recipes and make grocery lists for his family meals. He created a desktop application that allowed you to print your shopping lists. In 2008 we introduced the first recipe app for iPhone, then introduced iPad, Android and Windows Phone apps shortly after. By the end of 2013, we expect to pass 10 million app downloads.
What are you measuring?
KH: We measure all kinds of things, but Quantcast especially helps us better understand who is using our apps, how we’re doing on user engagement and repeat usage. Interest in meal planning tends to be seasonal, with strongest interest peaking around the holidays. One of our engagement goals is to win back users who may take breaks during the summer – we use social tactics, push notifications and email, and measure the impact on user retention. Quantcast demographic data also helps guide our targeting for acquisition campaigns.
Who’s your audience?
KH: We call our target audience the “chief household officer.” This demographic is primarily females 25-50 who like to cook, but also a significant number of males who do the cooking and/or grocery shopping. We tell busy moms and dads how BigOven will save their family time and money.
Why did you choose Quantcast Measure for Apps?
KH: One way we monetize is through advertising, and we want to ensure our mobile apps are visible to potential advertisers. That’s why we have a public profile on Quantcast. Because home cooks use our apps while shopping, we offer a great opportunity for advertisers such as McCormick and Kraft who want to reach shoppers in grocery stores.
What’s your favorite holiday recipe?
KH: We have tons of great holiday recipes and complete menus at BigOven.com. Try the reindeer cookies – they have pretzels for antlers!
Posted by Art Prateepvanich, Head of Product Marketing, Publisher Solutions
This week we released new enhancements to the Measure API so Measure users can now programmatically access their traffic and demographic data for all their properties, segments and platforms. We introduced the Measure API earlier this year to provide publishers with large numbers of properties or segments a more convenient way to download and analyze their Measure data. In this release, we’ve added multi-platform support, so publishers can now programmatically get their mobile app data in addition to their web data.
The following is available via the Measure API:
- traffic, upgrade/install and demographic data for owned properties
- filter by platform: web, mobile web and mobile app
- view data at the network, site, app or segment levels
What can you do with the Measure API?
The API makes traffic and demographic data available programmatically, so an account owner can download and analyze their data more easily, and with greater detail than possible with the standard dashboard. We’ve heard a number of interesting uses from our conversations with early API users:
See how the demographic composition of a particular property changes over time. By downloading demographics at regular intervals, a property owner can identify trends, and gauge the success of audience engagement efforts over time.
Buzzfeed, an early test user, measures audience engagement with their content by author and topic. They are using the API to download traffic and demographics by topic over time, to understand trends in their readership. Explains their Lead Data Developer Steve Heinz, “The API enables us to access all of this data very dynamically and integrate it with our internal systems. With mobile tracking as one of our top goals next year, using the API to slice and dice our data by platform will be invaluable.”
Power Your Dashboard
Keep tabs on daily traffic. Use the API to pull in traffic data into an internal dashboard, so the whole organization can stay on top of site health and trends.
Sell More Effectively
Use the API in combination with Measure audience segments to better understand the audiences behind a publisher’s targetable ad inventory. Using demographics by segment, a publisher can equip ad sales teams to tell a more effective audience story and create more compelling proposals.
For more information about the API, and to sign up, go to developer.quantcast.com. After completing the signup process, Measure users can use the interactive console at developer.quantcast.com/io-docs to execute API calls against their live data without writing a single line of code.
For any questions or feedback about the Measure API, please contact us at email@example.com.
Posted by Jon Katz, Product Manager and Art Prateepvanich, Head of Product Marketing, Publisher Solutions
Attribution is one of the most critical issues in digital advertising today. Proper attribution provides insights, incentives and controls over ad spend.
However, our current attribution models are flawed; they are too simple and easily gamed, as they measure from only the conversion event and skew metrics and incentives to bias the lower-funnel tactics. Measurement from more than just the conversion event is needed, and adding a second signal of measure further up the funnel is the right next step. If the industry chooses to adopt it, attribution will evolve to a new level of control and effectiveness.
Several of the leading attribution companies are exploring this option, but it requires greater storage and infrastructure to implement, so it needs everyone’s support. It is time to move from one signal of measure to two. This is a simple step that will have profound impact on how we view upper- and lower-funnel programmatic planning. A second point of measure will enable new metrics, insights and incentives for more efficient spend resulting in more conversions and less gaming of attribution.
The skill set and data required for effective upper-funnel prospecting is fundamentally different from lower-funnel retargeting. Ideally the two tactics work hand in hand to maximize total conversions across the full funnel. However, when only measuring from the lower-funnel conversion, they compete for credit and retargeting always wins because it is closer to the point of measure. By creating separate metrics for each phase independently, they now can work holistically together in a more efficient partnership to increase total conversions.
Adding A Second Signal For Dual Credit
Here’s how to do it: Split the funnel in two by using the first site visit as the second point to measure from. The first site visit is a natural delineation point between prospecting and retargeting. Consumers might visit a site multiple times before they convert, but retargeting can’t start until that first visit happens. This provides a clean break in the data and a natural hand-off between tactics. The site visit is the natural transition from upper and lower funnel and it touches every converter. Now we have new perspective and new metrics for prospecting and retargeting independent of each other.
Notice in the above illustration that a conversion is still required in both the upper-funnel (prospecting) and lower-funnel (retargeting) phases. That means credit is only attributed after the conversion. Just getting a site visit isn’t good enough. We still incentivize the converting customer, but now we have two separate incentive structures. Prospecting is about getting someone who has not been to a site to visit and convert; retargeting is getting someone to convert after they visit, but now they work hand in hand without competition and with full transparency.
It is important to remember that the job of your attribution model isn’t about rewarding or punishing vendors or departments on a plan. Attribution is an optimization tool for maximizing conversions. The byproduct of the optimization process is that sometimes budgets are cut, increased or decreased, but the goal should always be towards maximizing the desired outcome.
This measurement approach can produce double-digit increases in total conversions. The reality is that last touch attribution skews the influence of lower-funnel activity for traditional metrics like CPA, ROAS or ad efficiency that often result in overbudgeting and oversaturation with retargeting, meaning fewer conversions.
Let’s look at the example below:
Notice the difference in CPA calculations. In the above scenario the campaign data is the same for both reports; the dual-credit report just accounts for upper-funnel metrics independently from the lower funnel and is able to break out ad effectiveness more cleanly.
It is important to note that the resulting optimizations you might enact would be vastly different based on the conversion and CPA data presented in each report. This campaign example (it has been cleaned for illustrative purposes) produces a 30% increase in conversions simply by moving budget to vendor 1, an upper-funnel prospector, and vendor 4, a retargeter. The phased partnership results in more conversions without any additional budget.
Just budgeting a balance of prospecting and retargeting tactics on a plan isn’t the same as measuring them. Dual measurement is essential for having true perspective into your marketing funnel initiatives, and this two-phased dual-credit approach is the next step.
Marketers can ultimately assign whatever value or use whatever multitouch strategy they want within each phase. It is completely flexible, provides separate incentives and is less easily gamed. While this discussion has focused primarily on programmatic, this model can, and should, be applied across all digital marketing channels.
Adding a second signal of measure provides a natural next step in the evolution of attribution today. Two-signal attribution also lays the foundation for further advancements: adding a third and fourth signal to measure from for potentially even more control and insights. But let’s not get ahead of ourselves. It’s time to give upper-funnel attribution its due respect and split the funnel!
Originally published in AdExchanger on December 13, 2013 by Seph Zdarko, Head of Attribution Initiatives and Partner Strategy
Today, Quantcast has partnered with Twitter to help advertisers amplify their social efforts and reach their precise audience via Twitter tailored audiences!
Quantcast’s big data set and advanced machine learning help you define your best customer’s profile and advertise online to many more just like them. This new integration now lets you reach those audiences on Twitter. Quantcast can build your custom audience profile of existing and prospective customers, which you can use for advertising with Twitter’s Promoted Products.
To learn more and to take advantage of every opportunity to reach the right person in the right place, please send a note to firstname.lastname@example.org.
Posted by Nicole Beno, Product Marketing Manager
Last week, Quantcast hosted a large group of developers at the monthly Silicon Valley iOS Developers’ Meetup. Michael Kamprath, VP of engineering at Quantcast, gave a presentation on how he and his team built the Measure for Apps SDK and what obstacles they faced. Michael emphasized the need for transparency, privacy and ease of implementation to a captive crowd.
Three hot topics stood out amongst the sea of questions:
- Transparency: Developers should know exactly what they are implementing. Quantcast embraces this philosophy and distributes the Measure for Apps SDK as source code.
- Privacy: Developers are not lawyers, and privacy laws change often. This is why Quantcast’s Terms of Service document has current, updated privacy language that it recommends developers include in their privacy policies.
- Detailed demographics: You can get age, income, gender, ethnicity, household composition and education levels from the Measure for Apps SDK. Quantcast uses the same methodology it has been using for years of accurate demographic reporting on the Web.
Many thanks to our friend Tim Burks, the Meetup and Renaissance Conference organizer, for being a great connection to the iOS developer community. We at Quantcast hope to host many more compelling meetups in the future. If you are a developer, check out our Measure for Apps SDKs here, and let us know what else stands out to you. Contact us at Measure@quantcast.com with any questions or to receive our regular Measure Newsletter.
Posted by Maryam Motamedi, Product Marketing, Measure
Black Friday is the busiest shopping event of the year, capturing $59B in 2012 according to the National Retail Foundation. Black Friday is also no longer a day – most retailers extend their activities through the weekend, and Walmart and Best Buy open at 6pm on Thanksgiving Day, before most families have even carved their turkeys. The majority of “window-shopping” and list making happens much earlier, with shoppers searching for leaked deals online up to 3 weeks in advance of the big event. Given our web-wide visibility we examined our audience data to understand how shoppers are preparing for Black Friday – we analyzed their demographic profiles, how they look for deals and where they are looking.
The Black Friday Hero: Moms 25-44
Who’s preparing to shop on Black Friday? We looked at our real-time dataset of anonymous user behavior to find out.
We began by examining the demographics of consumers searching for “Black Friday” online in the past three months through November 18th.
We found the Black Friday deal searcher strongly skewed towards females aged 25-44. Based on our findings, more of these shoppers had an income between $50-100k and a college education than the US Internet population. Just as interesting is who isn’t looking for Black Friday deals. Men, Asians and Hispanics, Seniors (65+) and high income-earners ($150k+) all significantly under-index for Black Friday searches.
Not All Mobile Is Created Equal
Looking at the platforms Black Friday searchers are using, we found the mobile revolution is making its presence felt. US Black Friday searchers are 1.5x more likely to use an Android device than the typical US user online.
We spoke to Michael Brim, owner of BFAds.net, one of the largest Black Friday deal sites, who related how BFAds.net’s mobile traffic has grown dramatically. According to Mr. Brim, “Mobile (phone and tablet) web now represents almost 40% of BFAd.net’s total traffic. While we have seen year-over-year increases across all devices, mobile web traffic is up a combined 250%.”
Mr. Brim also notes that despite the huge traffic growth from mobile devices, a large majority of shoppers are still completing their online purchases on the desktop or tablet rather than on their smartphones. Smartphones may be powering research, but online commerce is saved for bigger screens and the comfort of home.
Interests in Family and Home
As a final element for this research into Black Friday shoppers, we looked at what keywords they searched for and organized the data in a couple of ways. First, we looked at which retailers over-indexed in searches. Second, from analyzing clusters of related keywords by interests we can understand what types of content users were searching for and engaging with.
Family and home oriented big-box retailers dominated the top lists as many of people’s interests revolve around family. Other notable interests include travel and sports.
In examining this year’s pre-Black Friday activity, we found Moms aged 25 – 44 are the driving force, and that’s not surprising given the stereotypical image of the Black Friday parent braving the crowds to find their children the hot toy for the season. Last year, Gallup conducted a poll that found adults 18 – 29 were the most interested in Black Friday shopping, and a story developed that Black Friday has made a dramatic skew towards youth. Based on our analysis of this year’s Black Friday shoppers, we think 2012 may have been an anomaly.
Posted by Art Prateepvanich, Head of Product Marketing, Publisher Solutions and Samuel Lo, Data Anthropologist
It’s every advertiser’s goal to capture their customers’ attention at the right time and place, but with so much content out there across so many devices, that window of opportunity becomes more and more important. As an industry, we need to find relevance―at the risk of getting lost in the noise.
In a webinar with the American Marketing Association (AMA) and Critical Mass this week, we explained how machine learning helps drive better advertising decisions and identified the key ingredients to successfully leverage real-time display buying. Listen to our VP of Performance Engineering, Michael Recce, leverage his background in neuroscience and artificial intelligence to simplify the science behind predictive modeling, in the full recording here!
And if you have any questions about getting started with programmatic buying, please shoot a note to email@example.com.
Posted by Stephanie Park, Demand Generation Manager, Performance
For a brand advertiser, the goal of an advertising campaign is to increase awareness of the brand among the target demographic. While this sounds fairly simple, a concept like “brand awareness” has always been difficult to measure.
To measure and optimize their campaigns, brand advertisers are using various metrics and vendors. Many turn to brand surveys that, while popular, are often cumbersome and expensive. Others are beginning to measure in-target delivery using a campaign audience validation service like Nielsen Online Campaign Ratings™. These reports indicate how many campaign impressions were delivered to their intended audience. Still, because of the ambiguous nature of measuring brand awareness, many online advertisers fall back on click-through rate (CTR) because it’s easy to measure and understand.
We have found that optimizing for CTR is a losing proposition and published our findings in a recent whitepaper. CTR doesn’t work for any campaign, because people who click on ads have a consistent, distinct profile: they are either under 18 or over 50 with a household income of less than $50K. Chances are, this “clicker” demographic is not any advertiser’s target audience.
Delivering Targeted Audiences
We recently saw this dynamic play out for a brand awareness campaign that was initially optimized for in-target demographic delivery and then shifted to focus on clicks.
To promote its newest product, a national CPG brand was looking to reach its target audience of women 18-34 within a brand-appropriate environment. The advertiser ran its campaign across a premium women’s lifestyle network and used Quantcast Demographics to reach its target audience. Nielsen Online Campaign Ratings was used to validate the in-target delivery of women 18-34.
Using its web-wide visibility and audience modeling against each U.S. Internet user, Quantcast was able to deliver exceptional targeting accuracy for the campaign: a 69% overall lift in audience composition for women 18-34.
Once the advertiser chose to optimize the campaign for CTR, however, the audience composition of women 18-34 tumbled 36%.** This means that only 38% of the campaign impressions were delivered to the brand’s target audience of women 18-34, according to Nielsen Online Campaign Ratings.
As a result, more impressions were delivered to “clickers” – who are either very young or older with a low household income This clicker audience looks nothing like the CPG brand’s target audience.
Moving Beyond Clicks
Simply put, you get what you optimize for. While CTR may seem like an intuitive way to measure brand awareness, optimizing a campaign based on clicks means you’re optimizing away from your target audience. As evidenced by the results above, optimizing toward any metric that is not aligned with your target customer means your budgets are wasted on advertising to the wrong audience.
At Quantcast we want to ensure that brand advertisers are getting the best value for their ad spend. Brand advertisers want to reach their target audience, so a more optimal way to measure brand awareness is by measuring in-target delivery – not clicks. Third-party audience validation services can help advertisers understand whether their campaign message was served to their target audience. When combined with viewability metrics, in-target delivery becomes the Holy Grail that all brand advertisers should seek from their display campaigns.
Posted by Jag Duggal, Senior Vice President, Product Management
*Source: Nielsen Online Campaign Ratings, Benchmark Report, Q4 2012
**Source: Nielsen Online Campaign Ratings, April 2013
As brand budgets transition from TV to digital, audience guarantees and validation are becoming more commonplace. In a world where brands are seeking greater accountability, publishers must deliver in-target audiences with great efficiency and accuracy – using fewer impressions to reach a brand’s target means overall inventory can be better monetized.
With the availability of data and innovations in targeting, publishers now have a wealth of options to meet campaign needs.
Matt Clark, head of publisher development at Quantcast, explores the ways in which publishers can take advantage of audience data to help meet guarantees and garner exceptional yield in this session at the recent Digiday Publishing Summit.
Originally published in Digiday on November 1, 2013