Contextual ad targeting paves the way for new opportunities
Advertisers and marketers are always looking for ways to remain competitive in the current digital landscape. The challenge of signal loss continues to prompt marketers to rethink their current and future strategies. With many major browsers phasing out support for third-party cookies due to privacy and data security concerns, marketers will need to find new ways to identify and reach their target audience. Contextual ad targeting offers an innovative solution; a way to combine contextual signals with machine learning to engage with your consumers more deeply through highly targeted accuracy. Contextual advertising can help you reach your desired audiences amidst signal loss – but what exactly is contextual advertising, and how can it help optimize digital ad success?
In a Q&A with our experts, Jason Andersen, Senior Director of Strategic Initiatives and Partner Solutions with Experian, and Alex Johnston, Principal Product Manager with Yieldmo, they explore:
- The challenges causing marketers to rethink their current strategies
- How contextual advertising addresses signal loss
- Why addressability is more important than ever
- Why good creative is still integral in digital marketing
- Tips for digital ad success
By understanding what contextual advertising can offer, you’ll be on the path toward creating powerful, effective campaigns that will engage your target audiences.
Check out Jason and Alex’s full conversation from our webinar, “Making the Most of Your Digital Ad Budget With Contextual Advertising and Audience Insights” by reading below. Or watch the full webinar recording now!
Macro impacts affecting marketers
How important is it for digital marketers to stay informed about the changes coming to third-party cookies, and what challenges do you see signal loss creating?
Jason: Marketers must stay informed to succeed as the digital marketing landscape continuously evolves. Third-party cookies have already been eliminated from Firefox, Safari, and other browsers, while Chrome has held out. It’s just a matter of time before Chrome eliminates them too. Being proactive now by predicting potential impacts will be essential for maintaining growth when the third-party cookie finally disappears.
Alex: Jason, I think you nailed it. Third-party cookie loss is already a reality. As regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) take effect, more than 50% of exchange traffic lacks associated identifiers. This means that marketers have to think differently about how they reach their audiences in an environment with fewer data points available for targeting purposes. It’s no longer something to consider at some point down the line – it’s here now!
Also, as third-party cookies become more limited, reaching users online is becoming increasingly complex and competitive. Without access to as much data, the CPMs (cost per thousand impressions) that advertisers must pay are skyrocketing because everyone is trying to bid on those same valuable consumers. It’s essential for businesses desiring success in digital advertising now more than ever before.
Contextual ad targeting: A solution for signal loss
How does contextual ad targeting help digital marketers find new ways to reach and engage with consumers? What can you share about some new strategies that have modernized marketing, such as machine learning and Artificial Intelligence (AI)?
Jason: We’re taking contextual marketing to the next level with advanced machine learning. We are unlocking new insights from data beyond what a single page can tell us about users. As third-party cookies go away, alternative identifiers are coming to market, like RampID and UID2. These are going to be particularly important for marketers to be able to utilize.
As cookie syncing becomes outdated, marketers will have to look for alternative methods to reach their target audiences. It’s essential to look beyond cookie-reliant solutions and use other options available regarding advertising.
Alex: I think, as Jason alluded to, there’s a renaissance in contextual advertising over the last couple of years. If I were to break this down, there are three core drivers:
- The loss of identity signals. It’s forcing us to change, and we must look elsewhere and figure out how to reach our audiences differently.
- There have been considerable advances in our ability to store and operate across a set of contextual signals far more extensive than anything we’ve ever worked with in the past and in far more granular ways. That’s a huge deal because when it comes to machine learning, the power and the impact of those machine learning models are entirely based on how extensive and granular the data set is that you can collect. Machine learning can pull together critical contextual signals and figure out which constellations, or which combinations of those signals, are most predictive and valuable to a given advertiser.
- We can tailor machine learning models to individual advertisers using all those signals and find patterns across those in ways that were previously impractical or unfeasible. The transformation is occurring because of our ability to capture much more granular data, operate across it, and then build models that work for advertisers.
Addressability: Connect your campaigns to consumers
How does advanced contextual targeting help marketers reach non-addressable audiences?
Jason: Advanced contextual targeting allows us to take a set of known data (identity) and draw inferences from it with all the other signals we see across the bitstream. It’s taking that small seed set of either, customers that transacted with you before that you have an identity for, or customers that match whom you’re looking for. We can use that as a seed set to train these new contextual models. We can now look at making the unknown known or the unaddressable addressable. So, it’s not addressable in an identity sense, it is addressable in a contextual or an advanced contextual sense that’s made available to us, and we can derive great insight from it.
One of the terms I like to use is contextual indexing. This is where we take a set of users we know something about. So, I may know the identity of a particular group of households, and I can look at how those households index against any of the rich data sets available to us in any data marketplace, for example, the data Yieldmo has. We can look at how that data indexes to those known users to find patterns in that data and then extrapolate from that. Now we can go out and find users surfing on any of the other sites that traditionally don’t have that identifier for that user or don’t at that moment in time and start to be able to advertise to them based on the contextually indexed data.
Historically, we’ve done some contextual ad targeting based on geo-contextual, and this is when people wanted to do one to one marketing, and geo-contextual outperformed the one to one. But marketers weren’t ready for alternatives to one to one yet. We want marketers to start testing these solutions. Advertisers must start trying them, learning how they work, and learn how to optimize them because they are based on a feedback loop, and they’re only going to get better with feedback.
Alex: Jason, you described that perfectly. I think the exciting opportunity for many people in the industry is figuring out how to reach your known audience in a non-addressable space, that is based on environmental and non-identity based signals, that helps your campaign perform. Your known audience are people that are already converting – those who like your products and services and are engaged with your ads. Machine learning advancements allow you to take your small sample audience and uncover those patterns in the non-addressable space.
It’s also worth noting that in this world in which we are using seed audiences, or you are using your performing audiences to build non-addressable counterpart targeting campaigns, having high-quality, privacy-resilient data sets becomes incredibly important. In many cases, companies like Experian, who have high quality, deep rich training data, are well positioned to support advertisers in building those extension audiences. As we see the industry evolve, we’re going to see some significant changes in terms of the types of, and ways in which, companies offer data, and make that available to advertisers for training their models or supporting validation and measurement of those models.
Jason: Addressable users, the new identity-based users, are critical to marketers’ performance initiatives. They’re essential to training the models we’re building with contextual advertising. Together, addressable users and contextual advertising are a powerful combination. It’s not just one in isolation. It’s not just using advanced contextual, and it’s not just using the new identifiers. It’s using a combination to meet your performance needs.
It’s imperative to start thinking about how you can begin building your seed audiences. What can you start learning from, and how do you put contextual into play today? You are looking to build off a known set and build a more advanced model. These can be specialized models based on your data. You can hone in and create a customized model for your customer type, their profile, and how they transact. It’s a greenfield opportunity, and we’re super excited about the future of advanced contextual targeting.
Turn great creative into measurable data points
Why does good creative still play an integral part in digital advertising success?
Jason: Good creative has always been meaningful. It’s vital in getting people to click on your ad and transact. But it’s becoming increasingly important in this new world that we’re talking about, this advanced contextual world. The more signal that we can get coming into these models, the better. Good creative in the proper ad format that you can test and learn from is paramount. It comes back to that feedback loop. We can use that as another signal in this equation to develop and refine the right set of audiences for your targeting needs.
Alex: If you imagine within the broader context of identity and signal loss, creative and ad format becomes incredibly powerful signals in understanding how different audiences interact with and engage with different creative. In the case of the formats that serve on the Yieldmo exchange, we’re collecting data every 200 milliseconds around how individual users are engaging with those ads. Interaction data like the user scrolling back or the number of pixel seconds they stay on the screen, fills this critical gap between video completes and clicks. Clicks are sparse and down the funnel, and views and completes are up the funnel. All those attention and creative engagement type metrics occupy the sweet spot where they’re super prevalent, and you can collect them and understand how different audiences engage with your ads. That data lets you build powerful models because they predict all kinds of other downstream actions.
Throughout my career, I learned that designing or tailoring your creative to different audience groups is one of the best ways to improve performance. We ran many lift studies with analysis to understand how you can tailor creative customized for individual audiences. That capability and the ability to do that on an identity basis is starting to deteriorate. The ability to do that using a sample of data or using a smaller set of users, either where you’re inferring characteristics or you’re looking at the identity that does exist in a smaller group, becomes powerful for being able to customize your creative to tell the right story to the right audience. When you layer together all the interaction data collected at the creative level on top of all the contextual and environmental signals, you can build powerful models. Whether those are driving proxy metrics, or downstream outcomes, puts us in a powerful position to respond to the broader loss of identity that we’ve relied on for so many years.
Our recommendations for marketers for 2023 and beyond
Do you have recommendations for marketers building out their yearly strategies or a campaign strategy?
Jason: Be proactive and start testing and learning these new solutions. I mentioned addressability and being in the right place at the right time. That’s easier in today’s third-party cookie world. But as traditional identity is further constricted, you will have these first-party solutions that will not be at scale, so you’re less likely to find your user at the scale you want. It would be best if you thought about how to reach that user at the right place at the right time. They may not be seen from an identity basis. They might not be at the right place at the right time when you were delivering or trying to deliver an ad. But you increase your chance of reaching them by building these advanced contextual targeting audiences using this privacy-safe seed ‘opted-in’ user set; this is a way to cast that wider net and achieve targeted scale.
Alex: Build your seed lists, test your formats with different audiences, and understand what’s resonating with whom. Take advantage of some of the pretty remarkable advances in machine learning that are allowing us, really, for the first time to fully uncork the potential and the opportunity with contextual in a way that we’ve never done before.
Jason: At the end of the day, it’s making the unaddressable addressable. So, it’s a complementary strategy; having that addressable piece will feed the models. But also, that addressable piece still needs to be identity-based, addressable still needs to be part of your overall marketing strategy, and you need to complement it with other strategies like advanced contextual targeting. The two of them together are super complimentary. They learn from each other, and it’s a cyclical loop. Now is the time to take advantage and start testing and understanding how these solutions work.
We can help you get started with contextual ad targeting
Contextual advertising can help you stay ahead of the curve, identify your target audience, and continue to drive conversions despite signal loss. We’ve partnered with Yieldmo to help make sure that your marketing campaigns are reaching the right target audiences on the platforms that are most relevant. To get started with contextual ad targeting to reach the right audience at the right time and drive conversions, contact our marketing professionals. Let’s get to work, together.
Find the right marketing mix in 2023
Check out our webinar, “Find the right marketing mix with rising consumer expectations.” Guest speaker, Nikhil Lai, Senior Analyst from Forrester Research, joins Experian experts Erin Haselkorn, and Eden Wilbur. We discuss:
- New data on the complexity and uncertainty facing marketers
- Consumer trends for 2023
- Recommendations on finding the right channel mix and the right consumers
About our experts
Jason Andersen, Senior Director, Strategic Initiatives and Partner Solutions, Experian
Jason Andersen heads Strategic Initiatives and Partner Enablement for Experian Marketing Services. He focuses on addressability and activation in digital marketing and working with partners to solve signal loss. Jason has worked in digital advertising for 15+ years, spanning roles from operations and product to strategy and partnerships.
Alex Johnston, Principal Product Manager, Yieldmo
Alex Johnston is the Principal Product Manager at Yieldmo, overseeing the Machine Learning and Optimization products. Before joining Yieldmo, Alex spent 13 years at Google, where he led the Reach & Audience Planning and Measurement products, overseeing a 10X increase in revenue. During his time, he launched numerous ad products, including YouTube’s Google Preferred offering. To learn more about Yieldmo, visit www.Yieldmo.com.
Latest posts
In the digital age, print coupons are dinosaurs, right? Not one bit. In fact, according to Experian Simmons, users of printed coupons — those obtained from newspapers, magazines, mail, etc.—outnumber users of digital coupons by a margin of almost 3-to-1. As of February 14, 2011 (the latest date for which data was available at the time of this post), 68% of all U.S. adults said their household uses print coupons, a number that has remained relatively unchanged during the past five years. 68% of all U.S. adults said their household uses print coupons, a number that has remained relatively unchanged during the past five years. By comparison, Experian Simmons also reports that 22% of all U.S. adults say their household uses digital coupons obtained from email or the Internet. That figure may be lower than the usage reported for any measured type of print coupon, including those handed out in or near stores, but adoption of digital coupons is growing: in 2005, just 12% of American adults used digital coupons. Smartphones adoption will continue to propel digital coupon use to historic highs in the months and years to come. According to an analysis featured in the 2010 U.S. Household Consumer Trend and Benchmark Report, 34% of U.S. iPhone owners use digital coupons compared with just 21% of non-iPhone owners. Interestingly, use of print coupons among iPhone owners doesn’t suffer a bit. In fact, as of February 14, 2011, 68% of iPhone owners reported that their household used print coupons versus 64% of non-iPhone owners, making it obvious that merchants should give their customers an option of using both print and digital coupons. For further consumer insights, download the 2010 U.S. Household Consumer Trend and Benchmark Report, which includes trends on economic outlook by household income, charitable contributions and planned automobile purchases.
Spiders, Rams, Seminoles, Golden Eagles, and Bulldogs. This is one way to describe the diverse collection of Cinderella teams that have advanced to the Sweet 16 in this year's NCAA men's basketball tournament. Four of these teams, the University of Richmond, Virginia Commonwealth University (VCU), Florida State, and Marquette, take their double-digit seeds to the next round in hopes of reaching the Elite 8. Butler, last year's Cinderella story, is seeking a visit to the Final Four for the second straight year. Interestingly, Richmond, VA is the home market for two of the Sweet 16 teams. These are the University of Richmond and VCU. Even more interesting is the fact that these two teams are on a collision course. With wins in the next round they would meet in the Elite 8 for a prized spot in the Final Four. With their surprise victories in the tournament so far, what these teams have in common is being labeled “bracket busters.” A more extensive market analysis uncovers other similarities, plus some notable differences. Here are highlights from profiles of the home market areas for this year's Cinderella teams using data from Experian's Mosaic consumer lifestyle segmentation system and Experian Simmons market research. The following statistics are based on the home markets of the five Cinderella teams. All four Cinderella teams hail from markets with above average interest in college basketball. Milwaukee, home of 11th seeded Marquette, has the highest concentration of adults who are interested in college basketball (28.1%). This is 11 percent relatively higher than percentage for the total U.S. Milwaukee also has the highest percentage of adults who said they watched last year's men's NCAA Division I tournament (17.3%). This is a relative six percent higher than the total U.S. The 10th seeded Florida State Seminoles hail from Tallahassee, which has the second highest percentage of adults with an interest in college basketball (26.9%). Richmond, where both 11th seeded VCU and 12th seeded Richmond are based, is just behind Tallahassee when it comes to the percentage of adults who are interested in college basketball (26.7%). Indianapolis has the lowest percent of residents who are interested in college basketball (26.3%), but that's still a relative four percent higher than the U.S. as a whole. Although interest should be very high this year, Richmond and Indianapolis (15.8% each) have the lowest percentage of adults who watched last year's tournament. Cinderella Team Market Snapshots Richmond, VA Home market of: Richmond Spiders, VCU Rams Sweet 16 opponents: Kansas, Florida State The top two segments in Richmond representing 30% of the market's households are: Metro Minority Communities (18.1%) comprised of married couples and single-parent minorities earning above average incomes from a mix of service industry and white-collar jobs in transportation, health care, education, and public administration. Urban Commuter Families (11.5%) comprised of upscale, college educated Baby Boomer families and couples living in single detached homes in city neighborhoods on the metropolitan fringe. Other interesting facts: Most over-represented segment is Metro Minority Communities (3.8 times the national average) Percent of adults with interest in college basketball is 26.7% Percent of adults who watched last year's men's college basketball tournament is 15.8% Tallahassee, FL Home market of: Florida State Seminoles Sweet 16 opponent: VCU Rams Similar to Richmond, the top two segments in Tallahassee are Metro Minority Communities (14.8%) and Urban Commuter Families (6.9%). The two segments that account for the next highest share of households are: Struggling City Centers (6.7%) comprised of young, single and single-parent minority renters living in low-income city neighborhoods. Rural Southern Living (6.5%) comprised of lower-income blue-collar couples and families living in sparsely settled mobile home communities. Other interesting facts: Most over-represented market segment is College Town Communities (6.2 times the national average) Percent of adults with interest in college basketball is 26.9% Percent of adults who watched last year's men's college basketball tournament is 16.8% Milwaukee, WI Home market of: Marquette Golden Eagles Sweet 16 opponent: North Carolina Tarheels The top segment in Milwaukee representing 11.2% of households is Urban Commuter Families (as described above). The two segments that account for the next highest share of households are: Small-town Success (10.5%) comprised of white collar, college educated, middle-aged working couples living in newly developed subdivisions outside the nation's beltways. Steadfast Conservative (9.3%) comprised of high-school educated mature singles and couples living in middle-class urban blue-collar neighborhoods. Other interesting facts: Most over-represented market segment is Successful Suburbia (3.6 times the national average) Percent of adults with interest in college basketball is 28.1% Percent of adults who watched last year's men's college basketball tournament is 17.3% Indianapolis, IN Home market of: Butler Bulldogs Sweet 16 opponent: Wisconsin Badgers There are four equal size segments that account for just over 30% of Indianapolis households. These include Steadfast Conservative (8.9%), Urban Commuter Families (7.8%), and Small-town Success (7.1%). All three of these segments are also among the top five in Richmond, Tallahassee, and Milwaukee. What makes Indianapolis unique from the other three markets is a higher percentage of New Suburbia Families (7.2%). These are young, affluent working couples with pre-school children concentrated in fast-growing, metro fringe communities. Other interesting facts: Most over-represented market segment is Successful Suburbia (2.8 times the national average) Percent of adults with interest in college basketball is 26.3% Percent of adults who watched last year's men's college basketball tournament is 15.8%
Tournament play begins this week in the 2011 NCAA Men's Division I Basketball Tournament, which means office productivity is likely to take a hit as fans jump online to watch live streams of games being played during working hours. With online viewing options expanded to mobile and other digital platforms this year, fans have more avenues than ever to get their March Madness fix. In fact, according to a recent estimate by Challenger, Gray & Christmas, total online tournament viewership during work hours is likely to reach 8.4 million hours during this year's tournament. In this latest installment of Experian Marketing Services' continuing March Madness consumer coverage, we'll profile the work life of online game streamers. Is there one down the hall or in the next cube? The answer is almost certainly “yes,” but the “who” may surprise you. According to Experian Simmons, just over 5% of all U.S. adults and nearly a quarter of adult NCAA men's tournament viewers (24%) qualify as likely online game streamers. For the purpose of this analysis, likely online game streamers is defined as those U.S. adults who watched the last NCAA men's basketball tournament who also sought out sports information online or watched online video in the last 30 days. These likely online game streamers must have also visited either cbssports.com or espn.com in the last 30 days. Fully 79% of likely online game streamers are employed either full-or or part-time, with 59% working 40 or more hours a week. Department managers and IT staff-have reason to be concerned about a loss in productivity during March Madness: fully 79% of likely online game streamers are employed either full-or or part-time, with 59% working 40 or more hours a week. Don't be so quick to suspect that colleague who always shows up late and goes home early as a game streamer. A safer bet would be the guy who's always at his desk when you get in and still there when you head out. In fact, one-in-ten adults who work more than 40 hours a week (11%) are likely online game streamers, meaning they're more than twice as likely as the average adult to be checking out the game online. Remote employees who work at home often get a bad rap with office “suits” sometimes assuming their pajama-clad colleagues fall prey to distractions. Actually though, Americans who work from home are no more or less likely to be likely online streamers than those who don't work from home. Likewise, the self-employed are no more or less likely to be online game streamers than laborers who work for “the man.” Interestingly, Experian Simmons found a direct correlation between company size and a worker's chance of being a likely online game streamer. Specifically: Those who work in companies with fewer than 100 employees are 17% less likely than the average American worker to be likely online game streamers. Those who work in companies with between 100 and 499 employees are just two percent less likely than average to be likely streamers while those employed by companies with between 500 and 999 employees are eight percent more likely to be online game streamers. Employees of companies with 1,000 or more employees are the most likely culprits with the group on average being 17% more likely to be likely online game streamers. As such, it's no surprise that Fortune 500 companies are the most at risk of having offices full of online streamers during March Madness. Employees of Fortune 500 companies are fully 66% more likely to be online game streamers than those who Americans employed by a non-Fortune 500 company. Finally, the best insight into whether your office mates are streaming basketball games online instead of working is by looking at their paycheck (not that we encourage that of course). Specifically, as income rises, so does one's chance of being an online game streamer: Employed Americans who personally earn less than $25,000 annually are the least likely to be online game streamers, scoring 50% below average on this metric. Those who earn between $25,000 and $49,999 are only 15% less likely to be game streamers. If you know or suspect that your colleague earns upwards of $50,000 a year, it's a good idea to keep an eye on them for the rest of the month; workers with incomes between $50,000 and $74,999 are 33% more likely than average to be likely game streamers and those who earn between $75,000 and $99,999 are 75% more likely to be likely game streamers. Your colleagues earning $100,000 or more annually are the most likely to be streaming online, with those personally taking home between $100,000 and $149,000 being a whopping 164% more likely than the average employee to be streaming games online. And those earning $150,000 or more annually being fully 176% more likely to be online game streamers. Moreover, one-in-five adults who earn $150,000 a year or more fall into our likely online game streamers segment compared with just 5% of all U.S. adults. The first match-up to be played during the traditional workday tips off at 12:15 EDT on Thursday March 17th when West Virginia takes on the winner of the second round-one play-in game. Armed with this information, you should be able to catch-or join-your office's online game streamers in the act.