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
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The annual back-to-school season is in high gear and Moms are preparing lists and sizing up their children’s clothing and school-related merchandise needs. It’s an important time of year for retailers, as apparel, shoes, electronics, furniture, computers, backpacks and school supplies will account for the bulk of consumer spending during the back-to-school shopping season. Many marketers have historically grouped the back-to-school audience into one collective segment of households with school-age children. This leaves money on the table because there are better ways to target Moms with kids when developing a back-to-school promotional strategy. Just like the inventory of new clothes and notebooks that retailers have neatly arranged on store shelves, families with school-age children come in an assortment of sizes, shapes and colors. What is the most effective way to segment the back to school audience? This begs the question “” Marketers can always turn to basic data elements for segmentation. These include age and gender of children, number of children in the household, parent’s age, household income, and the full spectrum of school classifications (preschool, elementary school, middle school, junior high school, high school, etc.). Though a more powerful approach would be to utilize a segmentation methodology that recognizes the lifestyle and behavioral differences among households that are most likely to contain school-age kids. Here are three snapshots of family-oriented, children-centric market segments that are highly likely to be responsive to a wide variety of back to school promotional offers. All three segments have been selected from Experian’s Mosaic lifestyle segmentation solution. Babies and Bliss Description: Babies and Bliss represent the premier lifestyle for large families in America. With a majority of households containing at least five people, this segment is a haven for large broods living in new suburban subdivisions. Parents in this segment tend to be in their 30s and 40s. There is a wide range of kids in these households, from preschoolers up to those in high school. There is also money in this segment, reflecting the high educations and low six-figure incomes that come from dual earners employed in professional and technical occupations. Some key traits of Babies and Bliss households include upscale tastes, large families, well-educated, conservative views, financially-savvy, convenience, and power shopping. Implications: Given their large families, it's not surprising that Moms from Babies and Bliss households are value-conscious shoppers who seek appealing deals for quality merchandise. They carry coupons, like to comparison shop when buying expensive items and head to the clearance rack first whenever they buy clothes, which tend to be conservative in style. In the mall, these Moms follow their children's lead but also remain very open to consider generic store brands rather than high-priced name brands. They like to shop (it's practically a sport) and are happy to open their wallets at department stores, specialty shops, catalogs and online sites. They especially pride themselves in being very Internet-literate. With their jobs, kids and errands, they appreciate the convenience of shopping online and are receptive to email ads, sponsored Websites and Web page links. Families Matter Most Description: A fast-growing segment, Families Matter Most consists of young, middle-class families in suburban locations leading active, family-focused lives. Nine out of ten households have kids (nearly two-thirds have multiple kids). These young, middle-class families have settled into a landscape of recently built subdivisions. Many adult household members are urban exiles who've sought a suburban setting with room for kids to grow. They are proud of their new homes, schools and shopping centers, where they can find everything they need just a short drive away. Families Matter Most distinguish themselves by having adopted attitudes and routines to help them effectively juggle the responsibilities of work and child-rearing. Some key traits of Families Matter Most households include sprawling families, family values, casual perspectives, price-sensitivity, credit revolvers, conformists and risk avoidance. Implications: Families Matter Most are casual in their attitude except when it comes to their children. They take their role as parents very seriously, which they describe in conservative terms. They avoid risks and feel little need to make a statement with their possessions. As shoppers, Moms from Families Matter Most households are price-sensitive consumers who look for discount stores that offer durable and comfortable fashion. They are worried about spending money impulsively and try to be informed consumers. Most are happy to wear last year's fashions and they tend to zero in on a few stores that carry the affordable brands they prefer. With their growing families they shop most often at discount and mid-market national chains including Target, Walmart, Kohl's, Toys R Us and Old Navy. Cul de Sac Diversity Description: Cul de Sac Diversity consists of ethnically diverse, middle-aged couples with school-aged children. This segment has an above-average concentration of Hispanic and Asian household members. Nearly one-third of Cul de Sac Diversity adult members are foreign-born. They are three times more likely to be bilingual compared to U.S. adults overall. These upper-middle-class households are well on their way to achieving the American dream. 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Deploying a back to school marketing strategy that treats all households with school-age children as one undifferentiated market is like creating a basic lesson plan and applying it to all grade levels of a one-room schoolhouse. Instead, marketers are encouraged to study their target audience more closely. With key insights in hand, they will have acquired the necessary prerequisites for graduating to a strategy that acknowledges the shopping characteristics and needs of a diverse and potentially lucrative audience of back-to-school Moms and their children.
Segmentation Layering For many marketers, segmentation is like breathing – it comes naturally and is a part of everything they do. To better connect with your target audience, use a good segmentation system with multiple layers that provides a breakdown of essential information while tying in lifestyle and transactional data. Consider marketing to parents. The most basic information includes demographics such as age, income, presence of children, etc. Add to that lifestyle information – the family has two working parents who rely heavily on the Internet for research and purchase convenience. The transactional data can really set apart where a parent falls on the parenting lifecycle. For example, is the parent still purchasing diapers and feeding supplies for their infant or bedding, towels and a coffee maker that might indicate their “baby” is headed to college? Both parents may look similar when comparing demographic and lifestyle information but the transactional data differentiates their needs. According to Experian Marketing Services: Parents use the Internet far more than the average American Moms are 34% more likely to buy products online and 33% more likely to participate in a blog than the average adult. "Marketers are targeting more carefully based on both the parents' life stage and consumer behavior,” says Jan Jindra, senior product market manager at Experian Marketing Services. “Younger parents, and those of smaller children, have different information needs than parents of older or college-age children. It's not only the life stage they're in, but the lifestyle," Jindra says. Read the full article and check out the latest in marketing to parents in DMNews: http://www.dmnews.com/household-brands-observe-parents-needs-in-defining-segmentation-tactics/article/205902/.