Listen to this blog
Marketers across all industries are forced to continually identify their biggest advertising levers - the sources of growth requiring the least resources. This forces CPG brands to rethink their own advertising methods and re-evaluate the legacy tactics we’ve have held so dearly for years.
With today’s competitive ecosystem in mind, DemandJump took a hard look at the most common ad targeting techniques and created a new approach to digital advertising: Dynamic Journey Targeting.
The Dynamic Customer Journey refers to the websites visited, queries searched, and digital interactions made as consumers develop demand for a product or service. As all consumer trends are ever-changing, so too is the journey taken to any purchase decision. Consumers navigate a variety of devices and channels, through a variety of ever-changing pathways, before they ultimately decide to buy anything. So, identifying the correct ad placements is essential for marketers to allocate their ad budget efficiently.
Dynamic Journey Targeting (DJT) refers to making sense of dynamic internet behavior to advertise along the Dynamic Customer Journey. It surfaces websites and search terms navigated as consumers develop demand for a product or service. With these recommendations, advertisers are empowered to selectively target advertisements to optimize performance (through programmatic advertising or direct media buys).
As opposed to targeting audiences, DJT analyzes internet traffic to uncover the true buyer journey for any product. This means that DJT does not handle any personal data, as it instead looks at internet behavior on an aggregate scale. It then gives advertisers a transparent view of their ads’ placements to evade the risk of ads delivered on fraudulent, low-performing, or simply irrelevant sites. When using DJT, advertisers show up where relevant and helpful, instead of annoying and ignorable.
These are websites built to look like legitimate publishers, but in reality offer no real value to consumers. They generate fake views and clicks with the help of fraud bots to attract advertisers to pay for ad placements. This means when advertisers completely disregard ad placement, they accept the risk that their ads could be wasted on cash-outs. Dynamic Journey Targeting identifies this behavior and avoids such sites.
But fraud aside, within programmatic advertising, it’s easy to let a machine do the thinking for us, and to not look closely at where our ads are showing up. Programmatic’s automated nature opens the door to a lot of less-than-optimal ad placements. In fact, many sites on which ads are displayed drive little performance due to their irrelevance to consumers’ current intentions.
In order to avert these risks, advertisers need to take a closer look at where our ads are showing up. We need to ask ourselves, “does it make sense for my ad to be seen here?” And, “is this ad placement really optimizing for performance?”
This is a relatively easy thing to do manually - on a small scale. But considering the reach and frequency most advertisers prefer to achieve, it’s almost impossible to curate large enough lists of high-performing sites without the help of Dynamic Journey Targeting.
When someone is in a mobile gaming app, they usually just want to keep playing their game. So when an ad shows up at the bottom of the screen, they will likely click out of it to get back to gaming asap. This thought process, then, explains all the accidental clicks coming from mobile devices. Many times, individuals will "fat thumb" the ad displayed and get sent to a landing page instead of hiding the ad as they intended.
Expanding this thought process to all ad placements explains the low click-through rates across display. When people are in a certain mindset of research (let’s say reading about fashion) they tend to ignore ads that don’t promote their current intentions of internet exploration, as these ads are deemed unnecessary to the task at hand. When ads don’t align with consumers’ current intentions, they are irrelevant. Due to this, they will be judged as ignorable and get little engagement.
A 2018 Gartner poll found that while 47% of consumers find it helpful to receive recommendations based on browsing history, 50% of consumers are not willing to give up their personal data get them. This suggests that they like relevant ads that help them discover interesting topics, but they don't want to be targeted in a way that feels invasive of their privacy. Advertisers, then, face conflicting sentiments when attempting to meet these needs. Audience targeting does help advertisers with the issue of ad irrelevance, but this method of targeting is limited in its ability to do so without getting too personal.
We could never meet consumers with hyper-relevant advertising if we solely focus on their audience data. We will never build a complete enough picture of our audience (especially with privacy regulation) to know the exact moment when an audience is interested in our ad. Closely watching their every move to understand when they are ready for us to show up feels simply an invasion of privacy. Advertisers must look to different forms of behavior analysis to target ads in the right place and right time. In identifying appropriate ad placements, advertisers satisfy these consumer wants in a mutually beneficial fashion.
In the Deloitte study referenced above, consumers sited irrelevant ads as a main reason for avoiding ads in at least one medium. If advertisers don’t find a way to advertise to consumers in a way that is helpful, we’ll never get to reach them in the first place - as our ads will be blocked entirely.
Two realities make audience targeting insufficient: the unreliability of audience data and the irrelevant placements of audience-targeted ads.
With GDPR, CCPA, and the default deletion of cookies from browsers like Safari and Firefox, audience data powering programmatic advertising is diminishing - and advertisers are moving away from programmatic advertising because of it. GDPR limits the ability of publishers to place cookies on visitors’ browsers without their consent, and even attempts to limit browser fingerprinting (a newer technique that constructs browser identities without cookies by matching the IP address with publishers’ visitors).
In 2017, Deloitte conducted a study highlighting the inaccuracy of third-party audience data. Here, they showed consumers the information that third-party data sources believed to be true of them (such as classifications of their demographics and other personal interests). Shockingly, 71% of these individuals said at least half of their personal information was incorrect.
You can check this audience inaccuracy out for yourself too. If you search google for the cookie data that companies have on you, you can see the audience information marketers believe to be true - and how wrong it can be.
I checked mine, and here’s what I found. As you view this, know that I am a 23 year old male with no kids.
This data suggests that I belong to Generation X (age group 39-53), while also being 80+ years old. It also assumes that I have children ages 6-10. This is comical - and disappointing to advertisers as we can’t rely on the data being sold as a way to efficiently allocate advertising budget.
This audience inaccuracy makes sense when one understands the way cookies work though. The techniques used to create audience segments analyzes cookies on browsers in attempts to build paper mâché representations of individuals. As sophisticated as these tools are, they still generate audience segments based on assumptions. They use previous website visits to indicate interests in certain topics or even intents to buy certain products.
As brought to attention in Jim Spanfeller’s 2016 Digiday commentary, in 2016 there were over 25 million cookies for new car intenders. Considering the average car buying cycle is 12 weeks, this cookie counting suggests that over 108 million cars would be bought in 2016. The reality was, however, that only 17.5 million new cars were purchased. This is an example of cookies classifying an audience so loosely that they overestimated market size by a multiple of 6. Does this constitute efficient targeting?
Though audience targeting places ads in front of individuals who are more likely to be interested in certain products (based on their previous browsing history), targeting audience alone does not create relevant ad experiences. As we discussed earlier, when a consumer is reading a blog about cooking, how likely is it that they change what they’re doing to read about a totally unrelated topic? Not very. This is why the average click-through rate across all display ad formats is just 0.05%. Consumers have developed banner blindness because advertisements rarely help them explore their current internet journey.
Because it can produce similar results to audience targeting without any personal data, many advertisers see contextual targeting as the solution to the limited availability of audience data. Digiday reports that programmatic audience spend is down as advertisers shift to direct media buys and contextual targeting. But compared to Dynamic Journey Targeting, this technique is still incomplete.
Contextual targeting scans the internet for images and words relevant to your ads, and it finds placements that are not as ignorable as general audience targeting.
Contextual targeting does not factor in how closely connected a website is to a brand’s competitive ecosystem, or how frequently visited it is along a consumer's buyer journey. It solely identifies topical relatedness. Contextual targeting might find an article that discusses outdoor activities, but not necessarily one that is regularly visited before buying camping equipment like DJT.
Most importantly, consumer behavior evolves daily, as do the channels and content they navigate along their dynamic customer journey. Without a real-time view of consumer behavior, marketers lack a full understanding of their customer's true interests. And their ads will quickly fall out of relevance as a result. Dynamic internet data is what powers Dynamic Journey Targeting, and it's why DJT is essential to any digital marketer.
Current users of dynamic journey targeting see boosts in ROAS of 240% compared to traditional targeting options. Due to its focus on high performing sites along the buyer journey, users regularly cut their ad spend in half and double digital marketing performance.
When using dynamic journey targeting, companies build better brand relationships through two key aspects: the establishment of a mutually beneficial ad delivery (where ads always help rather than annoy) and their avoidance of personal data in the process.
With Dynamic Journey Targeting, consumers get a better ad experience from first touch-point all the way through customer re-engagement. This is made possible by the advertiser’s ability to know where consumers want to hear from them, and the placements of their ads where relevant and helpful. The byproduct of DJT, then, is an internet decluttered of infrequently engaged ads. As advertisers choose to selectively advertise along the Dynamic Customer Journey, they no longer compete for ad space where their ads are not performing highest or where they're irrelevant.
If this future of display advertising is appealing, reach out. Let's chat about your customer's dynamic journey through the internet.