It’s not just you—online ads really are getting worse for everybody.
With the reduction in targeting capabilities due to new privacy regulations and evolving consumer behaviors that have upended third-party cookies, digital ads are being shown to less relevant users, resulting in diminished advertising effectiveness.
What’s more, signal loss is also making it more challenging to accurately measure and attribute conversions to specific ads or campaigns, leading to major gaps in understanding the true impact of advertising efforts. As a result, brands have dialed back ad spend and platforms have reported massive hits to revenue.
Today, advertising signal loss is among the biggest challenges facing marketers. To meet this moment, brands need to rewrite the advertising playbook and seek out new ways to activate data-driven insights.
The new data landscape
With the impending demise of third-party cookies, brands must pivot to first- and zero-party data solutions to regain lost signal and deliver personalized experiences.
By directly engaging with consumers and incentivizing them to proactively share their preferences, purchase history and other personal information as they see fit, brands can build trust, foster meaningful connections and gather valuable insights about their audience.
The stage is set for a transparent, mutually beneficial exchange of value—brands can leverage interactive experiences, such as mobile applications, consumer-engagement platforms and other types of content, to collect information directly from consumers who consent to sharing. Compared to what’s possible with third-party data strategies, this approach allows brands to uncover even more nuanced preferences, better understand the buyer journey and deliver hyper-targeted messaging, relevant offers and recommendations.
Next-gen rewards and consumer-engagement platforms like Fetch can provide brands with a privacy-first solution that’s proven to increase market share, drive incremental sales, incentivize repeat purchases, convert competitive buyers, and even drive consumers into specific stores and restaurants.
Find high-intent consumers
Marketers have traditionally relied on demographic data to inform their ad targeting, spending millions to identify their target audience based on deterministic information like age and gender.
But consumers are not a monolith, and demographics don’t tell the whole story. As such, this kind of demographic-based targeting has always been fundamentally flawed, because not everyone within a target demographic is a potential buyer.
The most powerful indicator of purchase intent? Past-purchase behavior.
If you know Customer A regularly buys Brand X, you also know that they can be incentivized to try competitor Brand Y. The strength of intent-based targeting based on past-purchase behavior is behind the rise of retail media networks, which leverage purchase history to inform targeted ads within their ecommerce environment. But these networks are limited to data collected within the retailer’s owned environment.
When considering platforms, look for those that can build intent-based audience segments based on a consumer’s entire previous purchase behavior, across all stores and including both in-store and ecommerce transactions.
By focusing its targeted, on-platform messaging on the highest-intent consumers and tracking performance within the platform’s closed-loop system, brands get verified incremental return on ad spend—something no other media channel can provide.
Define accurate measurement
As signal loss continues to cause challenges in attributing conversions and assessing advertising impact, brands need to embrace new measurement methodologies to gain a comprehensive understanding of their campaigns’ effectiveness. Advanced analytics, AI-driven modeling and multi-touch attribution can bridge the gaps in signal loss and provide a holistic view of the customer journey.
Proving advertising effectiveness is a long, complicated (and often convoluted) process. With traditional media channels today, there’s no standard measurement methodology and no way to provide physical-world attribution. Media mix modeling has its place, but it’s retroactive—advertisers must wait for metrics to populate to get an understanding of how the campaign performed.
And at a time when marketing dollars are precious and incremental results are paramount, ad-tech service providers must be able to quantify the value that brand partners get from the platform relative to the cost of doing business.
To solve this, Fetch developed a new kind of measurement framework: Verified incremental return gives marketers the ability to evaluate the incremental revenue generated by a campaign compared to the baseline performance. By using randomized control trials (RCTs) and a holdout group as a control, Fetch can accurately measure the incremental lift and attribute it to the specific campaign or offer.
Similar to the way pharmaceutical trials are executed, Fetch randomly assigns users into either “treatment” or “control” groups. The platform serves partner offers to the treatment group and withholds those offers from the control group. The holdout (control) group is used as a baseline to determine incremental lift, or the isolated effect that the offer had on metrics like sales, units or trips. Because Fetch has billions of receipts from millions of users, the platform can ensure that the randomized control and test groups exhibit the same behavior—similar purchase patterns, brand spend and category spend—as verified by transaction data.
In today’s competitive environment, brands need information that allows them to act quickly and decisively if they want to win. Currently, there are new platforms that can deliver superior advertising signals and cutting-edge measurement capabilities within a privacy-first framework.
By tapping into these new resources, brands can develop an even deeper understanding of their target audience’s preferences, behaviors and purchase-based history.
—
As chief revenue officer, Robin Wheeler spearheads Fetch’s sales organization. Over her 20+ year career, she has led revenue teams at major social and traditional media companies. Prior to joining Fetch, she held several leadership positions at Twitter, most recently as VP of U.S. client solutions, where she worked with some of the country’s biggest brands.
