The Battle for ATTENTION: Will AI Save The Advertising Industry in a Privacy-Driven Economy?

More Money, More Problems: Why Attribution is Harder Than Ever

By SCUBA Insights

In a fast-paced, competitive subscription economy, brands are battling it out for customer engagement, conversion, and retention. From programmatic ad buying on CTV, OTT, and OOH to manual ad buying and walled garden platforms, advertisers already face a complicated digital landscape. It’s more challenging than ever to optimize ad campaigns and spend, especially if brands can’t do it in real-time and are using less than convenient applications to do so.


But the domino effect doesn’t stop there. Once marketers actually deploy their ads across varying media channels and ad platforms in an optimal way, they have another difficult hoop to jump through: attribution. 


Attribution has long been a challenge for marketers and advertisers. Now, though, that challenge is even more complex and amplified with the rise of cross-media channels, massive volumes of streaming user data, and the demise of third-party cookies. Without the ability to track attribution quickly and accurately, a brand’s overall ad spend and ROI are undoubtedly impacted. 


New challenges, new ecosystems, and new methods of advertising make ad attribution more duplicitous than ever. In this blog, we'll take a closer look at new ad tech attribution challenges in the market, why they’re not easy to solve—and how brands might be able to achieve accurate, real-time ad attribution.


New Ad Tech Attribution Challenges in a New Economy

The ad tech landscape is complex and dynamic, with multiple channels and devices that consumers use to interact with brands. New and old channels, like OTT, VR/AR, and in-game have emerged, evolved, and matured over the last ten years—and are now offering and capable of providing new forms of advertising to their users. Gone are the days of receiving Netflix DVDs in the mail or checking TV guides for when a missed episode with reair. Streaming brands like Roku, or cable TV on-demand services like DirectTV, have entered (and helped built) the real-time subscription economy. 


Consumers have more control and access to media and entertainment than ever—with options literally at their fingertips whenever, and wherever they want. It’s also prime for the ad world, too.


And yet, these new (and old) ecosystems are still experiencing iterations and evolutions, forcing brands to be agile and ready to adjust. If you don’t know how to successfully track and measure ads across these new ecosystems, you can’t possibly expect to achieve accurate attribution.


Advertisers face challenges in attributing the impact of ads across each media channel to customer behavior and ad ROI accurately. Some of the most significant challenges:


  1. 1. Fragmented & Complex Customer Journeys 


Consumers are everywhere, all the time, every day. This has been a long-standing challenge to advertisers and brands who want to track, report, and build customer audiences. Fragmentation is nothing new. However, new layers of fragmentations are.


Whether consumers are flipping between streaming platforms on their TV console, tablet devices, between apps, or even ordering from a food app while streaming a show on their phone—customer journeys are increasingly complex and fragmented with new channels and platforms. This in turn makes tracking ad performance and attribution harder. The consumer journey is more fragmented than ever, with multiple touchpoints, making it difficult to attribute the impact of each channel accurately.


  1. 2. Dated Data Infrastructure


Almost everything is evolving in the real-time subscription economy: channels, ads, consumer behavior—the list goes on. One thing that hasn’t changed? Data infrastructures.


A majority of data infrastructures, including data warehouses, data lakes, and CDPs, were initially built for static data and purely for data collection. They are legacy solutions to former data challenges from over ten years ago. And while they still serve the purpose of why they were built, they aren’t equipped for today’s data challenges. They don’t have the ability to collect data in real-time, without tedious ETL or data cleaning, and can’t run fast analytics. It also means these dated systems end up getting layered with other tools, which increases complexity, delays time to insights, and limits a brand’s ability to quickly attribute channels and campaigns. 


3. Privacy, Cookies, & Customer Trust


It’s no surprise that privacy and customer trust have become major players in the advertising world. New privacy regulations pose new challenges (and more work) for brands to accurately track attribution, campaign performance, and user behavior in general. The demise of third-party cookies has amplified this challenge, giving brands fewer data and visibility into what their customers are doing and how they’re interacting with ads. 


Instead, customers have more agency than ever in controlling their privacy preferences, enabling ad blocking, and disabling cookies. And if brands can’t embrace this new shift in privacy and customer expectations, brand trust will suffer. Adapting to these new limitations and expectations is a challenge in and of itself, but it also makes tracking attribution even more difficult than it already was. Finding ways to track and identity attribution in a privacy-first manner—and with fewer third-party cookies—is an obstacle most brands still haven’t figured out.


  1. 4. Customer Data Inconsistencies & Siloes 


Advertisers use multiple data sources to measure attribution, including clickstream data, conversion data, view-through data, and more. Not only is this data siloed because they come from varying channels, but it could also pose new data inconsistencies. Metrics and forms of measurement vary across platforms, making it quite the challenge for advertisers to paint an accurate picture of attribution and ad performance. Not to mention the perpetual challenge of unifying siloed customer data, which is tedious and time-consuming—delaying real-time business decisions and the capacity to easily pinpoint attribution.


5. Limited Analytics


Most brands utilize customer data platforms (CDPs) to collect and store customer data to leverage for identity resolution and audience building. Originally built in 2013, CDPs weren’t built to perform robust, real-time analytics. CDPs unify customer profiles, but they don’t have the capacity to connect profiles across active users’ interactions across multiple media channels creating missed opportunities every second, minute, and hour. And for advertisers, this creates another challenge to accurately—and quickly—pinpoint attribution and ad performance.

Short-Sighted Attempts Are Costing Brands Money

There’s quite a bit at stake for brands and advertisers when it comes to ad attribution, performance, and ROI. Hence, they’re highly motivated to find solutions, workarounds, partnerships, and any tool available to stay successful. Some common attempts to overcome these challenges often include:


  • Purchasing and adding new applications to layer on top of their dated data infrastructure, CDPs, and analytics tools.
  • Buying (and spending a lot of money on) customer data and insights from third-party companies like ZoomInfo, 6sense, and others.
  • Hiring more technical members to their analytics and engineering teams to beef up their efforts to dive into analytics, cleaning, and unifying data, and pulling reports.
  • Nothing! Some brands have chosen to bury their heads in the sand, and instead, continue to increase or maintain their marketing and ad spend in the hopes of more revenue.


Regardless of how brands have attempted to deal with these new attribution and ad ROI obstacles, it’s costing them a miraculous amount of money and time—and it’s not working. New methods and tools, like ML, multi-touch attribution, and cross-device identification can be leveraged, yes. 


But, they can’t be leveraged successfully if a brand is relying on legacy solutions, outdated BI tools, and data infrastructures that were built for static data collection. 

A New Ad Ecosystem Needs a New Solution

If advertisers and brands want to truly adapt to the new digital ad landscape, they need a new solution—that’s actually built for today’s needs.


Scuba Analytics is that solution.


Scuba Analytics solves the challenges above and helps brands achieve their ad attribution, spend, and campaign goals with real-time customer intelligence in the following ways:


  • Real-time attribution and campaign optimization: Optimize attribution measurement and lead propensity modeling with real-time insights into ad performance. Brands can track ad engagement (or lack thereof) and performance to minimize ad fatigue, ad blocking, and ad avoidance. Instead, brands can make in-flight changes to personalize ads and increase contextual relevance.
  • Hyper-personalize ads & audience segmentation: Use ultra-granular insights about customer behavior and interactions with ads to identify areas of success and opportunities to drive ROI, while providing relevant and contextual ads to users. In tandem, build ML models for adaptive ad buying & optimizing attribution measurement
  • Real-time sub-second ad campaign analytics: Optimize in-flight campaign performance to drive greater reach and frequency and inform future strategy.


Scuba’s modern architecture was built in the post-social network era to handle scale and truly democratize decision-making for marketing teams with actionable data. Scuba will enable you to complete in the real-time subscription economy like never before. Take us for a test drive and compare today.

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