How Automation Can Make Performance Marketing More Efficient – Without Sacrificing Creative
Performance marketing has gone through a period of significant automation over the past decade. From advanced audience targeting algorithms to programmatic media buying across digital and traditional platforms, these advancements have created exponential value and performance for brands. The growth in performance media has also created a significant increase in the number of campaigns, and targeted and native content need to keep pace in these media channels.
Now we are in the era of generative AI, which promises to automate content production and accelerate speed to market. But, as we lean heavily on these tools to enhance content efficiency, we may have reached a tipping point where we are creating more content in service of media algorithms, rather than for the benefit of consumers and brands.
Instead of prioritizing generative AI tools solely for improved content efficiency, there is actually more short- and long-term value if we use them to drive greater insights to inform campaign briefs and media planning, as well as collaboration among the marketing teams.
This rebalancing of innovative tools to drive consumer strategies coupled with automation media and targeting capabilities will ensure we are creating better, more consumer-centric work that drives the next level of growth in performance marketing.
Performance marketing doesn’t have an efficiency problem
Ad Age recently noted that 45% of current CMOs believe AI will automate marketing tasks, allowing for cost savings and a reduction in the size of their marketing departments. Meanwhile, Forbes listed 15 marketing insights to improve performance marketing, and not a single insight was focused on cost efficiency and savings.
Instead, experts from across the media industry called for investment in generative AI to create a more holistic and sophisticated approach to performance marketing that includes using brand, organic social and PR principles/tactics as well as consumer analytics to find new ways to connect with an increasingly savvy consumer.
Shifting focus: from efficiency to effectiveness
The value of generative AI isn’t about replacing human expertise but arming marketing teams with the critical insights that inform smarter briefs and tools to create breakthrough concepts that ultimately drive stronger performance.
There are three key areas of the campaign workflow process where investing in generative AI can generate the most impact.
Advanced consumer insights for campaign briefs
Marketing strategists have limited time and data to create impactful campaign strategy briefs. They rarely have the bandwidth to turn various business and consumer data points into unique and salient consumer truths, which are the cornerstone of any successful campaign.
They need access to tools that aggregate and analyze in real time a rich set of first-party and third-party consumer data to generate real consumer insights. They need to be able to quickly prompt a system with business objectives and consumer targets and receive a list of recommended consumer truths for validation.
Companies like Adobe and Salesforce have been working generative AI predictive analytics tools into their enterprise marketing workflow solutions. However, both solutions still require significant investment in data integration and customization periods for complex model trailing to realize the value for everyday use.
Real-time creative-led media modeling
When creative teams develop campaign concepts, they are grounded in consumer insights. But stakeholder review conversations often include a lot of subjective feedback. This feedback can lead to the selection of a suboptimal campaign concept for consumer and secured media placements.
Creatives could mitigate subjective feedback if they ran their concepts through a real-time media simulation and generated expected results. It would be highly beneficial if these tools could offer real-time feedback as creatives fine-tuned creative variables, audience targeting and media strategies to identify the optimal solution.
Canva offers multiuser creative workspace for prototype creation and ad tech integration. While this solution isn’t plug-and-play, they do offer the ability to build a custom generative AI-based solution at a price that is reasonable for small businesses.
Consumer view of campaign messaging
Brands build their content calendars based on business and campaign objectives, but looking at a consumer view is often more useful. Pulling together sample creative by consumer is a very manual and time-intensive process, but it typically uncovers a significant amount of repetition and inconsistencies across the various campaign activities.
By using generative AI analytics to aggregate and synthesize the messages consumers receive, as well as campaign performance metrics, it is possible to identify trends such as areas of low resonance, oversaturation and underdeveloped opportunities. By doing so, it becomes possible to gain valuable insights that can inform future campaign planning, allowing for greater efficiency and resource allocation.
I have not yet come across any companies that are developing a unified solution integrating digital asset management, marketing calendars and audience data into a single platform. However, this intersection of insights is where generative AI can help drive consumer-centric insights for performance marketing planning.
Striking the right balance
The automation we’ve built into performance marketing today is only as good as the inputs we use to fuel them. To date, we’ve relied too heavily on the algorithms of our platforms to handle all the heavy lifting of finding authentic consumer connections.
Generative AI isn’t just about cranking out content faster; it’s about smarter, more innovative marketing. By using AI for deep consumer insights, data-driven concept validation and thorough consumer content audits, we can optimize our internal talent to craft campaigns that truly resonate with consumers and drive the next wave of growth.
Source: AdExchanger
