Every digital advertising organization wants to be “data-powered.” Data is the linchpin for advertising strategies, campaigns and outcomes. All the ingredients for digital advertising are contained and structured within vast data sets that fuel our campaigns, including:
- Audience data (whether first-party or third-party)
- Signal data (measures of buyer intent)
- Location data
- Promotional data (offer terms, specials, other promotions)
- Inventory data (what we’re promoting)
- Campaign creative data
Despite this, for many of my 20 years working in advertising, manual labor has hindered advertisers’ ability to apply their data effectively. Scaling campaigns, accounts, growth opportunities, revenue: All of these are limited by repetitive, labor-intensive processes, limiting the advertiser’s ability to act quickly on performance data. It’s the gap between being a “data-informed” organization and a “data-powered” one.
The good news? Automation is changing this paradigm.
Automation is the key to becoming data-powered at scale. But this approach must be viewed holistically. Imagine one set of data, dynamically connected directly to all major advertising publishers, and managed through a centralized system.
The potential benefits can be transformational. Ads can instantly reflect specials or inventory changes. More successful channels can be automatically extended across similar accounts. Data can be mined for insightful takeaways, and automatically generate a reporting deck.
This is what it means to be data-powered: You are managing advertising directly from your data sources.
The challenge for many advertisers lies in becoming operationally ready to embrace this fundamental change. If you’re one of them, here are the foundational steps you need to take:
Structure your data foundation for scale
Advertisers face the most challenging scenario: High data complexity and high data volume. And you can’t easily lessen the impact of either one. For example, even if you reduce data volume by excluding certain variables from audiences, you’re still dealing with highly sophisticated data sets.
Understanding the interplay between data complexity and data volume is critical if your teams are going to increase capacity without increasing costs or headcount. Advertisers must therefore adopt scalable data architectures that can handle both complexities and volumes seamlessly, and structure them in a way that supports scalability.
Clean, structured data is at the root of everything that follows: precise advertising execution, improved performance and profitability, and staff retention. To ensure you are set up for success, conduct a data audit. Here are the key considerations and questions to ask:
- Is my data format consistent across clients/segments?
- Is my data normalized or hand-entered (which may be prone to inconsistencies)?
- Does my data support quality decision-making for my teams today?
- Is my data readily available to integrate with my ad technologies and systems?
Apply artificial intelligence and automation strategically
Once you’ve audited and structured your data, you can leverage both AI and automation for scale. While often discussed hand-in-hand, AI and automation each play distinct roles in data-powered advertising.
AI adds a layer of intelligence to your work without the overhead of rule systems and if-then-else decision points. AI excels at answering questions, generating things or performing analyses. AI can also add variation to tasks, like keyword generation or ad copy, freeing up teams to work on more high-value tasks.
AI also can quickly detect patterns and anomalies in your data sets. Once patterns are identified, it’s possible to set up automations that exponentially scale your team’s capacity.
Automation, on the other hand, is deterministic and repeatable, making it ideal for repetitive processes. Think about some of the mundane, time-consuming tasks on your plate: moving data between spreadsheets, daily budget and bid adjustments, or building updated reports. This is where automation shines.
For advertisers, automating recognizable trends across different datasets opens up unlimited potential for scale, but it’s most effective when applied toward those that tend to consume the most resources for today’s ad ops teams: budgeting, account and campaign launches, campaign management and reporting.
Bring AI and automation together
Once you start using data, AI and automation synchronously, you need to be extra careful that you don’t fall into the trap of asking questions that require more answers. It’s tempting to throw every question at these tools, but it’s not the best strategy. Your stakeholders and clients don’t want more complex decision trees. They want results.
Your teams should be surgical in using automation to ask (and answer) the right questions. This ensures that you focus on a few critical metrics in order to extract valuable, actionable insights.
Bottom line: Automation should be taking those big, important business questions and turning them into execution. Keep it simple, keep it focused and let the system do the heavy lifting. Tech should serve your strategy, not the other way around.
Data, AI and automation will continue to play a larger role in the daily lives of ad teams everywhere. Setting your organization up for success today will help you use data to seize new opportunities and navigate tomorrow's challenges with agility.
