For many media strategists and buyers, machine learning remains a bit of a mystery. Sure, the ability to apply sophisticated computer systems and artificial intelligence to problems sounds exciting, but can it actually help with optimizing media buying based on complex human behaviors and dealing with day-to-day advertising challenges?
Simply put: Yes, it absolutely can. Facebook has been building its own machine-learning based tools to make customer discovery more efficient, measurement more accurate and personalization more targeted. In doing so, we’ve seen that the technology has a number of powerful capabilities that significantly boost campaign performance as well as team productivity. Moreover, these benefits can be realized right now—not some day in the far-off future.
Specifically, machine learning can already make life better for media strategists and buyers in these four key ways:
1. Improve advertising effectiveness by increasing liquidity
Machine learning is especially valuable for media buying because it boosts liquidity—a state in which every dollar flows to the most valuable impression.
Through the use of serial algorithms and predictive analytics, machine learning can quickly find the optimal audience for each ad. Paired with automated systems, the right creative can then be served to the right person at the right time—and at the right price.
This can make media buying both faster and better. Machine learning and automated systems rapidly process massively complex data sets and adjust in real-time, enabling teams to continually improve campaign effectiveness.
“Using automated, machine-learning driven optimizations always helps over trying to do it manually. We immediately see much better performance.”
2. Simplify campaign setup and optimization
Another benefit of machine learning is that it streamlines campaign management.
Specifically, media buyers and strategists surveyed who use machine learning-enabled tools say these systems reduce the amount of manual effort needed to tackle tasks such as campaign optimization/bid strategy, budget management and campaign setup.
This simplification is largely due to the fact that machine learning and automated systems tend to work best when given leeway, since broader campaign goals and parameters increase the available options. In other words, reducing the number of ad sets and campaign restrictions enables more automated optimizations. The Social Group™ team is Facebook Blueprint certified and can help envision, create, launch and manage your social media campaigns.
“We’ve been able to shift away from manual managing of performance to observing performance and spending time pulling out deeper analysis.”
3. Gain more (very valuable) time
Simplified campaign management delivers something of immense value to media buyers and strategists: more time.
Of those surveyed, some 77% of media buyers and strategists in the US who use machine learning-enabled tools say these systems save them significant amounts of time spent on manual and tedious tasks, with most saying they gain back between one and four hours per week, on average.1
Again, these benefits are driven by the autonomous and self-learning capabilities of the machine learning tools. The systems are able to process ever-changing data points quickly and efficiently on their own, enabling team members to save the time previously spent on manually defining and honing campaigns. If you’re looking to create new Google Ads campaigns or improve upon existing ones, our Search Engine Marketing team can help.
“We used to have 50 ad sets live at any time for a campaign, and we were constantly having to figure out what was working. Now thanks to machine learning-based liquidity tools, the accounts have fewer ad sets and are easier to manage.”
4. Be more strategic and creative
Why is gaining time so important? Because it allows for all that brainpower to be applied to other things.
Strategists and buyers surveyed say they put the time they gain from machine learning tools toward a mix of tasks, including developing client strategies, planning and testing new campaigns, analyzing the performance of existing campaigns and making creative optimizations. 1
Ultimately, that’s what makes machine learning even more valuable: Not only does it improve campaign performance, but it also enables media strategists and buyers to be more focused, strategic and creative.
“Before we had to do so much manual budget shifting. Now we can use that time on strategy. We can look at different audiences, tests and creative optimizations. It helps us be more forward-thinking.”
Something important to note about machine learning is that in order to harness its many benefits, strategists and buyers may need to rethink how they work. For example, it may be necessary to embrace broader campaign parameters and budget strategies, reduce the number of ad sets and reconfigure how performance is reported to clients.
While making these changes may require significant short-term effort, it will set teams up for greater success in the rest of 2020—and well beyond.
What it means for marketers
Greater liquidity leads to better results.
Machine learning matches advertising spend to the most valuable impressions in real time. This ability to rapidly process data on the fly delivers continually improving campaign effectiveness.
Campaign management can be easier.
Machine learning and automated systems work best with more leeway. This means less manual effort is needed to tackle tasks such as campaign setup, optimization and measurement.
More time enables teams to focus elsewhere.
By simplifying campaign management, machine learning tools free up time for strategists and buyers. This gives teams the opportunity to be more focused, strategic and creative.
Harnessing the full power of machine learning takes work.
Machine learning often requires new ways of working. Teams may need to embrace broader campaign parameters, reduce ad sets and reconfigure reporting to clients.
Source: Facebook for Business; Parter Agencies (The Social Group™)