How does machine learning on Facebook work? In a nutshell Facebook uses serial algorithms and predictive analytics to find the right audience at the right time at the best price. For the last 10 years industry has used traditional ad exchanges to bid on specific inventory against a predetermined audience – choosing ad units, identifying prospective customers, setting price ceilings. We now set a campaign goal for the machine learning to steer toward. The learning models and algorithms then predict which opportunities are most valuable based on incoming data in real time as ads are served, making the adjustments needed to help ensure every campaign meets its goals at the best possible price. It’s still early for machine learning in advertising and there is a lot to still be uncovered and put to the test. At Wright Social so far we are finding new efficiencies in campaign management. Additionally, we are spending less time on the tools and more time focused on nailing the message and creative as well as mining the data to inform new advertising opportunities. Facebook highlights what machine learning means for marketers in 2020:
Loosen the reigns when striving for liquidity.
Broad parameters work best here. By remaining agnostic about certain elements of a buy, teams help ensure the system can access as many opportunities as possible—and find the best ones for meeting campaign goals.
Choose goals that align with real-world business outcomes.
Systems work toward these goals like a ship following a navigation signal. Choose the right one, and you can be rewarded with a more efficient campaign; choose the wrong one, and you may find yourself off course.
It’s an exciting time in Facebook Advertising with opportunities to cost effectively build your ag-related brand, increase your visibility, explore new markets and generate consistent leads from social media efforts. We can’t wait to see how this platform transforms over the next 10 years and where ag sits in this picture.
