Return to Retail: How Deep Learning Boosts the Effectiveness Rate of Video Ads
The pandemic was an extraordinary time for the retail community and, in 2022, retail marketers are still adjusting to the post-pandemic marketplace and the major, lasting shifts in consumer behavior.
For example, with brick-and-mortar outlets forced to close during lockdown, the popularity of online shopping rose significantly during the pandemic. Another trend that has outlasted the lockdown era is that consumer appetite for video content is stronger than ever and demand is still growing. Digital video advertising itself has evolved too, with shoppable ads now available on many platforms to dramatically accelerate the sales process for retailers.
With spiked interest in online shopping and video advertising, how can retail brands make sure they stand out from the crowd with their digital ads and boost those all important key performance indicators?
The answer is by using "deep learning" to power their digital campaigns. But what is deep learning and how exactly does it help to improve the impact of video campaigns?
Deep Learning Explained
It’s important to understand the distinctions between artificial intelligence (AI), "classic" machine learning, and the cutting-edge deep learning technology that we’re discussing here.
In basic terms, AI refers to any technology (usually computer software) which attempts to simulate human intelligence. Machine learning, meanwhile, is a subset of AI that can learn from data and then apply its learnings to inform decisions. Classic "machine learning" isn't able to make decisions without human input to guide its actions, and it struggles to handle unstructured data sets. It's unable to improvise to make performance decisions on its own.
By contrast, deep learning technology is capable of making its own decisions, needing only the desired objective as input. Based on neural networks akin to the human brain, deep learning algorithms can respond to real-time events based on its knowledge of previous similar events.
Deep learning allows advertisers to maximize the efficiency of their contextual targeting strategies by making the most out of every single ad impression.
The technology can analyze masses of user information in fractions of a second and decide not only which ad creative to present, but also whether its display is likely to trigger the desired action. Deep learning tech can make literally millions of these decisions in a short period of time, something that's far beyond the capabilities of even the world’s best media planners.
With deep learning, multiple targeting strategies can also be supported, including contextual, behavioral and demographics, that adjust content to the preferences of the target group. For example, different creatives can be used based on target group interest.
By identifying a customer’s browsing habits (in a future-proof and privacy-compliant way) deep learning helps advertisers to design better customer journeys when it comes to sequential targeting.
Using the data from deep learning, advertisers can identify specific touchpoints in the user’s interactions with the brand to create an advertising strategy that, when executed, will make video ads significantly more effective. Our own research found that campaigns utilizing deep learning in this way are up to 50 percent more efficient compared to those using standard machine learning approaches.
By applying the power of predictions in real time, deep learning delivers more efficient media buying and continuous optimization of your buying strategy, ensuring both value for money and maximized impact for your brand.
Mateusz Jędrocha is the head of upper-funnel solutions development at RTB House, a company which creates truly personalized marketing campaigns powered by deep learning technology that cut through the online noise..
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