|Why is Deep Learning the way of the Future for Effective Marketing Campaigns|
Why Deep Learning is the Future of Successful Marketing Campaigns?
If you have a product to advertise, you want your advertising efforts to bring unquestionable and measurable results. And with the cookie-free future fast approaching, it’s vital to test modern technologies now, to maintain or improve those results. You don’t want to be left behind.
New deep learning-based AI solutions are much more efficient than standard machine learning AI-based approaches. By helping marketers create a segment of one for more granular customer segmentation, this new technology offers more opportunities for advertisers to convert window shoppers into buyers through personal advertising. They are also designed to respect the needs and privacy of Internet users with non-intrusive, privacy-respecting advertising.
Here’s an overview of deep learning and how it can help your advertising strategy.
What is Deep Learning?
Deep learning is an evolution of standard machine learning technology. It structures algorithms in three layers, known collectively as the artificial neural network (ANN). An outstanding feature of deep learning is its ability to work with large, complicated, or even unstructured data sets, from which each algorithmic layer can extract more complex features. And because ANN mimics how a human brain works with information processing and decision making, it can try many different things before making a final decision.
- First layer: place a user in a specific group
- Second layer: define niches or specific interests
- Third layer: assigns the user the correct stage of the funnel
Another advantage of deep learning, as opposed to standard machine learning, is the ability of algorithms to identify patterns without pre-set parameters determined by a human operator. This means that it can work with human supervision, much more flexibly than legacy solutions.
Deep Learning in Advertising
The ultimate goal of deep learning is to make online ad experiences more meaningful to users and allow marketers to apply more granular targeting while keeping privacy intact.
By using more powerful algorithms, it can offer unmatched accuracy and scale when it comes to targeted advertising.
This technology makes it possible for marketers to model user behavior and intent based on hundreds of anonymous metrics along with cross-platform activity and behavioral comparison, as well as hidden data such as time between products viewed or sequence of purchases. visited pages. The algorithm then analyzes this data to interpret exactly what the user was doing and can predict which products they are likely to be interested in.
Thus, you can provide potential buyers with deeply personalized results and tailored product offerings.
For advertisers, deep learning also addresses the biggest challenge in the online advertising industry: optimizing marketing budget and ad spend across all platforms. Effective targeting made possible by deep learning means that budget is spent only on users who are truly interested in a brand’s message, resulting in more completed views and helping to reduce cost per completed view ( CPCV).
For example, deep learning helped a fashion company deliver high levels of personalization and engagement with the right audience after acquiring a third-party brand. Thanks to deep learning, a three-month video ad campaign had 4x more reach, more than 6x more user engagement (CTR), and 3.9 million full video views at 80% average viewability.
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Deep Learning and The Marketing Funnel
The application of deep learning to retargeting campaigns is already well known and very successful. An e-commerce platform in the fashion industry harnessed its power for a remarketing campaign and saw an 18% increase in return on ad spend (ROAS), leading to a 66% increase in average value of orders (AOV) and an increase of 200%. in income.
However, retargeting is not the only bowstring for deep learning. Not only can deep learning algorithms use proprietary data and embed privacy to target the entire marketing funnel, but this powerful technology is also capable of accurately assessing where the user is in the funnel and then determining which ad should be shown.
- New to the brand – Aspirational content to strengthen your first impression
- Product Research Stage – Informational ad content that gives them more information about specific products
- Ready to buy – Presented with the right product to drive them to a quick conversion
Because deep learning can take into account different aspects of the context to select the perfect placement to best fit each opportunity, this enables precise and scalable targeting of audiences interested in a brand’s content.
Deep learning was instrumental in helping an automotive company maximize the reach of a geo-targeted CTV and OTT campaign. By identifying the top categories of context and targeting high-quality platforms, 96% of measured impressions were viewed and the cost per view was 25% lower than the industry average.
This aspect of deep learning completely revolutionizes the way advertisers can think about marketing campaigns, because solutions based on this modern technology help improve campaigns with different objectives. It also changes the meaning of the word “conversion” from basic monetization to continually building lasting brand awareness and maintaining deeper connections with prospects at every stage of their journey through the funnel.
Make testing new technology part of your plans
Deep learning technology is more effective than standard solutions.
However, if brands want to optimize the results of their campaigns both now and in the future without cookies, they need to start reviewing their current processes and investigate the solutions and tools that ad technology providers are currently working on.
A partner that can demonstrate the effectiveness of its tools and solutions is one that brands should trust.