How Automation Can Help Marketers Solve the Optimization Problem

October 19, 2021, 04:10
By Cole Tone

The marketing industry, like many others, is constantly evolving. New technologies and platforms are constantly emerging, and what’s considered industry wisdom one day is often outdated by the next. Right now, the dominant trend in the programmatic ecosystem is software as a service (SaaS), because it gives both agencies and brands transparent control over how their campaigns are managed and implemented. 

Given concerns about brand safety and advertising waste, it’s understandable that marketers would want to have complete oversight into every part of the marketing process. At the same time, it’s not an exaggeration to say that there are some parts that are just, well, boring. The reality is that by automating those tedious tasks, marketers will be empowered to focus on the creative, strategic work that will inevitably have the greatest impact for their brands. 

One element that has consistently been a human resource struggle for marketers is the optimization problem: pull the data, analyze it, and then manually optimize the media strategy based on those insights. This process is time consuming, labor intensive, and generally not a fun job to do – plus there’s the fact that no matter how much time you spend on it, there’s always something more that can be done. 

A deep learning algorithm, on the other hand, is great at that sort of work. It takes all the data you can give it and turns it into a fully customized algorithm, which can automatically adapt in real time to changing conditions. Your deep learning algorithm never has to go home to take the dog out, take a vacation, a weekend off, or even sleep. 

Deep learning uses neural networks – a system composed of layers of artificial nodes – to comb through data in order to find patterns and make predictions. These days, deep learning is embedded in everything from our phones to our voice assistants to our cars. There’s no question it makes our lives easier – as I and many others have discovered over the past year and a half, facial recognition is much more convenient than when I’m wearing a mask and have to key in my passcode every time I want to access my phone. 

At the end of the day, there are just some things that deep learning can do more efficiently than humans – and media optimization, thanks to deep learning, is now one of those things. However, unless your organization already has a robust data science team (which, these days, is extremely expensive and difficult to come by), you will need to find a partner who can develop these automated algorithms on your behalf. That’s where Cognitiv comes in: we build the algorithms that automatically optimize your media buy. Once an algorithm has been trained, it autonomously adjusts, retrains and reinforces itself based on the positive and negative outcomes that come from its predictions. 

Cognitiv’s goal is to fundamentally change how marketers do their jobs. Freeing marketers to pursue the type of creative, high-level work that excites them the most, and has the greatest impact for the brand. By outsourcing the tedious work of optimization to a fully automated, custom algorithm, marketers can be assured that their media buying will be as targeted and accurate as possible, while adapting in real time to any changes in the market condition and consumer behavior.

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