Getting to the bottom of deep learning

In short, deep learning is a subset of machine learning, a form of artificial intelligence (AI) that uses advanced algorithms - called neural networks - that learn to make predictions and identify patterns. This approach requires much more data and computational power than traditional machine learning algorithms and also learns to generalise and successfully handle unseen content when exposed to new topics.

New technologies and these improvements in AI can easily solve complex problems and, as a consequence, have been massively beneficial to marketers. These tools can quickly process an entire set of big data and come up with a “hyper-targeted” solution. As such, the adoption of deep learning is evidenced across various marketing disciplines.

The use of machine learning tools to optimise the sales process has been a trend for many years, allowing marketing and sales teams to generate a better return on investment. Equally, personalised content, emails, and advertisements are the driving factors for improving key KPIs.  Marketers are now recognising how effective it is to touch the customers' pain points and provide unique solutions to them. This is done by analysing the customers’ data - like recent searches, websites they visit, and how they respond to certain ads shown to them - and deep learning has the potential to take personalisation a step forward by recommending the solutions to the customers even before they search for them. Such hyper-personalisation also solves the privacy concerns that many people have in mind, like tracking, breaching, or selling data.

Machine learning can also help to increase the chances of communicating with a prospective audience in the most effective manner to increase the conversion rate with time.

The Smartology SmartMatch platform takes this one step further by pairing content generated advertisements with hyper-contextually relevant articles on the world’s leading publisher sites. The platform’s sophistication stems from the development of a public pre-trained deep learning model that is trained using a content set comprising over 3 billion words. Smartology has fine-tuned and further specialised this deep learning model using thousands of examples from an in-house manually-curated collection of contextual matches built from our publisher and advertiser content archive.

This deep learning model transforms publisher and advertiser content into weighted representations of the most significant parts. These are compared during the matching process to find two content items that share context. Think of it like DNA, where two siblings can be shown to be related by the amount of DNA segments they share.

On top of this sits a brand suitability model, which evaluates publisher content with regard to various unacceptable semantic categories - such as job loss, natural disasters and racism. This model can quickly analyse and weight words and phrases used in publisher content to determine the probability of it being unsuitable. This level of sophistication enables brands to confidently deliver their content generated advertisements alongside trusted media owner articles, while also being safe in the knowledge that advanced semantic brand suitability and brand safety concepts are prevalent and will more effectively prevent matches with any unsuitable articles. A more nuanced and advanced approach than blunt keyword exclusion list tools that are more frequently adopted by our competitors.

Brand suitability, brand safety and privacy are all a major concern, but with the deep learning algorithms embedded into SmartMatch, the delivery of a successful marketing campaign makes for a happy brand and a happy customer. 

So, when people are talking about AI, it is deep learning and its derivatives that are at the heart of the most exciting and smartest products, and Smartology’s SmartMatch SaaS platform has ‘smart’ as its namesake for this very reason.