Deep learning is a branch of Artificial Intelligence (AI) that offers innovative applications in many areas of technology. Algorithms driven by deep learning are being used to recognize speech, predict earthquakes, identify images, detect cancer, and make predictions. Deep learning trains computers to learn on their own.
If we pay attention, today, the human-to-machine bond has improved. For instance, our computers are much more practical and sophisticated than before. Now we have a touch screen, keyboard with swipe touch. At the same time, human-to-machine interfaces have evolved greatly as well. The mouse and the keyboard are being replaced with gestures, swipe, touch and natural language, ushering in a renewed interest in AI and deep learning.
Marketers need a clear understanding of their audience so they can distinctly identify where there needs overlap with what marketers are offering. By considering AI and especially deep learning tools, it will provide reliability and quality for further deployment. It will allow brand marketers to actually understand the market landscape, measure the brand’s position as opposed to competitors, create a strong relationship, and hold on potential opportunities. This intelligence tool provides marketers with innovative engagement strategy, in-depth audience knowledge in a multi-dimensional manner, from demographics and geographic, to psychographics to behavioral patterns, giving brands meaningful understanding of their target segments on what they do, what they like, and what they care about.
Deep learning is definitely a complex model. Because of its algorithms and the excessive number of data that we need to train the networks, deep learning requires a lot of computational power. We live in an era where traditional methods of managing data and analyzing have become very unwieldy. Multiple sellers and marketers are having a hard time juggling the large volume of data available and targeting their products and advertisements to the right audience. Today, companies rely on the capability to produce reports that will give them an understanding of what the different data tells them. Through this traditional analytic models, companies can collect, analyze, and manage enough data.
The advantage with the traditional model is that in order to fix a problem, the machine will solve it by breaking it down to different parts then combine the results at the final stage. The biggest advantage of deep learning algorithms is that they try to acquire high-level features from data in a progressive manner. Also, deep learning methods continuously improve and adopt changes in the primary information pattern. Greater personalization of customer analytics is one possibility. Another great opportunity is to improve accuracy and performance in applications where neural networks have been used for a long time. Traditional marketing seems to be dead, but you can still attract and reach a significant amount of your audience.
For many years, the word “funnel” was used to understand the consumer’s mind. Before consumers will choose potential brands then marketers will then direct them and move them through the funnel and at the end, those consumers will emerge with the brands they chose to purchase. Today, the world is quite different, the funnel concept does not effect decision-making. With the evolution of the internet, social media channels and mobile devices, people have taken control of the marketplace.
Consumers are becoming more demanding. They want everything immediately. When they visit a website, they want the information to be provided right away, they don’t want to scroll the entire page to find it. In addition, they don’t need a professional to call them, they will find that information by themselves. The buyer used to rely on advertising to purchase a product, however today, they are looking at reviews and ratings, product details, talking to relatives and friends; it’s more by social engagement.
As we move toward an increasingly mobile world, advertisers need to rely more and more on a social network to deliver their message. By using data set and machine learning, they will be able to achieve the best results. Even though advertising is trying to avoid machine learning algorithm, advertising is another area where some advertisers increase the relevancy of their ads and boost the return on investment of their advertising campaigns and their engagement rate.
Today, deep learning makes it possible for networks to leverage their content in order to create data-driven predictive advertising, precisely targeted display advertising and more. The role of advertisers is to create appealing content, create content that will inform and persuade their audience.
Machine learning technologies and AI are able to analyze data set from your content, then delivers intelligent suggestions. This help creates results that users were expected and improves engagement rate. Not letting big data do correlation work for you despite the fact that the machine does a really good job, it does not prevent humans intervention; we still need to check and make sure our goals are aligned.
For example, if the data set is small then sometimes using traditional models may yield more accurate results. Although some machine-learning specialists argue a properly trained deep-learning neural network can still perform well with small amounts of data. With Apple’s face ID present on the newest iPhone Xs, X, and XR, when setting up your phone, you can configure the face recognition, you are training the algorithm by scamming your face.
Every time you log in, your camera captures several data points which create a depth map of your face and will allow your phone to recognize your face or not. Furthermore, automatic machine translation used with deep learning is achieving top results in automatic translation of text and translation of images. Deep learning applications are changing the way we look at technology.