Deep Learning and Its Impacts on Marketing Analytics

Deep Learning and Its Impacts on Marketing Analytics


Have you ever wondered how machines know things about us? We now have Siri, Apple’s virtual assistant, that’s able to translate human voice to a wide range of commands. For instance, iPhone owners can get answers to their questions, find a way to obscure locations, send a message, and facetime their friends using only Siri.

Additionally, automated driving allows us to go hands-free on expressways, which can be even tested in city traffic. If you are a Tesla owner, then you know the drill.

We’ve seen a significant improvement in biology and medicine. Scientists have created new molecules for DNA-based drugs and vaccines.

This term deep learning is used quite a lot, but do we actually know what it means and how it will shape the future of marketing analytics? Let’s find out in today’s article because we will be answering many questions.

What is deep learning?

Deep learning developed from machine learning is an improved performance of artificial intelligence models. The big step when it came to machine learning was the application of Neural Networks-Models. They were constructed to mimic the human brain and process a lot of data.

Surprisingly, these models provided highly accurate outputs in clustering, predictions, and classifications. So, naturally, deep learning comes as a next step in the evolution of Neural Networks.

If we want to make things even more simple, we could say that deep learning is a machine learning technique where massive neural networks learn from a large pool of data and deliver accurate output.

As we mentioned previously, deep learning is at the center of innovations like virtual assistants, hands-free driving, instant audio translation, image recognition, etc. But, how does deep learning affect marketing? Let’s explain!

Improves visibility and builds awareness

Companies can apply deep learning to historical marketing data to target a specific audience and even identify potential fields that haven’t been tapped yet. Deep learning can mix and match many data sources and learn from them. This way, it identifies patterns and segments that traditional techniques or machine learning can’t accomplish.

It’s a great way to spot new business opportunities or launch new products or services. For example, Coca Cola started using smart vending machines with integrated virtual assistants to help customers find their favorite drinks and blends.

Recently, we have seen ads that have been created through deep learning; music made using bots, image selection for dynamic placement, data used from social media, and many others.

You have probably noticed how deep learning is affecting the ad industry. While this approach is still limited, we have super personalized offers and ads that lead to a higher engagement. Deep learning goes even one step further by recommending birthday presents based on the interaction on social media.

Creates differentiation

Differentiation is a way consumers experience or depict a particular brand. For example, websites create a more customized experience based on customers’ profiles to ensure their clients get relevant content and become more attached to the brand.

An excellent example is the Netflix streaming service, where there are no two customers with the same or similar viewing content.

While it’s not a new concept, deep learning also boosted the capabilities of chatbots, allowing them to have a better response to various customer queries. This way, each client gets an elaborate response that helps them decide whether to buy a particular product. On some occasions, we can even say that chatbots are nearly the same as their human counterparts.

Increases conversion

We often see brands that lose customers to the competition. Even though companies use multiple things to stop the conversion and attract clients to their corner, deep learning is the most sophisticated one.

One of the best examples of learning in marketing and recommendation engines can be seen at OTT and e-commerce platforms. And they are only getting better as they draw data from individual customers and inject it into deep learning.

Convenience is one of the best ways to draw conversion, and voice research has become a big thing in India since people feel more comfortable speaking than typing. Additionally, tools like Amazon Echo and Google Home allow customers to research e-commerce websites, using only voice commands.

Retains customers

Customer loyalty is one of the assets you shouldn’t neglect. However, it’s quite challenging to meet customers’ expectations and constantly exceed them in today’s competitive market. But deep learning can help here.

Especially in the fashion space, deep learning can predict clients’ preferences by using photos they’ve posted on social media. They can offer a discount on particular products or intentionally push products they will appeal to.

Based on customer purchase patterns, their past search behavior, and the cost of the products they used to buy, e-commerce retailers can apply deep learning to predict the outcome of the purchase and ship products in advance to the warehouse.

Amazon is one of the leading representatives of this model. Once you deliver products on time, you are encouraging customers’ loyalty.

In this article, we wanted to explain a couple of ways how deep learning is affecting e-commerce and online marketing. We only mentioned a fraction of things we believed were important. However, deep learning covers a wide range of topics that require years if not decades of research.