Machine Learning Offers New Opportunities with the Evolution of Branding Signatures

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Shutterstock Licensed Photo - By Elnur

Artificial intelligence has been one of the most disruptive new technologies to affect the marketing profession in the last 50 years. One study found that 53% of marketers plan to use machine learning in some capacity. At Smart Data Collective, we have discussed many of the ways that AI and machine learning have changed the face of performance marketing. However, brand marketing is also evolving with new technological advances.

Machine learning is changing the way that companies position their brand image. Some experts are debating the long-term impact on the marketing profession, but others are focusing on integrating new machine learning tools into their branding strategies. They are using machine learning to improve the designs of everything from their logos to the channel letters of their literature.

Mostafa Elbermawy, an author with Single Grain, wrote a very interesting article on the importance of AI in branding. Other experts have shared similar insights.

Benefits of Machine Learning for Brand Positioning

We want to talk in greater detail about the role of machine learning in branding signatures. However, it is important to first address the overall impact of machine learning on brand marketing, since all of these practices are so strongly intertwined.

A few of the changes machine learning has brought to brand marketing are discussed below.

Artificial intelligence is helping improve company cultures

Elbermawy made a very good point about the benefits of artificial intelligence for company cultures. The company culture is arguably the most important facet of any branding strategy. Companies will have a much easier time flourishing if they create a strong company culture. Positive company cultures are more productive, have lower turnover and forge stronger relationships with customers. On the other hand, toxic company cultures tend to have very poor brand images.

Machine learning is changing the dynamics of company cultures. New algorithms make it easier for companies to identify shortcomings in their business models and adapt the company culture accordingly.

Better targeted marketing

We have previously discussed the benefits of using machine learning to improve the targeting of online ads. Those articles focused on targeting in the context of performance marketing. However, precision targeting is just as important when it comes to brand marketing as well.

You need to make sure that your brand is properly positioned for your target demographic. Fortunately, new advances in machine learning have made it easier to identify your market and understand what they are looking for. This enables you to better position your ads for a strong brand image.

Adapting your brand literature for your target audience

The aesthetics of your logos and branding literature are essential for building a strong brand. Machine learning has made it a lot easier to create visuals that appeal to the underlying psychology of your target audience.

Machine learning algorithms are able to track how different consumers respond to various creatives. This enables companies to choose the best brand elements for their marketing strategies.

How machine learning is improving the aesthetics of branding signatures

As mentioned above, machine learning is playing a vital role in the evolution of logo design and other branding elements. Here are some examples:

  • Sentiment analysis to gauge response to brand messages. In the old days, brand positioning decisions were too subjective. Decision makers substituted their own judgment for their analysis. Machine learning is changing this reality. Companies can use machine learning to conduct sentiment analysis, which helps them understand the perception customers have of different brand messages.
  • Computer vision to analyze user generated content.  Software applications like GumGum are able to recognize different products and types of content throughout the Internet. They can can evaluate user generated content in various industries, which helps you determine what types of content your target customers are looking for. After all, content they generate clearly resonates with them, which means it would be a good basis for your own branding content.
  • Conduct more accurate split tests with website designs. You need to carefully test different website designs. Machine learning has made this a lot easier. You can use the data from your split tests to create higher quality content for other brand literature. 
Sean is a freelance writer and big data expert. He loves to write on big data, analytics and predictive analytics.