This article was originally published in the Elevate Your Marketing newsletter and has been repurposed and republished here with the author’s permission. Here is the link to the original article. 

AI has found its application in a wide variety of areas in marketing. From content and SEO to videos and ads, there isn’t any area in marketing that has remained untouched by AI. But today, we will be covering a topic related to the application of AI in marketing that many haven’t even heard about – Brand Language Optimization.

Below is a summary of what we will discuss in this blog post:

• What is Brand Language Optimization?
• How is AI helping in Brand Language Optimization?
• What are the Brand Language Optimization tools available in the market today?
• What type of companies should use an AI-based Brand Language Optimization platform?

What is Brand Language Optimization?

To understand Brand Language Optimization, we need to first get a sense of what ‘brand language’ is.

Brand language refers to the set of tools and communications a company or brand uses to create a brand identity and image with the following attributes:

• Consistency
• Attention-seeking
• Relatability (for customers/consumers and prospects)
• Credibility
• Objective-driven

And Brand Language Optimization is – as the term suggests – the process of optimizing a brand’s language so that it meets all or some of the above attributes or goals. Now, if you are wondering whether we are making a simple concept sound complicated, think about the challenges large brands like Amazon, Nike, and Coke might have to face to maintain their brand voice for decades across multiple regions, channels, marketing collaterals, languages, etc.

How AI is helping in Brand Language Optimization

Even for giants like Amazon or any other large brand for that matter, maintaining a consistent voice wherever they are is a daunting task, even with the army of people they have. This is predominantly because of the following reasons:

• These brands have a wide global presence with communications happening in a variety of languages.
• Though there are processes and guideline documents in place, not every process is centralized or done by following a ‘bible of branding’.
• There is a ton of data to be analyzed in the backend to make something like a uniform brand voice possible.

This is where AI comes to the rescue. Artificial intelligence combines various techniques such as Natural Language Processing (NLP), Machine Learning (ML), Natural Language Generation (NLG), and Dynamic Content Optimization (DCO) to analyze historical data and deliver the best experiences to consumers.

Now, let us spend a minute understanding how these techniques work together in tandem.

While NLP is about processing and analyzing loads of content and deriving patterns out of it, NLG takes it one step further by using the insights from NLP to generate content. The Gmail autocomplete feature, AI-based content generation, automated ad copy creation, etc, are all examples of applying NLP and NLG to marketing. Machine learning augments and improves this process by continuously learning using the data obtained from multiple touchpoints including email, website/app searches, chat interactions, support queries, etc.

Let us now come to DCO.

Dynamic Content Optimization or DCO is the process of delivering personalized messages at scale to consumers or website visitors at the right time to improve the likelihood of purchase or conversion. This can be done across a variety of channels including CTV (Connected TV).

What tools are available in the market for Brand Language Optimization?

Brand Language Optimization as a term is not very popular or widely used today. It is a relatively new concept that cuts across multiple facets of marketing such as marketing analytics, automated content generation, and personalized messaging. Owing to this, there are three types of players in this space:

• Players who call out themselves as Brand Language Optimization providers. An example of such a platform is Phrasee.
• Companies that position themselves as technology providers for personalization at scale or creative marketing automation. Not all of them necessarily leverage artificial intelligence in their solutions. It can also be noted that their primary objective might not be offering a consistent experience but rather the ability to automate and scale marketing experiences with ease across multiple channels. Examples of such providers include Jivox, Omnisend, and Celtra, just to name a few.
• Customer Data Platforms (or CDPs): CDPs like Emarsys and Blueshift are used to collect and unify customer data from multiple sources, derive insights out of it using ML, and deliver a personalized experience in an automated manner across multiple platforms.

A quick look at how Phrasee is changing Brand Language Optimization using AI

Among the many companies I mentioned above, Phrasee seems to be the only player positioning itself as an end-to-end Brand Language Optimization solution provider. Please watch the below video to understand how the platform works: