Using NLG, computer vision and AI for branding enables your business to continuously improve your brand communication and identity over the long term.
There are certain taglines you never forget—I'm Lovin’ It, Vorsprung Durch Technik, Think Different, The Best A Man Can Get. Each of these has been engineered specifically to play in your head like the irresistible hook of a chart-topping tune. Taglines are key components of an organization’s identity—which is essentially how their audience views them as a brand. Your organization’s identity is heavily shaped by the choice, tone and timing of the words used in your interactions with your audience. Collectively, these three parameters define your unique brand language in your marketing campaigns. The uniqueness of your brand language personifies you in the minds of your customers. Consistency is key in an organization’s brand language—not everyone can get away with tweets like these unless their identity and language are long established as irreverent, witty and youthful. However, not all businesses have to forcefully adopt a wisecracking tone, with many opting for elegance and professionalism in their interactions, and others preferring functionality over form to get their message across without fuss.
Ultimately, you choose your brand language based on the preferences and tastes of your target audience. The idea of “perfection” in brand language doesn’t exist—you must adopt the one that keeps you relevant and successful for several years.
Creating your own brand language is challenging enough. Maintaining it over several decades can be even harder due to the level of research, guesswork, course corrections and monitoring involved in the process. Using AI for branding, establishing and maintaining brand language becomes significantly easier for businesses of all shapes and sizes.
Creating a branding strategy is an arduous process involving many minds and several thousand hours of work. Firstly, it involves the process of collecting data from your target audience from different sources. This includes information about their purchase records and opinions posted on online public forums and social media. This process already creates a large pile of data to be assessed. Lest we forget, this is only the data collected from your target individuals in one region. Over several years, you expect your organization to grow and increase its presence in multiple cities, states, and countries across the world, making data collection even more challenging. Also, as one can expect, data collection is a continuous process as you need to always know the pulse of your target customers dynamically. Put all these factors together, and the process of data collecting becomes nearly an impossible task, even with entire armies of marketing professionals and researchers.
And all of the above is just the process of data collection. Once consumer data is captured, you will have to spend an endless number of hours trying to find useful insights from the ginormous reserves of consumer-related information. The insights will be related to demand patterns, seasonal purchase preferences, customer likes and dislikes and market competitor data. At the risk of reiterating, data collection and analysis are never-ending processes. Once these two processes are managed efficiently, you can create a branding strategy, language and identity to expand your customer base and maximize your profits. However, there is yet another element that needs to be factored in while forging your branding strategy. Once your business expands to other countries, you will have to create customized marketing strategies for each of them. Accordingly, you’ll need to adapt your brand language and identity to fit the sensibilities of that region. A one-size-fits-all approach will not be useful as people in that region may not understand certain references that locals in your home country may do.
Due to all these reasons, using AI for branding and strategy formulation makes a lot of sense.
Already, AI-based tools can be configured to carry out several digital marketing operations. The technology will have a similarly positive impact on branding as well. Businesses can utilize AI’s data processing and pattern recognition capabilities to create effective branding strategies. They can, for instance, use AI to analyze past marketing campaigns to identify the ones that worked the best. This can inform the marketing teams’ decisions on future branding and marketing initiatives. Using AI for branding in this way improves customer experience, maintains brand reputation and betters online engagement to improve conversion rates. Basically, AI helps in market research, the creation of strategies, as well as sentiment analysis and semantic analysis. However, the effectiveness of AI in these areas depends on how accurately the input data represents the consumer. This demands the collection of accurate consumer data from various touchpoints.
Consumer data can come in from several different channels, such as survey responses, social media posts, comments and contact center records. Tools like Natural Language Processing (NLP) and computer vision can help in collating the data from these sources and deriving actionable insights. Through semantic and sentiment analysis, such data can be used to accurately guess audience opinion and overall market trends.
While conducting online consumer surveys, machine learning and Natural Language Generation (NLG) tools can autonomously formulate follow-up questions. The formulation is based on the replies and opinions of previous respondents. Natural language processing is a key component in understanding and uncovering the underlying meaning behind seemingly incoherent responses in textual or audio formats. The data received could also be in the form of images from various records. Computer vision is used to discover insights from images, video data and other visual sources.
Using AI for branding in the above ways enables you to widen your respondent pool even more. The collection of more data enables you to get detailed information about consumers from different regions. Also, more inclusivity and diversity of data enables you to eliminate any biases from branding-related analytics. As you know, AI-based tools tend to perform better with time as the algorithms are exposed to a greater volume of data. This improvement is used to make surveys better by including specificity in questions to get detailed behavioral information about your customers.
Automating the data collection and analysis processes with AI also helps to save the time spent on manually creating reports, scanning social media pages and going through customer emails. You can use AI to handle these tasks, enabling your personnel to concentrate on more important tasks such as establishing a hypothesis, verifying the findings from studies, making leadership decisions and communicating details of the findings with stakeholders. Secondary research is as important in creating branding and pricing strategies as the primary one. Using AI for branding also involves scanning thousands of primary data sources—the thousands of online newspapers, sales reports and magazines spawned by digitization— to understand wider market trends. Manually handling any of these tasks will take hours, if not days, to carry out. AI-based automation saves a lot of time in your marketing and branding operations. Once you have the analyzed and processed data, NLG streamlines the process of developing a language for your branding based on the findings from such data.
NLG builds on NLP and pattern recognition for creating blocks of text. Widely known examples of NLG are the auto-complete options in emails and instant messaging apps. The GPT-3 can also be cited as an example of advanced NLG. Employing NLG and AI for branding helps you to create autogenerated emails and responses with the exact tone of marketing that you seek to use in your communication material. Naturally, you would want your tone to stand apart from the rest of your market competitors. The research and analysis abilities of AI and the text-generation ability of NLG make it possible to create your distinctive brand language. The Washington Post is a good example of an organization using NLG to generate and publish online content autonomously.
You need to create tailored marketing content for your consumers in different regions with the AI insights generated from audience data. The words and tone used in such content must align with the identity of your brand. NLG allows your marketing team to prepare or choose a template with which your marketing identity can be built or reshaped. Such templates are mainly driven by multiple ideas and narratives you want to use in your ad campaigns and public interactions. NLP and NLG make the process of zeroing in on an original narrative easier by focusing on your audience's demographics and requirements. As stated earlier, audience data collection and analysis provide the perfect platform for NLG systems to create your brand language.
Once you’ve created your unique brand language, you must ensure that no interaction or ad campaign misses out on using it. For example, lets’ say a company wishes to avoid using a specific word—perhaps one associated with its competitor—in its ads, social media posts or emails. AI will, firstly, help to detect any instances of the word in any content created. Then, the firm’s NLG system will work on replacing the word with its alternatives to keep the message of the communication and brand language intact. While this may seem like an easy exercise that may not need the usage of AI and NLG, you must remember that the changes may be needed on thousands of emails, ads, and social media posts from the past and the present. AI and NLG make it possible to have these changes made in a few seconds.
As you can see, AI is becoming a key component of marketing and branding, with many businesses around the world using the technology to handle those functions. Leveraging technologies such as NLG, computer vision, and AI for branding helps you maintain seamless consistency in your marketing material. In more relatable words, AI-based automation makes your brand language not just merely good, but Finger-Lickin’ Good.
Naveen is the Founder and CEO of Allerin, a software solutions provider that delivers innovative and agile solutions that enable to automate, inspire and impress. He is a seasoned professional with more than 20 years of experience, with extensive experience in customizing open source products for cost optimizations of large scale IT deployment. He is currently working on Internet of Things solutions with Big Data Analytics. Naveen completed his programming qualifications in various Indian institutes.