As humans, we have to use language to speak, read, write and to synthesize the world around us while making decisions thousands of times a day. But how has science and technology become involved in language processing over the past 25-30 years? The answer – through significant advances in Natural Language Processing (NLP).
NLP is a field of computer science and computational linguistics that has a basis in Machine Learning and Artificial Intelligence. NLP makes it possible for computers to understand, interpret and manipulate the human language. It can structure highly unstructured data sources or components. We have an incredible amount of languages and dialects, and within each language is a unique set of grammar and syntax rules, term and slang. NLP makes it possible to process all these variations and add numeric structures to natural language data and allow us to use it for many different applications.
This all sounds great in theory, but can NLP be used in the marketing domain? It definitely can, and it already has been applied in marketing to help infer meanings from human languages. NLP algorithms are being used to make marketing more predictive, personalized, effective, efficient and profitable. It is even projected to become a multi-billion-dollar industry by 2020.
Personalized Marketing
Personalization is one of the key aspects of success in marketing. Target customers come from very different demographics and therefore, the linguistic style of marketing messages plays a vital role in the success of marketing campaigns. NLP algorithms added to marketing automation solutions allow us to communicate to specific audience segments in their own linguistic styles which increases engagement with messaging. Adobe Corporation is already deep into incorporating algorithms in their Marketing Cloud offerings to implement “Linguistic Personalization”. Their first step towards automating message personalization is algorithmically inserting adjective and adverbs that have been found to evoke a positive sentiment in specific audience segments and sharing those segments with Adobe Campaign.
Smart Marketing Content
NLP algorithms are particularly adept at analyzing marketing content, which means the computer can turn your content into data, which it can then process and interpret. It then modifies the text in a marketing communication/email, based on target audience or relevance of a topic. Adobe’s software research team is actively utilizing the power of NLP to produce more effective content. Their team is working on algorithms that can automatically understand the content writers’ context and automatically construct a query that can retrieve relevant content for re-purposing. Once the NLP algorithm helps to create the content, it can then modify that content based on the requirements of the delivery channels.
Sentiment Analysis
Sentiment Analysis within Social Media applications is one of the most commonly used capabilities of NLP. For marketers, having the ability to identify ‘propensity signals’ from social media posts can be extremely profitable. For example, someone who posted on Facebook that they “just saw the new Tesla on the street and thinking about getting it too” is expressing a propensity to purchase, which is valuable information to a marketer. NLP algorithms can mine social media for such expressions of interest and return social accounts matching the potential customer’s criteria.
The Future: NLP and Adobe Campaign
Imagine if we could have a tool that would automatically write a personalized message based on the target audience’s behavior, demographics and psychographics? That would be game changing and significantly increase customer engagement rate and LTV. Adobe is actively working on enhancing their NLP algorithms, and my assumption is that these algorithms will be standard, out-of-the-box calculations in the Adobe Marketing Cloud apps in the near future. The future of marketing could be here sooner than we think!
