According to figures from eMarketer in 2016, advertising spend in the Middle East and Africa was expected to be in the region of $5.5 billion in 2019, up from $2.4 billion in 2015. Much of this today gets spent on ads on Google, Facebook/Instagram, Twitter, Youtube and Snapchat alone. And this is expected to keep growing as technology becomes more pervasive.
There is a strong business case for using behavioral data in digital marketing, as it offers marketers granular behavioral insights that can help to create consumer segments that align a target audience with brand attributes. Businesses can then build clusters of consumers with similar attributes, and focus marketing efforts on the most promising segments by personalizing products and services and designing messages that connect with the target audience. When a brand speaks the language of the target audience, it resonates and helps increase brand loyalty.
Behavioral data can be developed by analyzing people’s core personality traits. Comprehensive insights about customers’ personalities, values, habits and preferences, can then help marketers set their strategy while understanding customers’ behavior on an individual level.
One domain of artificial intelligence (AI) that has immense potential for marketers is Natural Language Processing (NLP), which uses machine learning algorithms to enable computers to read, understand and derive meaning from written language. The language people use is a reflection of their mental processes and personality—therefore, with its language analysis capabilities, NLP can help decode human behavior.
For example, the “Big Five Personality Test” analyzes human personality to identify behavioral traits. Meaningful insights are derived about a person based on five attributes: extraversion, openness, conscientiousness, agreeableness, and neuroticism. These traditional style personality tests involve long questionnaires that often take 30-45 minutes to complete and longer still to analyse. However, NLP can analyse an individual’s personality on the Big 5 scale using a sample of text written by an individual in less than two minutes, with approximately the same accuracy as a traditional personality test.
Once the written text has been provided for analysis, the computer will use algorithms to extract meaning associated with every sentence and collect essential data. Machine learning algorithms identify and extract natural language rules, so that unstructured language data can be converted into a form that computers can understand.
Cambridge University and IBM have now both run extensive tests to prove the efficacy of the NLP assessment models. NLP is also being used in a number of technology apps such as Google Translate, and personal assistant apps such as Siri and Alexa.
The behavioral data and insights that can be obtained from NLP has potential in several domains, including recruitment, education and career counseling, leadership, performance management, innovation and marketing. This can be further integrated with demographic, engagement and purchase data to create customer personas and micro-segments for personalized digital campaigns. This could have a direct impact on ROI—after all, in today’s digital world, understanding consumer behavior is the key to success.