By Dmitry Sverdlik, CEO and Founder, Xenoss
Artificial Intelligence has shown a relentless ability to grab headlines in 2023, often for negative reasons, but in MarTech at least, there are plenty of reasons to see the positive side. In fields such as predictive analytics, personalization and competitor analysis, AI represents a tangibly revolutionary force. Data from Statista indicates that the global market value of AI in marketing is projected to soar to $107.54 billion by 2028, up from $15.84 billion in 2021.
So what are the use cases in marketing, and how can businesses harness the power of AI-driven features?
Natural language processing (NLP): unlocking customer insights and tailoring strategies
NLP is capable of analyzing vast amounts of data to gain a deeper understanding of customers, identify emerging trends and customize marketing strategies. NLP empowers the use of chatbots and voice assistants while facilitating content generation, and among its primary applications in marketing, sentiment analysis stands out.
By employing sentiment analysis, marketers can gauge the opinion, emotions and attitudes of customers towards their products or services. Armed with real-time insights into customer sentiment, they can swiftly respond to both positive and negative feedback. This valuable knowledge enables marketers to fine-tune campaigns, enhance customer satisfaction and cultivate stronger brand loyalty.
Predictive analytics: unveiling patterns for personalized experiences
Predictive analytics harnesses the power of machine learning models to analyze data and identify patterns, and one application of such user behavior forecasting is to deliver personalized experiences. By segmenting similar users based on known information, predictive analytics provides relevant recommendations that resonate with individual preferences.
A powerful tool in the realm of predictive analytics is offered by Qlik Sense. This comprehensive solution incorporates APIs, a multi-cloud architecture and an associative engine, empowering users to seamlessly analyze data, uncover valuable insights and make data-driven decisions.
Augmented analytics: Contextual insights and automated data integration
In contrast to predictive analytics, augmented analytics relies on context and knowledge to flag insights, so that marketers are no longer obliged to rely on large datasets. In the marketing domain, augmented analytics can be used in data preparation and integration, automating the gathering, cleaning and integration of data from diverse sources.
These sources can include CRM systems, social media and web analytics platforms and customer databases. Augmented analytics also generates interactive visualizations, dashboards and reports, empowering marketers to analyze data more effectively. They can then leverage these algorithms for customer segmentation, enabling targeted strategies and personalized messaging.
Computer vision: Reshaping marketing through visual recognition
Computer vision holds immense potential to reshape marketing through its ability to identify objects in images and videos. One powerful application is the use of Generative Adversarial Networks (GANs) to create original visual content. GANs consist of a generator that produces content and a discriminator to discern between real and generated visuals.
Branded image recognition is another valuable application of computer vision in marketing analytics and targeted advertising. Companies such as GumGum employ this approach to identify brand logos, capturing users’ attention at the opportune moment. Moreover, computer vision enables the analysis of user interactions with advertisements, shedding light on which branded elements resonate with the target audience.
Large language models (LLMs): Empowering marketing communication
Large Language Models (LLMs) have the primary purpose of reading, understanding, and generating natural-sounding text, and with the 5th regeneration of ChatGPT on the horizon, LLMs are becoming increasingly appealing to marketers. Presently, LLMs excel in summarizing market research surveys, automating reporting processes, predicting ad performance, and generating marketing copy. And meanwhile, LLMs are still in their early stages of development, so their capabilities for marketing purposes will continue to expand.
What to consider before implementing AI in MarTech
Implementing AI capabilities into your MarTech product may appear enticing, but it’s crucial to recognize that it can be a resource-intensive endeavor. Before embarking on AI adoption, a comprehensive evaluation of its pros and cons is criticall. Products that are most likely to benefit from AI adoption include those with clear use cases such as matching, recommendations and prediction, as well as those handling substantial data volumes suitable for processing, and those that tackle a multitude of diverse tasks that would benefit from automation.
If you’re not prepared to invest the necessary time and financial resources into AI development or if you lack sufficient data for training machine learning models, implementing AI-powered features may not be the optimal choice. However, it may be wise to engage data engineers who can help assess the value of AI for your product.
By involving an experienced team, you can identify technological gaps within your MarTech product and determine which features and tasks can be enhanced through AI implementation. This collaborative approach ensures a strategic alignment of AI capabilities with your specific business needs.