Leveraging AI in Marketing to Drive Better Results

The expanding role of artificial intelligence in marketing enables teams to better engage customers, identify promising new markets, increase sales, and allocate resources more effectively. Marketers who integrate AI technologies into their campaigns position their organizations for growth, especially in highly competitive industries. In this article, we’ll explore the impact of artificial intelligence in marketing, examining the primary branches of AI and how today’s teams are using them. We’ll also delve into the challenges organizations encounter when integrating AI into their marketing processes, calling out common pitfalls and sharing our recommendations for avoiding them.

Artificial Intelligence Technologies for Marketing

Artificial intelligence tools help marketers process and make sense of the increasing amount and diversity of marketing-relevant data. The AI family tree is a large one with many branches. Let’s start by looking at some of the most popular AI technologies and how marketers use them.

Machine Learning (ML)

Machine learning is a subset of AI that uses data and algorithms to enable machines to learn in a way similar to how humans do. These systems learn from data, allowing them to make increasingly accurate predictions or decisions over time without explicit programming. This technology is commonly used in AI-driven customer segmentation, personalization, and predictive analytics use cases.

Natural Language Processing (NLP)

Natural language processing allows machines to understand, interpret, and generate human language, both spoken and written. NLP combines computational linguistics with machine learning and deep learning models to power language translation apps, email filters, digital assistants, and more. NLP is behind various marketing applications, including sentiment analysis tools, customer service chatbots, content generation apps, and social listening tools.

Computer vision

Computer vision uses machines to identify and extract information from visual data such as images and videos. Computer vision models are trained on massive amounts of visual data and make complex decisions or recommendations based on this data. In marketing, computer vision is used to conduct sentiment analysis using images and videos shared by customers on social media and other online channels, helping marketers better understand how their customers perceive their brand.

Predictive analytics

Predictive analytics uses statistical algorithms and machine learning techniques to analyze historical data and discover patterns and trends to predict future behavior or outcomes. Marketers can use predictive analytics tools to forecast customer churn, identify potential high-value customers, and personalize promotions more accurately.

Personalization engines and recommendation systems

Personalization engines are software designed to deliver highly relevant product recommendations, content customization, and targeted marketing campaigns to customers. By analyzing customer preferences, past interactions, and online behavior, AI algorithms may dramatically improve the quality and accuracy of personalized marketing initiatives. Using similar methods, recommendation systems leverage artificial intelligence to suggest products, services, or content to customers based on their behavior and preferences and are an integral part of ecommerce platforms and content streaming services.

3 Examples of AI-Enabled Marketing

As artificial intelligence tools grow more powerful, understanding their role in marketing is essential for businesses striving to successfully navigate competitive and dynamic markets. Here are three use cases illustrating how the marriage of artificial intelligence and marketing paves the way for innovation and growth.

Multilingual campaigns

Neural machine translation (NMT), a combination of NLP and machine translation, is an artificial intelligence technique marketers use to run multilingual ad campaigns. NMT automatically translates one language into another and even captures the cultural nuances present within languages. Marketers can use NMT to broaden the reach of their campaigns, enabling them to target a more diverse customer base. 

Advanced A/B testing

A/B testing is often a time-consuming, error-prone process. AI’s predictive capabilities assist marketing teams in selecting the options most likely to convert before they’re used in real-world scenarios, such as website CTAs or landing page layouts. AI can also track what types of messaging individual customers respond to, logging which tone generates a more favorable response. Using this data, marketers are able to adjust their messaging to match customer preference at a granular level. 

Competitive intelligence 

By proactively identifying opportunities, risks, and threats, competitive intelligence tools help businesses stay one step ahead of their competitors. Artificial intelligence-enabled tools may be deployed to track competitor pricing, promotions, and new product development, giving marketing teams the lead time to formulate a response. AI-enabled competitive intelligence can also spot changes in consumer tastes and preferences, identify new, underserved markets, and proactively respond to customer product or service issues using social listening.

Common Challenges of Using Artificial Intelligence in Marketing

Although the deployment of AI in marketing holds immense potential, its integration can present a series of challenges for marketers to navigate. Here are five of the most frequently encountered obstacles businesses face when incorporating AI into their marketing initiatives and how to overcome them.

Data quality and accessibility

Artificial intelligence algorithms are heavily reliant on relevant, high-quality data. When the data they use is inaccurate or incomplete, the quality of their predictions and insights will suffer. Using clean, relevant, and complete data to train models is essential. But when data is scattered across different systems and sources, it presents a significant challenge. A modern cloud data warehouse can store massive amounts of data from numerous source systems, acting as a single source of truth for all relevant data and ensuring AI models have access to the diverse training data they need. 

Data storage and processing infrastructure restraints

Artificial intelligence tools consume enormous amounts of data storage and compute resources. Organizations may lack the ability to store large amounts of data affordably. In addition, the compute resources required to power AI marketing tools may cause resource contention issues when running alongside other business systems. A modern cloud data platform solves both of these issues. It provides cost-effective storage for near-infinite amounts of data and rapidly scalable compute resources capable of powering the most intensive AI applications without impacting other areas of the business. 

Cost

Integrating AI into marketing processes may involve significant investment in data infrastructure, specialized talent, and team training. But the cost of maintaining the status quo while other organizations embrace change and innovation often outweighs the up-front costs accompanying any technology upgrade. Using flexible, scalable cloud resources managed by vendors is one way to reduce infrastructure and associated costs.

Data privacy

As the use of sensitive consumer data grows more prevalent, it is essential to ensure compliance with data privacy regulations and consumer protection laws. Artificial intelligence algorithms use consumer data along with other data sources to improve the accuracy of their predictions and decisions. Without robust data governance standards, businesses are more vulnerable to financial and reputational damage. The best cloud data platforms are designed to keep sensitive data secure. With a suite of comprehensive data security features and a robust, resilient cloud infrastructure, marketers are free to  focus their full attention on using their data, not securing it.

Learning curve during implementation

Building out the required AI capabilities in marketing can be a steep learning curve, especially for marketers still reliant on legacy tools and techniques. Although many AI-enabled marketing tools are designed to be user-friendly, setting up and maintaining them may require hiring someone with specialized skills in data science, machine learning, and AI technologies, presenting a barrier to entry for some smaller organizations. Depending on the use cases, smaller organizations may benefit from partner services companies to handle the heavy lifting.

Maximize Your AI-Enabled Marketing with Snowflake

Behind every successful AI-driven marketing program is a robust digital infrastructure purpose-built to meet the compute and storage requirements of resource-hungry tools. The Snowflake Data Cloud is designed to support the most intensive AI-driven marketing initiatives. Backed by a scalable and secure cloud data warehouse, marketers can unify their fragmented data, seamlessly deliver superior customer experiences, and maximize return on marketing and advertising investment. 

Learn more: Using Snowflake and Generative AI to Rapidly Build Features