Unleashing AI in martech: A guide to cross-functional collaboration

AI is rapidly transforming marketing, offering new opportunities for personalization, customer engagement and efficiency. Marketing technologists, data engineers, data analysts, domain experts and project managers must collaborate effectively to leverage AI fully. This collaboration is essential for exploring AI use cases in marketing, integrating data from various sources and building effective AI models.

The transformative power of AI in marketing

AI’s impact on marketing is vast and multifaceted. Here are some key use cases:

• Customer segmentation: AI can analyze vast amounts of customer data to identify distinct segments based on behaviors, preferences and demographics. This allows for highly targeted marketing campaigns.
• Predictive analytics: By analyzing historical data, AI can predict future customer behaviors, helping marketers to anticipate needs and adjust strategies proactively.
• Personalization: AI algorithms can create personalized content and recommendations in real-time, enhancing the customer experience.
• Chatbots and virtual assistants: AI-powered chatbots can provide instant customer support, improving response times and customer satisfaction.
• Campaign optimization: AI can continuously analyze campaign performance data and optimize marketing efforts in real-time, ensuring maximum ROI.

Use case example: AI for audience segmentation

Let’s consider the use case of AI for audience segmentation. Traditional segmentation methods rely on broad categories such as age, gender or location. AI, however, can delve deeper, analyzing data from multiple sources to identify more nuanced segments based on behavior patterns, purchasing history, social media activity and more.

For instance, an ecommerce company might use AI to segment its audience into categories like “bargain hunters,” “loyal customers” and “impulse buyers.” Each segment can be targeted with tailored marketing strategies for higher engagement and conversion rates.

Dig deeper: AI transformation: How to prepare your marketing team

Overcoming the limitations of out-of-the-box martech features

While many martech platforms offer built-in AI features, they often fall short due to data silos. These silos occur when data is isolated within different departments or systems, preventing a holistic view of customer information. As a result, out-of-the-box AI solutions might not provide the best results, as they cannot access and analyze all relevant data.

To overcome this, connecting data from various source systems and performing feature engineering is essential. This involves:

• Data integration: The first step is to integrate data from different sources, such as CRM systems, social media platforms, website analytics and more. This requires a robust data integration strategy that ensures data is accurately and securely transferred.
• Data cleaning: Once the data is integrated, it must be cleaned to remove duplicates, correct errors and fill in missing values. This step is crucial for ensuring the accuracy and reliability of the AI model.
• Feature engineering: This involves transforming raw data into something that can be used by AI algorithms. This might include creating new variables, aggregating data or normalizing values.

Dig deeper: What does ‘better data quality’ mean for marketers? And how do we get there?

Building an AI model for marketing: A step-by-step process for multiple stakeholders

Building an effective AI model for marketing involves several steps:

• Define objectives: Clearly define the business objectives and desired outcomes of the AI model. This helps in setting the right direction and evaluating the model’s success.
• Data collection: Gather data from various sources, ensuring it is comprehensive and relevant to the defined objectives.
• Data preparation: Clean and preprocess the data to make it suitable for analysis.
• Model selection: Choose the appropriate AI algorithms based on the problem. This might involve machine learning techniques such as clustering, classification or regression.
• Training and testing: Train the model using a portion of the data and test its performance on a separate data set. This helps in assessing the model’s accuracy and robustness.
• Deployment: Once the model is validated, deploy it into the marketing technology stack, ensuring it integrates seamlessly with existing systems.
• Monitoring and optimization: Continuously monitor the model’s performance and make necessary adjustments to improve its effectiveness.

To successfully implement AI in martech and manage all these moving pieces, it is essential to make the most of the unique skill sets of marketing technologists, data engineers, data analysts, domain experts and project managers.

Marketing technologists

• Business acumen: Understand business objectives and marketing operations processes.
• Governance and tagging: Ensure proper data governance and tagging practices.
• Data definition and metrics: Define data standards and metrics for consistency and accuracy.
• Martech expertise: Proficient in martech tools and systems, enabling effective integration and utilization of AI.

Data engineers

• Data integration: Skilled in integrating data from multiple sources, ensuring seamless data flow.
• Data cleaning: Expertise in data cleaning and preprocessing, ensuring data quality.
• Data architecture: Design and maintain scalable data architectures that support AI initiatives.

Data analysts

• Data visualization: Creating clear and informative visualizations to communicate data insights.
• Statistical analysis: Conducting analyses to understand data patterns and trends.
• Reporting: Generating reports that summarize findings and support decision-making.

Domain experts

• Industry knowledge: Deep understanding of industry-specific trends and challenges.
• Regulatory compliance: Ensuring that AI applications comply with industry regulations and standards.
• Customer insights: Providing insights into customer behavior and preferences specific to the industry.

Project managers

• Agile methodology: Applying agile principles to manage AI projects efficiently.
• Stakeholder communication: Facilitating communication between different teams and stakeholders.
• Risk management: Identifying and mitigating potential risks throughout the project lifecycle.

Dig deeper: How to transform martech and multichannel marketing for the AI era

A collaborative process for building AI models

The process of building AI models involves close collaboration between marketing technologists, data engineers, data analysts, domain experts and project managers:

• Requirement gathering: Marketing technologists gather requirements based on business objectives and define the scope of the AI project.
• Data integration: Data engineers integrate and preprocess data from various sources, ensuring it is ready for analysis.
• Data analysis: Data analysts interpret data trends, generate insights and provide actionable recommendations to refine the AI model.
• Model development: Data scientists develop and train the AI model, leveraging their expertise in algorithms and statistical analysis.
• Domain insights: Domain experts provide industry-specific insights to ensure the model aligns with market realities and regulations.
• Project management: Project managers oversee the entire process, ensuring timely delivery, stakeholder communication and risk management.
• Implementation: Marketing technologists implement the model into the martech stack to ensure it aligns with marketing strategies and operations.
• Continuous improvement: All teams work to monitor the model’s performance, making necessary adjustments and optimizations.

Transforming martech with AI: The cross-functional team advantage

Integrating AI in marketing offers immense potential, but achieving success requires a cohesive effort from diverse professionals. Marketing technologists, data engineers, data analysts, domain experts and project managers form a comprehensive team, each bringing unique skills and perspectives. 

By fostering collaboration among these various roles, organizations can overcome data silos, seamlessly integrate data from multiple sources and build robust AI models that drive personalized, data-driven marketing strategies.

This comprehensive teamwork is essential for achieving AI success in the ever-evolving marketing landscape, delivering exceptional customer experiences and maintaining a competitive edge.

Dig deeper: How to do an AI implementation for your marketing team