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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 work together effectively to fully leverage AI. This collaboration is essential for exploring AI use cases in marketing, integrating data from different sources and building effective AI models.

The transformative power of AI in marketing

The impact of AI on marketing is enormous and multifaceted. Here are some key usage scenarios:

  • Customer segmentation: AI can analyze vast amounts of customer data to identify individual segments based on behavior, preferences and demographics. This enables highly targeted marketing campaigns.
  • Predictive analytics: By analyzing historical data, AI can predict future customer behavior, allowing marketers to anticipate needs and proactively adjust strategies.
  • Personalization: AI algorithms can create personalized content and recommendations in real-time, improving the customer experience.
  • Chatbots and virtual assistants: AI-powered chatbots can provide immediate 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: AI for audience segmentation

Let’s look at the use case of AI for audience segmentation. Traditional segmentation methods are based on broad categories such as age, gender or location. However, AI can dig deeper and analyze data from multiple sources to identify more nuanced segments based on behavioral patterns, purchase history, social media activity, and more.

For example, an e-commerce company can use AI to segment its audience into categories such as “bargain hunters,” “loyal customers,” and “impulse buyers.” Each segment can be targeted with customized marketing strategies for higher engagement and conversion rates.

Dig Deeper: AI Transformation: How to Prepare Your Marketing Team

Overcoming the limitations of off-the-shelf 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, not allowing for a holistic view of customer information. As a result, out-of-the-box AI solutions may not deliver the best results because they cannot access and analyze all relevant data.

To overcome this, connecting data from different 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 transferred accurately and securely.
  • Cleaning data: Once the data is integrated, it must be cleaned to remove duplicates, correct errors, and complete missing values. This step is crucial for ensuring the accuracy and reliability of the AI ​​model.
  • Functional engineering: This involves turning raw data into something that can be used by AI algorithms. This may include creating new variables, aggregating data, or normalizing values.

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 determine the right direction and evaluate the success of the model.
  • Data collection: Collect data from various sources and ensure it is comprehensive and relevant to the defined objectives.
  • Data preparation: Cleaning and preprocessing the data to make it suitable for analysis.
  • Model selection: Choose the right AI algorithms based on the problem. This may involve machine learning techniques such as clustering, classification or regression.
  • Training and testing: Train the model using part of the data and test its performance on a separate data set. This helps assess the accuracy and robustness of the model.
  • Stake: Once the model is validated, deploy it into the marketing technology stack so it can be seamlessly integrated with existing systems.
  • Monitoring and optimization: Constantly 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 skills of marketing technologists, data engineers, data analysts, domain experts and project managers.

Marketing technologists

  • Business insight: Understand business objectives and marketing operations processes.
  • Management and tagging: Ensure good data management and tagging practices.
  • Data definition and statistics: Define data standards and metrics for consistency and accuracy.
  • Martech expertise: Proficient in martech tools and systems, which enable effective integration and use of AI.

Data engineers

  • Data integration: Skilled in integrating data from multiple sources, ensuring seamless data flow.
  • Cleaning data: Expertise in cleaning and pre-processing data, 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.
  • static analysis: Perform analysis to understand data patterns and trends.
  • Report: Generate reports that summarize findings and support decision making.

Domain experts

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

Project managers

  • Agile methodology: Apply Agile principles to efficiently manage AI projects.
  • Communication with stakeholders: Facilitating communication between different teams and stakeholders.
  • Risk management: Identifying and mitigating potential risks throughout the project life cycle.

Dig Deeper: How to Transform Martech and Multichannel Marketing for the AI ​​Age

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:

  • Collect requirements: Marketing technologists gather requirements based on business objectives and define the scope of the AI ​​project.
  • Data integration: Data engineers integrate and pre-process data from different sources so that 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 and ensure timely delivery, stakeholder communication and risk management.
  • Implementation: Marketing technologists implement the model into the martech stack to ensure alignment with marketing strategies and activities.
  • Continuous improvement: All teams work to monitor model performance and make necessary adjustments and optimizations.

Transforming Martech with AI: The Cross-Functional Team Advantage

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

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

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



Dig deeper: how to implement an AI implementation for your marketing team

The opinions expressed in this article are those of the guest author and not necessarily those of MarTech. Staff authors are listed here.