Summary: 14 Data + AI Challenges from 57 Leaders in MarTech

The webinar, hosted by Anthony Lamot and Jonathan van Driessen from DESelect, explored 14 key data and AI challenges identified from interviews with 57 marketing operations leaders. The challenges spanned across integration issues, data management, personalization, process complexities, knowledge gaps, resource constraints, AI use cases, internal collaboration, customer lifecycle understanding, actionable insights, marketing attribution, cross-channel marketing, and the impact of the economy on marketing strategies. Read the full report here.

1. Lack of Integrated Systems and Complex Data Models

• Challenge: Systems are often not fully integrated, leading to complex data models.
• Recommendation: Engage experts for integration, particularly those with both technical and marketing experience.

2. Effective Data Management and Dependence on Others

• Challenge: Managing data across systems is tough, often leading to marketing “ping-pong.”
• Recommendation: Empower marketers to access and use data independently, minimizing delays.

3. Data Utilization and Personalization

• Challenge: Difficulty in translating data into actionable personalization.
• Recommendation: Focus on merging and managing data at the segmentation level rather than at the content level for easier management and maintenance.

4. Complexity in Systems and Processes

• Challenge: Complex projects and processes can be overwhelming.
• Recommendation: Use standardized templates and consider taking a step back to streamline processes across teams.

5. Knowledge Gaps

• Challenge: Lack of training and knowledge transfer leads to poor tech adoption.
• Recommendation: Invest in continuous training and knowledge sharing to ensure team competency.

6. Resource and Budget Constraints

• Challenge: Teams are expected to do more with less due to budget and resource cuts.
• Recommendation: Measure campaign velocity to gauge efficiency and streamline processes to reduce unnecessary resource use.

7. Understanding AI Use Cases and Related Concerns

• Challenge: AI potential is recognized, but clear use cases are still being explored.
• Recommendation: Experiment with AI but ensure human oversight remains central to avoid poor automated decisions.

8. Internal Collaboration and Scaling

• Challenge: Coordinating across teams and scaling efforts can be difficult.
• Recommendation: Implement marketing request forms and project management tools to manage tasks efficiently.

9. Understanding Customers and Their Lifecycles

• Challenge: Gaining a holistic view of the customer lifecycle remains challenging.
• Recommendation: Map out the entire customer journey and identify content gaps to enhance customer lifecycle management.

10. Lack of Actionable Insights and Changing Stakeholders’ Mindset

• Challenge: Access to actionable insights is limited, and some teams are stuck in outdated marketing mindsets.
• Recommendation: Consolidate data into centralized systems and emphasize the value of personalized, data-driven marketing over traditional methods.

11. Marketing Attribution Problems

• Challenge: Demonstrating the value of marketing efforts is complex and often misleading.
• Recommendation: Use attribution models cautiously and be aware of factors like “dark social” that aren’t easily measurable.

12. Cross-Channel Marketing and Traffic Control

• Challenge: Managing message frequency across channels to avoid overwhelming customers.
• Recommendation: Implement traffic control systems to optimize message delivery and avoid customer fatigue.

13. New Systems, Legacy Systems, and Knowledge Transfer

• Challenge: Balancing new technologies with legacy systems while ensuring knowledge retention.
• Recommendation: Maintain a living internal wiki for continuous knowledge transfer and onboarding.

14. Economy

• Challenge: Economic uncertainty impacts budgets and customer spending power.
• Recommendation: Be prepared for fluctuations and focus on delivering value efficiently.