Educating people and training algorithms: The new high-performance model for marketing management

(This article was originally written for Marketeer magazine in PT)

It became a constant challenge for organizations to understand and evaluate the benefits of both workforces – human and technological – working together in a hybrid performance model.

In fact, this is a scenario that companies always face when they bring and incorporate new technologies within the working methods implemented. With the latest advances of the artificial intelligence (AI), some critical questions arise in the horizon:

? Until when will it make sense to continue educating people to operate tools that are now being managed more efficiently by AI?

? When to train (your own) algorithms so that they further increase the business’s competitiveness?

? How to combine human-machine capabilities to create a high-performance hybrid model?

The impact of AI on marketing tools

The incorporation of AI into marketing tools has increased considerably in recent years, moving from the traditional ingredient component (like “intel inside” concept) to an interactive application interface, receiving input from users, that has already started impact certain marketing tools and tasks:

• Email Marketing: Automate email marketing campaigns, including segmenting lists, personalizing content, and optimizing send times;
• Content Creation: Create articles, product descriptions, and social media posts, with supremacy in generating large volumes of content in a short period of time;
• Chatbots and Virtual Assistants: Provide customer support and lead generation;
• Social Media Management: Scheduling, content suggestions, sentiment analysis, and even responding to frequent customer inquiries;
• Search Engine Optimization (SEO): Provide insights for keyword research, content optimization, and backlink analysis;
• Pay-Per-Click Advertising: Bid management, ad optimization, and audience targeting in PPC campaigns;
• Personalization: Product recommendations and marketing campaigns based on user behaviour and preferences;
• A/B Testing: Optimize website elements and marketing campaigns;
• Predictive Analytics and Reporting: Analyzing large datasets and generating reports with actionable insights, and predict customer behaviour, such as churn prediction and lead scoring

It's important to note that the degree of automation and AI integration varied among businesses and industries. Some organizations embraced AI and automation extensively, while others were in the early stages of adoption.

Considering the dynamics of the Martech industry, new AI-powered tools and capabilities continue to emerge, creating increasing opportunities for instrumental changes in the marketing functions landscape.

The key moment to incorporate AI into marketing tasks

The decision to stop training people to use a tool and start training a machine to perform the same task depends on several factors.

This decision must be guided by an in-depth and case-by-case analysis, considering its alignment with the business objectives and taking into account various scenarios, which involve the complexity of the tasks; the potential benefits of AI as well as its limits and available resources:

• Return on Investment: Assess whether time, effort, and resources required to train the machine justify the expected benefits in terms of efficiency, accuracy, and cost savings;
• Complexity of the Task: Some tasks may be too complex or require human judgment, intuition, or creativity;
• Repetitive and Time-Consuming Tasks: Strong candidates for automation to be performed consistently, quickly with zero fatigue, freeing up human resources for more creative and strategic work;
• Scalability: Consider whether the task needs to be scaled up to handle larger volumes of data or operations. If there is a growing demand for a particular task, automation can ensure scalability without a linear increase in human labour;
• Consistency and Accuracy: If the task requires a high degree of accuracy, AI can reduce the risk of human errors, and following predefined rules, patterns and standards, complying with specific protocols;
• Intensive data analysis: Extreme capacity for data analysis, patterns and insights, ?inferring diagnoses and conclusions of various types (investigation, investment, etc.);
• Data Availability: Automation relies on historical data to train machine learning models. Ensure that sufficient data is available to train and validate the machine effectively;
• Technology Readiness: Assess the readiness of AI and machine learning technologies for the specific task. Some tasks may require more advanced AI capabilities, which may not be readily available;
• Ethical and Legal Considerations: Evaluate whether automating specific tasks aligns with ethical and legal guidelines

Training and educating marketing teams about AI

Training and educating marketing teams about AI and its applications in performance operations is crucial for successful integration:

1. Assess current knowledge and skills
2. Create a learning plan
3. Organize training sessions
4. Encourage self-learning
5. Hands-on experience
6. Experimentation and Prototyping
7. Cross-Functional collaboration
8. Case studies and Use cases
9. Regular updates and discussions
10. Feedback and Evaluation
11. Advanced Learning

Practical examples

1. Customer Segmentation:

• Task: Use AI tools to segment your customer base on behaviour or purchase history
• Objective: Create targeted marketing campaigns for each segment

2. Predictive Analytics for Sales:

• Task: Develop a predictive model to forecast sales for a specific product or service
• Objective: Improve inventory management and marketing strategies based on sales predictions

3. Chatbot Implementation:

• Task: Design and implement a chatbot for your website or social media platforms
• Objective: Enhance user engagement and provide 24/7 customer support

4. Dynamic Ad Generation:

• Task: Use AI to create dynamic ads that adjust content based on user behaviour or preferences
• Objective: Increase ad relevance and click-through rates

5. E-mail personalization:

• Task: Implement email personalization using AI to tailor email content to individual subscribers
• Objective: Improve email open and conversion rates

6. Email Campaign Automation:

• Task: Set up an AI-driven email marketing campaign that automatically sends follow-up emails based on user behavior
• Objective: Nurture leads and increase conversion rates

6. A/B Testing:

• Task: Conduct A/B tests for a marketing campaign, with AI helping to analyze and interpret results
• Objective: Optimize marketing strategies based on data-driven insights

7. Content Recommendations:

• Task: Implement an AI-driven content recommendation system on your website
• Objective: Increase user engagement and time spent on your site

8. SEO Optimization:

• Task: Use AI tools to optimize website content, including meta tags, headings, and keyword placement
• Objective: Improve search engine rankings

9. Data Visualization:

• Task: Use AI-powered data visualization tools to create interactive dashboards for marketing data
• Objective: Enhance data-driven decision-making

10. Competitor Analysis:

• Task: Use AI tools to analyze competitors' online presence, keywords, and ad strategies
• Objective: Identify opportunities to outperform competitors.

Encourage team members to choose projects aligned with their interests and the specific goals of your organization.

Training AI algorithms to operate marketing tools involves a combination of goal setting, data preparation, resource engineering, ML techniques and interactive and recurring optimization to produce results with an effective impact.

These models are trained periodically so that they adapt to changes in marketing dynamics and are aligned with business strategies and objectives, becoming considered new value-added assets.

This investment can lead to more effective marketing initiatives that improve customer experiences, increasing the profitability of operations and providing brands with a highly competitive advantage in their respective markets.

The high-performance hybrid model

From the moment that certain marketing tools are managed by AI models, it becomes essential to ensure that marketers gain skills on how AI works within the application context of the tools, and how to collaborate effectively with them – In this case, educating and training people and machines to work collaboratively.

Marketers are needed to define strategies, produce creativity, interpret the insights generated by AI and to ensure that this entire process is aligned with the brand's values and objectives (ethical and responsible compliance).

The implementation of AI models in marketing tools desirably requires a combination of multidisciplinary experiences working in close collaboration between marketing professionals, data scientists and AI specialists in a successful functional symbiosis.

The future of digital marketing tools training

All these circumstances will have a future impact on current training programs to use marketing tools – and in particular on digital tools applied in marketing management.