Enhancing B2B Personalization with Human-ML Integration:
ML has become crucial for business-to-business (B2B) companies seeking to offer personalized services to their clients. However, while ML can handle large data volumes and detect patterns, it often needs a more nuanced understanding that human insights provide, especially in building relationships and dealing with uncertainties in B2B contexts. The study explores how integrating human involvement with ML can enhance personalized information systems (PIS) for B2B applications. By developing a research framework and applying it in the energy sector, the study demonstrates how combining human expertise with ML algorithms improves personalization, achieving above-average performance metrics like precision, recall, and F1 scores.
The study addresses a significant gap in the existing literature by detailing how human insights can practically augment ML capabilities. It highlights B2B firms’ challenges in adopting ML for personalization due to theoretical gaps, privacy concerns, and AI fairness. The study presents a model outlining the stages of human-ML augmentation, from understanding business needs to model deployment and evaluation. The study aims to bridge the gap between academic research and practical implementation by offering theoretical insights and practical examples, advancing B2B personalization strategies through effective human-ML collaboration.
Enhancing Machine Learning with Human Insights:
Integrating human expertise with ML can create collaborative intelligence, leveraging each other’s strengths to push business boundaries. Key human contributions include developing theoretical frameworks to enhance model interpretability, using expert knowledge to select features and algorithms, and combining intuitive judgment with ML’s analytical speed for better data collection. Additionally, human insights can help assess customer feedback, ensuring fair and ethical ML outcomes by mitigating biases and improving model accuracy. These human-machine Learning collaborations are valuable in B2B personalization, optimizing recommendations, and addressing data limitations.
Research Framework for Human-AI Integration:
To optimize human-AI models, firms often start with AI for initial data analysis and then use human expertise to refine results, aiming to balance cost and efficiency. This approach is particularly useful in B2B contexts for personalized marketing strategies. A proposed framework integrates human insights throughout the ML process, starting with theoretical foundations (e.g., U&G theory), selecting suitable ML techniques with expert input, and choosing relevant features. Human judgment also enhances data collection and model evaluation, ensuring the accuracy and fairness of recommendations. Feedback from customers, especially those dissatisfied, is assessed by experts to improve model performance and reduce biases.
Methods:
The study investigates an integrated human-ML model-based PIS in the energy sector, blending traditional data mining methodologies like CRISP-DM and SEMMA with human insights. The process involves four key phases: (1) Premodel Creation using U&G theory for content identification, expert knowledge for ML technique selection, and fuzzy Delphi method for feature selection; (2) Data Collection and Preparation through structured interviews; (3) Model Creation with Python; and (4) Model Evaluation using precision, recall, F1 metrics, and expert judgment to refine the model. This approach aims to enhance model effectiveness by integrating human expertise with data-driven methods.
Empirical Research:
The study developed a human-ML integrated PIS for the energy sector, focusing on B2B transitions to sustainable energy. In the model-creation phase, the content was crafted using U&G theory, and a decision tree-based collaborative recommendation method was chosen due to its efficiency with limited item feature data. Initial feature selection employed the fuzzy Delphi method, supplemented by ML techniques, to identify crucial features like age and job discipline. Data were gathered from 1,155 B2B visitors at industry events. The ML model, implemented in Python, was tested through feedback rounds, evaluating performance with precision, recall, and F1 scores, all exceeding the acceptable threshold, confirming the model’s effectiveness.
Discussion and Implications:
While ML excels in quantitative tasks, human judgment remains superior in subjective evaluations due to its intuitive and insightful nature. The study presents a model integrating human expertise into the CRISP-DM data mining framework to enhance ML processes for B2B personalization. Key stages include using marketing experts for theoretical foundation and feature selection, IT experts for data handling, and human judgment for model evaluation. The study highlights the benefits of combining human insights with ML for improved personalization and addresses concerns about ML biases. Future research should explore additional human-ML integration points and the theoretical basis for hybrid models.
Sources:
- https://www.researchgate.net/publication/377056614_Augmenting_machine_learning_with_human_insights_the_model_development_for_B2B_personalization
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