Precise and effective software promotion strategies are crucial for software companies. By introducing causal machine learning techniques to analyze software promotion data provided by software companies, we aim to reveal the extent to which the promotion strategy of technical support services affects sales effectiveness and explore the heterogeneity of this effectiveness across different customer groups. The results show that the introduction of technical support can generate a 60% increase in sales revenue for software companies. By comparing multiple causal machine learning models, Linear DML emerge as the optimal model, which is used to further explore customer subgroups with high causal effects and to interpret the model with SHapley Additive exPlanations (SHAP). We find that features such as larger company size, more personal computers, fewer employees, and less IT spending are customer features that are more responsive to technical support services. The findings are expected to provide software companies with a theoretical basis for developing more effective promotion strategies and identifying promotional customer groups.