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Sales and marketing activities are vital in helping companies achieve their growth, profitability, and brand awareness goals. However, the success of these activities is closely related to the budget spent, smart analysis, and strategic decision-making.
Businesses leverage data analytics and machine learning models in their campaigns to establish effective communication with their customers and potential customers. Therefore, predicting campaign success with artificial intelligence and discovering the features that influence success can help businesses use their resources more efficiently.
AI algorithms are trained with much campaign data to predict campaign success. Some AI models contribute to creating more effective campaigns by allowing us to examine campaign-related features.
One way to enhance AI models’ performance is to train them with more data and optimize their hyper-parameter settings.
Hyper-parameters are parameters that directly affect an artificial intelligence model’s learning process and performance but are not learned by the model itself.
Hyper-parameter optimization involves finding the optimal values for these parameters, enhancing the model’s prediction accuracy. Well-tuned hyper-parameters reduce the error in the model’s predictions and increase the reliability of strategic business decisions, helping to minimize risks.
We optimized the hyper-parameters using the Grid Search algorithm. When comparing the prediction models before and after hyper-parameter tuning, we observed a 3% increase in accuracy.
This improvement in AI predictions enables more accurate forecasting of the success of campaigns created with Next4biz CRM, giving businesses a competitive advantage over their rivals.
You can access the full text of our scientific study titled “The Impact of Hyper-Parameter Tuning on Campaign Success Prediction,” presented at the 12th National Scientific Studies Congress, by clicking here.
