AI tools have significantly boosted my approach to client targeting in media sales. For example, I once used an AI-driven customer analytics platform to analyze audience data for a digital ad campaign. It identified patterns in viewer behavior, revealing an untapped demographic interested in health content. This insight allowed me to tailor proposals with personalized ad placements that resonated with this group. As a result, the campaign saw a 35% increase in engagement. Additionally, AI tools automated lead qualification, giving me more time to focus on client relationships and strategic planning, which further strengthened our results.

More organisations are continually taking advantage of machine learning methods to give predictive outputs based on the data they have been trained on, particularly in sales for predicting market opportunities. Often, these models are created in R or Python, but other tools exist. ML Methods: . Linear Regression, for continuous variables (i.e. a line of best fit) . Logistic Regression, for categorical binary output based on probability (i.e. prediction is either A or B) . Random Forest, utilises multiple layers of decision trees to reach an output (can handle regression or categories) . Neural Networks, take advantage of deep learning to reach a final output, in this case more data may mean higher accuracy (unlike some other methods)

AI tools can analyze vast amounts of data to identify patterns, refine audience segments, and predict client needs. They help personalize pitches and optimize ad placements for maximum ROI.