Customer Segmentation: Deep Learning’s Natural Language Processing abilities allow large retailers to increase datasets, including their potential clients’ social media profiles, with the objective of obtaining more precise segmentations. Additionally, Artificial Intelligence can also allow the companies to choose the most appropriate channels to use to market to their potential clients.
Content Personalization: The majority of the content that is shown to shoppers online is irrelevant or not suited to their preferences, which reduces the conversion rate. Just as with customer segmentation, AI offers additional unstructured datasets for a better multivariate analysis, with the goal of identifying a larger number of correlations than rule-based systems.
Price Optimization: AI is able to optimize prices in way that is more sophisticated than using traditional methods like “cost-plus,” “relative to competitors,” or “odd-even pricing” ($0.99). By identifying the correlations between datasets, Artificial Intelligence can better optimize the relevant factors, including price elasticity, revenue, profits, product availability, and the phases of the product lifecycle. It can even take willingness to pay into account.
Churn Prediction: Traditional programs waver when it comes time to incorporate new information sources, maximize the value of multivariate datasets, or offer detailed recommendations. When AI predicts customer churn, it can identify the main churn indicators more effectively and improve the adjustments made, through a more precise prediction of the format and content of the interventions, so that they will be successful.