Next generation predictive marketing based on self-learning algorithms
The technology of Machine Learning & AI Marketing is based on two recommendation models. Each is optimized to support a specific marketing approach. For inbound marketing – affinity analysis (or the so-called Inbound Predictive Marketing) and for outbound marketing – behavioral analysis (the so-called Predictive Outbound Channel). Used in tandem, the models enhance both inbound and outbound marketing activities.
The mechanism of affinity analysis relies on sophisticated algorithms used in association analysis. By thoroughly analyzing transaction data and correlations between specific products and in categories, they calculate the optimal combination of items in each offer. After the resulting data is parsed and modeled, a frame with product recommendations can be shown to each customer. In addition, the use of metadata makes it possible to instantly react to changes in customer preferences.
The system can employ machine learning to compare predictions from product association analysis for end customers on an ongoing basis. Then it assigns scoring to each given recommendation in order to indicate how likely that product is to be bought by individual customers. Moreover, by updating product exclusion grids, the algorithm ensures that products are not recommended to customers who already bought them.
Predicting Customer Journey models and AI-driven recommendations
SALESmanago Copernicus – Machine Learning & AI module can automatically select appropriate products for each customer and recommend the best possible way of communication with them based on their Customer Journey. Moreover, the system stores information on the behavior of anonymous website visitors, allowing the content to be personalized even for unidentified contacts. This means that all customers are subject to analysis of their Behavior Mechanisms.