In the following is a list of the most important predictive analytics approaches that allow to forecast consumer behavior and customer needs which should be useful for your business planning as well as revenue and profit chances:
- Profiling is used to describe individuals based on characteristics or behavior to make better business decisions. You can group, for instance, similar profiles by shared characteristics such as demographic, geographic, psychographic and behavioral characteristics.
- Association analysis searches for patterns where an event is associated with another event. A typical example is the shopping cart analysis which calculates the purchase probability for each product with the aim to identify patterns and rules in the buying behavior to gain a competitive advantage.
- Logical regression answers with which probability of success an event depends on certain variables. Hence, it is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables.
- Decision tree analysis presents decision rules on the basis of which data are classified and clearly displayed graphically, helping to describe the target group to be analyzed more in detail.
- Best next offer attributes each customer to the products that he/she most likely will buy next. in corresponding ads these products can be offered to the customer on the next visit to an online shop.
- Cluster analysis is a group formation process that looks for patterns in data with the aim to identify homogeneous subsets from its heterogeneous entirety. This can be used for a wide variety of tasks, e.g. for the development of a product for a specific target group.
- Linear regression is a method to predict or estimate the numerical value of an observed adjoining variable, e.g. the annual sale per customer. Forecasts or trends can then be derived from the acquired knowledge.
By Daniela La Marca