How To Calculate Purchase Propensity With Machine Learning

Calculation of purchase propensity via means of machine learning proved its efficiency. Down below, an in-depth analysis of why and how we calculate purchase propensity with machine learning follows.

Marginal propensity of consumers toward a product or a service is generally guessed by suppliers, so demand shifts, market movements, and financial alterations, although they have an impact, are overlooked. In fact, not being able to keep records and data causes so many problems in terms of calculations of these metrics that providers are left to guess only. Problem is that purchase propensity is a major driving force of most transactions. Who will buy this product? and which product is going to be bought by whom? are the questions pulling the strings of selling strategies. However, without a proper data collection and means of utilizing them, every strategy is dull. In particular, without a proper way of calculating purchase propensity, selling strategies are missing on so many levels.

Why we are yearning the information on likelihood of purchases for specific products has simple grounds. In order to put a sound selling strategy to work, one must know the facts on products’ place in market demand. That is, individual likelihood to buy a product or a service. Chances are high that a person signals if s/he is going to buy a certain item. Those signals may be ultimately exposed, or they can be hidden in so many layers of consumer behavior that revealing them needs too much work. Nonetheless, wherever the situation places, a certain amount of time and effort must be put on revealing purchase propensities of individuals. Thus, in a world where almost everybody becomes consumer, a willing consumer and an unwilling one must be differentiated in terms of their buying behaviors. Hence, both conventional statistical methods and the newest methods must be utilized. However, now the best way to calculate purchase propensity is machine learning.

Machine learning algorithms deal with purchase propensity predictions as they are dealing with other applications: correlation calculation. That is, as an individual's inclination to behave in a certain way changes, some parameters of this behavior can be nested into each other – forming a prediction model to anticipate customer propensity. Of course, this needs a vast amount of data to even start. However, e-commerce and e-marketing, where data collection is so easy (relative to off-line retail), have become so widespread that the only thing left is just turning that data into information for prediction models. Web behavior, email engagement, and feedback to websites, all are a huge source of data for starting out machine learning model formation.

Enhencer utilizes best algorithms to construct a predictive purchase propensity model for you. In incremental means, a propensity score is attained to every class (or group) of consumers. Then, a model is offered in which purchase and non-purchase classes (binary classifier) are tested. Hence, with further teachings and tests, classifier is expected to have a maximum accuracy on attaining propensity scores. If model is working well, we can see similarities between model and former marketing data. This way we are running an A/B testing to see if model works. Thus, with that model running we can spot the purchaser and non-purchasers so that a sound selling strategy can be formed such as factoring out the non-purchasers and leaning towards the highest likely one to purchase.