0Logistic regression is a form of regression which is used when the dependent is a dichotomy (yes or no) and the independents of any type (either continuous or binary).
Logistic regression can be used to predict a dependent variable on the basis of continuous and/ or categorical independents and to determine the percentage variance in the dependent variable explained by the independent variables. The impact of predictor (independent) variables is usually explained in terms of odds ratios. This is one of the most prefered linear classifier model that is used in solving many problems in our client services industry in India.
One important point to say is that the choice of any model selection in solving a practical use case only depends on the type of the dependent variable and often the underlying probability distribution. It does not depends on the types of the independent/ predictor variables.
Where it is used:
Logistic model is used in various instances. Among them the following are very common where it is used quite often:
- Customer attrition model/Churn model: To predict the customer likely to attrite from a bank/financial institution/telecom services
- Next purchase propensity model: To predict whether a customer is likely to purchase if we target by a promotion/campaign
- Cross sell model: whether a customer is likely to buy a new product or a service across the all possible products/services
- Upsell model: Whether a customer likely to buy more in the next quarter than his existing purchase pattern if we target them with right promotional offer
- Customer conversion model: Whether the prepaid customer for a telecom giant will convert to a postpaid customer if we target them suitably
- Insurance model: Whether a customer is likelihood to be hospitalized in the coming quarters.
This model is one of the predictive models that used across many industries like aviation, bank and finance, retail, pharma, CPG, telecom, shipping line, online retail, E commerce, FMCG etc.
Aim to build logit model:
The basic aim to build such model is to investigate the probability of a customer/patience is likely to respond to a defined event. Event is the fact that we are trying to predict. Based on the predicted probability right business steps should be taken to optimize the margin of the business. To study the drivers responsible for an event is also another parallel objective in building such models. By building logit model
- The right targeting list could be generated to maximize the response rate
- Right set of customers could be targeted to cross sell or upsell any product/service
- Right business decision could be taken in advance the value the customers when they are active in the system
