Problem Statement
Customer Segmentation
The major issues that any marketer faces are client segmentation, churn prediction, and customer lifetime value (LTV) projection. Businesses have a tonne of marketing-related data from a variety of sources, including email campaigns, website traffic, and lead information.
PS Number: PSDAT007
Domain Bucket: DATA ANALYTICS
Category: Software
Dataset : NA
It is possible to anticipate the likelihood that a user will upgrade to the commercial version based on their past behaviour and their behaviour during the trial period. A programme may launch customer interventions to convince the consumer to convert early or more effectively participate in the trial if the choice problem were modelled.
Background of the Problem
Customer segmentation, churn prediction and customer lifetime value (LTV) prediction are the main challenges faced by any marketer. Businesses have a huge amount of marketing relevant data from various sources such as email campaigns, website visitors and lead data.
Objective
For example, given the pattern of behaviour by a user during a trial period and the past behaviours of all users, identifying chances of conversion to paid version can be predicted. A model of this decision problem would allow a program to trigger customer interventions to persuade the customer to convert early or better engage in the trial.
Summary
Using data mining and machine learning, an accurate prediction for individual marketing offers and incentives can be achieved. Using ML, savy marketers can eliminate guesswork involved in data-driven marketing.