While running direct marketing campaigns, businesses must map the right customers to a given promotional offer to maximize the campaign effect. For example, which customers should receive a discount on subscription, to minimize the business overall churn rate.
Different methods can be used to identify the right set of target customers for campaigns, such as, manual spreadsheet-based statistical modelling and outcome modelling. These methods, however, have some limitations like:
- Randomized and inaccurate list of target customers
- Lack of granular details such as which customers are most likely to respond to marketing campaigns
- Low marketing ROI due to poor response rate from customers
Machine Learning (ML)-based uplift modelling is a promising approach to overcome the above limitations. It allows businesses to categorize customers as the ones who are likely to respond positively to a campaign and those who would remain neutral or even react negatively.
An uplift model increases marketing ROI by determining the right target customers.
A well-executed uplift model would improve a business marketing efficiency and help in driving higher incremental revenue. The successful implementation of the model requires the right set of enablers such as raw data acquisition, feature engineering, and AI/ML model development.