In today’s competitive digital environment, Digital Service Providers (DSPs) focus on targeting the right set of customers with personalized marketing campaigns. However the different approaches to determine the right set of customers such as, manual spreadsheet-based statistical modelling and outcome modelling have shown following limitations.
- Randomized and inaccurate list of target customers
- Lack of granularity on which customers are most likely to respond to marketing campaigns
- Minimum marketing ROI
DSPs need to look beyond the traditional approaches and adopt ML-based uplift modelling to identify the right set of target customers.
The Machine Learning (ML)-based uplift modelling enables DSPs to increase the uplift score, which helps in gaining granularity in terms of which probable churners have higher likelihood to respond positively to a personalized campaign, thereby improving marketing efficiency and driving higher incremental revenue.
The successful implementation of the model requires right set of enablers such as raw data acquisition, feature engineering, and AI/ML model development.
Download this insight to know more about:
- How DSPs can develop a ML-based uplift model architecture and thereby increase the uplift score
- Which are the most critical features to be selected for an effective uplift model
A leading DSP in the Americas achieved the following business benefits after implementing uplift model – Increased the uplift score by 18.2%, resulting in improved customer retention and increase in marketing ROI.
- Ankit Tomar
- Dinesh Singh GC
- Prashantkumar Maloo
- Rohit Karthikeyan
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