Increase the ROI in Telemarketing by +250% by Prioritizing the Top 30% Leads

(1 – 2 minute read)


NGOs aim to transition one-time donors into consistent monthly contributors. Telemarketing (TM) has proven to be an effective channel for this purpose. However, NGOs also prioritize minimizing administrative costs to ensure maximum funds are directed towards their cause. This cost-efficiency is a key metric for donors, influencing their trust in the NGO. This presents a challenge: How can NGOs determine the most effective leads to call?



The challenge for NGOs is to find the right balance between investment in telemarketing and the returns it yields. The solution lies in leveraging data more intelligently. While Machine Learning (ML) is not a new concept, its application in predicting the outcomes of outbound activities has been limited. One potential barrier has been the perceived high cost and time commitment associated with hiring data scientists to develop and refine these models. Additionally, manual model creation lacks automation and is heavily dependent on the continuous involvement of the data scientists. An alternative approach is to partner with Allyy, which have demonstrated significant success in this domain.


Integrating Machine Learning through offers a strategic approach to outbound telemarketing activities. By analyzing data from past campaigns, Allyy accurately predicts which leads are most likely to respond positively. scores and leads into “A, B, C, & D” rankings, allowing NGOs to allocate resources effectively. In one case study, while the baseline hit rate across all leads was 5%, Allyy accurately forecasted that “A” leads would achieve a hit rate of +25% and “B” leads between 7-8%. Conversely, “C” and “D” leads performed below the 5% average. By concentrating on the top 30% of leads from the “A” and “B” categories, the NGO was able to engage 70% of potential donors, enhancing the ROI by 250%.

If you have the data

–  you might as well get the most out of it, and in most cases, that is with

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