## **6.4 Implications for Practitioners**

The policy implication of our mechanisms analysis is clear. Organizations running social media ad campaigns with social objectives should adopt more nuanced targeting approaches to effectively reach their target populations. Specifically, these organizations need to implement _explicit_ targeting strategies to overcome optimization algorithms of social media platforms that prioritize users that are cheaper to reach to maximize overall engagement numbers. In our study setting, for example, these are wealthier users that may frequently check their social media but are at lower risk of contracting malaria.

In our evaluation of the real-world campaign, Malaria No More employed geographical targeting based on states with high incidence rates, and also segmented audiences using various demographic groups. These groups were defined by age, gender, and levels of urbanization, reflecting the personas of interest. Yet, these audiences were likely too broad, and did not adequately consider the higher engagement costs of users living in non-solid dwellings. Our trial shows that more sophisticated targeting tools, like Facebook Lookalike Audience, can enhance the targeting precision of ad campaigns. Our recruitment method via Facebook ads indicates that reaching individuals in non-solid dwellings could be almost 10 times more costly than those in solid dwellings. Therefore, unless we specifically target these more vulnerable groups, the optimization algorithms are likely to concentrate on lower-risk individuals in order to meet engagement metric goals within a set budget.

We propose a three-step approach for practitioners aiming to ensure their social media ads effectively reach and impact populations of their interest. First, during the formative research stage, organizations should identify these target populations where the potential impact of intervention is greatest. Second, practitioners should move beyond the basic targeting options provided by online platforms and employ more sophisticated tools such as Lookalike models to ensure inclusion of these key groups in the campaign’s audience. Third, practitioners should be mindful of the tradeoff between maximizing on-platform engagement and concentrating on vulnerable populations that are most likely to benefit from the campaign. Although the cost to reach and engage these at-risk users may be higher, the potential for significant developmental impact per individual reached is also greater.

# **7 Cost-effectiveness**

In this section, we evaluate the cost-effectiveness of the investment in advertising. To determine the range of overall effectiveness in terms of malaria cases avoided per million per month, as _recorded_ in health facility records, we use the most conservative impact estimates from Table 5. These include monthly reductions in overall national incidence (3.12 cases) and urban incidence (6.10 cases). Considering that these impacts represent the average during the treatment and after-treatment period from September 2020 to May 2021—a span of 9 months—we multiply the monthly reduction by 9. This calculation suggests a total estimated reduction in reported malaria incidence of approximately 28.08 and 54.90 avoided cases per million people in the three study states.

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