Monitoring family planning progress requires accurate and timely estimates of key indicators such as the modern contraceptive prevalence rate (mCPR), defined as the proportion of women of reproductive age using modern contraceptive methods. However, large-scale survey data, often the primary source of these mCPR estimates, are infrequently collected, leading to data gaps. Family planning service statistics, routinely collected during service delivery, provide a supplementary data source. From these statistics, an indicator known as Estimated Modern Use (EMU) can be derived, but it is a biased estimator of mCPR and has uncertainties that need to be considered. This thesis focuses on advancing the methodology, application, and usability of EMU by quantifying and accounting for biases and uncertainties and ultimately better supporting low- and middle-income countries in tracking family planning progress. First, we refine the derivation of EMU by improving upon necessary adjustments for missing private sector contributions to family planning service statistics databases. Previous methods carried out adjustments assuming constant contraceptive supply share distributions over time and without quantifying uncertainty associated with supply share estimates. We update and improve upon the EMU calculation to reflect time-varying contraceptive supply and capture uncertainty in the private sector adjustment, resulting in observation-specific uncertainty previously unseen in EMU derivation. These improvements are demonstrated through country-level case studies. Next, we develop a new approach to incorporating EMUs into the Family Planning Estimation Tool (FPET), which generates estimates and short-term projections of mCPR. We use a Bayesian hierarchical modelling approach to estimate data type-specific EMU uncertainty and across country variance parameters before incorporating the resulting estimates into FPET. We introduce a Bayesian hierarchical model when using EMUs in FPET to capture uncertainty, accounting for country- and type-specific uncertainties through the hierarchically estimated variance hyperparameters. Model validation results and anonymised country-level case studies highlight the impact to mCPR estimates when including EMU data in FPET using this approach. Validation findings demonstrate improved predictive performance with EMU inclusion compared to relying on survey data alone, while case studies provide further insights into its effects across different country contexts. Finally, we present a paper to describe the details and implementation of ss2emu, an open-source R package, developed to perform the most advanced SS-to-EMU calculation process in R. This tool complements existing workflows performed by country-level data experts, providing reproducible datasets and visualisations for use in FPET. By offering a scalable and user-friendly solution, the tool enhances accessibility and empowers users, such as family planning monitoring and evaluation officers, to make more informed decisions in family planning monitoring. Together, these contributions improve the accuracy, integration, and usability of EMU as a family planning indicator, enabling countries to better monitor progress toward family planning goals and address data gaps with confidence.