Within higher education, there is campus-wide discourse increasingly calling for preparing graduates for a globalized world, internationalizing the curriculum, and building international partnerships. Yet, some of the departments -- notably language programs -- are absent in the discussion regarding solutions and allocated funds (Tardy, 2015; Warner, 2011). Despite this disconnect, there remains a critical need for the transcultural competencies, multilingual abilities, and a globalized perspective that are frequently incorporated into language learning courses in higher education. As one way of addressing this, there is a continued call for transdisciplinary approaches and collaboration in SLA research (Douglas Fir Group, 2016) and transdisciplinary course offerings (Modern Language Association, 2007). These suggestions coincide with continued conversations and underlying pressing concerns among language programs in HEIs that they increase or maintain student enrollment to persist in an increasingly market-oriented approach to education. This paper seeks to address these looming issues by highlighting data analytics use from other higher education fields (e.g., student success and retention efforts) into different programmatic and transdisciplinary curricular models as a solution to the challenge of offering cross-disciplinary language courses that increase student enrollment.