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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64145
Volume:
10 |
5 |
May 2026
Pages:
1-11
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
MIT-ADT University
College of Management
MIT-ADT University
College of Management
Early childhood education institutions depend heavily on referral networks and outreach channels to drive admissions, yet most preschool operators lack systematic methods to evaluate which lead sources translate most consistently into actual enrollment. This study applies enrollment analytics to a dataset of 91 admission enquiries compiled from the Oxford International School (OIS) network across five branches in Pune, Maharashtra, to examine the concentration, geographic distribution, and referral performance of school partner leads. Descriptive analysis and branch-level disaggregation reveal that School Partner Referral is the exclusive lead source in the current dataset, accounting for 100% of recorded enquiries, with 45 unique school partners generating 90 admissions across branches. Branch-level analysis shows that OIS Chinchwad accounts for the highest enquiry volume (43 enquiries, 47.8%), followed by OIS Wadgaon Sheri (18 enquiries, 20.0%) and OIS Nigdi (16 enquiries, 17.8%). Partner productivity analysis uncovers high concentration: the top 5 school partners contribute 35 out of 90 referrals (38.9%), indicating a power-law distribution in referral generation. An enrollment funnel model and dashboard framework are proposed to operationalize these findings for admission counselors. Practical implications for lead diversification, partner incentivization, and branch-specific outreach strategy are discussed.
KEYWORDS
This study demonstrates that systematic lead source analytics can extract actionable strategic intelligence even from a relatively small, single-channel enrollment dataset. By applying descriptive analysis and partner productivity modeling to 90 OIS Pune enquiry records, we identify three findings with immediate practical value: the enrollment pipeline is geographically concentrated (OIS Chinchwad accounts for nearly half of all enquiries); the partner base exhibits a power-law productivity distribution (the top 5 partners generate 38.9% of referrals); and at least one branch (OIS Undri) shows clear signs of under-activated partnership potential. The proposed dashboard framework translates these findings into a real-time decision support tool that branch managers and the central admissions team can use to monitor pipeline health, prioritize partner engagement, and identify early warning signals of enrollment shortfalls. The tiered partner classification (Tier 1, Tier 2, Tier 3) provides a simple, actionable schema for differentiating partner management strategies without requiring sophisticated modeling. At the same time, this study is transparent about its boundaries. The dataset covers only the enquiry stage of the admission funnel, making conversion rate analysis impossible in the current form. The exclusive presence of a single lead source type limits comparative channel analysis. Addressing these limitations through improved data infrastructure — specifically, mandatory funnel-stage tracking and multi-channel lead attribution in the ERP system — is the most consequential next step for the OIS Pune admissions team. Looking ahead, the integration of full-funnel data (enquiry → visit → enrollment), multi-year trend analysis, and predictive modeling of partner churn risk are the most productive research directions. More broadly, this work contributes a replicable methodology for early childhood education operators across India who collect ERP data but have not yet taken the step of analyzing it systematically. The goal is not to replace the relationship-driven, trust-based admission process that defines quality preschool enrollment — but to make that process more visible, measurable, and strategically informed.
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Publication Date
5/27/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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