This cross‑sectional study characterizes the socio‑economic and financial features of 35 companion animal clinics in Ankara Province, Türkiye. Using a 44‑indicator questionnaire, the authors applied Principal Component Analysis followed by k‑means clustering to group clinics by socio‑economic focus and financial performance. Four clusters were identified with distinct cost structures, income levels, service mixes and profitability. The study highlights that clinics that align operating costs with income sources and adapt services to client needs tend to perform better financially.
Urban companion animal practice involves clinical care alongside business management and client communication. In Türkiye, private veterinary clinics for pets have increased rapidly in metropolitan centers such as Ankara. This expansion, together with changing pet ownership patterns and client expectations, has created a competitive environment in which clinics face rising operational costs and evolving service demands. The authors note a relative lack of comprehensive studies that combine socio‑economic and financial data to classify veterinary clinics and inform strategy; this study aims to address that gap by applying multivariate clustering techniques to clinic data.
A cross‑sectional, interview‑based survey was conducted with 35 companion animal clinics located in Ankara Province. The questionnaire, administered in person between 10 June 2019 and 19 February 2020, was built from 44 indicators identified in prior literature to represent socio‑economic and financial aspects of clinics. Each interview lasted approximately 60 minutes and combined multiple‑choice and open‑ended questions. Participation was voluntary; an informed consent form was provided and all collected data were anonymized. Because no comprehensive official registry of eligible companion animal clinics was available during the study period, the investigators could not determine the total target population or calculate a response rate or sampling proportion.
The 44 indicators included qualitative and quantitative variables summarized in the article's tables. The authors collected data on clinic characteristics, service offerings (including diagnostics, grooming, pet hoteling and specialty services), staffing and equipment investments, and monthly financials. Five financial metrics—net profit margin, cost‑to‑income ratio, labour productivity, profit per patient, and asset turnover ratio—were calculated to evaluate financial wellbeing and operational effectiveness. For each metric, tertile segments were created to support comparative analyses.
The study used standard financial ratios to quantify clinic performance. The five metrics listed above were computed from survey data to provide comparable indicators of profitability, cost control and efficiency across clinics. These metrics were incorporated into the multivariate analysis to inform cluster formation and to interpret differences in financial outcomes among groups.
Principal Component Analysis reduced dimensionality of the 44 indicators and supplied component scores that were then used with k‑means clustering. The analysis identified four clusters of clinics with distinct socio‑economic and financial profiles.
Cluster 1 (10 clinics, 28.57%): These were multi‑service clinics offering a broad range of services and demonstrating higher investments in staff and equipment. They reported the highest monthly operating costs ($8,616.77) and the highest monthly income ($10,671.77), reflecting both elevated expenditures and revenue associated with service diversity.
Cluster 2 (8 clinics, 22.86%): Clinics in this cluster were characterized by moderate costs and relatively stable income. They appeared oriented toward operational efficiency and delivery of targeted services rather than maximal service breadth.
Cluster 3 (8 clinics, 22.86%): This group included cost‑efficient clinics with a focus on cat care. They achieved the highest reported profitability ($5,093.65), suggesting that lower cost structures and concentration on specific client segments can improve net returns.
Cluster 4 (9 clinics, 25.71%): Clinics specializing in exotic animal care had the lowest monthly income among clusters ($5,023.83) and faced challenges in maintaining regular profitability.
Differences among clusters were mainly attributed to operating cost structures and strategies, sources of income, and the variety of services provided. The findings indicate that financial performance is influenced by how clinics balance investment in capabilities with the revenue potential of their service mix.
The cluster analysis demonstrates diverse business models within companion animal practice in a major Turkish city. Multi‑service clinics invest in capabilities that increase both costs and income, while more focused or niche practices may control costs and attain higher margins in specific contexts. Exotic animal clinics faced particular revenue constraints in this sample. The authors suggest that understanding these profiles can help veterinary professionals and policymakers develop strategies to improve resource allocation, match services to client needs, and enhance financial sustainability.
Methodological limitations noted by the authors include the voluntary recruitment of clinics and the absence of an exhaustive registry to define the target population, which prevented calculation of a response rate and limits generalizability. The survey design relied on self‑reported financial and operational data collected via in‑person interviews.
The study provides a classification framework for companion animal clinics based on socio‑economic and financial indicators, identifying four clusters with differing cost and income patterns and variable profitability. Clinics that align their operating costs with income streams and adapt services to client demand can improve financial outcomes. The authors propose that these insights can inform clinical managers and policymakers seeking to optimize clinic strategy and performance.
The authors report that the minimal data set used for analysis is available in the UNIPD research data repository as cited in the article. No specific funding was received for the work, and no competing interests were declared.
Acknowledgment details are provided in the original article.
Reference listings and the article DOI are provided in the source publication. Specific citations, figures and tables referenced in the analysis appear in the original PLOS ONE article.