Sikka Fee Survey: A Data-Driven Approach to Veterinary Pricing

Summary

Fee Survey leverages Sikka.ai's 15 years of expertise and vast database to deliver data-driven fee recommendations for veterinary practitioners looking to enhance production, attract and retain patients, and control costs. Given the industry's ongoing economic fluctuations, it is strongly recommended that veterinary practices refresh their service and product fees at least twice a year. Our comprehensive veterinary dataset—spanning millions of real-world transactions across treatments, diagnostics, inventory, and additional services—shows that regular fee updates are critical for staying aligned with rising costs, shifting salary benchmarks, evolving insurance reimbursements, and competitor pricing. Leveraging insights from large-scale vet fee data enables practices to adjust their pricing strategically, remain competitive, and maintain financial sustainability in a dynamic market.

Veterinary practitioners can greatly benefit from optimizing their service fees on a quarterly basis using Fee Survey from Sikka. This tool provides anonymized, region-specific insights into how much other veterinarians in their geographic area (Geo zipcode) are charging for procedures and products, using aggregated, non-identifiable data. This valuable competitive intelligence enables veterinarians to strategically update their fees to increase production while remaining appropriately positioned in their local market. To achieve optimal results, service charges should be revised in alignment with fluctuations in expenses, including equipment, lab supplies, salaries, and broader economic conditions.

What is Fee Survey for Veterinarians and How Can it Help the Veterinarian Practices

The Fee Survey For Veterinarians provides information on the 50th, 60th, 70th, 80th, 90th and maximum percentiles for each veterinary procedure or product based on Geozip code.

The Fee survey assists veterinarians in gaining an understanding of the current procedure and product fees in their local area to decide whether to raise or lower the fee and explore the possibilities of boosting their revenue. Everything around the practice is changing, including prices, compensation, the fees other dentists charge, what insurance plans cover, the state of the economy, and the price of supplies and lab work.

A veterinarian will experience a loss if they charge too little for the procedure or product compared to other vets in the neighborhood, and they risk losing pet patients if their fees are too high. Therefore, charging a fair price aids dentists in increasing their earnings and retaining the pet patients.

How the Fee Survey is Calculated 

Unlike other medical fields, such as dentistry, veterinary medicine does not have standardized procedure codes. Hence, we utilize veterinary service procedure descriptions to group procedures by geozip. Unstructured veterinary procedure descriptions require thoughtful preprocessing to ensure consistency and improve the quality of downstream analysis. By applying Natural Language Processing (NLP) techniques, the text is normalized so that similar entries are treated uniformly. This process is essential to normalize the text, ensuring semantically similar entries generate consistent embeddings.

To extract meaningful insights from unstructured data like text, it’s crucial to convert it into a structured format that machines can understand. Word embeddings play a key role in this process by transforming raw data into vector representations that capture both semantic meaning and context. These representations enable various downstream tasks such as classification, clustering, and similarity search by offering a consistent way to analyze complex information. Without embeddings, the nuances and inconsistencies in unstructured data would make it difficult to build scalable, intelligent solutions.

Once the data was embedded, unsupervised clustering methods were applied to uncover natural groupings and patterns, without relying on predefined labels. By comparing multiple combinations of embedding and clustering techniques, the best-performing setup was identified based on its ability to accurately capture the structure and meaning within the data. This approach proved especially effective in organizing unstructured veterinary text, enabling clearer insights and better data-driven decision-making.

Based on the resulting clusters and geozip codes, average and percentile prices were then calculated. These metrics were then combined with the Cost of Living Index (COLI) to derive a multiplier—representing the percentage by which veterinary service fees should be adjusted for specific geozip codes. This process enabled the recommendation of updated veterinary procedure fees, including the 50th, 60th, 70th, 80th, 90th, and maximum percentile values for each procedure description across different geozip regions (Fig. 1).

Figure 1: Sample Data