As all veterinary practice owners and managers know, data is the backbone of any successful hospital. Whether clinical data on pets, financial data on the practice, or operational data on staffing and utilization, it all tells a story. But what if that story could go deeper? What if data could provide a more targeted experience for pets, their owners and clinic teams? Big data, the next evolution of how veterinary clinics leverage information, will do just that.
So, what is big data? At the clinic level, it includes lengthier data sets that historically exceeded the capacity of Microsoft Excel and other traditional software packages. Such data sets can be quite voluminous — possibly millions of rows. At the onset, big data sets might not be well structured and need cleanup before processing, or they might require stringing together several unconnected data sets to tell a more insightful story.
Once consolidated, big data can include internal data, such as clinic-level transaction or appointment information, and external data, such as Google review ratings or publicly available sales information from competitor clinics.
Practice Management Insights
Moving beyond the standard outputs provided by practice information management systems or financial software, savvy clinic managers have become aware of the wealth of granular, big data-driven insights available to practices.
The first step is understanding that veterinary clinics of every size, even small ones, have access to big data. That realization leads to the next question: What insights could I gain from the data?
The easiest way to understand the performance of a clinic’s operations is to follow the insights developed from detailed internal data sets, such as transactional information. Transactional data, for example, can tease out insights such as:
- Pricing: Actual prices paid, minus discounts or coupons, for products and services.
- Bundling opportunities: Bundling routine veterinary visits with other services, such as dental cleanings or prescriptions.
- Appointment efficiency: Quantifying the degree to which clients schedule visits within a reasonable time frame or whether new pet owners leak to competitors because of scheduling delays.
- Fully loaded costs: How much do labor and products cost when you consider all the direct and indirect costs?
- Trending: Insights into medical procedure frequencies, seasonal impacts, and other operational and financial developments.
While traditional financial statements or PIMS outputs remain helpful in understanding a clinic’s performance, going deeper into data helps explain why something might be occurring. For example, financial statements showing a decline in sales do not, on the surface, explain why the drop is happening. However, by synthesizing internal data further — for example, by gathering and aggregating all transactions over a set period — a clinic manager might learn whether the decline is related to pricing, volume, changes in the service mix or other factors. That “why” provides a greater degree of actionable information for identifying a more precise fix.
Clinic Growth 2.0
Imagine you are a practice owner looking to build or buy an additional clinic. Gaining insights into the level of spending on pet care or the ratio of people to pets in a given trade area would be enormously helpful in deciding where to open a hospital. While such information is readily available and primarily used by clinics during expansion efforts, big data can take those discrete data elements a step further by stringing together otherwise separate variables.
Typical external big data variables across a trade, or geographic, area might include:
- Pet spending and ownership.
- Trade-area characteristics, such as income and population density.
- The level of competition and the features of competing clinics.
- Aggregator considerations, such as the percentages of corporate-owned clinics and independents.
- DVM and staff tenure.
- Foot-traffic information using publicly available cell phone location data.
- Online review scores for nearby clinics.
- Resident psychographics, such as Experian’s Mosaic.
What makes external big data so powerful is you can take all the variables that made your clinic successful and apply appropriately weighted correlations of the variables to the geographic area where you are considering opening another hospital. This opportunity allows a clinic owner to distill the trade areas with the highest probability of having a makeup similar to your current location.
Conversely, you also can consider variables, such as levels of competition, that have impeded your current location’s success. You can develop a statistically backed expansion plan by finding trade areas that mirror the best drivers of your historical success.
The Technology
Developing the insights described here might mean synthesizing millions of rows of data, which can exceed the capacity of standard desktop software. In the digital age, however, veterinary clinics are accelerating to operate in environments where such data becomes necessary to understand clinic performance fully.
Tools that can make the process more accessible to clinic owners and managers include:
- Big data aggregation software: Microsoft Power BI and Alteryx, for example, allow the synthesis of larger quantities of data, such as analyzing all transactions at a clinic. Practices with an in-house data specialist or corporate support center might download the applications, or you might trust your technological capabilities.
- PIMS aggregation tools: Large multisite practices sometimes want to consolidate data across clinics that use different software systems. Fortunately, software companies have developed various tools to accomplish the task. For example, iVET360 can ingest data from separate PIMS and harmonize much of it into one template.
- Machine-learning software: For more advanced modeling of clinic growth, machine learning provides the statistical rigor to consider all trade-area variables and model the future of a potential clinic based on data variables that drive success at other veterinary hospitals. External support is likely needed to apply machine-learning techniques at most clinics.
Further big data analysis can be found through various sources. At the simplest level, you can hire freelance data scientists for one-off or regular data cleanup and aggregation exercises. However, for advanced assistance, leveraging a more focused data scientist might be required for customized help, such as machine-learning work.
Over the next few years, we will see more data tools that will make data cleanup, synthesis and insight even easier.
A Competitive Edge
In the new era of practice management, many veterinary clinics are leveraging big data to maintain a competitive edge. The marketplace provides evidence that clinics with a better grasp of their big data, especially internal information, might trade at higher multiples than their peers.
Ultimately, data is valuable only if it drives an actionable insight and is tangible for a broader team within a clinic. As competition within the veterinary industry intensifies, the hospitals positioned to use internal and external big data sources will be better able to serve pets, clinic staff and their communities.