Why Health Care Organizations Should Care More About Web Analytics

Health care organizations – hospitals, physician practices, and specialist clinics – are primarily concerned with keeping their patients healthy and happy. But they’re businesses too, and they operate in a highly competitive environment. To succeed at both, health care organizations should do everything they can with data and analytics to understand their patients and optimize the patient experience.

Unfortunately, while health care organizations usually succeed in keeping people healthy, they often fail at making the most of the data they have and understanding the analytical tools available. Part of the reason is that health care organizations are like most small to medium enterprises (SMEs) – they lack the in-house resources to cultivate data infrastructure and the expertise to conduct analysis. Instead, they focus their technical thinking on the latest advancements in medicine and their data analysis on patient outcomes and costs. Plus, there is always a concern about sensitive patient information that puts them on the defensive instead of on the cutting edge. As a result, many health care providers have been left out of the big data revolution.

There’s good news, however. Sophisticated tools developed for big companies with big data have become more accessible than ever. They range from advanced web analytics to optimize user experience to predictive modeling that will help them understand what patients want and when. In this article we will focus on web analytics — because its one of the easiest, and most visible, places to start.


Making the Case for Advanced Web Analytics

While many health care organizations invest in search engine marketing that will drive traffic to their sites, often less emphasis is placed on understanding what happens when they get there. But there are good reasons to pay close attention:

  1. Create Positive Patient Experiences: Your website is an increasingly important part of patient experience. For some patients, it is their first interaction with you, and you want it to be great. To keep patient satisfaction scores high, health care providers should dig deep into analytics to find what it takes to create breakthrough user experiences.

  2. Understand What Patients Want: Studying how patients come to the site – and what they do when they get there – will tell you what they are looking for and why.

  3. See Where They’re Coming From: Health care providers are particularly sensitive to distance, especially in densely populated areas where patients have abundance of choice. Geographical analysis can tell you where visitors are coming from and what they’re looking for. Combined with demographics and historical analysis, you can make strong correlations that can help you understand where to focus in the future.

  4. Filter User Behavior by Type: Good analytics can tell you who is visiting and how often they come back. Plus you can differentiate between patient traffic and visits from other health care providers.

  5. Understand How Your Marketing is Working: Attributing the success of campaigns is always tricky, but much harder if you don’t have good analytics. Confirm what’s working and whether advertisers are telling the truth.


A Short List of Tools for Consideration

Free tools like Google Analytics are good for general trends and inexpensive SEO tracking applications like Moz are easy to set up, but there are many more options to go deeper:

User Behavior
Tracking visits and behavior on an individual basis is essential for advanced segmentation and user experience decisions. We use Piwik and Opentracker, and we recommend you check them out too.

Personalization, Targeting & Message Optimization
Designing a single experience for all visitor segments naturally involves compromises. Using a service like Optimizely allows for content to be specifically changed and targeted to known visitor criteria meaning the right people receive the right message.

Predictive Modeling
The greatest predictor of future behavior is past behavior. We use the scientific statistical programming language R to create models that are reproducible, reusable and can be compiled into friendly applications with Shiny.

Media Mix Modeling
Different marketing efforts and channels work in concert.  The effect of these combinations is important to understand where to spend the next marketing dollar. The answers can be surprising and sometimes counterintuitive.  We use R with the new ChannelAttribution package to understand how marketing budgets should be geared to effect the greatest returns.

Want to learn more about how we can help your organization? Drop us a line at hello@deducive.com.