Web Analytics in Aggregate Are Telling the Wrong Story

Introduction: Web Analytics are a Signal, Not a Smoking Gun

A potential client approached me to analyze their web analytics across various brands to uncover insights and improvements. After taking an initial look, I realized that their problem was several nuances deep and was going to be difficult to explain. The crux of the issue was each site had their own different issues. Some didn’t convert well, some had technical errors, some had out-of-date information. The ensuing improvement roadmap was tough to explain and expensive. This request highlighted common issues in understanding web analytics that I wanted to highlight

The Dilemma of Web Analytics in Aggregate

Clients often share their analytics in aggregate, like those from their suite of pediatric dental websites, but struggle with understanding what is happening. Typically this is done to save time but the problem with most DSO or MSO groups boils down to a crucial point: you’re comparing apples to oranges.

The desire for a high-performing website is universal. However, the approach to understanding web analytics can lead to misconceptions. Aggregating data from multiple sites can paint an inaccurate picture, potentially leading to decisions based on misleading information.

Why Aggregated Analytics Can Mislead

Aggregating web analytics is tempting due to its simplicity, especially when dealing with vast amounts of data. However, this approach can inadvertently lead to self-deception, especially for businesses managing multiple brands with distinct user experiences and structures. When you lump together analytics from varied sites, you risk overlooking the nuances that differentiate their performance. You should be able to distinguish between nuance and real movement if you’re doing it right:

  • Was the uptick in traffic a seasonal fluke or a real win from your latest strategy? 
  • Is the user experience consistently smooth across sites, or are disparities influencing your numbers?
  • Is the site technically sound or is it creating a higher than average bounce rate?
  • Is the behavior different based on acquisition source?
  • Are the type of consumers getting what they want? The researchers, the convenience shoppers, the loyalists…
  • Are we measuring the timeline on when the new patient called or the actual first appointment date?

Analytics Data Should be Owned by You, not the Marketing Organization

While a good performance marketer will tie their performance to production and therefore tell you a more accurate story, you still need to be able to audit them of sorts. You should have analytics that you know are the same regardless of who’s working on the website. If your web company or performance marketer is any good, they’ll actually prefer this. It’s usually better that the data tells the story rather than perception and intuition.

There’s nothing wrong, in my opinion, with having your marketing company or web company create the analytics event tracking for you – that can get overwhelming quick. But make sure you agree on what the metrics are that you’re grading against. Some metrics are helpful to give you a clue of what’s happening and some can be directly affected by the performance marketing company. 

Additionally, these metrics should be owned and managed by you, both for HIPAA and data integrity reasons. Using unbiased, third-party data ensures decisions are based on accurate and relevant information. Tools like Patient Prism, Peerlogic, or Freshpaint with your Analytics solution can provide this unbiased data, regardless of your website platform or marketing methods.

Strategies for Understanding Web Analytics

To truly grasp the “why” before the “how much” behind your analytics, consider two approaches:

Pre-made Platforms

Utilizing platforms like Optimizely or VWO allows for detailed variable analysis through extensive data collection. However, this may not be feasible for organizations with limited resources.

Remove Variables Where Possible (The Carenetic Approach)

Simplify by standardizing website elements across your brands while maintaining unique identities. This method reduces variability, making analytics comparisons more straightforward. For example, ensuring similar navigation and call-to-action (CTA) features across sites can provide a clearer baseline for testing and analysis.

Here’s a small example of some home pages that are extremely similar but keep the brand style intact. In this case, the DSO wanted to keep the sites fairly consistent between brands. Additionally these two sites have very similar company size, services, and revenue numbers. Now we can take this baseline and perform several experiments such as the one you see here with two different hero offers. 

In summary, websites can LOOK wildly different from domain to domain, but the underlying structure, strategy, and CTA can be very similar and easy to maintain or compare. To be clear, I didn’t say make all the sites exactly the same and cookie cutter, I said to remove some variables.

 

Conclusion: Streamlining for Clarity

Understanding web analytics requires a thoughtful approach that considers the unique aspects of each site while avoiding the pitfalls of aggregated data analysis. By employing targeted strategies and leveraging unbiased data sources, businesses can gain clearer insights into their web performance, leading to more informed decisions and improved outcomes.

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