Healthcare marketing analytics 101
Analytics has a reputation for being too recondite or too expensive, but everyone can utilize the predictive and insightful power of data. Here’s how you can get started.
How many times have you heard the phrase “data-driven analytics” tossed around in seemingly disparate contexts? Some firms try to impress you with this corporate verbiage, but it’s just gobbledygook: The word “analytics” implies that you've analyzed some data set, so analytics by definition must be “driven” by data; without data, there’s nothing to analyze.
According to Verified Market Research, big data is invaluable to healthcare marketing for information on customer preferences, market trends, unknown correlations, and hidden patterns, which in response facilitates enhancing business decisions. Big data analytics in healthcare market size is also big money—valued at USD 29.30 Billion in 2020 and is projected to reach USD 59.10 Billion by 2028. So, if it seems like we’re being fastidious about the term, it's because we are: Eliminating these buzzwords from your vocabulary is an important step toward converting the threat of big data into an opportunity.
To help you in this undertaking, we’ll distinguish between data, analytics, and insights, and show you how you can use this information to your advantage in the healthcare marketing landscape.
The difference between data, analytics, and insights
Data is the subject of your evaluation; analytics are the methods you use to evaluate it; and insights are the results of your evaluation. Evaluating data is tantamount to baking, where the raw ingredients such as milk, sugar, and eggs are the data you collect. Since you need these raw ingredients to bake a cake (well, a good cake, at least), it would be redundant to say that you've made an “ingredients-driven” cake. The same holds true for analytics.
Insights are the results of your analysis: newly discovered trends, evidence supporting beliefs you assumed to be true, your customers’ psychographic patterns, and so on. “Data-driven insights” is an equally redundant term because insights are the result of data analysis; otherwise, they are only conjecture.
Here's an example: A marketing team claims that anesthesiologists are more responsive to website ads than cardiologists because they have higher click-through rates. However, their data analysis reveals that the noticeable difference in engagement rates is actually caused by the inferior quality of their cardiology banner ads, not by a natural difference in anesthesiologists' enthusiasm for clicking on ads. This demonstrates how analytics are necessary to provide insights that debunk unfounded claims, which are often the result of people taking superficial metrics—open rate, the number of clicks, and so on—at face value.
Why good marketing analytics are important
First and foremost, good analytics add value to your marketing campaigns. If you place ads in electronic tables of contents (eTOCs) instead of eNewsletters because your data shows that orthopaedic surgeons are more likely to open eTOCs, then this insight adds explicit value to your campaign—namely, more impressions and exposure among your target audience. Similarly, patterns in data may cause you to reallocate your budget to the best-performing campaigns, or to stop spending on campaigns that aren’t performing as well. This makes your campaign more efficient, which saves money.
Second, analytics can help marketers answer their most difficult questions—especially the five “Ws” of marketing. Marketers often have a general idea of who they want to reach (for example, a pharma company developing a new cancer drug will want to reach oncologists) but may not be able to pinpoint specific customer attributes they want to target. However, deep technical analysis will help you employ audience segmentation, which allows you to group and target customers by specific attributes. In this case, you might want to reach key opinion leaders in oncology who are receptive to new cancer drugs. This will certainly enable you to describe more thoroughly what your audience wants and why you want to reach this precise audience with your specific message.
Lastly, analytics-based marketing illuminates where your target audience is and when they are most accessible. For example, your analytics might reveal that emails to key opinion leaders in oncology are most successful during the afternoon work hours, but banner ads on an oncology journal’s website perform best before and after work hours.
What are good marketing analytics?
There’s no tried-and-true formula for good analytics, but your analytical undertakings should be inquisitive in nature. Marketing analytics are concerned with answering vexing questions, elucidating unfamiliar parts of the market, and providing evidence that supports or disabuses you of your current marketing assumptions. Test your long-held assumption that physicians in the Northeast are more receptive to surgical device marketing than physicians in the Midwest, for example, you will find evidence to support your claim or discover new marketing opportunities that have been hiding in plain sight.
One example of good marketing analytics is pharma companies' ability to track increases in prescriptions for their drugs and assign such increases to a specific campaign. This can be hard to track, but regression models using a glut of easily obtained metrics can predict a marketing campaign’s effect on prescription rates. And, more importantly, they can track the campaign to specific physicians based on their National Provider Information (NPI), adding behavioral data for future analysis.
What macro data should you collect?
There are certain macro and micro metrics you should be analyzing regularly. Forbes suggests that customer behavior and needs, changes in the market size and composition, and demand forecasts are the most important macro trends for marketers to follow. Trends in market growth and demand are important for assessing how your brand is performing in the market and how well you should expect it to perform in the future. You should be happy if your revenue from surgical devices increases by 25% when the market grows by only 20%, for example; conversely, you'll need to assess where you can improve your marketing tactics if your revenue increased by only 10%.
What micro data should you collect?
Similarly, Forbes suggests that marketers stay abreast of micro trends such as brand performance, changes in your competition, and marketing and sales channel analytics. The internet offers a cornucopia of easily accessible data about your brand and your competitors, thanks to LinkedIn and Glassdoor. You can analyze this unstructured data to define actionable insights on how to make your brand stand out.
Sales channel analytics may seem like an obscure metric to track, but deep analysis can highlight strengths and reveal vulnerabilities in your conversion process. Look to your website’s traffic report for a glimpse at how many visitors you failed to convert and at what step in the buying process they bounced. If you find that most of your visitors leave immediately, for example, you should look to simplify your web page, because you've likely made the buying process too convoluted for them.
How to take your analysis to the next level
The caches of data you collect (or should be collecting) are the ideal starting point for analysis—they contain everything you need to know about what made certain campaigns successful and how each of your customers responds to your advertising. You can look for broad trends, like visits to your website or clicks on eTOC advertising, or specific patterns among physicians in certain specialties, nurses at different hospitals, or even whether emails with exclamation points in the subject line perform better. These results, no matter how insignificant they may seem, enable you to add tiny details to the caricature that represents your target customers.
This customer detailing is tantamount to the practice of audience segmentation, which simply means grouping your audience by specific shared characteristics—like grouping physicians by specialty, for example. Using more technical data analysis tools can help you craft more specific segments, such as physicians within the same subspecialties who have similar behavioral, educational, and professional attributes. And while there’s no fixed way to segment your customers, the necessary ingredients are high-quality customer data and sound technical analysis.
Corporate buzzwords like “data-driven analytics” confound us into believing we’re dealing with Einsteinian complexities, but good analytics do the opposite: They simplify and succinctly describe the complex healthcare market with its millions of dissimilar customers. It’s easy to test the waters by analyzing the data you already collect—and once you start, you’ll be amazed by the predictive power of data.