Healthcare Digital Marketing: How To Get 46% More Visits In Three Months

Our client is a medical center with a wide range of services. There are about 20 directions in total, plus its own hospital and diagnostic department. The name of the center will not be disclosed under the terms of the NDA.

It was necessary to increase the number of calls to the diagnostic and surgical departments of the center. Both directions are the main source of income for our customer, and they are also linked to each other. The problem was ineffective advertising: the medical center increased its budget for advertising campaigns, but there were fewer and fewer new patients.

Key complexity factor: large amount of data.

The fact is that medical centers offer different types of services: diagnostic services, the possibility of performing surgical procedures, and specialist consultations. Each of these services has its own average check, which is different from the check in the other direction.

A large workload of specialists also complicates the task. It is associated with a high level of patency of the medical center.

All these factors together indicate that you need to set up high-quality analytics. You can use it to track parameters of various orders, starting from the number of patients and ending with the cost of attracting them.

Analytics allows you to work in several directions at once:

The medical field involves building analytics based on two factors — a web analytics system and call tracking.

Call tracking task: track the number of calls. However, you won’t be able to see their quality. As well as how targeted these calls are.

The task of the web analytics system is to identify the missing data that we mentioned above. 

We started by checking whether analytics and statistics are configured correctly, and how we set up previous ad campaigns. This step helped us identify problem areas: it turned out that incorrectly configured analytics and contextual advertising were becoming an obstacle to the development of the clinic.

Why was analytics configured poorly

This information should not be overlooked, as it is the basis for creating creatives that are suitable for the clinic’s patients and making changes to the site to make it more convenient. 

Why did contextual advertising contain errors?

Working with analytics

We needed to create more dynamic numbers. So we provided spoof numbers for each session, and additionally were able to collect correct statistics for all calls.

After that, we enabled dynamic number substitution. This helped to link calls to each of the sessions.

In the next step, we created static numbers specifically for two medical aggregators. Business cards with these numbers, in turn, were divided into different campaigns.

Next, we set up the ways that traffic channels were tracked – paid and organic.

We tested the entire stage of patient visits to the clinic, and then set up recording of actual visits — in this case, we were interested in repeat visits.

Result: we have more data now. We were able to conduct an analysis and understand how to optimize your campaigns further. We also identified all the sources from which real applications were received. Statistics of requests became available to us. We saw which requests were getting fewer requests.

In addition, we reduced the number of requests for such sources that were classified as undefined. As a result, we were able to react faster when the number of visits to the clinic fell.

Working with the context

We have regrouped our ad campaigns. The logic was simple: we only enable one campaign per destination.

This is how we managed the budget for the clinic’s services, we managed to optimize advertising costs, and now we can manage the workload of medical center specialists more flexibly.

After analyzing the previous work in each area, we had new data in our hands. They helped us adjust our budget. As a result, the focus was placed on the diagnostic and surgical departments of the clinic.

Now each business card in the ad campaign contained its own number, which we could use to track whether calls correlated and what position the ad impression occupied.

Another part of the work was to expand the semantic core. All the parts in the ads were filled in. We tracked the amount spent on different ad platforms and key queries. We disabled the ones that were ineffective.

As a result, we managed to collect additional target requests that did not show the client. Thus, the quality of the audience became higher, and after it, the conversion rate also increased.

It is important to note that contextual advertising requires careful system work. One-time actions will not bring the desired result. Therefore, the next stage of our work was optimization.

Working with ad campaign optimization

We have built our analytics so that it takes into account the final requests and transactions. To do this, we needed to conduct an analysis: we considered each advertising campaign in the context of what key queries were there, what devices or demographic factors, what sites and what direction.

This was done so that we could lower rates for those segments that turned out to be the most expensive and inappropriate. We disabled some of them. This applies to each slice.

The final rating of each source should be considered based on associated conversions. Some advertising campaigns will only work for the very first user interaction with the clinic. The other part will help the patient make the final decision and submit a request. Each of these channels is important to shape the user’s path.

All the data that we received during our work allowed us to adjust the budget for advertising campaigns and their bids. We understood which of them were inefficient and when it was time to turn them off.

First three months of work: