Are you planning to configure your analytics tool but unsure of what to measure? Here are 3 examples of business cases, along with what metrics were observed in each case and why.
Case 1: Using a symptom checker to drive bookings
This is a typical case, where a symptom checker is promoted to attract new users. When the new users finish the symptom-checking interview, they will see 2 types of information: (1) a summary of their symptom analysis and (2) a call to action inviting them to book a visit or become a member.
What metrics should you observe in this case?
- Entry page → Number of people redirected to the symptom checker (from the solution page or directly from the solution). User action: clicking on the symptom checker button.
- Beginning of the symptom check → Initial information (symptoms, age, sex). User action: completing the input fields.
- During the symptom check → Click Through Rate (CTR, percentage of the interview completed). This can be observed by mapping the “Next” and “Back” buttons in the checker. User action: clicking the “Next” or “Back” button.
- Click out rate → A.k.a. drop rate, comeback rate. This is the number of users that have dropped out (clicked out) to other parts of the website. To observe this, add an ID to each anonymous user. With that ID, you can roughly measure the number of returning users; however, this is probably cookie-based, and may therefore be somewhat inaccurate. User action: clicking any part of the website that leads away from the symptom check. This can lead to multiple events.
- Interactions with results screen → Measured by the CTA clickouts. To do so, mark the “Membership” or “Book a Visit” buttons. Conversions will indicate a successful transfer to membership purchasing or visit booking. User action: clicking CTAs for membership or visit booking; subsequent actions that are part of the sign-up flow (creating a username, verifying their email, viewing the “Thank You” page).
Case 2: Using a symptom checker with a chatbot
At large healthcare organizations, chatbots commonly help guide users and answer their common questions. This case is similar to case 1; however, it is preceded by a few extra steps.
What metrics should you observe in this case?
- Interactions with the chatbot window → User action, eg. opening a chat, closing a chat, typing/choosing a reply.
- Interest in a symptom assessment → User action, eg. choosing to use a symptom checker.
- See the list of metrics in case 1 (above).
Case 3: Using a symptom checker to navigate patients to the correct health services, as determined by their triage level
This functionality is often used by healthcare providers that offer a wide range of services. In this case, a symptom checker can be used to understand a user’s needs and filter the available services to present only those matched with the user’s symptoms, triage level, and recommended specialty. Again, this case is similar to case 1, but with some additional events to consider.
What metrics should you observe in this case?
- See the list of metrics in case 1 (above).
- If possible, track how the user came to your solution (i.e.the page that redirected the user to the target page, a.k.a the visit source).
- When running a campaign, links should be tagged with tracking codes - UTMs - to recognize what traffic the campaign generated.
- Track the number of completed symptom checks. Compare existing members vs. non-members-turned-members vs. non-members who stay non-members.
Do any of the above examples match your business case? If not, map out your user journey, decide when users make key decisions, and track those events. Feel free to discuss any of these topics with your dedicated Customer Success Manager.
AKw, PKu, ALE