The first year of our platform for online consultations with doctors was coming to an end. We launched 20 specializations and set up tools for traffic attractions, but the unit economy did not converge, and our business model was not ready for scaling. The team faced a difficult task: to reduce the economics of a unit of online consultations with doctors and prepare the product for scaling. This is what the unit economics of our product looked like at that time: we attracted users through bloggers on social networks and purchased targeted queries from search services. The average lead cost us about $40. The average cost of a consultation was about $15–20. An hour of a doctor's work cost an average of $6. We paid doctors for working hours, regardless of whether he was consulting at that time or not. Another part of the money was taken by the clinic, on behalf of which the doctors worked. At the same time, we managed to retain clients: 20–30% of patients contacted us for advice at least three times within six months. But even with such high retention rates, the service was in deep trouble. What growth points have we found? We considered four ways to make the service profitable: Look for doctors with a lower rate per hour. On the other hand, the quality of consultations could suffer in this case. Open our own clinic so as not to give part of the profits to partners. Switch to the doctor's payment upon delivery of the service. But then we would no longer be able to provide services at the time of need, which was our core value. Increase the cost of the consultation. However, according to the law, the doctor could not prescribe treatment, write out a prescription, or give a sick leave. It limited the value of an online consultation, and it was difficult to justify a price increase in this case. Looking ahead, I will say that we have implemented all of the above ideas and even opened our own clinic. And now let's dive in. Obtaining a medical license and opening a clinic We made the final decision to open our clinic after one of the partners reduced the number of shifts for their therapists from 4 to 1. As a result, we lost the specialists who carried the main burden. We launched the process without much hesitation, realizing that otherwise we risk being left without doctors at any time. And of course, when we had our own clinic, we no longer had to share income with partners. Finding doctors with a lower rate We took the selection and training of doctors very seriously: a personal interview with the solution of clinical problems, training, and a one-month trial period with selective verification of consultations. This allowed us to maintain a high level of quality. At first, we were afraid to look for doctors with a lower rate, because we doubted their qualifications. But, as it turned out, we feared in vain; we managed to find good specialists in regions remote from the capital. The level of their qualifications was just as high, and the cost of a consultation was 2-3 times less. At the same time, all quality metrics remained at the same level. Switching to pay-as-you-go The main problem we had to solve was that the hourly model was not profitable. It was necessary to use at least 70% of the working time of one specialist in order to make a profit. Only the most demanded specializations managed to provide such a level of employment. At the same time, if the load exceeded 80%, we lost clients; doctors could not cope with the flow, and patients had to wait a long time for a free specialist to appear. Perhaps this was the most difficult task for us. We called this process “the transition to the Uber model"— pay-as-you-go. For such a model to work, it was necessary to provide the optimal number of doctors for the growing flow of patients. Otherwise, clients who have to wait too long could leave and not return, the number of requests for consultations would fall, and doctors would become uninterested in responding to infrequent requests. We could lose both the doctors and the clients. We tested the new business model through an experiment. It was scary; we took a big risk. We measured the effectiveness of the new model by three target metrics: waiting time for a doctor's response, conversion to service provision, and profit from each specialty. With the help of the experiment, we managed to find a ratio of the number of consultations and doctors in which the connection of a doctor on request did not drop the target metrics. It turned out that for this, it was necessary to conduct at least 100 consultations a day and have at least 300 doctors in the system. With this ratio, there is almost always a doctor who is ready to pick up a consultation. However, this scheme did not work for all doctors but only for the most popular specialties. In their case, hourly pay was also most often worked in plus. It was not clear whether it was worth changing the work model or what to do with less-demanded specialties. We abandoned the most unclaimed specializations: there was no point in keeping doctors for the sake of several consultations a day. For the rest, we continued the experiment to understand how much the quality of service drops when switching to the “uber model” It turned out that with this model, in the case of proper communication with users, we lost only 5–10% of consultations while revenue grew by 2-3 times. We completed the monthly experiment with the following results: Our revenue has almost doubled. We decided to leave therapists and pediatricians on an hourly basis since the economy converged on them anyway, and maintaining the main value of the service ”we are always there"—turned out to be more important than saving 10% of the budget. We closed several rare specializations. We transferred the rest of the specialists to pay-as-you-go services. Experiment with pricing Raising prices is always unpleasant, but we could not help but do it. First, we increased the cost of all services by $1–2. The metrics didn't drop. Then we conducted an experiment with pricing for narrow specialists, and for some of them, we doubled the cost of consultations. As a result of the experiments, the conversion rate to the first consultation dipped slightly, and the revenue increased by almost 30%. It turned out that, in general, people are willing to pay more. It turns out we were afraid for nothing. Conclusion Thus, as a result of the experiments, in a few months we managed to bring the unit economy together and prepare the product for scaling. Sometimes you have to give up some of the solutions that are interesting and attractive to the client for the sake of revenue. You may think that the quality of the product has dropped, but it has not. Let you have just a good product, no frills. It is important to ensure that the business is profitable. Here are the lessons we have learned: Making unpopular decisions, such as raising prices, is always scary, but in fact, it turns out that it is not so catastrophic. Users who really need your product will pay a little more and wait a little longer. The main thing is to find a balance and not be afraid of experiments.

The writer is smart, but don't just like, take their word for it. #DoYourOwnResearch before making any investment decisions or decisions regarding you health or security. (Do not regard any of this content as professional investment advice, or health advice)

Boosting Customer Loyalty: Key Lessons Learned from a 20% NPS Drop

Read My Stories

Too Long; Didn't Read

Download free Tech Leaders Productivity Report!

Lessons From Our Story of Looking for a Working Business Model for an Online Consultation Platform

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

Boosting Customer Loyalty: Key Lessons Learned from a 20% NPS Drop

A Timeless Business Lesson About Nepotism and Meritocracy

Applying First Principles Thinking to your Startup Idea to Enhance Impact

Model Validation in C#: Unit Testing

Business Process Standardization for those who are not in the Fortune 500 ... yet

Boosting Customer Loyalty: Key Lessons Learned from a 20% NPS Drop

A Timeless Business Lesson About Nepotism and Meritocracy

Applying First Principles Thinking to your Startup Idea to Enhance Impact

Model Validation in C#: Unit Testing

Business Process Standardization for those who are not in the Fortune 500 ... yet

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps