Personalized Healthcare at Scale: Simplifying healthcare with recommendations
Halodoc offers variety of services to our customers from digital outpatient services enabling patients to consult with top doctors in a click of few buttons to purchasing medicines, health insurance, medical tests and in-hospital bookings and services. With the wide gamut of services offered, it becomes overwhelming for the users to select the right doctors to consult, medicines to buy or services to avail.
At Halodoc, our marketing and product teams have been working hard at delivering user experiences and propositions that are valuable to our customers and at the same time viable for our business. Balancing the two is an extremely delicate balance to achieve. One obvious way to achieve this is to personalize a user’s experience on and off the app through tailored content, recommendations, messages and notifications. We started in working on building a personalized experienced for the users surfacing the right products and services to the users based on their history on the platform, demographics and related products.
While our marketing and product teams had been doing this in a semi-automated way, we soon realized that in order to truly achieve personalization at scale, we will have to solve two problems:
- Engineering: create an end-to-end integrated technology platform
- Organisational: create a shared view of personalisation and everyone speaking the same language when it comes to it.
The ways to personalise a user’s experience are endless. There is indeed an amazing diversity of customers in terms of needs, preferences, expectations and past experiences. Taking this diversity into account is essential to send relevant communications, deliver valuable experiences and improve customer retention.
In this blog post, we will focus on engineering aspects and try to share our experience on this journey, our personalization platform and what the future looks like for us.
Tenets of Personalisation
For any organisation planning to encompass on this journey there are 5 key tenets to consider and strengthen before picking to solve this problem.
- Analytics & ML
- Content Pipelines
Very soon during our journey we realised that data will be the most powerful weapon in our arsenal alongside our people. What started as simply trying to understand what users are doing on the platform by tracking user actions as events, soon grown into more than billion events.
Our customer data was distributed across separate, disconnected systems typically managed by different stakeholders. Marketing, Customer Success, Product all viewed customers as differently (and rightly so) because all of them saw a side of the customer. We soon realised that this was a huge problem as everyone had a very different view of the user depending on which team you talk to and what that user wanted.
Single View of the Customer (SVC) is the first step to personalisation. Data consolidation using a data platform (DP) becomes key for this. more details on data platform here .
We leverage CleverTap to collect, unify and manage all user generated data events. The data platform becomes a source of user information for all of other systems which use this information to personalise user experiences. Typical use-cases being creating segments/audiences of users based on their traits/properties and curating on and off journeys for an immersive experience.
Along with our customer data in our data platform, we now have a lot of other sources of data which augment the user, order and overall end to end order fulfilment experience; like Orders data, Appointments data, other source data from production databases etc. All these sources are moved to the data platform for ease of accessibility and data discovery.
Analytics & ML
Having data in one single source is extremely powerful but unless you can use that data to draw actionable insights and patterns it doesn’t serve much purpose. In order to utilise the abundance of data we have, we built equally strong analytical tools and machine learning models to act on them. Our data science teams use the consolidated data to create insights which are used to power different products.
Models are either built using ML libraries like Scikit-Learn or Tensorflow and deployed through automated pipelines to our production. ML jobs identify product associations as well as the buying patterns of our customers to recommend products accordingly to them, does User segmentation into cohorts and recommends products, services for these cohorts.
The next and more or less obvious tenet in the personalisation journey is pipelines.
Easy way to explain pipelines is the plumbing required to move the data and insights from source to destinations.
For our marketing and product teams, the source is the data platform and our job was to build pipelines that work disseminate data into systems which put together experiences for the end user. We would power our promotions engine, our content management system and campaigns, and re-engagement systems through these pipelines.
There are different ways in which pipelines are built ranging from out of box vendor integrations, custom development through data dumps and API integrations to name a few. Teams are easily able to flow the audience created in data platform into these systems and ensure all communication is personalised.
While we have built a source to destination pipeline which ensures all systems see the exact same user data, the future that we embark on is building interconnected pipelines.
Penultimate step in scaling personalisation is auto production of experiences based on data informed content. Our content and experience production systems are currently in a semi-automated state. Our marketing and product teams utilise the templates, layouts and widgets and personalise the content for our users based on data we have. While this works well at a small scale, the scale that we aspire to be at now requires automation of content production, short real time feedback cycles and run-time experience optimisations.
We currently automate content eviction and sorting based on user preference, impressions and click through rates at different points in the user’s buying journey. This needs to evolve in an end-to-end journey optimisation next.
The final and most important step in this personalisation journey is Experimentation. For Halodoc it means rapid learning. What we learn from our data, the patterns we see, insights we get always leads to an experiment. We experiment a lot and run multiple A/B tests at times many throughout the day. While this helps us to learn a lot about our customers and what they want and like, this process is semi-automated right now.
The final step in scaling our personalisation journey is building an experimentation platform which empowers our teams to experiment and measure and visualise metrics impact easily.
Bringing it all together
All these tenets form the foundation for a strong scalable personalisation platform. While we experiment with many personalisation algorithms, along with some external tools, we are building our own personalisation service now which utilise all of this ground work, connected pipelines and then curate cross journey experiences for our customers. Stay tuned for learnings as they come.
Meanwhile here is a little something around our systems, pipelines and channels to munch on while we come back with more!
We are looking for experienced Data Scientists, NLP Engineers, ML Experts to come and help us in our mission to simplify healthcare. If you are looking to work on challenging data science problems and problems that drive significant impact to enthral you, reach out to us at email@example.com.
Halodoc is the number 1 all around Healthcare application in Indonesia. Our mission is to simplify and bring quality healthcare across Indonesia, from Sabang to Merauke. We connect 20,000+ doctors with patients in need through our Tele-consultation service. We partner with 3500+ pharmacies in 100+ cities to bring medicine to your doorstep. We've also partnered with Indonesia's largest lab provider to provide lab home services, and to top it off we have recently launched a premium appointment service that partners with 500+ hospitals that allow patients to book a doctor appointment inside our application. We are extremely fortunate to be trusted by our investors, such as the Bill & Melinda Gates Foundation, Singtel, UOB Ventures, Allianz, GoJek, Astra, Temasek and many more. We recently closed our Series C round and In total have raised around USD$180 million for our mission. Our team works tirelessly to make sure that we create the best healthcare solution personalised for all of our patient's needs, and are continuously on a path to simplify healthcare for Indonesia.