How Good Doctor, Indonesia's Leading Digital Healthcare Platform, Drove 51.78% More Transactions and 13.86% More Paying Users with Netcore's AI Decisioning Agents

Good Doctor is one of Indonesia’s leading digital healthcare platforms, giving users convenient access to online medical consultations, medicine purchases, health information, and integrated healthcare services. The platform connects more than 15 million users to a network of over 1,000 doctors and 4,500 healthcare facilities, and works with 65+ insurance partners and 3,500 corporate clients across Indonesia.

Industry Context

Indonesia’s connected healthcare market is expanding quickly, with telemedicine leading the category and the broader market forecast to grow at a CAGR of around 27% through 2031 (Mordor Intelligence). Patients increasingly expect the same immediacy and relevance from healthcare apps that they get elsewhere online, even though health needs are inherently personal and change fast, shaped by season, symptom, and individual circumstance. The market is expanding steadily within a consolidating field of established platforms, most competing on the same basic premise of access. For Good Doctor, staying ahead meant treating relevance, not just reach, as the next growth lever.

Good Doctor’s objective was straightforward: improve activation, consultation and purchase conversions, and overall marketing effectiveness, without simply pushing more messages to more people. That meant solving for three things:

• Reaching users based on their actual likelihood to engage or convert, not just their presence in the base
• Continuously identifying which content and journey paths performed best, without relying on manual testing
• Keeping campaign messaging aligned with fast-changing patient interests and seasonal health trends

The Core Gap

Good Doctor’s existing setup for App Push and Email wasn’t designed to prioritize content or traffic automatically; every new variant, segment, or trend required manual review. In a category where patient interest can shift within days, a seasonal illness, a sudden symptom, that manual layer meant campaigns were often optimized for what had already happened rather than what was happening. Left unresolved, this carried three compounding risks: users dropping out of onboarding before reaching their first consultation, marketing effort spread evenly across users regardless of intent, and campaigns arriving after, rather than during, the moment a patient was actually looking for care.

Good Doctor worked with Netcore to introduce AI Decisioning Agents across the user journey, systems designed not just to analyze data but to act on it directly: shifting traffic, selecting audiences, and surfacing trends without a person triggering each cycle. This covered the journey from first visit through ongoing engagement.

Step 1: Optimize the Onboarding Journey

Good Doctor built multiple onboarding journey variants, each testing different messaging on why and when to start a consultation. Netcore’s Decisioning Agent, running as the Journey Path Optimizer, continuously observed engagement and conversion across these variants and shifted traffic toward the best-performing path on its own, without a person running or interpreting a manual A/B test.

Step 2: Target by Intent, Not Volume

Instead of every campaign going to the full user base, Netcore’s Decisioning Agent, configured through Propensity-Based Segments, continuously scored users on their likelihood to purchase or repurchase and routed App Push and Email campaigns to the resulting high-intent segments directly, refreshing who qualified as behavior changed.

Step 3: Act on Emerging Trends

Netcore’s AI Insight Summarizer agent, accessed through Co-Marketer, continuously read behavioral and health-search data on its own and surfaced emerging patterns: which medicine categories were growing fastest, which symptoms were trending, and where demand was shifting, without the team needing to pull or interpret that data manually.

Step 4: Turn Insight into Campaigns

When the agent flagged a sharp rise in interest around sore throat medication, the team acted on that signal immediately, launching a targeted campaign while the interest was still active, pairing timely content with an offer relevant to what users were already searching for.

Results were tracked across three areas, onboarding, predictive targeting, and trend-led execution, and each ties back to a different lever of business performance: activation, marketing efficiency, and speed to relevance.

Faster activation, more users reaching value

A user who drops off during onboarding never becomes a customer. With the Decisioning Agent continuously reallocating traffic toward the best-performing path, more new users reached their first consultation, directly growing the base of engaged, addressable users (15 May – 15 June 2026):

• 10.23% higher Registration Rate – more visitors converting into registered users, widening the entry point to the funnel
• 10.09% higher First Activation Rate – more new users completing a first meaningful action, turning sign-ups into actual product usage

Better marketing efficiency, same effort, more outcomes

With Decisioning Agents continuously scoring and re-targeting high-intent users, the same campaign effort produced more results, improving return on marketing spend rather than requiring more of it. (Figures below are shown as a multiple of the previous rate, e.g. 2.0X means twice the previous rate.)

• App Push (May 2026): 22.38% higher Click Rate, 2.0X Conversion Rate – conversions roughly doubled from the same audience size and send volume

• Email (March vs April 2026): 3.7X Open Rate, 12.6X Click Rate – substantially stronger engagement per email sent, meaning fewer, better-targeted sends were needed to reach the same or better response levels

Faster time-to-relevance, demand captured while it’s active

Campaigns built on signals the agent surfaced in real time, rather than a fixed content calendar, converted meaningfully better, showing that timing, not just targeting, moves outcomes:

• 2.2X Open Rate
• 2.7X Conversion Rate

Overall business impact

Together, these improvements compounded into gains that tie directly to revenue and user growth:

• 51.78% more transactions – direct top-line growth in a category where transaction volume maps straight to revenue
• 13.86% more paying users – growth in the paying base itself, showing the gains came from converting more users, not just monetizing existing ones harder

Beyond the numbers

• Decisioning Agents now run continuously in the background, freeing their bandwidth- allowing them to launch more campaigns/ focus on other things
• Campaign planning is now patient-centric rather than calendar oriented.
• Marketing effort is increasingly concentrated on users most likely to act, improving efficiency without added headcount

Good Doctor plans to extend AI Decisioning Agents across more touchpoints in the user journey, building on the same foundation of journey optimization, predictive targeting, and trend intelligence established here. The intent is to keep personalizing care at scale as the platform grows, rather than treating relevance as a one-time project.