Healthcare Product Analytics Case Study

Overview

At a Glance

Healthcare Product Adoption Analytics Platform is a centralized analytics solution built for a US-based HealthTech company operating a clinical documentation platform. While the platform generated extensive customer and product activity data, management lacked a clear view of client engagement, feature adoption, integration usage, and platform activity across customers.

The organization needed consistent product usage reporting that could combine customer and activity data from Salesforce, Azure Blob Storage, and CSV files. Built using Stitch, Python, MySQL, and Tableau, the solution transforms fragmented data into structured analytics and interactive dashboards for tracking customer usage, feature adoption, integration activity, and usage trends.

Automated Product Usage Reporting

Feature Adoption Visibility

Cross-Client & Integration Analysis

Problem Statement

More product usage. More customer data. Less clarity on what was actually driving adoption.

As the HealthTech platform grew, customers were using more of its clinical documentation workflows, including assessment sessions, AI-generated notes, chart creation, templates, and connected integrations.

The challenge was not a lack of data. The company already had valuable customer and usage information across Salesforce, Azure Blob Storage, CSV files, and product activity records. The problem was that these sources did not provide one clear view of how customers were engaging with the platform.

Bringing information together from disconnected platforms requires a structured approach to data integration for business analytics, ensuring that information from different sources can be consistently processed, combined, and used for reporting.

Management could see individual records and activity, but it was difficult to answer broader product questions such as which features were gaining traction, which customers were using integrations, how engagement differed across clients and platforms, and whether usage was increasing or declining over time.

As adoption increased, five problems became harder to manage:

Fragmented usage data:

Customer, clinical workflow, and product-activity data were distributed across multiple systems and files.

Limited feature-adoption visibility:

Teams could not easily see how clients were using assessment sessions, AI-generated notes, charts, templates, and integrations.

Difficult customer comparison:

Comparing product usage across clients, locations, and connected systems required data from multiple sources.

Hidden usage trends:

Weekly and monthly changes in platform activity and feature engagement were difficult to identify from raw data.

Slower product decisions:

Without centralized healthcare product analytics, management lacked a clear view of customer engagement and feature adoption.

The business needed a clearer way to understand customer engagement and product adoption across the healthcare platform.

Our Solution: Centralized Healthcare Product Analytics

Turning fragmented customer and usage data into a consistent view of product adoption.

SculptSoft developed a centralized analytics solution that brings together customer information, call-related data, and product activity from multiple sources. Instead of reviewing each source separately, product and business teams can now analyze customer engagement, feature usage, integration adoption, and activity trends through one reporting environment.

Data Consolidation Across Multiple Sources

Salesforce customer data was replicated into a centralized MySQL analytics database using Stitch. Call recordings and related CSV files stored in Azure Blob Storage were also brought into the reporting workflow, allowing information from different parts of the platform to be analyzed together.

Data Processing and Business Logic

Python ETL scripts cleaned, transformed, and combined data from MySQL and Azure Blob Storage. Business rules were applied to create structured, business-friendly datasets for customer activity, feature usage, chart metrics, and other reporting needs. Building reliable analytics workflows also depends on effective data pipeline design and engineering, where data processing, transformation, and integration work together to prepare information for consistent reporting and analysis.

Analytics-Ready Reporting Layer

Processed data was organized into MySQL reporting tables and views before being connected to Tableau. This created a consistent reporting layer and reduced the need to build dashboard logic directly on top of raw source data.

Product Usage and Adoption Reporting

Tableau dashboards give management visibility into customer activity, assessment sessions, AI-generated notes, chart usage, templates, integrations, and broader platform engagement. Users can review trends across clients, system platforms, implementation stages, and time periods from the same analytics environment.

Key Features and Capabilities Delivered

A clearer way to see how customers use the platform, which features they adopt, and where engagement is changing.

Customer Usage Overview

Gives product and business teams a consolidated view of client activity, assessment sessions, and overall platform usage, making it easier to understand how actively customers are engaging with the product.

Feature Adoption Tracking

Shows how customers are using capabilities such as AI-generated notes, chart creation, templates, and other product features, helping teams identify which areas are seeing stronger adoption.

Integration Adoption Visibility

Tracks which clients and work locations are using connected integrations, giving management a clearer view of how integration usage varies across the customer base.

Cross-Client and Platform Analysis

Allows teams to compare usage across clients, locations, and system platforms instead of reviewing each customer in isolation. This helps highlight differences in engagement and product adoption.

Weekly and Monthly Usage Trends

Provides trend views that show how product activity changes over time, helping teams spot increases, declines, or shifts in usage patterns that may need further attention.

Flexible Management Views

Interactive Tableau filters allow users to analyze activity by client, account manager, implementation specialist, implementation stage, system platform, and date, so different teams can focus on the information most relevant to them.

Organizations looking to better understand how operational and product data can support business decisions can also explore SculptSoft’s Data Analytics Services.

Outcome and business impact

Product teams gained a clearer view of how customers were using the platform and where adoption was growing.

The analytics environment replaced fragmented usage analysis with a consistent way to review customer activity, feature adoption, integrations, and usage trends.

Management could move from isolated records to broader questions about how the product was being adopted across its customer base.

Business metric	Result	What made the difference
Customer usage visibility	1,652 clients represented in the dashboard data	Customer and usage information was brought together into one analytics environment
Assessment activity tracking	482,508 assessment sessions available for analysis	Structured usage datasets made high-volume activity easier to monitor
AI feature usage visibility	2,422 auto-generated notes tracked	Feature-level reporting showed how AI-assisted capabilities were being used
Feature and integration adoption	Clearer comparison across clients, locations, and connected platforms	Product activity and integration data could be analyzed through consistent dashboard views
Usage trend monitoring	Weekly and monthly engagement patterns became easier to review	Historical usage data was structured for trend-based analysis rather than isolated record checks

Business metric

Customer usage visibility

Result

1,652 clients represented in the dashboard data

What made the difference

Customer and usage information was brought together into one analytics environment

Business metric

Assessment activity tracking

Result

482,508 assessment sessions available for analysis

What made the difference

Structured usage datasets made high-volume activity easier to monitor

Business metric

AI feature usage visibility

Result

2,422 auto-generated notes tracked

What made the difference

Feature-level reporting showed how AI-assisted capabilities were being used

Business metric

Feature and integration adoption

Result

Clearer comparison across clients, locations, and connected platforms

What made the difference

Product activity and integration data could be analyzed through consistent dashboard views

Business metric

Usage trend monitoring

Result

Weekly and monthly engagement patterns became easier to review

What made the difference

Historical usage data was structured for trend-based analysis rather than isolated record checks

The result is a stronger product analytics foundation that helps business and product teams understand customer behavior, monitor adoption, and identify where engagement is increasing, declining, or needs closer attention.

Technology stack

Databases & Data Engineering MySQL, Python, Stitch

Cloud & Infrastructure Microsoft Azure, Azure Blob Storage

Third-Party Integrations & Enterprise Platforms Salesforce

Business Intelligence & Visualization Tableau

Potential Use Cases Across Industries

The same analytics approach can support other organizations that need to understand how customers use a digital product, which features are gaining adoption, and where engagement is changing. The exact setup would depend on available data sources, product workflows, and reporting needs.

SaaS & Product Companies

Software companies can use a similar approach to track feature usage, customer engagement, adoption trends, and account-level activity across different user groups or product modules.

Healthcare Platforms

Healthcare technology companies can bring together CRM, workflow, integration, and product usage data to understand how customers engage with digital tools and where adoption may need more attention.

Enterprise Software Providers

Companies with multiple clients, locations, or connected systems can use centralized product analytics to compare usage across accounts, monitor integration adoption, and identify differences in platform engagement.

Still relying on scattered product data to understand what customers are actually using?

As an AWS Select Tier Services Partner, SculptSoft builds product analytics solutions that bring CRM, usage, integration, and activity data together so product and business teams can track feature adoption, customer engagement, and platform trends more clearly.

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