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Re-imagining CX for Digital Therapeutics (DTx) apps in the USA

In recent years, there has been a significant rise in the popularity of digital therapeutics and wellness applications in the USA. 

These apps offer various services, from mental health support to personalized fitness plans. 

Digital therapeutics (DTx) uses technology to provide innovative solutions for managing and treating various medical conditions. 

The market for such applications has grown multifold. According to a report by Grand View Research, the digital therapeutics market in the USA was valued at USD 2.4 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 19.9% from 2021 to 2028.

As the demand for these apps continues to grow, companies must prioritize the user experience (UX) and customer experience (CX) to stand out in a crowded market. 

This blog post will explore the importance of re-imagining CX for digital therapeutics in the USA and provide actionable tips for creating a seamless and engaging user experience.

The Role of CX in Digital Therapeutics Apps

Understanding the User Journey

Understanding their user journey involves mapping the entire process from app awareness to achieving health goals.

The first step is understanding how users discover, search, and evaluate the correct application. 

Omada Health, a leading DTx application, promotes its services across social media platforms, blogs, and referrals. In addition, it partners with employers as health plan providers. It has partnered with over 600 employers, including CostCo, Lowe, and Cigna, to reach the maximum audience. 

Once you have the right users onboarded, ensuring that user engagement rates remain high on your platform is vital. Gamification is a commonly used technique to enhance user engagement.

Pear Therapeutics, which provides therapeutics against opioid-use disorder, substance-use disorder, and chronic pain, amongst others, has products such as reSET, reSET-O, Somryst, and Pear-006, which feature interactive lessons, quizzes, rewards, and feedback that help its users learn and apply new skills and behaviors. 

And, finally, leveraging the right strategies to ensure retention of the users. Memberships, loyalty points, and reward programs have worked well with freemium business models. These can reduce the CAC significantly while parallelly improving CLV. 

You can read Mantra’s take on strategies to improve loyalty in subscription-based services here. 

By understanding the user journey, digital therapeutics apps can deliver a more personalized and user-centric experience, leading to higher user satisfaction and improved outcomes.

Personalization

A one-size-fits-all approach often fails to impress the users as there are several nuances to consider while mapping out user journeys. 

Personalizing the experiences based on the user’s preferences, medical history, consumption behavior, and patterns helps hook the users effectively.

DTx companies should keep in mind the following principles while designing their UX: 

  • Tailoring the app experience to individual user needs
  • Offering personalized recommendations and content
  • Allowing users to customize their settings and preferences

Kaia Health uses artificial Intelligence and computer vision to provide physical therapy and pain management. It uses a personalized and adaptive approach to tailor its programs to each customer’s needs and progress. The company’s products, such as Kaia Back Pain and Kaia COPD, use the smartphone camera to track and analyze the customer’s movements and posture and provide real-time feedback and guidance. The company also adjusts the difficulty and duration of the exercises based on the customer’s feedback and performance.

Designing an Intuitive and User-Friendly Interface

Digitalization plays a crucial role in designing an intuitive and user-friendly interface that focuses on simplifying navigation. Here are some examples of how companies can optimize their user interface:

Contextual guidance: Digital transformation enables contextual guidance within the app. For example, interactive tooltips, pop-ups, or overlays that provide advice and instructions to users as they navigate the app help them understand its features and functionalities more efficiently.

Progressive disclosure: As a strategy, it enables progressive disclosure techniques, where information is revealed gradually as the user navigates through the app. This helps to give the user a manageable amount of information at once and allows for a more focused and streamlined navigation experience.

By leveraging these techniques, digital therapeutics apps can enhance the overall user experience and make it easier for users to navigate the app and accomplish their goals.

Leveraging AI for Enhanced CX

Companies can leverage AI to enhance digital therapeutics apps’ customer experience (CX), primarily through generative AI. Here are some examples:

Instant Customer Support: AI-powered chatbots can provide instant customer support within the app. These chatbots can answer frequently asked questions, guide users through the app’s features, and assist in real time. It helps improve the overall user experience by providing quick and efficient support without human intervention. For example, Mantra Lab’s co-creation, Wysa, is an intelligent conversational CX platform that helps assess the emotional well-being of its users and tracks how to improve the same. 

Improved App Performance and Functionality: ML algorithms can continuously analyze and optimize app performance. For example, machine learning algorithms can identify and fix bugs, improve loading times, and enhance the user interface.

Generative AI for Personalized Content Creation: Generative AI can create personalized content for users based on their needs and preferences. For example, Livongo Health, which combines connected devices, data science, and coaching to help people manage chronic conditions, uses a data-driven approach to personalize and optimize its services. The company uses generative artificial Intelligence to deliver tailored recommendations and nudges to its customers based on their preferences and goals. 

Ensuring Accessibility and Inclusivity

As we begin reimagining the customer experience (CX) for digital therapeutics applications, we must recognize that our target audience encompasses diverse users with varying needs and capabilities.

Designing for Different Devices and Platforms

To truly enhance the CX, designers and developers must embrace this diversity. Let us understand some ways in which companies can focus on accessibility:

Optimizing the app for various screen sizes and resolutions: In a world where the screen size of a smartwatch differs significantly from that of a tablet, it is imperative to ensure that the app’s layout remains intuitive and functional across all dimensions. This means responsive design that adjusts gracefully to varying screen sizes and resolutions.

Testing the app on different devices to identify and fix issues: Rigorous testing across various devices is the linchpin of success. Identifying and rectifying problems arising from device-specific nuances ensures a smoother and more inclusive user experience.

Considering Different User Needs

Inclusive design is about accommodating different devices and understanding and addressing diverse user needs. 

Making the app accessible for users with disabilities: Accessibility should be ingrained in the app’s DNA. This entails adhering to the Web Content Accessibility Guidelines (WCAG) to ensure that individuals with disabilities can navigate and utilize the app effectively. Features like screen readers, voiceover compatibility, and text-to-speech capabilities become essential.

Providing options for font size and color contrast: Recognizing the importance of readability, offering adjustable font sizes and high-contrast color schemes can significantly assist users with visual impairments. For example, Epic Systems, a healthcare software company in Wisconsin, powers many patient portals and apps used by healthcare providers across the United States. One of their offerings, MyChart, provides access to medical records and prioritizes accessibility. It includes features like screen readers and compatibility with voice assistance.

By designing for different devices and platforms while considering diverse user needs, we create a more equitable experience and extend the reach and impact of these groundbreaking healthcare solutions.

Conclusion

CX plays a crucial role in attracting and retaining users in the competitive landscape of DTx apps. By understanding the user journey, designing an intuitive interface, leveraging technology, and ensuring accessibility, companies can create a seamless and engaging user experience.

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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