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Improving CX for Shared Mobility Services in India

Shared mobility is an umbrella term for companies that enable individuals to access a vehicle as and when they require it.

Shared mobility services like Uber and Ola ushered in a new era of public transportation, which needed to be more active with the use of autos, buses, and metros in urban areas. Dealing with a heavily unionized industry, these companies helped open the ride-sharing provider market.

Before the pandemic, these companies saw enormous markets for their services. However, things hit a slump during 2020, with the back-to-back lockdowns in India and public concerns around health and hygiene. 

Most of these companies offered carpooling services, such as Ola Share or Uber Pool, discontinued due to changing consumer behavior.

As we look at revitalizing the sector post-pandemic, there is a need for improved customer experience (CX) to ensure sales hit higher levels than in the pre-2019 era. This article explores the challenges of CX for shared mobility services in India and potential solutions for improvement through digital initiatives.

Understanding Shared Mobility in an Indian Context

Shared mobility services include ride-hailing, bike-sharing, car-sharing, and other shared mobility services which typically rely on technology, such as digital platforms, to connect users and provide vehicle access. Some examples of shared mobility services are:

E-hailing: A service that allows users to book a ride with a driver using an app or website. The ride can be private or shared with other passengers. Examples: Uber, Ola, BluSmart, etc.

Car sharing: A service that allows users to rent a car for a short time, usually by the hour or day. Users can pick up and drop off the vehicle at designated locations or anywhere within a specific area. Examples: Zipcar, Zoom Car, Revv, etc.

Bike and scooter sharing: A service that allows users to rent a bike or scooter for a short time, usually by the minute or hour. Examples: Bounce, Yulu, Lime, Bird, etc.

Carpooling and ride-sharing: A service allowing users to share a ride with other users traveling the same route. Users can arrange the ride in advance or on demand. Examples: Blablacar, Quick Ride, Waze Carpool, etc.

How do these services benefit the customer?

In several ways, shared mobility services benefit the end users in India. Be it reducing traffic congestion and pollution by decreasing the number of private vehicles on the road. Providing affordable and convenient transportation options for urban commuters who do not own a vehicle or cannot afford other modes of transport. 

And enhancing accessibility and connectivity for rural areas and underserved regions that lack adequate public transport infrastructure or services, which could be highlighted as some of the key benefits. 

What are the concerns plaguing consumers today?

  • Safety and hygiene: India’s shared mobility services face challenges in ensuring the safety and hygiene of vehicles and drivers, especially during the COVID-19 pandemic. This raises user concerns about the risk of infection, theft, harassment, or accidents.
  • Data and technology: India’s shared mobility services rely on data and technology to provide efficient and seamless user services. However, there are challenges in collecting, analyzing, and sharing data across different platforms and stakeholders. There are also issues of data privacy, security, and quality.
  • Cost efficiency: Rising input costs and attempts from service providers to jack up prices through cases like surge pricing, night charges, etc., add to the overall costs that trickle down to the end consumers.

Mantra Labs recently surveyed whether consumers would want to use carpooling services such as Uber Pool and Ola, where 60% of respondents replied with a firm YES. 

  • Poor customer service: In India, the reliability of such transportation options could be better. Customers often deal with long waiting periods, last-minute cancellations, poor driver behavior, and inefficient customer complaints management.

Improving customer service through CX solutions

  • Education and Awareness

Education and awareness initiatives are needed to improve the customer experience for shared mobility services in India. These initiatives should emphasize the importance of safe, reliable, and efficient transportation services and the need to adhere to safety regulations.

Options to provide your tracking details to another mobile number, immediate notification if the driver deviates from a marked route, road safety assistance in case of an accident or encounter, etc., should be provided and highlighted upfront on the mobile app.

Through these initiatives, stakeholders will be able to use the services in a comfortable and mentally peaceful manner, likely improving both the usage and the experience.

Mantra Labs helped build the mobile app for India’s #1 shared mobility services provider from scratch. Discover how we created a seamless platform that works at scale.

  • Lower Prices

Most ride-sharing apps provide promotional codes, discounts through third-party apps, or even weekly/monthly passes to help combat high prices and surge pricing. However, users must be aware of these benefits and avoid a high price barrier.

Microsoft Edge provides a pop-up when a user is at the payment stage of their cart for any shopping website – with information on the discount coupons available. Having a similar setup while a user completes payment will ensure that consumers can utilize the benefits.

  • Loyalty Programmes 

Cashback and loyalty points are also efficient ways to reduce a consumer’s financial burden. They are improving customer retention and boosting customer satisfaction. Companies can use gamification tools to improve user engagement and the time consumers spend on their apps. 

Mantra Labs created a rewards program for Myntra’s End of Reason Sale, which allowed users to collect coins and rewards redeemable during their purchases. Similarly, offering premium services such as free upgrades, in-transit entertainment, and partner offers as rewards would increase user stickiness. 

Conclusion

Shared mobility services have great potential to reduce congestion, air pollution and increase connectivity in India. However, there are still many improvements in customer experience to ensure these services are utilized to their full potential. By implementing lower prices, pop-up notifications, cashback, and loyalty points, these services can become more accessible and attractive to consumers. These changes will improve customer experience and make shared mobility services a viable option for many people.

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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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