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3 Loyalty Retention Strategies for Your Subscription Service

Retaining loyal customers is pivotal to the success of any subscription-based service, as it’s important to keep customers subscribed and engaged with the service. While many businesses focus on acquiring new customers, it’s equally important to focus on retaining existing customers and increasing customer loyalty.

Subscription services provide customers the convenience of having products and services delivered to them regularly. In 2023, Subscription Cancellations are at an all-time high in the USA markets. With such a market sentiment, brands need to have a loyalty retention strategy in place. We have enlisted 3 Loyalty Retention Strategies for Your Subscription Service that can help you win your customers.

What is a Subscription Based Service Model?

A subscription-based service is a business model where customers pay a recurring fee to access a service or product. Subscriptions can be offered on monthly, yearly, or pay-as-you-go models. Subscription services can be offered as a one-time purchase or as an ongoing commitment. For example, YouTube Premium offers an ads-free experience on subscribing as a Premium user at their monthly or annual subscription rates. 

Why is Retaining Customers Important?

Keeping customers happy and engaged helps to maintain loyalty and increase the lifetime value of a customer. HubSpot Research found that, in cases of company error, 96% of survey respondents continue buying from a company they regularly purchased from if the company apologized and corrected the situation. Retaining customers helps to reduce the cost of acquiring new customers, as well as increasing revenue.

Apart from the recurring revenue that existing customers bring in, there are upsell and cross-sell benefits. As per a blog by Chargebee, the success rate of selling to current customers is 60-70%, while selling to a first-time customer is 5-20%.

Thus, in a market where customers are fairly risk-averse and want to minimize their spending, retaining existing customers is critical to the success of a service provider.

Types of Loyalty Retention Strategies

  • Rewards Programs

Rewards programs help companies retain customers by incentivizing them to continue using their services. According to Forbes Research, 79% of consumers say loyalty programs make them continue to engage with a brand. While 75% say that they are likely to make another purchase after receiving an incentive.

By taking a subscription, a customer takes up a premium loyalty program with the brand or company. If they receive reward points as a part of it, the same can be redeemed for future purchases.

Prominent brands such as Sephora, Amazon Prime & Starbucks leverage such programs to cultivate a loyal customer base.

For example, Mariott offers it loyalty program called Mariott Bonvoy Benefits which offers multiple benefits from the moment users sign up. Through its portal, users have a personalized journey designed to heighten their experience with Mariott, as well as avail the best of travel and hospitality offers.

  • Personalized Customer Experiences

According to Evergage, 99% of marketers say personalization helps advance customer relationships, with 78% claiming it has a “strong” or “extremely strong” impact. Personalization helps make a customer feel more valued, and at the same time, it enhances their productivity or experience by helping them cut across a vast superset of choices, narrowing it down to highlight relevant material or services.

Streaming giant Netflix provides its users with hyper-personalized content recommendations through a customized Home Page, a Shuffle feature, and Recommendation lists. This helps users to be more engaged with the content they watch, as they are provided options based on their watch history and persona.

As more users become cognizant of extended reality technologies, the usage of AR/VR to draw in more personalized customer experiences has also increased.

Mantra Labs helped a leading Luxury Home Decor brand design and develop their Augmented Reality experience to boost their in-app engagement.

  • Automated Retention Strategies

Digital technologies enable brands to stay closer to their users and provide contextual nudges which ensure that there is a high recall for the product or service provided.

As most brands will have relevant subscriber data, they can use this to share personalized emails, push notifications, and other automated messages that are tailored to the customer’s interests and preferences. These messages can be used to remind customers of upcoming subscription renewals, offer discounts or promotions, or provide helpful tips and advice.

Brands must also ensure that the renewal journey of their customers is seamless. Providing multiple payment options, and, options to save payment details for faster checkouts.

While auto-renewals are a convenient feature, brands should proactively notify users of pending renewals, and in case of a heavily underutilized subscription offer customized plans. Media giant Wall Street Journal makes it difficult for paid users to cancel their subscriptions, with a user having to call their Customer Care Center and manually record their request to cancel the subscription, as opposed to directly canceling it via a portal. While this might marginally reduce customer churn, customer satisfaction drops significantly.

Eventually, brands have to ensure there is a balance between revenue generation and customer experience for prolonged success.

Final Thoughts

Retaining customers is essential to the success of any business. And loyalty retention strategies can help to ensure customers remain engaged and continue to purchase products or services. As subscriptions cancellation hit an all-time high, brands need to make their offerings personalized to provide value for money to subscribers. As well as ensure there is a significant focus on creating a seamless customer journey to boost customer satisfaction across all segments.

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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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