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CX Trends 2021: Here’s how businesses are winning Customer Experience moments

5 minutes read

In the pandemic era and the subsequent lockdowns around the country, in-person customer experience seems like a far-fetched dream for most of us who have made our homes into our offices, our beds, or living rooms into our conference rooms, and vice versa.  

While brands across the globe are building processes for the new normal, where the virtual world continues to gain popularity, even as the second wave of the COVID-19 pandemic rages on, with a third wave expected to hit soon enough. 

The last 18 months since the pandemic struck have taught a crucial lesson to every business and marketeer by disrupting set notions and practices. The key to a thriving business in questionable times like these is to understand the importance of customer experience and travel up the graph from a good to a great one, in order to sustain oneself. Simultaneously, it’s important to workaround forecasts in a volatile setting for every business type to ensure preparedness.  

What began as a global healthcare crisis also led to a significant transition into a digital-friendly world. From work-from-home setups to e-commerce, getting food home delivered, and more, customers are more online than ever before, leading to a rise in digitally-savvy professionals driving and engaging in better CX.

If you are investing in CX, where do you begin?

According to research conducted by Gartner, companies that successfully implement customer experience projects begin by focusing on how they collect and analyze customer feedback.

Despite these turbulent times for people and businesses, customer expectations have seen an upward graph and so providing a top-notch customer experience is a challenge everyone is trying to meet in order to retain their loyal customer base. 

PwC, through their future of CX report, surveyed 15,000 consumers and found that 1 in 3 customers will leave a brand they love after just one bad experience, while 92% would completely abandon a company after two or three negative interactions. 

Whether you use surveys, web forms, or Net Promoter Score (NPS) programs, read through customer comments, suggestions, and opinions to see what they expect from you. Then, invest in those projects to meet their expectations.

Read on for trends we are seeing and expect to see in 2021: 

CX Trend 1: Going digital for customer interaction in the pandemic era: 

The shift to digital that has been aided manifold amid the pandemic, has seen consumer behavior move on for all services including e-commerce, finance, healthcare, wellness, and more. Forrester has predicted that 2021 will see digital customer service interactions increase by 40%.

According to Gartner, the new normal makes it mandatory for the service industry especially to transition to a “digital-first” strategy, thereby enabling improved customer interactions via proactive engagements on messaging platforms. By the year 2025, 80% of customer service organizations are expected to abandon native mobile apps in favor of messaging platforms for a more seamless customer experience.

Even at the workplace, a digital transition means conferences and seminars move to Zoom conferences (and other related apps) and webinars. The year 2020 also made way for a paradigm shift in the Ed-Tech space when educational models have moved online and full-time courses too are being held on the web. 

All Images Courtesy: zendesk.co.uk/CX Trends report 

CX Trend 2: The rise and stay of contactless service in the new normal: 

As the COVID-19 pandemic continues to impact both customers and organizations, it has instead generated a shift to a contactless approach as the best alternative of providing a service without person-to-person contact. According to a survey by IDC, over 36% of manufacturers said that their service or product installation will now have a contactless approach. The survey also predicts that by 2021, 65% of organizations will have shifted to a digital-first approach through automated ‎operations and ‎contactless experiences. However, it is also imperative that technicians, as well as customers, are safe amid this transition which might also see an increased implementation of the latest technologies and capabilities including artificial intelligence and augmented reality, and mixed reality for optimized service. 

This prediction by IDC also aligns with Forrester’s 2021 prediction that says that consumers will continue to prefer digital interactions and customer service, to keep themselves safe.

CX Trend 3: Emotive technology and why there’s a noticeable rise:  

The pandemic and the subsequent lockdown also led to an all-time high of reported mental health problems, which were largely said to have been triggered by social media. It is thus a need-based search for a solution to overcome issues such as these which also benefit consumers and businesses. 

According to Harvard Business Review, “When companies connect with customers’ emotions, the payoff can be huge.” The ability to generate positive emotions in a customer and leaving a good lasting impression is called brand intimacy which helps brands drive conversions and customer loyalty. 

In the present day, companies are dealing with a lot more data amassed from their customers which helps them figure out what their customers are feeling through the use of facial recognition, movement data, health data like heart rate and blood pressure, social media behavior, and more. 

One of the ways that this immense power on a customer’s lifestyle choices can be used for a good cause like being able to tackle mental health struggles including anxiety and depression, emotional health crisis, and more. 

Microsoft now plans to embed Teams with a series of “wellness” tools to address these crisis situations that will help monitor emotional health, mental health and provide necessary tips and tricks. Other apps including Wysa, Headspace, Calm and more help with a chat to help you feel at ease, extend therapist support when needed, and also provide guided meditation sessions to help keep your mind calm. High-stress levels and anxiety are also known to reduce immunity levels, which in turn might increase vulnerability to other health issues, and open up the unfortunate possibilities for other lifestyle disorders including hypertension and diabetes. 

CX Trend 4: Empathy, a core element in CX: 

Empathy has emerged as a core organizational capability in the year 2020 and so empathetic customer support is now imperative for customer service in 2021. According to Forrester, organizations must recognize the needs of their customers both physically and emotionally, to provide better empathetic customer support and experience. This metric has skyrocketed as consumers around the world have been adjusting to the pandemic, lockdown, and the new normal. 

A recent report published by Gartner predicts that by 2025, customers will engage a freelance customer service expert to address 75% of their customer service needs. Steven Petruk, President, Global Outsourcing Division at CGS, shares, “Amid the challenges of the pandemic, customer care centers have all but done away with any metrics around call duration and are actively encouraging agents to spend more time on the phone with clients. While empathy has not been an operational performance metric in the past, it absolutely is the prime area of focus now and will continue to be. In an effort to measure empathy, many companies are adding empathy-specific questions to their post-call surveys.” 

With an ever-changing business landscape, more so amid the second wave of the pandemic and a probable third wave expected soon, companies globally have an opportunity to re-strategize and plan their roadmap as a short-term goal depending on what might work best for them in the present situation, with the flexibility to rehash their MO every few months or annually. 

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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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