5 Reasons why Customer Service Chatbots are the Need of the Hour

3 minutes, 59 seconds read

Published on May 22, 2020

Updated on Aug 3, 2020

The rapidly advancing world suddenly came to a halt with the outbreak of the COVID-19 pandemic. If anything positive that has come out of this crisis is that it has made people more comfortable with technology. Even people from non-tech-savvy older generations are readily adopting technological advancements. Especially the customer service verticals (helpdesk and support portals) if businesses are seeking automation the most.

Chatbots have come a long way since they were first introduced. In 2016, Facebook allowed chatbots into its Messenger platform to let businesses deliver automated customer support, e-commerce guidance, content, and interactive experiences through chatbots. From answering simple queries to scheduling appointments, chatbots have evolved into AI-driven Virtual Assistants. Given the variety of purposes they solve, chatbots are here to stay. The chatbot market is projected to reach $1.25 billion by 2025.

Let’s look at some of the most pressing points which make Customer Service Chatbots so relevant in the current period.

1. The Need to Save Time, Money and Resources

The prolonged lock-downs have left a deep impact on the business cash-flows. To manage the business with limited resources and constraints on budget, this is the right time to integrate chatbots which can take up routine tasks and save bandwidth of human resources for more complex ones. These days, chatbots are available at affordable prices and even on monthly subscription models.

2. Elevate Digital Customer Experience

During the initial stage of the COVID outbreak, people struggled to get essentials. The volume of customer grievances and queries were very high. Businesses struggled to address them. AI-driven chatbots in such situations prove to be a great asset in acknowledging the problems and providing relevant solutions.

Voice-enabled customer service chatbots give a human-like experience to customers which helps add that personal touch in a digital environment. Unlike command-based chatbots, AI-based or Machine Learning chatbots can answer ambiguous questions. Based on the responses, chatbots are learning and can provide better answers over time. NLP chatbots will take the digital CX to another level which is a crucial differentiator for businesses in these times.

AI in Insurance will value at $36B by 2026. Chatbots will occupy 40% of overall deployment, predominantly within customer service roles.
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3. Build Customer Engagement and Brand Loyalty

One of the biggest pain-points of the lock-downs and social distancing is keeping the existing customers and clients engaged and building trust amongst them. Retaining brand loyalty has been challenging since customers during these times will watch out for businesses that provide the best services. Big names with a huge customer base may fail if they continue with legacy systems and traditional models even in these crucial times.

To keep the business running, organizations will have to engage with customers. Bots can derive data through it’s AI capabilities which can be used to re-engage with customers. Especially in the e-commerce sector, bots can remind customers of the unbought items from their wish-list, suggest items to pair with the selected ones, take feedback, and so on.

Customers remember brands that provide good services during difficult times.

4. Dealing with the Issues of Modern Workforce

Due to lockdown, organizations faced a pressing challenge to ensure the smooth functioning of business with a remote workforce. A part of this challenge was also to ensure healthy and transparent communication with the internal workforce i.e. employees.

Especially the larger organizations and MNCs faced communication challenges with their employees across the globe. For instance, the HR department might not be able to reach all its employees. This calls for a need for chatbots that can address some of the basic queries. As Gartner predicts — by 2022, 70% of white-collar workers will interact with conversational platforms daily. The current pandemic is just fueling the adoption of helpdesk automation.

5. Lead Generation

The business development and sales departments have a difficult road ahead. Given the economic slowdown, how to generate leads? Considering the current situation, many businesses are going digital as sales representatives cannot meet clients in-person.

In the B2C space, cold calling and email marketing will soon become redundant. The situation requires an interaction with people through which leads can be found. Bots can provide data on the back-end while interacting with prospects and help businesses reach out to them. Thus, enabling more sales conversions.

[Also read: Conversational Chatbots for SMEs to continue business from home]

What Does the Future Look Like for Customer Service Chatbots?

Modern customers include Millennials and Gen Z who represent 2 billion (27%) and 1.8 billion (24%) of the population respectively. They have a high affinity for self-service portals and look out for their query resolution instantly. Chatbots with integrated workflows can drive historical consumer data and accordingly suggest resolution.

Companies like Uber and Amazon are already deploying self-service customer support, which not only releases the load from call-centers but also satisfies the growing preference for convenience. According to a recent Salesforce survey, 60% of businesses are ready to adopt self-service portals and chatbots are a crucial part of facilitating this.

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:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, 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.

5 Reasons why Customer Service Chatbots are the Need of the Hour