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Why Insurance should take back control of the ‘funnel’ with a Lead Management Solution

While every organization manages their ‘sales funnel’ differently, they must all address the arduous task of handling leads competently. In the Insurance business, generating quality leads is proving to be a tough assignment for older agents, as their collective numbers dwindle down —  as more business is being transacted online, creating a complex web of lead data to consolidate, than ever before.

For an insurer or broker, unearthing excellent leads is only half the battle won — converting the semi-interested, prospective buyer to ‘loyal avengers’ of your brand is the true test. Without an adequate analysis of the lead data, insurance firms are more likely to let valuable customers slip through unnoticed. Infact, 80% of marketing leads are either lost or discarded because of poor lead management; while 80% of leads passed onto the sales agents are unqualified — according to research on lead management, surveyed among B2B organizations in 2019.

Documenting a prospect’s complete story of interactions & experiences with your organization, delivers timely insights into exactly how and when a prospect was converted from an ordinary ‘lead’ to a ‘customer’. Most of the sales follow-up process including managing leads, prospecting new business and dispatching service to existing clients — is time consuming and for the most part, manually done. On the other hand, outside competition via other brokers and organizations offering closely matched services and products are most likely to capitalize on the mistakes you have failed to identify early —  which brings us to — What is it about your business that will capture a prospect’s attention long enough to close a sale, build quality relationships, and encourage referrals? For an effort of this magnitude, the journey begins with a strong lead management framework.

The transition for qualified ‘leads’ as it evolves through the organization’s marketing and sales pipeline, eventually passes through several phases or ‘lead stages’.

Lead Management Accelerator Framework

Here’s why Insurance needs an Accelerated Solution

Lead Prioritization

  1. Clustering of leads based on assorted attributes —  profile, source, income, demographics etc.
  2. More targeted and focused approach on managing prospects.
  3. Helps improve the quality of leads to the caller/sales team.

Lead Allocation

  1. Profile analysis of callers to identify their strengths and allocate leads accordingly.
  2. Enhanced and optimized Lead Conversion thereby creating profitability.

Lead Disposition

  1. For time effectiveness quick, detailed & one-stop disposition.
  2. Seamless integration with insurance core platform allows quick access of quotes and payment service.
  3. Allow effective communication by means of various integrations like e-mail, SMS, dialers etc.  
  4. Various features like document repository, call scheduling, lead journey chart helps callers handle lead dispositions aptly.

The implementation of a strong framework makes a dedicated LMS solution for the insurance industry not only desirable, but strategically important for the industry.

From creating qualified opportunities and ultimately satisfied customers — Lead management is the backbone of a successful sales operation. The sales process should integrate with lead management seamlessly, which is why an automated LMS with customizable workflows harnessing Artificial Intelligence/Machine Learning is a complete solution. By automating the sales process we can ensure calls, demos, follow ups and meetings — even potential revenue — isn’t slipping through the sales pipeline undetected.

To know more about how Insurers can create workflow specific LMS solutions, get in touch with us — hello@mantralabsglobal.com


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Silent Drains: How Poor Data Observability Costs Enterprises Millions

Let’s rewind the clock for a moment. Thousands of years ago, humans had a simple way of keeping tabs on things—literally. They carved marks into clay tablets to track grain harvests or seal trade agreements. These ancient scribes kickstarted what would later become one of humanity’s greatest pursuits: organizing and understanding data. The journey of data began to take shape.

Now, here’s the kicker—we’ve gone from storing the data on clay to storing the data on the cloud, but one age-old problem still nags at us: How healthy is that data? Can we trust it?

Think about it. Records from centuries ago survived and still make sense today because someone cared enough to store them and keep them in good shape. That’s essentially what data observability does for our modern world. It’s like having a health monitor for your data systems, ensuring they’re reliable, accurate, and ready for action. And here are the times when data observability actually had more than a few wins in the real world and this is how it works

How Data Observability Works

Data observability involves monitoring, analyzing, and ensuring the health of your data systems in real-time. Here’s how it functions:

  1. Data Monitoring: Continuously tracks metrics like data volume, freshness, and schema consistency to spot anomalies early.
  2. Automated data Alerts: Notify teams of irregularities, such as unexpected data spikes or pipeline failures, before they escalate.
  3. Root Cause Analysis: Pinpoints the source of issues using lineage tracking, making problem-solving faster and more efficient.
  4. Proactive Maintenance: Predicts potential failures by analyzing historical trends, helping enterprises stay ahead of disruptions.
  5. Collaboration Tools: Bridges gaps between data engineering, analytics, and operations teams with a shared understanding of system health.

Real-World Wins with Data Observability

1. Preventing Retail Chaos

A global retailer was struggling with the complexities of scaling data operations across diverse regions, Faced with a vast and complex system, manual oversight became unsustainable. Rakuten provided data observability solutions by leveraging real-time monitoring and integrating ITSM solutions with a unified data health dashboard, the retailer was able to prevent costly downtime and ensure seamless data operations. The result? Enhanced data lineage tracking and reduced operational overhead.

2. Fixing Silent Pipeline Failures

Monte Carlo’s data observability solutions have saved organizations from silent data pipeline failures. For example, a Salesforce password expiry caused updates to stop in the salesforce_accounts_created table. Monte Carlo flagged the issue, allowing the team to resolve it before it caught the executive attention. Similarly, an authorization issue with Google Ads integrations was detected and fixed, avoiding significant data loss.

3. Forbes Optimizes Performance

To ensure its website performs optimally, Forbes turned to Datadog for data observability. Previously, siloed data and limited access slowed down troubleshooting. With Datadog, Forbes unified observability across teams, reducing homepage load times by 37% and maintaining operational efficiency during high-traffic events like Black Friday.

4. Lenovo Maintains Uptime

Lenovo leveraged observability, provided by Splunk, to monitor its infrastructure during critical periods. Despite a 300% increase in web traffic on Black Friday, Lenovo maintained 100% uptime and reduced mean time to resolution (MTTR) by 83%, ensuring a flawless user experience.

Why Every Enterprise Needs Data Observability Today

1. Prevent Costly Downtime

Data downtime can cost enterprises up to $9,000 per minute. Imagine a retail giant facing data pipeline failures during peak sales—inventory mismatches lead to missed opportunities and unhappy customers. Data observability proactively detects anomalies, like sudden drops in data volume, preventing disruptions before they escalate.

2. Boost Confidence in Data

Poor data quality costs the U.S. economy $3.1 trillion annually. For enterprises, accurate, observable data ensures reliable decision-making and better AI outcomes. For instance, an insurance company can avoid processing errors by identifying schema changes or inconsistencies in real-time.

3. Enhance Collaboration

When data pipelines fail, teams often waste hours diagnosing issues. Data observability simplifies this by providing clear insights into pipeline health, enabling seamless collaboration across data engineering, data analytics, and data operations teams. This reduces finger-pointing and accelerates problem-solving.

4. Stay Agile Amid Complexity

As enterprises scale, data sources multiply, making Data pipeline monitoring and data pipeline management more complex. Data observability acts as a compass, pinpointing where and why issues occur, allowing organizations to adapt quickly without compromising operational efficiency.

The Bigger Picture:

Are you relying on broken roads in your data metropolis, or are you ready to embrace a system that keeps your operations smooth and your outcomes predictable?

Just as humanity evolved from carving records on clay tablets to storing data in the cloud, the way we manage and interpret data must evolve too. Data observability is not just a tool for keeping your data clean; it’s a strategic necessity to future-proof your business in a world where insights are the cornerstone of success. 

At Mantra Labs, we understand this deeply. With our partnership with Rakuten, we empower enterprises with advanced data observability solutions tailored to their unique challenges. Let us help you turn your data into an invaluable asset that ensures smooth operations and drives impactful outcomes.

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