Overview
As a trusted brand in America’s solar industry, our client drives impact through innovative solutions and excellence. Their sales and marketing team sought to revamp their setup to enhance call center efficiency, boost ROI, optimize marketing spend, and automate reports. With data from Meta, Bing, and Google, they needed a unified system to streamline marketing and improve operations.
20%
CPS (Cost per Sale) Decreased
35%
Lead to Appointment Conversion Ratio Increased
~90
Man-hours/month saved by automating reporting
Customer Challenges
As the client scaled its marketing and sales operations, inefficiencies in data management and resource allocation emerged. These challenges affected decision-making, marketing performance analysis, and overall operational efficiency.
Marketing Spend Data Governance
The client faced inconsistencies in their Excel reports tracking marketing metrics due to manual downloads of spend data from Google, Meta, and Bing. They used to download CSV reports and compile them manually, which led to discrepancies from varying spend models for third-party leads and referral apps, as well as differences in figures from each portal.
Call Center Resource Shortages Impacting Customer Service
Resource shortages in call centers and sales teams across certain regions led to delays in customer service, ultimately resulting in lost business. Additionally, in some areas, the low number of leads relative to the available manpower resulted in inefficient resource utilization.
Inconsistent Channel Performance Insights
The inconsistent filters in Excel reports resulted in varied numbers, making it difficult to analyze channel performance effectively. While there were multiple reports tracking conversion rates, the client lacked a single, reliable source for comprehensive marketing analysis, which hindered their ability to draw meaningful insights.
Integrated Marketing Analytics Architecture
A centralized data framework that automates multi-channel data ingestion, enables advanced performance modeling, and delivers real-time insights for strategic marketing decisions.
Solutions
NeenOpal provided targeted solutions that enhanced data accuracy, channel performance insights, and resource allocation for the client through automation and machine learning. By streamlining data integration and using predictive analytics, we helped optimize marketing spend and operational efficiency.
01.
Developed Lead Spend allocation Model
We automated the tracking of owned media spend by integrating APIs from Bing, Meta, and Google into a Snowflake data warehouse. This approach eliminated many inconsistencies in incremental data loading and enabled the development of a lead spend allocation model. This model effectively allocated marketing costs to individual leads, taking into account marketing expenses, lead volume, and geographic factors.
02.
A single dashboard for Volume, Velocity & Conversion for Lead to Sales Funnel
We collaborated with stakeholders to standardize filters across various stages and integrated key dimensions after data ingestion. Building on the lead spend allocation model, we enhanced it with specific dimensions for deeper channel analysis. A Tableau dashboard was created for comprehensive marketing spend analysis, covering the entire process from lead generation to project closure. We emphasized key performance indicators (KPIs) such as Cost per Lead (CPL) and Cost per Sale (CPS) to provide meaningful insights.
03.
Optimized Call Center Resource Allocation with Forecasting Model
We implemented a machine learning model to forecast the number of leads, appointments, and sales by geography, optimizing resource allocation effectively. This model considered various factors, including marketing spend, lead source, geography, call center and sales rep availability, as well as historical conversion rates, ensuring resources were allocated efficiently in line with anticipated demand.
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Benefits
Significant Cost Reduction per Sale
The implementation of NeenOpal's solution incorporating data driven decision making led to a 20% reduction in Cost per Sale (CPS). Comparing KPIs such as CPS and Cost Per Lead (CPL) enabled the company to determine the most optimal lead source for their business.
Significant increase in lead to appointment conversion ratio
Enhanced manpower and resource planning, coupled with tracking key metrics and leveraging a machine learning model, resulted in a 35% increase in the conversion rate.
Reduction in Manual Effort
The automation of Excel reports via APIs and dashboards led to a substantial decrease in manual labor, reducing 90 man-hours every month which were previously needed in preparing data and reports manually.
Conclusion
Our customized solutions tackled the main challenges faced by the client's sales and marketing team in the solar industry. By improving data accuracy, providing better insights into channel performance, and optimizing resource allocation, we helped the team make smarter decisions. With the use of automation and machine learning, we addressed their immediate issues and set the client up for long-term growth in a competitive market, supporting their goal of delivering innovative solar solutions.
FAQ
What challenge was the energy client facing?
The client had inconsistent marketing data and manual reports, making it hard to analyze performance and allocate resources efficiently.
What outcomes did the client achieve?
The client reduced cost per sale, increased lead-to-appointment conversions, and saved significant man-hours through automated reporting.
Which technologies were used?
Solutions included Snowflake for data warehousing and Tableau for visualization and reporting.
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