As one of the most trusted brands in America’s solar industry, our client is dedicated to making a positive impact through innovative solar solutions and a commitment to excellence. Their sales and marketing team approached us to revamp their current setup, aiming to enhance call center efficiency, boost ROI, and optimize marketing spend while also automating manual reports. With data coming in from platforms like Meta, Bing, and Google, they needed a unified system to streamline their marketing efforts and improve overall operational efficiency.
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.
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.
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.
We provided targeted solutions that enhanced data accuracy, channel performance insights, and resource allocation for the client through automation and machine learning.
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.
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.
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.
At NeenOpal, we specialize in delivering tailored Business Intelligence solutions that address the unique challenges faced by our clients. Our expert team leverages data management and automation to create effective dashboards and models that deliver actionable insights, helping clients to enhance operational efficiency and make informed decisions.
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.
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