AI Recommendation Engine Drives Millions in Revenue for Tech Manufacturer
From smartphones and televisions to industrial machinery, cars and airplanes, a U.S.-based manufacturer and retailer of electronic components provides technology solutions to more than 100,000 clients.
To support a wide range of complex, high-volume custom product design, the company manages more than 100,000 individual SKUs. This requires highly knowledgeable sales and technical designers to identify core components and make recommendations for compatible product attachments or replacements to meet customer needs.
But relying solely on human-driven processes to execute this level of technical work had created challenges, including long sales cycles and missed opportunities for attach revenue.
Leaders within the organization recognized these issues and began looking for ways to supplement these processes with data-driven intelligence.
In addition to the DaaS solution, Insight provided OneDrive for Business to solve an ongoing issue for the client: lost data when transitioning devices. Rather than using a manual process or hands-on IT support for transferring data from old devices to new ones, all employees now use OneDrive and have access to their data from the cloud.
Industry: Manufacturing
The challenge: Simplify sales awareness and increase revenue related to product attachments.
The solution: A modern data architecture as the foundation for AI — and an ML model to make product recommendations based on historical data.
Insight provided:
Full implementation of Insight Lens for Gen AI Accelerator
ML model Proof of Concept (PoC) and build
Power BI integration with ML model for testing and validation
Build-out and automation of pipelines for rapid delivery
Integration of attach recommendations into CRM system and eCommerce website
Insight services: Consulting Services
The client’s senior vice president and staff attended an envisioning session focused on identifying potential applications for Machine Learning (ML) and Artificial Intelligence (AI). Many of the ideas generated in this session involved how AI could help address the challenges of making manual recommendations for compatible product bundles — as well as similar challenges for customers on the web.
An Insight representative also attended this session, and a conversation began around how Insight could help the company achieve these goals.
Over the next few months, Insight client executives and data engineers learned about the existing data environment and the client’s desired outcomes. The path forward became clear: to develop an ML model that could integrate with both the Customer Relationship Management (CRM) system and the eCommerce site. This would provide embedded product recommendations based on core component compatibility. But the client’s team quickly realized that before any AI work could begin, they needed to establish a modern data architecture to serve as the foundation.
Due to the intricacies of compatibility considerations and the need for historical data to inform the most successful product attachments, data visibility and accuracy were essential. Years of acquisitions and siloed systems had led to disparate data on products and customer usage that was largely documented through spreadsheets rather than pulled from a unified database.
To ensure the efficacy and scalability of the project, Insight recommended a two-phase approach — an initial phase to establish a single-pane-of-glass view into the client’s data and a second phase to build out the ML model.
$15 million increase in revenue pipeline in eight weeks
$50 million projected increase in annual revenue
Expanding capabilities to other business units
Support for viable alternative product recommendations amid chip shortages
Kicking off the first phase, Insight data engineers started locating and collecting the organization’s terabytes of CRM data into a Databricks Lakehouse architecture. The team leveraged Insight’s proprietary Lens framework, which provided a scalable, unified platform through which data could be cleansed and transformed for further use.
With this foundation in place, the team turned their attention to replacing the client’s existing product spreadsheets with real-time visualizations of this data. This enabled users to build highly accurate reports around historically successful attachments, as well as reveal sales trends and market penetration.
With a new Databricks environment, Insight data scientists began phase two of the project. Over eight weeks, a Proof of Concept (PoC) was built out for the ML model. The recommendation engine was integrated with Power BI to provide preliminary outputs for testing and validation. These were then provided to the client’s technical engineers to ensure the attach recommendations were both logical and aligned to parameters.
After building out and automating pipelines for rapid delivery, further testing and validation was performed with sales teams. These representatives leveraged the data in client conversations — further confirming the solution’s value in creating new revenue opportunities.
With this critical stage of testing and development complete, the project moved into production. Insight’s data team finalized the solution by integrating attach recommendations into the client’s CRM system and eCommerce website.
With the recommendation engine now up and running, the client experienced significant Return on Investment (ROI). After just eight weeks, the system increased pipeline revenue by $15 million — with a projected annual value of $50 million.
Based on this success, the model’s capabilities will be extended to recommend other component attachments — with the long-term goal of encompassing the full product portfolio. Even as external pressures (such as chip shortages) create unprecedented supply constraints, the company is well positioned to provide customers with not only attach recommendations, but also viable alternatives when products are out of stock.
In support of this new use case, Insight will leverage the existing data on component properties and compatibility to identify replacements with similar electrical properties and shorter lead times. This is expected to have a dramatic impact on revenue and customer outcomes in the coming months and years.
Beyond the immediate gains, the recommendation engine has demonstrated the viability and value of AI investments throughout the business. With a modern data architecture in place as the foundation for future initiatives, the organization continues its data-driven transformation, unlocking new opportunities for growth and competitive advantage.
Businesses everywhere are realizing massive performance gains — and laying the foundation for a data-driven future — with Insight’s help.
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