Off-the-shelf vs Custom Machine Learning Models?
When is building better than buying an off-the-shelf solution? Companies can engage in different approaches to model development. From fully...
Phase 4: Improve Portfolio Company Performance
A leading Private Equity and Venture Capital firm, with over $20B under management, requested an end-to-end data audit of their deal platform. They were looking to evolve the platform to make a scalable and unified sourcing tool with consistent architecture and infrastructure. They wanted an independent third-party to assess the technical decisions made to date in the development.
The firm needed help integrating 4 newly acquired CRM/ERP companies. Each acquired company had its own databases, in its own format. A lack of visibility into the sales process of these siloed data systems hindered coordination, planning, and tracking. The blind sales department had no concise data to direct their time and resources. Due to disparate data sets, the company had no insight into the efficacy of their upper funnel engagement or attribution across their sales cycle. As a result, they had a low conversion rate on sales efforts.
The company’s existing development team had no resources for an internally focused, stand-alone project so they hired Blue Orange. The goal was to provide architectural guidance on their data infrastructure to support unified data and sales optimization.
A complete project assessment was provided with a budget for the AWS data lake transition and the cost-benefit analysis of these improvements to the sales projections with the new system.
Vertical Private Equity Optimization
Stage Portfolio Performance
PHASE 1: Data Transformation: Created a scalable architecture that confidently could handle all data-driven operational growth and would not be outpaced by all the input.
PHASE 2: Data Visualization and Integration: Improved sales modeling and oversight with real-time, full-funnel dashboards.
PHASE 3: Predictive Analytics and Automation: Increased top of funnel conversion using ML prediction to improve lead segmentation. Quick results. They needed to solve their critical problems quickly and then add complexity later.
PHASE 4: Improve Portfolio Company Performance: Real-time data science as a service to your portfolio companies, as an investor, improves the company's performance and thus your investment.
When you invest in middle-market companies, they are unlikely to have sophisticated analytics departments. They might have an analyst who works with marketing data and one that works on financial analysis, while only briefing the other department on the results after some time. This disjoint and relatively immature system is not effective. It is an incomplete picture of retrospective data, that may or may not be helpful in projecting future potential. Reporting on past events is necessary, but it is not enough.
As an investor, offering real-time data science as a service to your portfolio companies, you will improve their performance. When a portfolio company becomes more profitable, your investment results will improve as well.
Specifically, we suggest helping companies address problems such as:
There are two pathways to bring data science and analytics capabilities to your firm. First, you can adopt the “build” approach – hire a whole department of specialists in data. This approach can work! However, it is slow and expensive to build such a department, especially if it is outside of your firm’s core competency.
The second choice: partner with a data science firm like Blue Orange. With this approach, you get data science expertise for your portfolio firms as needed.
Contact us to take a closer look at how Blue Orange makes both of these wins possible.
PHASE 1: Sales Optimization in Private Equity
PHASE 2 & 3: Deal Sourcing Platform