How PROPHET Uses Data Science to Drive Portfolio Optimization

In?a recent post, I described how every manufacturer should be focused on maximizing the value of their product portfolio (i.e., the total revenue available from all products), which represents an evolved way of doing business. Managing a portfolio calls for continuously assessing the relative contribution of each product category to total revenue and rebalancing sales focus and production capacity to ensure that the greatest total revenue is achieved.

In some ways, this approach is counterintuitive. It steers sellers away from selling as many sought-after products as possible for as high a price as it can fetch because that can lead to cannibalization of other inventory and diminish the value of a portfolio overall—if the impact on other products and channels isn’t considered. It’s also a departure from relying on last year’s pricing and limited or isolated pieces of market feedback to inform current prices.

A Platform Built for Portfolio Optimization

Although portfolio optimization via Excel is theoretically possible, it’s wildly inefficient; there is simply too much data for a human to process. Furious’ platform,?PROPHET, was designed and built for portfolio optimization.?PROPHET?uses sophisticated data science, software and algorithms developed over many years, as well as machine learning, to scale the processes of forecasting, pricing and inventory recommendations. Customized business logic gets smarter over time as?PROPHET?ingests and learns from your organization’s data, including information about products purchased, when, where, by whom, at what pricing and which products were bought together.

PROPHET?ensures that revenue and profit are optimized across all of a seller’s channels and products by pricing each product relative to its value within the entire portfolio of products. For example, if lowering the price of a product that is highly price elastic will significantly increase demand (sales volume) but will cannibalize the sales of a more profitable similar product, that decision to reduce price may not maximize portfolio value.

Exposing the Ripple Effects of Price Reductions

When working to close a deal, salespeople commonly offer price reductions to get a signature. In the absence of data insights to show the ripple effects of these decisions across the portfolio, this was a logical and effective way to conduct business. But now we know that uniform pricing and data-driven pricing recommendations improve a seller’s portfolio value and that variability is a sign of suboptimal pricing.

To that end,?PROPHET?arms salespeople with pricing recommendations on a per-deal basis as well as portfolio-level guidelines to ensure revenue is protected and pricing is optimized and managed strategically.

In an ideal world, you would see rate increases because of increased demand. But in practice, prices tend to remain exactly as they were prior to a spike. That’s because pricing is not updated frequently enough to reflect changes to demand, and salespeople negotiating prices typically don’t have access to all the information needed to make an informed choice. Therefore, portfolio value is not maximized.

To start using data science to optimize price and maximize portfolio value,?talk with our team.