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Sentrana Deploys Statistical Model for Deriving Market Costs - Sentrana Inc. has deployed a model which provides decision-makers with the ability to predict their competitors’ average costs using correlations among cost of products in their own transaction databases
Sentrana Deploys Statistical Model for Deriving Market Costs

 

NewswireToday - /newswire/ - Washington, DC, United States, 2009/06/08 - Sentrana Inc. has deployed a model which provides decision-makers with the ability to predict their competitors’ average costs using correlations among cost of products in their own transaction databases.

   
 
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Sentrana Inc., a Washington DC-based leader in revenue optimization technology, has deployed a model for a multi-billion dollar distributor in North America which provides decision-makers with the ability to predict their competitors’ average costs using correlations among cost of products in their own transaction databases. These results can be used to fine-tune the model-recommended prices for each product and customer combination.

For the customer, the most important consideration when selecting distributors is how much one distributor charges relative to what their competitors are charging for similar products. Product pricing typically uses a product’s cost-of-goods-sold (i.e. the cost of a product paid to a company’s supplier) as an essential ingredient. It is important to know your costs relative to your competitors’ costs when setting prices which are optimal. Since access to a competitor’s cost-of-goods-sold is often limited or non-existent, Sentrana’s new model looks for correlations of cost movements across many similar products within a company’s own database. Quantitative Modeling Manager Alireza Mahani explains, “This model enables us to identify key correlations among historical costs over time to arrive at the best prediction of the ‘market’ costs of items, and to highlight which products have deviated from these typical costs." For example, this model can show linkages between the price movements of different items within a category that typically move together, such as dairy products. If cream, milk, cheese, and butter historically follow the same pattern of price movement by moving up together and down together, then the deviation of price movement in one item is significant. If the cost of milk goes up by 1%, but the cost of all other historically linked items go up by 2%, the model predicts what competitors are typically paying for wholesale milk.

By downplaying your own costs when cost deflation occurs and instead using the costs that your competitors are paying as predicted by the model then pricing decisions will be stronger and buffer the organization from margin erosion in times of deflation and inflation. The model’s ability to estimate competitors’ prices solely from an individual company’s historical data adds to the functionality of Sentrana’s patented MarketMover™ platform by enabling further optimization of prices for each product and customer combination.

Sentrana Inc. (sentrana.com), based in Washington, DC, is a leader in providing revenue optimization solutions in highly competitive B2B & B2C markets. We provide end-to-end revenue optimization solutions by combining advanced quantitative methodologies with qualitative managerial inputs into our patented MarketMover™ platform. Our technology facilitates optimization of the marketing decisions at the individual transaction level that drive financial performance, allowing organizations to actively shape their demand environments. Our solutions enable our clients to make better pricing decisions, increase profitability, and unify disparate business units under a single revenue strategy.

 
 
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Written by / Agency / Source: Sentrana, Inc.

 
 

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Sentrana Deploys Statistical Model for Deriving Market Costs

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Publisher Contact: Bahia Simons-Lane - Sentrana.com 
202-507-4480 bahia.simons-lane[.]sentrana.com
 
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