PricefxPlasma, powered by Bain & Company, provides industry-level benchmarking to B2B enterprise companies that need to develop strategic insights into their pricing processes and performance to compare them to market averages.

As the only solution of its kind in the pricing world, PricefxPlasma provides customers with the unique business advantage of being able to improve their pricing strategy and execution by eliminating anecdotical evidence, generic and poorly defined benchmarks, and pure guesswork when assessing their current pricing within a marketplace context.

Users can leverage a pre-defined set of more than 20 industry-relevant performance KPIs to reveal insights into industry pricing and to measure their pricing performance against their peers.

It goes without saying that data confidentiality and security remain top priorities at Pricefx. To ensure this, customer data is anonymized by Pricefx via double-blinding, making it impossible to trace any individual customer data. No one outside of Pricefx has visibility to unique customer identifiers.

How does it work ?

• PricefxPlasma works with the following data from customers:

  • Transactions data
  • Quotes data
• Customer data is extracted and anonymized on a monthly basis.
• The anonymized data is transformed into standardized metrics that are then loaded into the PricefxPlasma platform, which aggregates and filters the metrics further to create industry-level benchmarks.
• The resulting benchmarks are distributed to the customers’ environments as a set of standard Pricefx dashboards and customers can also include this data in their own dashboards, allowing for a direct comparison between their company and the benchmark.
• The benchmarks are made available with a three-month delay, fast enough to allow for relevant analysis but sufficiently disconnected from the current market status to avoid any compliance issues.

What is the output ? 

Plasma dashboards provide out of the box to the user a pre-defined set of more than 20 market-relevant performance KPIs enabling data-driven assessment and benchmark to other companies.

Those KPIs are structured into 4 dashboards: 

• Executive summary – Primary dashboard that consolidates some of the most important KPIs which are present also in the other dashboards.
• Plan – Dashboard based on transactions data providing mostly insights on pricing business structure.
• Price – Dashboard based on transactions data providing insights on pricing processes and execution.
• Profit – Dashboard based on quotes data providing insights on pricing strategy and deal process performance.

For details see Plasma Dashboards.

Usage

PricefxPlasma is designed to answer the most mission-critical pricing analytical questions:

• What is a common price waterfall structure?
• What yield should I expect on price increases?
• What discount targets are reasonable for my sales force to hit?
• How many layers of approval should deals go through and how quickly should they be approved?
• What is the expected ROI from installing enterprise pricing software?

KPIs currently measured in PricefxPlasma include:

Pricefx continues to roll out new KPIs to benchmark against.

Confidentiality

PricefxPlasma benchmarking is based purely on anonymized and aggregated data.

• Customer data is anonymized by Pricefx via double-blinding, an automated process that removes customer identifiers by assigning a random key to the customer data immediately at the point of extraction to disassociate it from the customer, ensuring that it is impossible to trace individual customer data.
• Through the double-blinding process, no one outside of Pricefx has visibility to unique customer identifiers.
• Benchmarks are compiled from aggregated metrics with a number of anonymized entities to ensure that users cannot self-identify certain companies.
• In addition to improving statistical validity, this rule also protects the identity of the underlying entities. Benchmarks are not shown when sufficient data is not available, e.g., in case of more detailed KPI filtering by a user (by industry, region, etc.).
• Due to applied aggregation of data, it is impossible to conduct a competitive comparison on standalone items (e.g., product price or even product lines) for users to self-identify certain companies.