Using big data to make better pricing decisions

July 29, 2021

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Data. Data. Data.

It’s a twist on the age-old real estate agent adage of “Location. Location. Location.” to explain the wild fluctuations of home property values.

In this instance, the one-word, thrice-repeated maxim highlights the increasingly powerful role a company’s utilization of its own compiled information plays in its success.

The days of sole reliance on a traditional sales model are looong gone so it stands to reason the availability of infinitely more, highly specific and deeply tell-tale data points must be mined properly in order for any business to build upon its own experiences and learn from its history. Failure to do so is, well, a business failure.

Or, to twist another well-worn phrase, Mo Data, Not Mo Problems.

Ugh, ok, yes, we instantly apologize for that one, but it was just sitting there…

Anyway, hear us out.

Billing based on customer consumption can come in many forms. Depending on your business, you may wish to have your customers pay by the gigabyte, by the minute, by the gallon or, really, by any logical form of measurement, depending on the source and formats available.

Data mediation is the technology employed not only to ensure the billing based on this usage data is accurate, but, for the smart organizations, much more than that. 

What is data mediation, and why is it an essential system for any company with a usage-based model?

This usage data collected from all the different sources and different formats need to be properly converted, or normalized, for billing purposes, and that’s what data mediation does. In order to provide your customers with a straightforward invoice and calculate revenue correctly, that jumble of usage data must be converted to pricing. 

By combining mediation and billing, a company is capable of providing more pricing and packaging options to its customers. Having built-in mediation capabilities allows for automated collection and conversion of usage data in order to automate the rating and pricing process, more easily predictive avenues of revenue generation and, ultimately, a more pleasing-to-the-eye bottom line.

It’s no wonder RecVue got into the data mediation business as an integral part of the monetization platform.

Our built-in Data Mediation Layer (DML) resides on top of our usage platform for user-friendly usage data import and additional control for the user before data goes into the core repository and is consumed by the billing engine.

Increasingly popular usage billing needs have upped the ante for traditional CRM/ERPs to either (1) adapt through customization or (2) seek out separate solutions to handle data mediation.

RecVue is rightly proud of our close proximity to Oracle, but we recognize shortcomings when we see them. While Oracle can extract data and store it in its data model, it does not have a built-in mediation layer capable of consuming usage information, reconciling and rating that usage for billing purposes. RecVue does.

RecVue’s mediation layer has many steps in its use of measured or usage data. Typically those steps include: collection, normalization, data quality, aggregation, identity, logic and rating. 

Knowing the importance of properly ingesting this valuable data, RecVue was created with two things in mind: 

  • The ability to process high volumes of data, or large data sets
  • The ability to process complexity in terms of scale

When it comes to RecVue, it’s all about handling big volumes at scale. RecVue’s DML addresses control through configuration. This capability offers our customers better visibility to import data and exceptions through UIs. Using an intermediate staging process, data mediation is accomplished before RecVue tables are touched.

Technology solution provider, World Wide Technology (WWT), faced customer invoicing delays of up to four months+ due to an old-guard combination of manually collected usage data, multiple validation layers and hand-reviewed contracts. 

The benefits of RecVue’s DML as part of its applied monetization platform for WWT included the following:

  • Capability to read usage data from multiple input sources
  • Support for multiple source file-formats including CISCO, CAAS, Cloud Broker, etc.
  • Ability to monitor data flow and manage exceptions
  • Option to approve or reject data using the provided control screen
  • Ability to manipulate data to add more values before being consumed by RecVue

By being able to mediate usage data, along with automating contracts and deploying agile billing through RecVue, WWT reduced its time-to-invoice by 94%, from more than 120 days down to 1-3 days.

Normalize your organization’s data for invoicing through built-in mediation.

Data. Data. Data.

If you would like to learn more about how RecVue's Data Mediation Layer (DML) can help your organization’s pricing decisions, revenue intuition and bottom line, contact us for more information or to schedule a demo.

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