Embedding Algorithms Inside Revenue Operations

Conga

Conga

RVP, Sales Engineering

What’s meaningful, unique and defensible for our customers? A patent that impacts profit margin and win rates!  An algorithm that can be embedded in our CPQ code that recommends a “winning price range”. The ML algorithm considers a substantial number of weighted variables associated with a quote. It then recommends a price range to increase the probability that a customer will accept, whilst ensuring that no discounting rules are violated.

There are many business reasons for automating and optimizing a company’s revenue operations. A critical aspect of this is control and manageability of the quoting process and putting a stop to rogue discounting.  Many organizations hit by the recent COVID-19 crisis will already be looking re-build and re-model their ‘Quote-to-Cash’ processes and they will be taking the opportunity to improve on their old models. Using machine learning to capture IP and assist sellers to win business will be at the top of any CRO’s agenda.

Revenue Operations

The ML algorithm considers a number of weighted variables associated with a quote.

The automation of Rev Ops using algorithms now available enables a competitive edge which many businesses are using to win business much faster and more efficiently than their competition.  It’s no longer rocket science, but part of a growing field of how companies are choosing to run their business with the help of AI and ML.

 

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