Using Data Science to Optimize B2B and SasS Pricing

Price testing is a science in the retail space, but has been historically a challenge in the B2B space. There is a new model for using near-history proposal data to emulate retail price testing. New Data Science models can correct for false-signals, low sample volume and outliers.
Topics:
Accessing near-term proposals and outcomes
Using a “volume” threshold to determine if sub-optimization is possible
Higher Volumes:
. Optimize revenue
. Optimize market share
. Optimize margin
Lower Volumes:
. Optimize outcomes (sales success)
Modeling the data using AI
Data collection & AI modeling
Outcome pathways
Applying flexible pricing models (industry, size, time-of-quarter)

[@mur8all]

YOu need to trim the first 15 mins of this video