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The Need for a NexGen CPQ Solution | Pricing Matters

Price optimization to unlock ROI potential
The Configure-Price-Quote (CPQ) software market growth is exploding. According to Gartner, CPQ market revenue was approximately $878 million in 2016, with a 20% CAGR expected through 2020. However, CPQ is not new, and many organizations that have invested in CPQ solutions are failing to meet expected ROI. So, what’s going on?

Pricing has the potential to offer more financial value than the value gleaned from operational efficiencies associated with improved configuration and quoting. For example, a landmark study published in Harvard Business Review examined the unit economics of 2,463 companies and found that a 1% price improvement results in an 11.1% increase in operating profit. With this amount of bottom-line impact, price optimization is the key that can unlock ROI realization for CPQ.

However, price optimization is arguably a complicated challenge to solve. Identifying a customer’s willingness-to-pay for a product and/or service in real-time requires numerous data sources, complex predictive models, and intelligent software that is easy enough for salespeople to eagerly adopt. Typical challenges include data that is not readily available, frequently changing market conditions and large distributed user communities to manage. This complexity magnifies when considering that optimal pricing can span a large set of products and services in every quote. To achieve success, deep analytics powered by advanced techniques such as machine learning and artificial intelligence, are required.

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The Need for a NexGen CPQ Solution – Pricing Matters.

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