Predictive Pricing for enhanced Negotiation Success
A science and learning based solution that approximates the Customer ‘Willingness to pay’ per deal – the key-question in every negotiation based sales process.
How does it work?
Using advanced statistics and applied machine learning, algorithms are trained on transactional data(sales + quote + cost data) as well as reliable market/competitive data.
When is it useful?
In complex B2B pricing/negotiations settings which require increased commercial control and pricing process optimization. It enables pricing executives to go into negotiations full of confidence and science-backed intuition and ensures that deals are closed faster and with more precision.
How is it offered?
As a real-time, web-based solution via APIs in a CPQ/CRM or even Excel.
Algorithm Tuning to Meet Profit & Price Targets
PriceCypher allows sales and pricing managers to steer prices based on business targets and strategies. A simulation console shows the impact of price changes on KPIs such as volume, margin and conversion. Decision-makers can activate their simulated strategies (or choose the algorithm-calculated optimum) to steers prices to meet business targets. It is also possible to create price frameworks and guidelines which serve as boundaries for sales to operate within.
Deal Guidance with Predictive Pricing
PriceCypher provides a price recommendation (Predictive Price) per deal scenario. Historical transaction data is combined with market intelligence and competitive trends (as well as won-loss data if available for machine learning) to arrive at the customer willingness to pay. This price has the highest deal win probability and maximises deal revenue. Predictive Pricing algorithms can be designed for both renewal and acquisition customers.
Business Performance & Algorithm Monitoring
PriceCypher facilitates pricing governance and commercial control by enabling monitoring of price performance. Sales and Pricing can be monitored against pre-set business targets. It is also possible to do customer value management and design campaigns for customers and deals that are performing poorly compared to peers.
Machine Learning techniques enable the algorithm to continuously learn and adapt to ensure accuracy of predictions and realization of business targets.
According to a recent report, Gartner shows the rise of B2B Price Optimisation Solutions are examples of artificial intelligence/machine learning that can deliver substantial business benefits. Many large companies are leveraging these data science solutions to operationalise intelligent pricing and gain a competitive edge in the marketplace.
Pricing & Negotiating large B2B deals is complex, time-consuming, full of uncertainty.
Multiples complex factors need to be considered in determining a customer’s willingness to pay.
Companies rely on intuition, siloed data warehouses and spreadsheets to make pricing decisions.These methods outdated and wrought with inaccuracy & an utter waste of the salesforce precious time which needs to be spent building relationships with clients not crunching numbers behind the desk.
PriceCypher uses advanced mathematics and machine learning to create algorithms that calculate customer target price or willingness to pay.
These algorithms take into account:Transaction history(sales, cost, quote data), Market trends, Competitive information to yield a ‘PREDICTIVE PRICE’.
This intelligence is made available in real-time to empower sales executives to negotiate faster based on fact-based knowledge.