Big Data Price Optimization

Organizations that sell thousands of SKU's to a large and diverse customer base create an amazing wealth of transactional, product and customer data that can be exploited to understand price sensitivity and develop price recommendations that optimize revenue and profitability. By understanding the factors that drive price variability such as geography, customer spend, order size, channel, product differentiation, etc, unique price segments are created to accurately measure price sensitivity and provide pricing guidance to the sales team. Having access to data-driven insights that show what other "peer" customers with similar characteristics are paying enables sales teams to quote and communicate prices with confidence.

What To Expect
Case Study

Objectives

Outcomes

Improve pricing performance through a process that applies advanced statistical techniques to set prices and improve pricing outcomes
Leverage transactional, customer and product data to effectively groups segments that behave similarly in order to improve price alignment and profitability
Improve the discipline and efficiency of day to day pricing processes
Establish greater visibility into pricing performance and track results over time

Establish floor, target, and start prices for all products and customers in scope
Generate data driven pricing guidance to sales teams
Access to price optimization algorithms and data needed to generate price recommendations
Measure and track the effectiveness of price recommendations based on financial improvements

ROI

Watch Outs

The impact of a well executed price optimization can be very significant. We have seen returns on investment ranging from 10x - 20x.

Beyond the direct benefits to revenue and profits organizations who have adopted big data-driven pricing solutions have also managed to streamline their quoting, sales and customer negotiation process which frees up resources to improve other areas of the business.

Clean workable data is critical.
Identifying and aggregating data from multiple sources can be challenging and time consuming therefore having access to a strong IT or data governance team is very important throughout the process
It is important to establish a period for testing and validating price recommendations before they are fully implemented
Integrating price recommendations with current ERP systems can add complexity

$ 2 Billion Food Distributor
Optimizing price levels across thousands of customers and thousands of SKUs

Challenge	Solution	Results
1,000 price quotes per week and time is of the essence Pricing on intuition, experience and gut feeling - A function of both speed and complexity Significant price variability Limited visibility into price improvement opportunity Hard to adjust prices to reflect changes in commodity costs Multiple drivers of price - good/better/best product hierarchies, customer size, geography, competitive dynamics and more .	Understood historical transaction data across products and customer to identify price sensitivity and willingness to pay differences driven by factors such as restaurant type, geographic location, spend, product mix, etc. Generated price recommendations using a custom algorithm based on each unique market, customer - product combination in the form of a price range. Entered price recommendations into the system where sales reps could easily approve the price recommendation or change it before submitting prices to customers	System-generate prices were tested against a control group for 15 weeks Results from 15-week study showed average price improvements of 1.8% vs. control group generating over $960k in improved profits during that period Optimized price recommendations were deployed to the entire sales team .