If you would like to enroll in this course, please click here to purchase this course individually, click here to purchase the CPP bundle, or click here to purchase the CPE bundle.

You cannot improve what you cannot measure and nowhere is need for good analyses more acute and more critical to the bottom line, than in pricing.  However, effective pricing analysis is more difficult than traditional financial or sales analysis.  Corporate processes and data infrastructure, built to support these traditional analysis requirements, fail to realize the full potential of pricing analysis.  This workshop presents best practices in pricing analysis and decision support, from theory to practical implementation, and includes hands-on exercises. 

The online pricing course will teach you:

• Actionable data collection, synthesis, and validation techniques 
• Knowledge of the key difference between the theory and practice of pricing analysis 
• Common areas of pricing opportunity, and the pitfalls many businesses succumb to in attempting to capture them. 
• Ways to build credibility in the organization 
• How to create a virtuous cycle for pricing analysis, where successive efforts make future efforts easier and more powerful 

Key Pricing Topics Include:

Theory and Practice

• The theory of pricing analysis
• The actual practice of pricing analysis (and why the practice is so different from the theory)
• 6 important differences between consumer pricing and business pricing, and their implications for pricing analysis
• Why pricing analysis differs from financial and sales analysis

Data collection            

• Identifying the data you need
• Data collection and synthesis
• Data cleansing and validation techniques
• Where to put the data

Identifying opportunities

• Common pricing opportunities
• Looking back—the basics
• Looking forward—trend analysis (how you can become a hero or a goat very quickly)
• Competitive price tracking best practices

Institutionalizing change—reaping the rewards

• Getting support
• Overcoming objections to analysis
• Tracking progress while getting the attention of executives
• Keeping data fresh and useful