Blog, Pricing

4 Steps to Price Optimization: CPQ Series Part 2

By Frank Sohn
June 19, 2018

In the first post of this series on CPQ, I looked at intelligent CPQ and why it’s importantPrice optimization is a big part of the why so let’s spend more time now looking at what is required to use it.  To do this, we will take a deeper dive into the four how-it-works steps that I introduced in my previous post.

 

1. Develop a pricing strategy has been developed for every Route-to-Market (RTM)

RTM = 1.Sales   2. Channel Partners   3. eCommerce

While this may sound simple, it is not easy to do. If products are undercharged, money is lost. If products are overcharged, growth will be stifled. Finding the right product, for the right price, for the right customer, at the right time is key for any successful sales team. This is a challenging yet critical task which is why senior leadership typically develops a pricing strategy.

To be very clear, not every business needs to develop a new pricing strategy. If your business already has one and it is working, keep using it. Otherwise ask yourself these questions to understand if you need a new or modified pricing strategy:

  • Do you know what pricing strategy is currently used (per RTM)?
  • Have you ever tested different pricing strategies for your RTMs?
  • Did you ever discuss your pricing with your customers?
  • Do your sales results (Revenue, Margin etc.) across all RTMs meet or exceed your expectations?
  • Do you know when a deal will be unprofitable and therefore you should walk away from it?

If you answered any of these questions with “No” do a Price Analysis before proceeding to step 2.

Price Analysis

To determine what is working well and what needs improvement (per RTM) with the current pricing strategy, use a Price Analysis. This process will also help you determine if new business use cases need to be added.  An example here is to understand if it makes sense to charge for a service instead of charging for a product. Last but not least, you need to determine what other use cases should be considered for pricing (e.g. use of local currencies, exchange rate).

Note: Many businesses still use only a “Cost plus” pricing strategy or a “List less” pricing strategy.  While these are easy to understand, they are not the right pricing strategy for every business and RTM.

Consider also these and other possible pricing strategies:

  • SaaS Pricing Models (e.g. Freemium, Usage Based Pricing, Per User Pricing, Subscription Billing, Per Feature Pricing)
  • Price Skimming
  • Psychology Pricing
  • Price Anchoring
  • Value Based

There are many more pricing strategies; read this HBR article for additional “food for thought.”

Once the leadership team has agreed upon a Pricing Strategy for every RTM it is obviously key to execute it effectively. This means the pricing strategy has to be implemented in the CPQ solution.

 

2. Gather historical price data

Before a machine learning algorithm can be used, it needs to be trained on historical price data. This means some historical price data needs to be made available.  A good list to start with includes at least the following:

  • Customer data (e.g. geography, company size, deal size, hierarchy) to start the segmentation of customers
    • Product numbers (and/or services) sold including their base prices (e.g. list price, cost) and quantities
    • Discounts and promotions per product number (and/or service)
    • Competitor prices per product number (and/or service)

This list can obviously be extended as needed with more data, including  economic conditions, fixed and variable costs, product availability, etc.

 

3. Use Price Optimization in a “live” environment

In this step, sales uses a CPQ solution with Price Optimization capabilities with real customers. Information typically shown to users includes:

  • The optimal Price for Product A and Customer ABC
  • Example: Price for Product A is $168 for Customer ABC.

 

  • The Price Range a customer can buy the product profitably
  • Example: Price for Product A needs to be between $150 – $175.

 

  • Required approvals for a specific price point
  • Example: A Price below $160 for Product A requires the approval of the VP of Sales in Region North America.

 

  • On different devices (e.g. laptop, smart phone) different prices may be displayed to different customers
  • Example: Product A on a laptop is $165 and the same Product A on a smart phone is $160.

 

  • Visual Guidance (using traffic light guidance with red, yellow, green) to indicate if any price or approval actions are required
  • Example: Product A is shown with a RED icon because the customer’s requested price is $125 while the suggested Price Range is $150 – $175. The sales team needs to either increase the price or get the necessary approvals to proceed.

 

4. Monitor results and update data continuously to improve the accuracy of the machine learning algorithm   

In this step, the machine learning algorithm merges the historical data with newly gathered data from the live-environment, learns from it, and makes necessary adjustments for continuous improvement.  This ensures that the Price Optimization remains dynamic.

Change Management

To ensure Price Optimization efforts yield the expected benefits, the intended users must actually use it. To ensure this happens, a change management effort should be planned early on in any CPQ effort. The objective is to:

  • Minimize the resistance to organizational change through consistent involvement of key stakeholders
  • Maximize the collective benefit of improvements in an organization

A designated senior leader should promote and communicate the CPQ vision (with Price Optimization)  to key stakeholders, including: Sales, Sales Operations, Product Management, Product Engineering, Marketing, Finance, Legal, and IT right from the start.

How and why modern intelligent CPQ solutions interface with other systems such as ERP and CRM will be covered in my next post.

Editor’s Note: For more on Intelligent CPQ, please visit the Vendavo website.

  • CPQ , Intelligent CPQ , maching learning , price data , Price Optimization

    Frank Sohn

    Frank Sohn is President & CEO of Novus CPQ. A Configuration, Price and Quoting Expert with 20+ years of experience, Frank founded Novus CPQ Consulting in 2015 and has worked in CPQ and Quote-to-Cash business and IT roles since 1996 with companies like Hewlett-Packard, Juniper Networks, PriceWaterhouseCoopers and Consulting Companies in Europe and the US.