3 Pitfalls of Using Big Data in CPQ Software

3 Pitfalls of Using Big Data in CPQ Software

Configure Price Quote (CPQ) software has been a revelation for sales organizations over the past decade. Gone are the days of making CPQ decisions based on email conversations, what someone said in a meeting once or Excel spreadsheets. Information that used to be distributed across the organization in different business units and across platforms is now centralized, ready to be accessed and analyzed by decision makers.

Today, the next generation of CPQ solutions is here—moving beyond just a central repository that calculates quotes to a powerful digital platform that optimizes pricing strategies with the use of big data technologies. Organizations can now rely on data analytics and powerful artificial intelligence (AI) to automate many CPQ processes, ensuring the right information from across the organization is collected, analyzed and used to create actionable information.

However, with any powerful new technology, there are dangers. Organizations need to make sure they have solid big data policies in place to ensure they aren’t drawing wrong conclusions based on incomplete or inaccurate data. Analyzing the wrong data or looking at it through the wrong context could push businesses down the wrong path—an irreversible consequence that could lead to disaster.

Here are three pitfalls to avoid when implementing big data strategies with your CPQ solution:

  1. Beware of small sample size

Sports nerds and stat geeks know this rule well. Trends based on a limited amount of data can be dangerous and lead to wildly inaccurate conclusions. A young rookie who hits three long balls on opening day is unlikely to end the 162-game season with 486 home runs. That would be ridiculous as most Hall of Famers don’t hit that many in an entire career. Similarly, organizations shouldn’t automatically expect a strong sales month to lead to record profits. So many factors could have gone into the results—many of which were likely beyond the company’s control. Organizations need at least two years of data to draw any meaningful conclusions. This will iron out any abnormal or unanticipated factors and give analysts enough data to make sound recommendations that can reasonably be expected to lead to reliable business decisions.

  1. Filter out the noise

Listen, it’s easy to get stuck in the weeds with data analytics—especially when it’s a shiny new toy. Analysts want to dive in right away, start identifying trends and distribute recommendations to everyone from the product development team to the business development director. It’s important to take a step back and decide what attributes are relevant and what kind of analysis would contribute most to the business. Finding out how certain product versions compare against each other is possible, but is it meaningful if it only shifts margins by a penny? Big data analysis should focus on macro trends within your business and the industry rather than on individual products or services.

  1. Bank some early wins

Take your time and practice on a small scale before you jump in head first. Remember that digital transformation technologies such as big data and AI are already game-changing tools that represent a big change in the way you conduct business and your employees go about their day to day activities. Start with a modest goal of achieving some sort of high-level insight, see how the process worked out and expand from there. A deliberate approach can save a lot of complexity and heartache down the road, and the ability to show value can get user buy-in early in the transformation.

Big data is revolutionizing CPQ software, giving organizations accurate, actionable information they can use to make informed business decisions to stakeholders throughout the organization from product development and business development to sales and marketing. Organizations need to make sure they take a measured approach to implement the new technology, ensuring they aren’t basing decisions on flawed analytic techniques.

Ready to learn more?
Config Consultants is a systems implementer focused on creating Digital Transformations strategies. Our A5 methodology modernizes your lead to cash process and makes your supply chain lean by leveraging industry-leading packaged software, such as Salesforce, Oracle, Apttus, and Anaplan.

Visit our website and contact us to schedule a consultation.


By Dave Farley, Vice President of CPQ at Config Consultants