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Is Your Sales Team Wasting Time on the Wrong Quotes?

Ever wonder if your sales team is chasing shadows, spending time on quotes destined to flop? What if there's a way to pinpoint, with startling accuracy, which quotes are worth pursuing and which are not? Enter the revolutionary approach we've pioneered for one of our major clients, leveraging machine learning to transform their CPQ process.


In sales, time is as precious as the deals themselves. Yet, many teams operate on a 'quote and hope' basis, treating each potential deal as equally likely to close.

This is not just inefficient—it's an outdated strategy in an era where data can drive decisions.

For one of our key customers, we've flipped the script on how quotes are managed, implementing a machine learning system that predicts the success of each quote before resources are allocated.

Transforming Data into Strategic Gold

The cornerstone of our approach involves a detailed analysis and transformation of each quote into a predictive vector. We scrutinize every element: from the number of items quoted to the customer's historical behavior and market trends. This data is then translated into features that our machine learning models can use to learn and make predictions.

Choosing the Right Tools for Prediction

We don't rely on guesswork; we employ robust machine learning models tailored to the unique dataset of our client. Logistic Regression offers a clear, interpretable model for starters, while more advanced methods provide the depth needed for more complex data relationships.

These models train on real customer inputs — identifying which quotes turned into orders and learning the subtle cues that predicted success.

Proven Results in Real-Time

Instead of spraying efforts across all quotes, they now focus on those with the highest likelihood of closing, as indicated by our model. This targeted approach has not only improved their conversion rates but also enhanced overall sales efficiency.

Continuous Improvement and Future Plans

Machine learning models thrive on data, and each new quote provides feedback that refines and enhances the predictive accuracy of our system. We're committed to continuous improvement, leveraging every piece of data to sharpen the model's predictions. This ongoing process ensures that the sales strategy evolves in lockstep with changes in customer behavior and market conditions.

With this advanced CPQ strategy, we're not just assisting our client in making sales—we're helping them make smarter sales. By knowing which quotes to prioritize, their sales team can allocate their time and resources more effectively, boosting both morale and bottom line.


As we advance this technology, the potential for further integration and enhancement grows. Stay tuned as we explore new ways to expand this approach, making every quote not just a potential sale, but a strategic step towards profitability and efficiency.

Join the revolution where CPQ and data doesn't just support your business — it drives it.


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Ready to learn more? Check out the online ebook on CPQ with the possiblity to book a CPQ introduction with Magnus and Patrik at