The Science Behind Make Your Numbers

With Zilliant you can predict and pursue measurable revenue and profit improvements using data-driven science instead of the guesswork so prevalent today. Here’s how it works…

Zilliant science is a specialized form of applied mathematics and operations research, which means we make heavy use of statistics and optimization algorithms. Two of the most powerful sales applications in these fields, price optimization and cross-selling, are widely used in numerous industries including retail, airlines and consumer services. Yet despite their proven success in some industries, relatively few B2B firms have been able to apply these capabilities until now. Why? The usual B2B selling processes and associated data are not well suited for the use of classical analytic techniques.

Applying predictive science to the B2B sales environment is a tough nut to crack because of its inherent complexity. Typical commercial relationships involve numerous people, scores of products, and disparate transactional modes (e.g. contracts, spot quotes, matrix price lists). Adding to the ambiguity are external factors like economic cycles, product innovation, competition, channels and cost volatility. And when you consider how dynamic these variables are across markets and time, it’s no wonder that companies struggle to understand the buying behavior of their top customers, let alone how all of their customers behave.

So, how did Zilliant crack the code on price optimization and cross-selling in B2B? A big part of the answer is our library of proprietary models that we developed through our work with hundreds of B2B companies. Models are objective mathematical representations of the real world, and provide the foundation for most scientific inquiry. Our core models represent key B2B selling phenomena like price response, sales rep behavior and wallet share dynamics.

Of course, there’s more to our science than just models; we have a holistic scientific method that transforms raw market data into actionable guidance and strategic insights. The Zilliant Scientific Methodology comprises four key elements: sparse data mining, tailor-made response models, optimized scenarios, and active learning.