Leveraging B2B sales performance with data-driven pricing and visualization (Part 1) 

The need for data-driven pricing in B2B sales 

Integration of complex data structures 

In a B2B context, a vast amount of data is captured and stored across different systems, much like in a B2C context. However, B2B differs in its focus on relationships and long-term engagement. This distinction shapes the nature of the data in several ways. First, while the total transaction line volume in B2B often magnitudes smaller, the SKU count is not drastically different from B2C context. Additionally, due to the smaller customer base, B2B companies have a deeper understanding of each customer. This allows more detailed customer-related data, providing a clear picture of each customer and how they interact with the business. 

To be able to create a pricing system that looks beyond the net purchasing costs, all the different data sources need to be integrated while making sure the above-mentioned differences between B2B and B2C are considered.

The creation of a single point of truth 

The inherent nature of a pricing system requires the integration of different data sources. This can be the first step for a company to create a single point of truth, where data is integrated centrally. Once centralized, this data can be accessed not only by pricing and sales teams but also by other stakeholders across the organization. This approach facilitates data consistency and trust, enhances decision-making, and improves collaboration.

Dynamic price setting

While providing sales and pricing teams with a clear baseline for price setting, it’s even more critical to ensure the pricing system can react to market changes. Creating an automated process of re-running the pricing model, together with allowing pricing and sales experts to interact with specific pricing levers that influence the model, enhances its reliability. This approach also allows experts to integrate their knowledge directly into the model without requiring technical expertise. 

Data analytics decision-making culture 

Cultivating a culture where data analytics plays a crucial role during any decision-making process is something that everyone agrees upon, yet it is often overlooked. By implementing a data-driven pricing system, employees start to recognize and appreciate the role of data analytics, as it forms an integral part of the pricing system.

Principles of pricing model creation

Since different parts of the business may have different pricing methods, it may be necessary to develop tailored data-driven pricing models. In some cases, no model may be required at all, for instance: a few large customers that purchase only highly specialized products. However, regardless of the complexity of the data-driven pricing model, the following principles will always be applied:

1. Robust scope definition 

Defining the scope of in which the pricing model operates may be the most important part. It defines the boundaries for the pricing model’s logic and clearly outlines the stakeholders involved in its development. These boundaries are determined through cooperation with business experts and by considering the availability of data within the business. 

2. Pricing segment creation 

The next step is to define clear and comparable pricing segments. This allows pricing and sales to perform comparative analysis across their customer and product portfolios, uncovering hidden value buckets and providing valuable business insights. It is crucial that business experts not only agree on the segmentation approach but also fully understand it. This ensures that decisions can be made based on the defined segments and supports incremental improvements to the segmentation logic. 

3. Encoding price-setting expertise 

As previously mentioned, the involvement of business experts is crucial, as they hold key knowledge about pricing value drivers that are often not captured in any system. These drivers are typically specific to the business and require a tailored approach when creating a data-driven pricing system. This system should seek to encapsulate the intangible pricing expertise within the company into something more tangible. By integrating company-wide data sources with business expert knowledge, businesses can create a robust data-driven pricing system that not only identifies hidden value buckets but also provides further insights for sales and pricing teams during negotiations and decision-making processes. 

4. Allowing experts to steer the model based on their expertise

Having a pricing system that automatically recalculates its content is one thing, but it’s equally important to allow pricing and sales experts to directly influence the system. This can be achieved by providing a user-friendly way of encoding strategic and tactical price steering decisions. This further improves the pricing model accuracy but also deepens the experts’ understanding of how the model works. 

How to be sure of a success

Engage experts early  

Ensure that the right stakeholders are involved in the early development of a data-driven pricing system. This allows not only to integrate the right data but also ensures that their expertise is translated into the system. 

Data-driven culture 

Cultivating a culture where people are comfortable and capable of working with data is crucial. Often, business people know where to look for insights but may lack confidence or skills to explore it in the data, while technical teams are comfortable at analyzing the data but may not understand the business needs. Having a culture where both groups are comfortable working with data, will allow business people to quickly find insights in the data, while technical teams can focus on more complex analysis and improving existing systems.

Automation resistant 

Employees are often averse to automation as it is often seen as a boogeyman who is going to take their job. However, automation is primarily used for highly repetitive tasks, which often are prone to manual errors due to their disengaging nature. Automating those tasks allows employees to engage in more interesting parts of their job. 

Resistance can also occur to the automation of entire processes, making the argument that there is no human oversight over the process. In practice, a well-automated process does not lose human oversight, instead, it focuses human oversight on parts in the process where it is needed. This should make the process not only more efficient but also reduce errors and eventually lead to process improvement ideas down the line.

To conclude... 

In the first part of our series on leveraging B2B sales performance through data-driven pricing and visualization, we have explored the importance of integrating complex data and creating tailored pricing models. By embracing data-driven pricing, B2B companies can navigate through the market volatility and uncover hidden values, empowering sales teams to make informed decision. However, to truly maximize the power of it, it is essential to prevent the common challenges, by for example engaging the right experts. In part two, we will focus on the transformative role of data visualization in turning insights into actionable, decision-making tools for sales and pricing teams.