Personas, Roles and the Science Behind Negotiation Guidance
- Ioana Kocurova
Accelerate Negotiation Guidance addresses 3 major personas Pricing Managers, Pricing Scientists and Configuration Engineers. In this section we will address just the Pricing Managers and Pricing Scientists.
If you are a CE and want a more technical view and description of Negotiation Guidance, click here.
Negotiation Guidance Target Audience
For Pricing Managers who need to provide guidances to the sales team the Negotiation Guidance Accelerator is an interactive optimization model that guides users to define and drive the optimization of their Negotiation Guidance.
Unlike classical price optimization approaches,
our solution can integrate constraints and business rules as well as differentiated objectives along the segments.
For Pricing Scientists who need to improve or complement the approach, the Negotiation Guidance model is a set of Science bricks
that allows PS to independently improve and refine selected bricks.
Unlike classical price optimization approaches,
our solution handles the modification or replacement of bricks without having to develop anything on BE or FE side.
What is a Science Brick
In short, a science brick is what we consider at Pricefx any building block contributing to the Price Optimization features, such as:
Product Similarity
Clustering
Multifactor Elasticity
RHS Demand Elasticity
Forecast
Attribute binning
… and others
Science bricks are utilized for segmentation, elasticity, and clustering tasks performed by data scientists on a daily basis. Pricefx transforms these bricks into productized features by combining them with enablers (BE, Python, OE) provided by the engineering team. In simpler terms, we create and assemble these bricks into enablers, develop new science prototypes, test them using selected datasets or proof of concepts, and ultimately generate optimization accelerators. This process, known as optimization use cases, enables us to efficiently deliver pre-packaged solutions to users and customers in need. The pricing science team collaborates with the accelerator team and leverages the platform manager application to facilitate this process.
LEARN MORE: To see how you can deploy Accelerate Negotiation Guidance, click here.
To support the creation of optimization use cases and accelerators, we adopt a component-oriented vision where the bricks are assembled. However, a proper framework is required to run complex processes, often involving advanced machine learning algorithms or AI. These processes entail data preparation, data mapping, model training, and utilization in other models for optimization and prediction purposes. The framework eliminates the need to constantly adapt the UI and UX for each new use case, providing a seamless experience. Pricefx can deliver use cases based on the accelerator concept, making it easy to plug and deliver bricks with just a few clicks to any partition for any customer.
Sample Mini Use Case with Science Bricks - Multifactor Elasticity
As an example, the multifactor elasticity science brick, a component of this accelerator, utilizes transactional data, product information, and customer data.
By incorporating event-specific information such as Black Friday or a targeted week when an even is scheduled, it becomes an input for optimization and multifactor elasticity. Using the Python engine, this feature enables forecasting based on product attributes and similarity scoring. In turn, it facilitates the creation of new product clusters.
This is particularly useful for product recommendations, where similar products with better margins or lower prices can be suggested.
The data can be accessed from anywhere within the solution, and the results can be utilized across different parts of the application.