At the core of the dynamic pricing approach lies the pricing engine or algorithm. With that said, machine intelligence doesn’t entirely eradicate humans; instead, it forces both forms of intelligence to sync and work together. The key to designing the automated pricing engine is to involve users continually in the process of creating benchmarks, rules, and constraints, adoption of the engine, and transformation of its outputs into decisions.
The adoption of the dynamic pricing approach is unique for every company and not something that can be prescribed as a medicine. However, there are essential components of a successful transition that are typical for every company. These necessary elements relate to the two fundamental processes of the implementation of any solution: the building of the engine and its execution and hardwiring in the organization.
Before embarking on developing an engine, managers need to assess the available technology and data. The questions to answer here are
- How much quality-data does your company have?
- How automated are the processes of data collecting and processing?
- What data matters for the organization: Information on customers and competitors? What kind of information: Negotiation histories? Comments? Sales overrides?
- How does the current sales pricing engine work? Can you add any other inputs to the existing mechanism that would benefit the computational logic and result in a better pricing recommendation?
The pricing solution will largely depend on the type of business you’re operating; however, in terms of software architecture, there are typically two types of solutions available on the market. First, it’s a rule-based system, which operates on the knowledge base containing rules, and machine learning software, which mines data to find the approaches to solving an issue without direct programming.
In a rule-based system, the rules are represented in the form of “if-then” statements. When software discerns a particular pattern, an inference engine defines the relationship between the rules and facts. When a rule gets triggered, the software acts accordingly. Because software relies on the “built-in” knowledge to respond to the environment’s current state, it’s pretty inflexible and cannot react appropriately to the changing circumstances. As an inventory grows or some other factors change, the rule-based system requires more and more maintenance by adding rules, modifying the existing ones, ensuring rules aren’t duplicated, and so forth.
Software that’s powered by machine learning (ML), on the contrary, gains knowledge from data – the more data is fed into the system, the more it learns from it and improves its performance – it doesn’t need detailed instructions on decision-making. AI and ML-powered software allow for richer and more extensive analysis and hence, for broader functionality.
Some of the typical features of the ML-based pricing solutions include
- Granular customer segmentation and cluster analysis, which can uncover subtle customer behavior patterns and determine customer personas with surprising degrees of accuracy.
- Incorporation of large amounts of variables (including both internal and external factors) and massive amounts of data.
- Real time data market analysis.
- Possibility to align pricing recommendations with internal performance metrics, such as margin and inventory optimization.
- Price elasticity evaluations, which determine if any given customer is prepared to pay a new price.
Whether you try to build an engine yourself or find a partner with a dynamic pricing solution, it’s always a good idea to start with a pilot project and use it to gain as many insights as you possibly can.
As powerful as analytics can be, the successful outcome of your pricing endeavor largely depends on people working together and exercising control over the process. Ideally, the sales and marketing teams have to work hand in hand with data scientists and supplement their knowledge with the human side of pricing. The pricing team controls the model, and IT runs and enhances it.
Before endorsing the new dynamic approach into your organization, ask yourself the following questions:
- How should the switch to dynamic pricing be implemented and hardwired into your everyday pricing routines?
- How much of a stronger mandate does the new pricing routine require from you as a manager? How should the leadership of your organization be involved in endorsing the new solution?
- How much and what kind of training do your teams require before being comfortable using the new systems?
- Does the change in the mindset of your team members is necessary?
- How will you create a feedback loop between your teams to ensure continuous improvement of your pricing engine and strategy?
There’s no such thing as perfect data. However, starting even with a small number of use cases and improving the capabilities of your pricing engine one use case at a time, you can successfully implement a dynamic pricing strategy. Agile approach with continuous delivery and self-assessment is essentially the right strategy. The switch to dynamic pricing cannot happen overnight but should be implemented gradually. First, you may start with an initial appraising of the market and your industry’s pricing standards. Then, introduce a loyalty program, which will allow you to perform an initial segmentation of your customers into groups and implement different dynamic pricing strategies accordingly. Add demand, perception pricing, and competition-based pricing into the mix one step at a time, and you’ll end up with a holistically dynamic approach to pricing.