Introducing pricing software has many benefits but requires careful planning and implementation in order not to waste financial or organizational resources. In this article, our experts from Simon-Kucher Engine explain the most common pitfalls and help you successfully navigate your journey toward digital pricing.
The future of pricing is in software. But simply acquiring pricing software doesn’t automatically lead to success. There are many challenges on the way to introducing pricing software successfully – from choosing the right software, to managing various issues in implementation, including troubles with data management. Here are some tips on how to overcome the typical challenges.
1. Invest time to find the right solution
In our last articles in the series, we already discussed strategies for finding the right software – from CPQ to PO&M software to web crawlers. The search for the right solution should be taken seriously.
If you are not familiar with the market or do not have expert advice at hand, send out a request for information (RFI) to get an impression of potential options.
We highly recommend insisting on a custom demo. A typical standardized demo rarely verifies whether the software truly fits. Pricing processes are too different across industries and companies, and the devil is in the details. We recommend providing the software vendor with a description of your process requirements as well as (anonymized) sample data, so that they can prepare a customized demo. This allows you to validate whether the software fits your needs.
In the next step, conduct a proof of concept (POC) or pilot test: Most pricing logics and methodologies require significant amount of configuration to be implemented in pricing software. This means it is rather difficult to do a trial run and test the software. However, we still consider it useful to do a POC, which should include the following elements:
Manual vs. automated data upload: The interface connection typically takes a lot of time but is similar across different pricing software vendors. Therefore, the automatic data interface is worth skipping within a POC. With manual data upload, you can still check if the data can be easily configured to your needs. How does the software deal with data gaps and low quality?
Test algorithm: Compare the software’s price list output to your intended output. What does the software do with low data quality in extreme cases? Does the software generate the required analytics for you to validate the price changes?
Test international pricing: If you have a pricing governance model involving central prices combined with local derivations, check how the collaboration works between central and local pricing managers. Is it easy and efficient enough for a local pricing manager to check and confirm central recommendations? Does the software offer enough ways for the central managers to guide and monitor international prices?
Test discounting and rebates: Test the configuration of discount rules and discount levels. Can you simulate the impact of discount changes easily? Does the software enable you to check the compliance of discount conditions? Is there a convenient interface to set customer-specific discounts and approve them?
As part of Simon-Kucher's pricing strategy projects, our clients can start using and piloting Simon-Kucher Engine software. This software can be configured to your targeted pricing strategy. Just ask your Simon-Kucher consultant for access.
2. Manage pricing complexity
Introducing pricing software requires substantial investments and organizational efforts.
A major problem with pricing software is that there are often many specific exceptions and complex pricing mechanisms you need to configure in your pricing software. Considering these exceptions and individual rules takes a lot of time. The longer it takes to introduce the software, the more likely the business interest will fade. In practice, failed software implementation projects occur more often than you think – and consequently, expensive software is not used or gets stuck in the middle of the implementation process.
A few things can help manage complexity:
Create a global glossary of pricing and discounting terms. Often different terms are used for similar products and services internationally, and creating a glossary supports standardizing as many terms as possible across countries
Define how you group products within your pricing software. You want to combine products that are priced similarly, but also need to ensure you can easily implement your price governance
Reduce exceptions and manual overwrites before using the pricing software and avoid creating new exceptions
Re-evaluate your pricing model and algorithm. Often, a certain level of complexity is required for strategic reasons. However, complex pricing models are also more difficult to roll-out in software
With our Simon-Kucher Engine solutions, we focus on providing intuitive front ends allowing for flexible point-and-click configurations by business users. This allows for self-service (and low cost) exception handling and ensures the software ultimately fits your pricing strategy.
3. Data is key and needs to be prioritized
Data is key to any pricing software – whether you are using traditional or AI-based algorithms. Without high-quality input data, pricing software cannot contribute to success.
A frequent source of data errors is data captured through web scrapers, for example, price crawler data. If the algorithm gets the data wrong, it can have serious consequences for price calculations. We highly recommend using solutions with both an AI-based and an additional rule-based data quality check in to handle this
Another challenge is the data costs: There are many different cost bases. The costs may vary based on the manufacturing plant, they may be linked to different units of measure, and the time frame in which the costs are measured is highly relevant. You don’t want to use outdated costs for your future price list. Create a clear definition of when to use which costs and check if the right costs are also used by the pricing software
Often, pricing algorithms use different data columns that describe product attributes. For example, product material, product size, or other product characteristics. Sometimes these fields are empty. Here it is important to establish processes to fill data gaps, either within the source system, during the ETL procedures, or in the pricing software itself
The global glossary mentioned above can also apply to the data. This means that the algorithm can interpret the same things as the same. The more data is harmonized across countries, the lower the implementation efforts
In our SK Dynamica | Retail solution, we have a built-in data quality monitor that visualizes the quality of pricing data and can also be accessed directly by the pricing managers.
4. Get the team behind the software
Introducing new software can put a lot of pressure on the pricing and sales team. The team may not want to use the software because of its complexity or feel threatened about software telling them how to do their job.
Instead, pricing software should be designed and configured so that it makes life easier for every employee involved. This includes:
Fewer data inputs and data input automation where possible
More insights available at your fingertips to make better decisions and view the impact of past decisions (especially successes)
Options for employees to configure the software to their needs
Possibilities to collaborate with colleagues
Of course, communication and training are key, as with any other software implementation. If the team is enthusiastic about the software, that’s a big step toward successful implementation.