Buy vs Build a Pricing Solution: Critical Conversations
The crucial questions business and data science leaders need to discuss when deciding to buy or build a pricing solution.
As retailers build up the data science expertise in their organizations, many are keen to apply these skills to the pricing conundrum. Surely, they think, their data scientists can create a better pricing solution for much cheaper than the 3rd party platforms available for purchase today.
Even if the retailer does have the right skills in-house to develop a pricing application, is that the right investment for the retailer? Will the benefits received outweigh the burden on resources? And what about the ongoing maintenance requirements?
When considering the decision whether to build or buy a pricing solution, we recommend pricing leaders have an honest discussion with their IT and data science leaders to weigh the pros and cons of building a custom pricing application. To help make the right choice for your business, here are the most important questions to ask during those conversations.
The Key Question for Retail Executives
The most important question business and data science leaders can ask each other when debating build vs. buy is:
Does having a custom pricing application differentiate us in the eyes of our customers?
In the early days of applications, one retailer notoriously invested heavily in a custom accounts payable solution, much to the frustration of the merchandising team. Their mistake? No customer would be enticed to drive across town and shop at their store because of a unique accounts payable system. The same applies to pricing. A customer does not see behind the scenes and know if the price was generated through a custom coded pricing application or from a 3rd party pricing application. Nor would they care if they could see. The customer will only know if the price meets their expectations.
Retailers should focus on building applications that create differentiation and buy ones that assist internal processes and planning. Today, there is no need to create a custom application to calculate optimal prices. AI based pricing solutions enable retailers to ask ‘what if and why not’ to develop optimized pricing strategies that customers would be willing to drive across town for.
Key Questions for Business Leaders to ask Data Science Leaders
How fast can you deliver a working pricing solution?
Today’s development tools enable rapid prototyping, but that is not a fully scalable, tested solution. A production ready application that supports a retailer’s business needs can take a long time. Often, custom applications are never delivered due to complexity or lack of detailed requirements. Partnering with a vendor who provides pricing solutions eliminates application risk because a retailer will know exactly what capabilities they get along with committed future roadmaps.
What is the effort to get our data ready for use?
The key to pricing optimization is data. A dynamic pricing solution tests the limits of a retailer’s data architecture and data management practices. To generate the most accurate price recommendations, retailers need a minimum of two to three years of sales, location, product, and inventory data. The effort to collect and clean for pricing use can take from three to four months and sometimes as long as twelve months. If the data team is busy preparing the data for use, they will not have the time needed to develop custom applications.
Will you have resources available to provide support and updates?
There is no ‘normal’ in today’s market and the pricing application needs to adjust to changes in consumer behavior. COVID is the perfect example. If you were using a custom-built pricing solution, would resources have been available to update the application for impacts from the pandemic? Even without COVID, markets are changing, and the custom pricing application will need to be continuously updated. AI based pricing optimization solutions are supported and maintained by a team of retail and data experts ensuring accuracy during instability and that retailers have the most current pricing models tailored for their pricing strategy.
Key Questions for Data Science Leaders to ask Business Leaders
Do you have well-defined requirements for your pricing needs?
Does the pricing and merchandising team have the time and expertise to define solution requirements to build a custom application? Are the rules, functionalities, accounting, and other features clearly understood? Can they anticipate the functionality needed in the next 12 to 24 months? Applications are only as good as the requirements defined for it. The pricing solutions available today have spent many years listening to retailers and building upon the pricing expertise within their own organizations. 3rd party solutions also work with a wide variety of retail segments, meaning retailers get the benefits of a full industry view without additional effort.
Why reinvent what already exists?
Pricing is a key lever for differentiation and retailers need to be able to respond to market changes faster than ever while meeting customer expectations and delivering business results. Industry leading price optimization solutions meet the requirements of retailers around the globe, giving them deep data insights and practical pricing strategies to optimize for today and prepare for an uncertain tomorrow.
Finding the Answer
The most important thing when choosing whether to buy or build a pricing solution, is that retailers need a robust pricing platform that empowers the pricing and merchandising team to deliver immediate or long-term pricing recommendations that drive margin, profit and growth. When it comes to efficiency, resource allocation, differentiation and capabilities, the choice is clear – buying a third-party solution will best serve retailers and their pricing and business goals. Revionics helps enterprise retailers around the world harness data analytics to enhance their pricing strategies and make profitable pricing decisions that drive company growth.