Want to devastate your financial performance? Simple. Get pricing wrong. And yet, 42% of retailers still primarily use spreadsheets for pricing and 34% use internally built software.
What’s the problem with these traditional tools? For starters, both are incapable of supporting big data, machine learning and artificial intelligence (AI). You know, science.
Which is a shame because one of the beautiful things about price management in retailing is that it is a numbers game, meaning it is governed by hard facts and data-driven insights. As a result, pricing is a perfect candidate in the retailer’s org chart for algorithm-driven processes based on a scientific approach. In fact, industry leaders like Amazon and Walmart have embraced automated and algorithm-based pricing to deliver hyper-fast price changes, i.e. dynamic pricing.
In the Targeted Research Report “Raising Your Price Strategy IQ”RIS examines the role of data-driven pricing, challenges that hold retailers back, and the introduction of artificial intelligence (AI) and machine learning into next-generation tools.
Avoiding the Price Is Wrong
Here are a few key takeaways from the report:
- Retailers believe pricing software plays a huge role in a retailer’s financial performance. They gave it an 8.1 score, which is a very high rating on a 1-10 scale, where anything in the 5-6 range is neutral. Any score above eight is a strong endorsement.
- Since pricing software is so important one would assume retailers must make a serious effort to keep it up-to-date. This is mostly true. More than a third (35%) say their pricing optimization software is up-to-date, which means they have made an upgrade or new implementation within the last few years. Another 11% say they are deploying an upgrade or new implementation now but have not yet finished. Combined, the two above figures show that nearly half (46%) of retailers have advanced pricing software in place (or will have it soon), however more than half have allowed their software to fall behind.
- Of the retailers that have fallen behind, 38% have plans in place to upgrade to a new solution. This breaks out into 19% who will upgrade or implement a new solution within six months, 8% who will update within a year, and 5% who will update within two years. This is a high level of up upgrade activity for any technology commonly found in the retail enterprise tech stack.
- One reason for the large amount of upgrade activity is the high percentage of retailers that still rely on spreadsheets for pricing – 55% use a mix of spreadsheets and other software and 42% primarily use spreadsheets.
- The two top challenges retailers face in executing their price strategy indicate it is hard for them to keep up with a dynamic, competitive environment. The top challenges are responding to competitive prices, chosen by 45%, and developing agile responses to competitive changes in the marketplace, chosen by 42%.
- In a strikingly honest self-assessment, retailers give themselves a low score for understanding the role of AI in pricing. Based on a 1-10 scale where one stands for no understanding and 10 stands for complete understanding retailers gave themselves a 3.9.
Despite years of software development, optimization algorithms, and the incorporation of machine learning and AI, price management in many retailers is still reliant on imperfectly informed guess work.
The good news is retailers are in the process of doing something about it. A total of 38% plan to upgrade or implement a new price optimization solution within two years, which is a necessary step up from the high percentage of retailers that currently rely on a mix of spreadsheets and other software (55%) or spreadsheets alone (42%).
Pricing is a numbers game and advanced, data-based solutions are its natural allies. As retailers replace legacy tools and adopt more advanced solutions they will discover a new level of accuracy and improved financial results that come from an AI-fueled, science-based approach.
Click here to download the report and get a complete set of charts and analysis.