The Value of AI Pricing in Retail: Highlights from Revionics’ and Conrad Electronic’s Presentation at The EHI Retail Institute Conference in Germany
(This blog is the first in a series on The Value of AI Pricing in Retail, which was a topic explored at a recent EHI event in Cologne.)
Earlier this month retail executives from around Europe came together in Cologne, Germany for the EHI Retail Institute Conference, which focused on how AI will transform the retail industry in the coming years. The theme — “Artificial Intelligence and Analytics” — ignited exciting exchanges among retail leaders around the latest developments in this area. A strong consensus is that AI is a “must have” for retailers, ad nowhere more so than in the areas of price and promotions.
During a joint presentation title “AI Pricing Solutions; Looking Beyond the Hype,” which I delivered together with Ales Drabek, Chief Digital and Disruption Officer at Conrad Electronic SE, we spoke to AI in the context of our own personal and organizational experiences. Here are some of the highlights:
Leveraging AI in retail helps retailers make better decisions, empowers them to react with more agility to market developments, provides up-to-date shopper and competitive insights, allows retailers to continually improve and generates new profit-generating possibilities. While the majority of retailers don’t yet leverage AI-based pricing, a Revionics-commissioned Forrester Consulting global study found that 76% of retailers believe AI-driven pricing would have a positive impact on shoppers.i
One reason the adoption of AI in retail may be slow is many retailers don’t truly understand what AI and Machine Learning is; therefore, during the presentation the speakers defined it in Layman’s Terms:
- AI is the broader concept that machines should be able to carry out tasks in a way we would consider them smart or intelligent.
- Machine Learning is a current application of AI based on the idea machines can access data and learn for themselves (i.e. without explicit programming).
- AI and Machine Learning consists of a VERY broad set of techniques and toolboxes.
- Concepts and many techniques for AI have been around since 1950’s – it’s not new.
Adopting “Market Ready” AI Solutions is Critical
Revionics has been helping retailers leverage proven AI, SaaS-based solutions that deliver quantifiable results. What qualifies an AI solution to be market ready and deliver business impact? Here’s our view:
- It has learned from a lot of real-world data: AI solution providers like Revionics have learned from years of real-world data instead of just training data. Real-world data is chaotic, but road-tested science can quickly analyze and prompt action on this data, all while detecting early changes in shopper, competitor and market behaviors.
- It matured with prior input and proper guidance: AI learns from feedback cycles and from comparing forecasted to actual impact to evolve and improve continually. Variables can be adjust over time and differ by retailer, category, situation, season, channel and location. This time-based learning allows the AI solution to make the best recommendation and to know how best to respond to new data sources.
- It is created by scientists and retail experts who have successfully used AI to solve the problem: Being an applied AI practitioner takes specialized skills. Writing production-ready software is not easy as it sits at intersection of several IT, AI and retail business disciplines. In addition, just because someone does AI in one domain doesn’t mean they will be successful in another. Retail is a very different animal from other industries such as healthcare, finance, CPG, etc.
Revionics has been leveraging AI and Machine Learning in its price, promotion and markdown optimization solutions for years. Retailers around the world have been leveraging these solutions for more than a decade, including in-depth capabilities such as forecast risk classification; attribute modeling; product attribute interference; probabilistic modeling cannibalization; demand modeling and forecasting; demographic demand analysis; demographic store modeling; store-zone clustering; and markdown cluster optimization.
Rooted in both retail and science, our solutions help retailers around the world harness the power of AI, which results in a stronger ROI, an enhanced competitive advantage and a more finely crafted improved price image.
Check back soon for a related blog post that will focus on Conrad’s real-world example of AI-based pricing and promotion at work.
i Retail Success Requires Personalized, AI-Driven Pricing Strategies, A Revionics-Commissioned Study by Forrester Consulting, October 2018