Dynamic Pricing? Yes, You Can!
February 28, 2019
- The future of the pricing industry, the trends that shape it and the latest developments on the market
- How using AI-driven algorithms to make optimal pricing decisions in real time helps your business increase revenues or profits
- How access to all your data across all channels and systems combined with powerful AI can drive your pricing decisions
Traditionally, when we think of dynamic pricing, we think of large institutions with access to massive amounts of data. Not anymore. Time for you to take a slice of that pie.
When Gabriel Smith, VP for Product Strategy at Pricefx, and Chris Herbert, President of Silicon Valley Pricing, discussed the future of the pricing industry in our recent webinar, they offered some key insight into the trends that shape it, the latest developments on the market and how dynamic pricing will come to rule it.
Here are my takeaways:
Dynamic Pricing: AI and Machine Learning
The words on everybody’s lips these days are “artificial intelligence” and “machine learning dynamic pricing.” In the webinar, Gabe started with some definitions. Anything we think that a computer can do, we can refer to as artificial intelligence (AI). Machine learning (ML) is a subset of AI, including statistical techniques and algorithms – also used in price optimization. These algorithms make optimal pricing decisions in real-time, helping business capitalize on prime selling opportunities. Yet, as Gabe noted, while ML has proven to be a success, it’s not suitable for every company.
Wrong Tools = Wrong Results in Dynamic Pricing
You cannot deliver the right results with the wrong data. As dynamic pricing has become more and more standard across industries, attempting to succeed without advanced price optimization is like bringing a knife to a gun fight.
Gabe had an interesting story to illustrate this:
“We have a customer that was competing against a company using algorithmic and dynamic pricing. This customer ended up implementing our solution to compete, and thanks to this, they uncovered a trend that showed their competitor was dropping prices at 4:30 PM every Friday. They would drop their prices and capture sales over the weekend and raise the prices on Monday morning at 8 AM. After discovering this, our customer could respond appropriately (dynamic matching of pricing, etc.) and their competitor didn’t even know what hit them. Implementation of the right tools saved our customer a substantial portion of sales.”
Divide and Conquer
Hearing this, Chris wanted to know how machine learning could help companies build a segmentation model. Gabe explained that machine learning comes up with data-driven recommendations by looking at different dimensions and how those influence customers’ willingness to pay. However, there’s still a need for pricing and data science, it shouldn’t be entirely data-driven. This aligns with the recommendation to use Pricefx as a framework tool to have things done faster, while the system algorithmically improves itself.
Delivering Pricing Your Customers Can Value
In summary, it’s evident that any pricing solution must be broad and deep enough to capture omni-channel complexity. The key factors here are data openness and integration with ERP systems. Having access to all of your data across all channels and systems through price management software with integrated and powerful AI means the system is not only constantly learning and getting smarter, but it is also driving your pricing decisions based on historical and real-time data. This ensures that you are delivering the right prices at the right time, staying ahead of your competition and delivering value.
Want to watch the complete conversation and learn how you can utilize dynamic pricing and price optimization? Click here.