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How AI-Powered Pricing Transforms Data into Dollars

Who are your customers and what are they willing to pay? If you get that right, margins increase, profits soar, and your business grows. If only pricing were that easy.

But it isn’t: it’s hard. The volume and velocity of change in today’s global marketplace isn’t slowing down. Businesses are getting bombarded with constant fluctuations in costs, currencies, supply chains, and demand patterns. If you look at the wrong data points using stale assumptions, you’ll fail to act on changing market dynamics. That’s why many businesses struggle to stay on top of their profitability goals.

Businesses can’t afford a fuzzy view of their profitability

To find profit opportunities and avoid leaving money on the table, you need lots of answers.

As you evolve your pricing journey, you understand more than just the basics of who is buying and what they are buying; you have answers to things like:

  • Why are they buying?
  • Where are they buying?
  • How often are they buying?

As your pricing sophistication grows, you start to dig deeper:

  • Why are they buying from us?
  • Who else is buying?
  • When are they buying?
  • How do things like discounts impact buying?

However, without having a way to translate all this data among the noise of geopolitical events, buying behaviors, cost changes, and more, you’re still only getting a partial glimpse of the complete market picture. You need to know what really matters for each sale, each customer, each purchasing moment. This is where AI and dynamic pricing can play a pivotal role.

Get a clear view of every transaction

AI-powered pricing relieves the pricing burden currently shouldered by many pricing professionals by proactively detecting and responding to market changes. You’re able to gain insight into drivers of price and demand, and answer incredibly important questions, such as:

  • How influential is competition in certain markets?
  • How are cost fluctuations impacting profitability?
  • How does seasonality influence a customer’s willingness to pay?
  • How does a particular customer value a product’s specifications or attribute more than another customer and therefore be willing to pay more?
  • If a customer is buying a product from us for the first time, have they bought other similar products? What does that tell us about their willingness to pay?
  • How volatile are supply chains and how does this affect a specific transaction?
  • If we increase the price, how much would the win probability of a specific transaction change?

Price optimization is about knowing what the right price is based on all the different features and characteristics of a sale, so you can get the win probability you want and the margins you want. And, at PROS, we have found that the best way to optimize prices is by leveraging neural networks.

Unlike traditional segmentation approaches to price optimization, our Gen IV AI-powered dynamic pricing takes a radically different approach to price optimization, which can truly transform how you view your customers, markets, and pricing strategies.

A crash course in price optimization—the old and new way

In the past, pricing technology relied on a segmentation-based algorithm. A segmentation-based algorithm looks at all your historical data, and whenever you have a new sale, breaks that data up into segments and finds the slice of data that is most like the scenario that you're trying to price, then prices accordingly.

However, traditional segmentation-based pricing falls short in today’s dynamic market, and here’s why:

  • Segmentation requires a history of similar transactions. You may not have sold a particular product configuration to a particular customer in the past, so there is no historical data to draw from. Or you may not have sold it enough times. Segmentation can’t help in this case.
  • There isn't easy detection of seasonal patterns. With seasonal patterns that change as often as the ones we see today, such as a large spike in December for certain products, traditional segmentation again falls short because you may not have enough examples of selling that exact product within that particularly narrow timeframe. That means you could be missing margin and revenue opportunities.
  • It's difficult to assess changes in competitor prices and costs. With traditional segmentation, it becomes difficult when you have a situation that you haven't seen before, such as costs increasing more than they ever have in the last five years, or when competitors have usually offered 5% discounts but now they’re suddenly offering 8%. In today’s environment, businesses need to understand what that relationship is, not just try to look for similar data in the past. 
  • Segmentation doesn’t use all your data. A segmentation-based approach breaks up your data into chunks so that you're only using one segment of your data to help you price. But what if there is relevant data in every other segment? This information can help you understand how a particular customer may react to a price in a specific scenario, even if that transaction wasn’t in your segment.

Why segmentation-less pricing is better

Rather than using only a sliver of your data, PROS AI considers all the data that you have available—transactions, attributes, product data, customer data, competitive data—and feeds that data into a model to deliver the optimal price for a sales transaction.

3 key features of PROS dynamic AI-powered pricing include:

#1—Price prediction

The goal of price prediction is to understand the relationships between willingness to pay and all the other characterizations that you’ve seen in the past. For example, the model understands how geography or seasonality affects price prediction. It learns all those relationships by looking across the entire data set to understand historical behavior and then forecast future behavior based on current conditions. And the beauty is that you don’t need to have a certain number of transactions for this to work.

#2—Elasticity estimation and price optimization

You could have done a great job of collecting data but there may be something going on that your data doesn’t capture so you wouldn’t get a signal to increase or decrease your prices. PROS AI continuously assesses the response to a particular price that you’ve put out into the market and builds an elasticity curve to understand the relationship between win probability and price. This win probability curve is continuously learning and adjusting so you'll be able to understand based on data whether you need to increase or decrease a price to adjust for what you're seeing in terms of win rates. This is a crucial part of dynamic pricing—not just being able to use historical data most effectively, but also being able to respond to current trends in the most effective way.

#3—Strategic and business rules

PROS AI enables you to add in your own strategic and business rules. For example, you may have contracts with certain customers that require certain prices. Or you have cases that require approvals. All of this is built in so that the AI is working to help you meet the needs of your business.

What life looks like for AI-powered pricing analysts

A unique price for every transaction—PROS AI considers every variable and dynamically prices for a specific transaction on that specific day. If a variable changes, such as the shipping method or sales channel, the price might change. If you price the same product two weeks from now, the price may also change because PROS AI dynamically assesses what the pricing effect is for that week of the year, as well as the direction the trends are changing.

More time on strategic work—Rather than being bogged down in spreadsheets, PROS AI automatically serves up pricing recommendations so you can spend more time on strategy to understand how you well you’re executing prices.

A complement to current pricing strategies—PROS AI is a complement to current pricing strategies, enabling the entire pricing team to be more informed and focused on strategic decisions. The AI enables you and your team to understand what’s going on in the business, so you have a complete picture of who you’re selling to and why they're buying.

Pricing transparency—PROS AI enables you to examine claims that the AI is making, such as why a win rate is expected to be 45%, or why selling in a particular geography would increase the price by 15%. It’s easy to see how the AI produced a particular price. This gives both your pricing team and selling team confidence to know that the customer will accept a different price—and the data is there to back it up and substantiate that price change.

Harmonized pricing across channels—AI-powered prices can be sent to any number of different channels where you interact with your customers, whether that's within CRM or CPQ for building out quotes and agreements, self-service eCommerce sites, or other channels.

Thrive in the new era of AI

Learn more about how PROS dynamic AI-powered pricing can help you stay ahead of the competition and maintain profitability by joining our upcoming webinars or viewing the full recording from this webinar here.

About the Authors

Bryan Kruming Headshot

Bryan Kruming has worked within the technology industry for over 15 years, including the last 10 years specializing in solution consulting for ERP, CRM, CPQ, and most recently, Pricing solutions. He has experience with a variety of industry verticals, with his primary areas of focus being the chemicals, wholesale distribution, high-tech, and oil & gas industries. He's based out of San Diego, CA.

Kaavya Muralidhar Headshot

Kaavya Muralidhar is a Product Manager at PROS, leading strategy and implementation of AI-based price optimization software used by some of the largest enterprises in the world. Leveraging her background at the intersection of AI, Design, and Business, Kaavya's expertise lies in creating pricing software experiences that are seamless, delightful, and empower customers to excel financially through volatile market conditions.