Dynamic pricing or flexible pricing isn’t new.
Before the 19th century, customers haggled for discounts, and loyal patrons were often given a “friendly price.” Mass marketing introduced a little more science by using demographic research, and more recently big data and analytics, but lost the shopkeeper’s ability to directly negotiate with a customer. Prices now are mostly set according to market segment and supply-demand, not based on firsthand buyer knowledge.
Dynamic pricing today is human-driven, assisted by big data and analytics. Airlines change their pricing based on peak times. Uber charges more for high-traffic hours, during driver shortages or for traveling in certain neighborhoods.
Dynamic pricing can also be applied based on customer segments. People in segment A get pricing X. People in segment C get pricing Y. Pricing managers can control this kind of supply and demand / yield management pricing with rules in an ecommerce solution.
However, marketing has gone through a revolutionary shift to personalization. Customers are interacting with brands in new ways, on more platforms. They want the company to know who they are, no matter what method they are using, and expect consistently personalized service. The new commerce challenge is that companies must now attempt to sell to (and price for) a market segment of one.
That is where dynamic pricing engines using artificial intelligence come in.
Pricing and the customer journey
Dynamic pricing engines use machine learning technology to accurately personalize pricing in ways that were previously impossible. What’s becoming possible through machine learning is the ability to pinpoint the price individual customers are willing to pay and at the same time optimize for revenue. To achieve this, many pieces of data need to come together.
1. All about the customer
A dynamic pricing engine uses machine learning algorithms to read a customer like a seasoned merchant. It looks at real-time behavior: how many times have they looked at the item, where the pointer is positioned on the page, the products they’ve compared. It calculates their price threshold from how much they’ve spent before and the kind of discounts they’ve applied. It will recognize whether it’s a first visit or someone who’s used or even purchased from other touchpoints. These systems also take into account similar customers to assess intent.
2. Pricing in context
Today, pricing is based on segmentation and mapping customers to a persona. Contextual pricing will take personalization to the next level, where recommendations will be just about you and for you, since companies can piece together purchase intent from an individual’s behaviors. This means they can offer differential pricing or progressive discounting that might encourage purchase if a customer is on social media versus if they are using a laptop to explore flights through Expedia.
Prices can be adjusted according to selling context or other conditions. Anonymous buyers that responded to a Facebook post may get a different price from loyal app subscribers who have bought two or more times before. It is also possible to offer contextual pricing within an experience, which often occurs when upselling, bundling, or cross-selling. For example, educational organizations could offer students taking online courses the ability to order supplemental materials from within the course environment. The system presents the offer when a student attains a poor or non-passing grade on a test or assignment.
3. Competitive intelligence
Pricing intelligence software not only evaluates customer behavior and internal supply and demand, but can monitor billions of product prices on other sites and analyze market shifts. Walmart uses this technology to change prices 10,000 times a month, based on competition, traffic, time of year, and inventory. The free WiFi in its store also allows them to track how many smartphone users are in the store and monitor the type of price apps that are being used for comparison shopping.
The new price equation
According to Accenture research, while 86% of business leaders agree on the importance of a differentiated customer experience, only 23% achieve strong returns. One pivotal factor is price, which should be as fluid as the experience itself.
The new price equation should include expectation + history + behavior + channel + context. Fixed prices don’t reflect value, or create opportunities to reward customers or respond to their purchase cues. Dynamic pricing engines are optimized to give customers what they want at a price that they accept–and that maximizes revenues. Machine learning continuously improves the model over time, given increased information about a customer’s behavior as they interact with the brand.
It’s a way to build relationships – and revenue.
The Accenture survey found that 60% of customers were willing to pay more for quality, experience and product options. They are also more likely to return for repeat purchases. In this way, dynamic pricing engines facilitate not just a single sale, but a lifetime of customer value.
Is your commerce system capable of leveraging dynamic pricing? Learn more about cutting edge commerce technologies at elasticpath.com
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