How to Enable Surge Pricing for Your Products
What is it
“Dynamic pricing, surge pricing or time-based pricing is a pricing strategy in which businesses set flexible process for products or services on current market demands. “ – Wikipedia.
How it’s done
The prices are updated based on signals that takes into account competitor pricing, supply and demand for the product/services, and other external factors in the market.
Industries that use it
Dynamic Pricing Basics
Value based pricing strategy is dependent on factors such as geography and demographics; therefore, is difficult to ascertain. Consumer’s willingness to pay can be an indicator for the perceived value. That is where the concept of price elasticity kicks in.
We say a good is price elastic when an increase in price causes bigger percentage of fall in demand, e.g. particular brand of ketchup sauce. The idea is that a fine balance needs to be maintained between dynamic pricing and price elasticity so not to discourage the consumers. Elasticities are location, time and retailer dependent. Dynamic pricing can be utilized by businesses for revenue and product maximization strategies. Retailers can combine this with pricing strategies to aim for volume maximization. Exercising caution before implementing dynamic pricing strategy is recommended and should be done in conjugation with other signals such as consumer budget, degree of necessity and brand loyalty.
Benefits of Dynamic Pricing in Retail (*Source Ecommerce Wiki.)
This flexibility in offering prices allows retailers to increase:
Other advantages includes
Machine Learning and Artificial Intelligence to the Rescue
Machine learning algorithms use computational methods to learn information directly from the data without relying on a predetermined equation as a model. The algorithm adaptively improves as the number of sample available for learning increases.
The idea is that with more data, you can ask better questions to your model. Dynamic pricing involves analyzing a lot of variables and mining huge amount of data where there are no pre existing rules or formulas. Therefore, it is natural to use machine learning for forecasting demand planning.
Artificial Intelligence is making bots and apps smarter each day. AI is making personalized recommendation of goods and services that caters to your choices, budget and unique situation.
Leveraging Machine Learning and AI for Dynamic Pricing
The core of Artificial Intelligence (applied Machine Learning) is reshaping how organizations enable dynamic pricing for their products and services. Traditionally, the pricing of goods were based on the principles of demand and supply with the advent of AI organizations now have to factor in market dynamics, consumer behaviors and their perspective. Rule based pricing models are not dynamic and hence AI based algorithms are reshaping the industry. These models take data from disparate sources, analyze them to find patterns and make recommendations at scale.
Since the parameters in the algorithm are in a constant state of change, humans wouldn’t be able to keep up with them which is why the neural network based algorithms are getting so popular. As data volumes continues to grow, the popularity of these algorithms won’t fade away anytime soon.
How Other Business Found Success by Enabling Dynamic Pricing
In 2014 Amazon introduced predictive stocking patient. This technology enabled the retailers to know what product the customer will order in advance and at what price point. It allows retailers to reduce delivery time, and helped Amazon with better inventory management. Other benefits includes better control on pricing and overall market strategy.
Gebni, an app launched in 2017 is helping restaurants acquire take-out orders during the slow periods. It sets dynamic pricing based on an algorithm that reduces the price of menu items according to demand. Discounts change in real time, ranging from a moderate 2 percent to a whopping 35 percent — even on a $10 item. This app was launched in February, 2017 and has been a success.
Dynamic Pricing on Google Cloud Platform
Cloud platform is the technology enabler where services are offered on a single platform making it easier to design, deploy and maintain solutions without the hassle of micro-managing. Google Cloud Platform is one of the leading vendors when it comes to deploying AI-Ml based solutions.
Google BigQuery is warehouse that enables integration of third party data (public or otherwise) with your own companies data. This OLAP data warehouse is serverless and can do analysis on Terabyte volumes of data in minutes.
BigQuery can form the data source to Google Cloud Machine Learning. The Machine Learning Engine allows models to be trained and evaluated. The trained model can be utilized for serving predictions for the new data coming in. Some of the features that may be part of determining the dynamic pricing will be magnitude of popularity, page views, time, week day etc.
Market signals for trends can be obtained using the Natural Language API which can analyze the sentiments of your brands from social media and review site. AutoML is bringing in a whole set of pre-trained and pre-built models that can analyze sentiments from audio and video data.
If your organization has in house applications that tracks market signals, it can be deployed on Google Compute Engine, or App Engine. This will not only speed up the data gathering process, but all of the architecture can be hosted in one platform for true end to end analytics.
Datastudio can be utilized to view interactive reports and dashboards. This will help your organizations to form strategies in real time.
Check out Planning in a Box, an application that makes inventory management easier for Amazon sellers, here. It has dynamic pricing capabilities.
We at Pluto7 consulting Inc, a Google partner can help your organization enable dynamic pricing strategies. Over the years we have helped organizations to make data driven decisions using ML and AI on Google Cloud Platform. To learn more visit our website.
Transform your business by leveraging the power of Machine Learning Artificial Intelligence, Analytics, and IoT solutions.