How Machine Learning is reshaping Sales and Price Optimization
June 5, 2021
5 min read
If one evaluates the parameters that contribute to the success of a product/service, the pricing model adopted plays a crucial role in it. Not surprisingly, businesses pay enough attention to pricing strategies often experimenting with new technology, process changes and methods to control/cut prices while optimizing revenue and profits. It is all the more true in the current state of global crisis. Fortunately, businesses are intelligently relying on AI and Machine Learning applications for optimizing product pricing so as not to let global market and economic conditions leave a terrifying impact.
This is true in every respect. Pricing is often considered one of the most important determinators for purchase. Even if one considers the case of mobile applications. While it may surprise many as to how free apps make money, the secret lies in the pricing strategy: Offer the product for free, get users engaged and then charge a premium (through freemium pricing or subscription models) to earn revenue.
The market is changing, the audience is changing, the environment, economy and every facet of doing business is changing. Traditional pricing models are often insufficient and technology driven solutions are simply put, the way out. With industries across insurance, hospitality, travel, e-Commerce, retail among others, leveraging the potential of Artificial Intelligence and Machine Learning algorithms for revising their dynamic pricing strategies, let’s dive in and understand the why’s and how’s involved.
What is Price Optimization and how Machine Learning solutions can help
Let’s begin by first understanding ‘What is price optimization?’, why businesses need it and how it can help businesses in keeping customers loyal and competition at bay.
Price optimization or algorithmic pricing is the process of setting prices driven by ML algorithms in order to maximize profits, increase market share, or reach other business goals.
Traditionally, price sets were determined by human decisions based on a number of data/metrics and analysis. This meant that these were naturally more susceptible to manual errors and even time-bound. These would also be concentrated on focused groups of audiences and markets. While this was working very well for businesses at large in the last decade, times have changed and there are better ways to optimize revenue channels and price using Machine Learning algorithms.
How Pricing Algorithm works and the role of Machine Learning
When it comes to pricing strategies, scalability is a prominent factor to consider. Traditional models often failed to offer scalability whereas dynamic pricing models facilitate that.
Let’s understand that with an example on how Machine Learning influences pricing. Say you have to fly from London to Frankfurt on the 15th of next month. You play around for the next three days, and check the ticket prices on the company website from your smartphone and laptop. You will notice how the price fluctuates almost every time that you check. Magic? Not really.
The impact of Machine Learning models in business can effectively be seen here. By using ML applications and the core concepts of dynamic pricing strategy, businesses target different groups by using data analytics to analyze market trends, demand fluctuations, customer behavior, and other such factors.
As you might have guessed, the core of pricing algorithms depends on the price and demand function.
Business Use cases of pricing optimization
Machine Learning for Retail Price Recommendation:
Retail pricing has to take into account multiple factors so as to determine the initial price, discounts (if any), and promotional prices. Doing this manually is guaranteed to result in errors and even opportunity-losses. With the right availability of data and a sound database, Machine Learning can help with this.
For instance, take the case of Competera, which offers a pricing management software, that uses neural networks to analyze over 60 factors to provide an optimized price for an item. A pricing optimization case study conducted on Intertop, (a client with 114 stores and 14 mono and multi-brand channels across 25 cities in Eastern Europe) demonstrates its success.
The solution offered two primary features: (1) Elasticity-based markdown suggestions and (2) analytics for well-informed pricing decisions with a single click. The markdown suggestions were provided after analyzing historical data points and business rules of repricing.
Pricing impact on the Hotel Industry:
The hotel industry uses algorithmic pricing extensively. The industry is ruled by several factors such as weather, holiday season, location, competition, macroeconomic factors etc. In time, it is not surprising that the sector relies heavily on technology especially for amplifying customer engagement through AI personalization.
Pace, a startup headquartered in London launched an industry-first hourly automated adaptive pricing software. The software uses ML algorithms for price optimization, allowing hotels to maximize profits. The software takes into account customer demographics, such as age, profession, and even accounts for the time of the year to provide hotels with competitive pricing to ensure they are always ahead of the curve.
In barely three years since its inception, the company has gathered leading clients with claims to having optimized £1+ bn in revenues.
Using Machine Learning in pricing for E-Commerce:
Another industry that leverages different pricing models is the online and eCommerce sector. The dynamic pricing algorithm for e-commerce has proved to be incredibly useful for retailers, especially considering how easy it has become for online shoppers to compare prices and switch between platforms/vendors.
Rue La La, an online retailer faced a unique challenge. The brand organizes online flash sales for one day to four days (AKA events) on an assortment of similar items (AKA styles). These events provide the biggest share of the revenue for the brand, an optimized pricing model was required for the same.
Researchers Kris Johnson Ferreira, Bin Hong Alex Lee, and David Simchi-Levi helped address this problem for the brand.
Using decision tree price optimization, a demand prediction model was built with items aggregated by style (price determinator). Considering that historical data on the first exposure styles were unavailable, the model was built according to Rue La La’s sales transaction data from 2011 until mid-2013. With inputs from the demand prediction model, the researchers developed a price optimization model that helped to grow revenues of the company by 10% in six months.
What’s important to note here is that: (1) For Machine Learning models to be successful, there’s a tremendous dependency on the quality and quantity of data; (2) To improve engagement, it is crucial to focus on designing elements. In general, most eCommerce businesses pay sufficient attention in building AI-driven UI/UX designs to ensure higher engagement and subsequent conversions.
Machine Learning: Smart solution or Disruptive technology?
And that brings us to the most poignant question: Is Machine Learning the solution to price optimization challenges?
As can be seen from the use cases narrated above, a simple answer would be ‘yes’ – yes it can definitely help in streamlining processes and even cutting costs writing paper for optimizing profits. But that being said, it is also true that it comes with its own challenges. As a top AI service consultant, at Day One Technologies we believe that Machine Learning technology is evolving, and its true potential is far from being realized.
In its current use, it has been of assistance in that any Machine Learning driven effective pricing solutions would ideally make strategic suggestions based on:
- Historical data
- Demand forecast and history
- Price points and sales metrics
- Price and non-price factors
- Recommended pricing and review based on product/service lifecycle
A disruptive technology for sure, but Machine Learning is here to stay. And if you think that your business can grow with the right technology and software support Contact Day One today for a discussion. If not you can always Subscribe to our Newsletter for the latest updates from the world of AI technology and business.
Testimonials What people have to talk about us
Vere360 – VR based Immersive LearningReading Time: < 1 min
Day One helped Vere360 “fill skill gaps” and build a platform that would cater to their niche and diverse audience while seamlessly integrate the best of #AI and #VR technology.
Ms. Adila Sayyed Co-Founder
1TAM – Video Blogging ReimaginedReading Time: < 1 min
‘1TAM’ was only for iOS with gesture-based controls, advanced video compression techniques, and a simple architecture that allowed actions to be completed in 2-3 taps. The real challenge for ‘1TAM’ was to keep it distinct which bought brilliant results with all the strategies and approaches implied for best video compression techniques.
Anwar Nusseibeh Founder
Fit For Work – The Science of Workplace ErgonomicsReading Time: < 1 min
Day One Technologies came with the expertise that was required and helped in building a platform that is edgy, functional, and smart, delivering engagement and conversions at every step.
Ms. Georgina Hannigan Founder
Finch (previously Trio) – Growth with Investing, with benefits of CheckingReading Time: < 1 min
The Finch (previously Trio), one of our clients today has reached this level with our expertise and with a great team of developers in Day One, who have made every stone unturned in making this project a big success.
Neel Ganu Founder
SOS Method Meditation for ‘Busy Minds’Reading Time: < 1 min
Day One Technologies helped in building an innovative mobile app (for #iOS and #Android) that’s easy-to-use, engaging, and data-driven to help users reap the most at every point.