In recent years, artificial intelligence has enabled pricing solutions to track buying trends and determine more competitive product prices. While static pricing keeps prices absolute, dynamic pricing adjusts prices to offer customers different prices based on external factors and their individual buying habits.

While there are some dynamic pricing options on the market, automatic dynamic pricing software is in its infancy. Most dynamic pricing solutions aggregate available pricing data from across the web, pulling in data from a company’s competitors or from the prices that are available to consumers of different regions. 

Generally speaking, however, dynamic pricing solutions use machine learning to find a customer’s data patterns. These patterns are unveiled by analyzing a variety of sources, such as loyalty cards and postal codes, in order to predict what the customer is willing to pay and how responsive they might be to special offers.

Machine learning algorithms can also reveal pricing gaps of which businesses can take advantage. Once the patterns are revealed, the machine learning system could adjust and determine product prices that are best suited to the person’s spending habits.

For instance, if an online shopper’s behavior shows that he does not spend time comparing prices within a certain price range, machine-learning algorithms take note of this pattern and take this into account next time.

In this article, we aim to provide business leaders with a glimpse of:

  • Which dynamic pricing applications are currently available
  • How businesses can use dynamic pricing machine learning systems to increase revenue

Dynamic Pricing

Pace

Pace claims to have developed a software that uses machine learning to enable hotel management to explore pricing that matches supply and demand. This could allow hotels to maximize their profits by offering the price that customers are willing to pay based on their demographics and the time of year among other factors.

Hotels could forecast increases or decreases in bookings based on certain customer demographics and adjust prices accordingly. For example, students and elderly customers might pay less than people staying at the hotel for business. Holidays may also affect the price for each of these segments. Perhaps students would end up paying more than non-student customers on certain holidays because demographic data shows they’re willing to do so.

After taking Pace’s price suggestion into consideration, management can finalize a price and post it to the front desk and to hotel booking sites such as Booking.com and Expedia.

Pace has not released any videos demonstrating their software. However, CEO Jens Munch states that the application’s algorithms update every 6 hours, which leads us to believe that Pace may not require a team of data scientists to effectively use, but we cannot verify this.

At the moment, the company’s clients include small hotels such as Hunger Wall Residence, TelePort Hotels, Smart Hotels, and Star Lodge Hotels. Pace does not mention any larger clients on their website.

Artur Matos is the CTO at Pace, holding a Doctorate degree in Computer Science from the Nagoya University, where he conducted research in genetic programming, genetic algorithms, and other evolutionary computation techniques.

Perfect Price

Perfect price is an AI-powered dynamic pricing solution that claims to enable companies, such as car rental companies, to do dynamic pricing.

According to Shartsis, Perfect Price calculates pricing based on microsegments to better fit price with demand. Traditionally, car rental companies segment based on time of day, boosting morning prices to match business travelers who are assumed to be more willing to pay. With Perfect Price, every car class and location is set as its own microsegment, and it can separate days into 24 microsegments.

Shartsis claims the application is able to determine if a certain car shows higher demand in a specific area and at a specific time of day, resulting in surge pricing while not affecting other car classes. Shartsis further claims that the system’s machine learning algorithm is able to determine this pattern for one location and one car class out of millions of combinations. With automated pricing capabilities, the system could need minimal human oversight, he added. How exactly the software does this does not seem to be publically available.

The application’s predictive capabilities could enable businesses to conduct pricing simulations that test if customers would prefer to ride new car models over older models. It could also test promotions and price changes on whether or not they affect revenue.

Perfect Price does not provide any video demonstration of their application, but the company makes available some case studies. In one study, the Betabrand crowdfunded clothing community reports that it needed to reflect new prices on its products, but did not know how to test it. They turned to Perfect Price, which used the e-commerce edition of its application to experiment with new pricing and promotion.

Because Betabrand usually uses Google Tag Manager (GTM), Perfect Price worked to connect the two systems, which took half a day. Using historical data from GTM and Perfect Price’s own data allowed the system to recommend new prices. According to the case study, the system recommended product price increases averaging 25%.

Perfect Price does not mention any larger clients on its website.

CTO and Co-founder Youngin Shin is responsible for data mining and machine learning within the company. He earned his PhD in Computer Science from the University of Texas in Austin and has worked at Twitter, Microsoft, and the Walt Disney Company.

Aggregate Pricing Data

Incompetitor

Incompetiror, developed by Intelligence Node, is a retail product index that gives the user access to competitors’ catalogues and pricing, allowing the user to use those prices as a benchmark for their own pricing structure. The company claims that the software uses machine learning to track more than 1 billion unique products across more than 130,000 brands over 1,100 categories.

Based on the application’s product index, the company claims the application allows businesses to view and compare their competitor’s products to determine if theirs are overpriced, about equal, or underpriced.

To use the application, the user inputs their business website into the search engine, chooses the industry to which the business belongs, and lists their top 5 competitors. Incompetitor then finds and presents the users with the pricing information of the user company’s top 5 competitors. The data is presented by price range and product performance, allowing the user to see trends and competitor activity.

The 2-minute video below, found on Incompetitor’s homepage, demonstrates how Incompetitor enables users to view the competitor’s catalog and show changes, such as added, removed, and out-of-stock products, as well as monitor product and category launches and stocking trends. The customizable dashboard also provides relevant statistics, such as the competitors’ ranking, stock counts, price and discounts for any product. The user can also drill down to view more detailed data at a subcategory and product attribute level.

E-commerce businesses with this information could spot what is currently popular in their market and know exactly which products shoppers want. They could access rich market data down to individual SKUs, product attributes, categories, and brands and gain visibility into product catalogs.

They could even pinpoint hot categories and products for timely promotions to increase sales. Additionally, knowing what shoppers are buying could help businesses adapt their merchandise and prevent out-of-stocks or excess inventory.

The company recently released a case study of a home and decor retail client, which studied the difference between the competitor’s catalogs at its online and physical stores. Intelligence Node claims that the Incompetitor application allowed the client to compare similar and exact product matches, view detailed product attributes, and improve its own product assortment and pricing.

After comparing the client’s online and in-store assortment, the client decided to increase its assortment of 2-seater fabric sofas by 1.5 times. In the dining table category, the client increased prices by 2.5% while still staying competitive compared with similar competitors’ products. Intelligence Node claims that these decisions, guided by results of the application, helped the client improve margins. It should be noted, however, that the client’s business name was not revealed and we caution readers to view the information with some reservation given the lack of transparency.

The company also lists Unilever, Walmart, and Jockey as its clients.

Leading the AI initiatives within the company is CTO and co-founder Slavcho Ivanov, who earned his Master’s degree in AI from the Sofia University St. Kliment Ohridski.

Wise Athena

Wise Athena is a software for consumer packaged goods (CPG) that helps companies determine the best pricing for their products and trade promotion decisions. The company claims its software calculates pricing based on econometric science and predictive AI.

Wise Athena also claims to have the ability to automatically select a product’s data attributes or specifications, and it computes for loss in sales volume, as well as the revenue or market share of a product when the same company launches a new product. It also computes for potential change in the product’s demand when the price for another product changes, as well as competition, leading price, and total sales. Wise Athena reports that their system updates its machine learning models monthly to maintain or increase prediction accuracy.

In the 4-minute video below, Wise Athena shows how the application computes predictions and optimizations. Users can include as many data categories as needed to accurately predict prices. The application also predicts volume and margins and can be navigated up to the SKU level.

Wise Athena claims to be able to analyze all of a CPG company’s different products so they can optimize prices and trade promotions to maximize revenue.

According to Wise Athena, CPG companies could optimize as many products as needed. In a case study, Wise Athena assisted a cleaning supplies client in Latin America with pricing 200 to 300 unique product SKUs. The SKUs were priced differently based on their category, fragrance, use, assigned store location, and what sales are currently active at that store. The identity of the client was not revealed, so we caution readers to accept the information with reservation.

Wise Athena claims to be able to predict the volumes and margins that each price point would generate with 98% accuracy and claims that businesses can start to see results in about four weeks.

The company also lists Beiersdorf and Heinz as its clients.

Ana Montoro Rosado, Chief Data Scientist, was responsible for machine learning and econometrics for pricing and trade promotions optimization for more than two years prior to working at Wise Athena She holds a Master of Science degree in information technology and communications engineering from the Universidad Rey Juan Carlos.

According to the Company’s LinkedIn profile, at least 5 data scientists are employed at Wise Athena.

Navetti PricePoint

Navetti PricePoint claims their software controls, manages, and measures pricing using machine learning and analytics methodologies across the entire pricing process, CEO Andreas Westling said.

Now on its fourth version, the application consists of modules that group similar products, optimize local list prices from sales companies, dealers, and distributors, and takes into account incentives, discounts, and payment terms. The company claims that these modules help the businesses optimize prices.

The software also offers templates that can be used to generate reports that a marketing team can use to make decisions.

Navetti has marketing videos available, but they do not seem to have a demonstration of their software.

Navetti claims that it helped Electrolux increase profitability by developing a spare parts pricing structure. Prior to implementing Navetti, Electrolux’s local prices were reportedly set by 29 pricing principals with no common strategy in each market. The price discrepancies generated complaints from customers and exposed the client to trade risks. The company needed to establish a common understanding of consumer pricing across markets.

The first step was to classify 55,000 items into 740 families. In the PricePoint application, the spare parts documentation team enters the cost as a reference, categorizes it and sets the value drivers. The new part displays on the dashboard of the central pricing team, who confirms the target price and distributes it to all markets. The case study claims that one entry into the system generates the correct value-based price for each of the 29 markets.

Since the implementation of value-based pricing and the PricePoint software, Electrolux’s net sales and profitability reportedly increased by 6.5% in the first year and 5.4% the following year.

Navetti also claims motorcycle company Pierce used PricePoint to optimize prices across product categories, markets, and competitive situations. The client also used PricePoint to simulate pricing scenarios, although did not specify how the application helped the company achieve better revenues. In the same video, however, Navetti claims its application can help clients achieve a 30% or more increase in annual sales revenue.

Among Navetti’s other global clients are Airbus, Tetra Pak, Olympus, Kia, and General Electric Healthcare.

Navetti was recently acquired by Vendavo, a provider of cloud-based commercial software for sales and pricing. We could not find evidence of any C-level executives on the team with a background in AI.

Concluding Thoughts

Dynamic pricing algorithms consider factors such as competitors’ pricing, consumer behavior, location, time of day, and seasonality to determine how much shoppers are willing to pay for a product or service.

Based on our research, industries that are likely to use AI-driven pricing software include consumer goods, fashion, hospitality, and transportation.

Of concern is the fact that many of the dynamic pricing companies we found were not transparent about how users interact with their software; demonstration videos and similar explanations were largely unavailable. This may be due to the fact that the software could require teams of data scientists to operate, and that isn’t an easy sell to businesses looking at dynamic pricing options.

In addition, many companies made available case studies that did not name the client that supposedly used the software to achieve success. This may be due to the “sensitive” nature of determining competitive prices, but it may also reveal the dubious nature of the case studies themselves. We caution readers to question why a company may not provide transparent case studies before they buy from them. Machine learning systems can often take a long time to integrate, and companies are not likely to make that clear.

Of the companies covered in this report, Navetti has the largest client list, and Wise Athena seems to have the most robust AI science behind it, employing 5 data scientists on their team.

 

Header Image Credit: RVAnews.com

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