Pricing Glossary

Pricing science

Pricing strategy

Pricing strategy refers to how a company uses pricing to achieve its strategic goals, such as offering lower prices to increase sales volume or higher prices to decrease backlog. Despite some degree of difference, pricing policy and strategy tend to overlap, and the different approaches and methods are not mutually exclusive.

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• Pricing policy: Pricing policy refers to how a company sets the prices of its products and services based on costs, value, demand, and competition.
• Discrimination: price discrimination is when a retailer sells certain products for a lower price during intense demand from price-sensitive customers and increases the price during times of high demand to sell to price-insensitive customers.
• Coupons/vouchers: coupons or vouchers are code-based, one-time discounts provided by retailers to customers, usually on a segmented basis.

Price setting

The act of determining and assigning prices to specific products, categories, or assortments.

Price optimization

Static pricing

• A price set by a retailer and not changed at any point in the product’s lifecycle.

Dynamic pricing

• Dynamic pricing is a method whereby retailers continuously and (semi-)automatically adjust the prices of their products to match market demand to increase sales opportunities and optimize profits. In other words, variable prices are used instead of fixed prices.
• Pricing solution software evaluates relevant factors like demand, inventory, and competitors’ prices to gauge the optimal price for a given product. Based on the retailer’s pricing strategy, the algorithm adjusts product prices to increase and/or maximize sales and profits.

Rules-based pricing

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• Competition-based pricing is a form of rule-based pricing, which is about matching or beating a competitor’s price for any given item.
• Cost plus pricing: cost-plus pricing is the simplest method of determining price and embodies the basic idea behind doing business. You make something and sell it for more than you spent making it (because you’ve added value by providing the product). Many companies use cost-plus pricing as their primary pricing strategy when releasing products.

Machine learning-based pricing

• Advanced pricing solutions use machine learning algorithms to determine optimal prices. These can measure customers’ willingness to pay using price elasticity for any given product. Combined with forecasting algorithms, machine learning algorithms can forecast the price-demand curve of each item. Based on this, companies can automatically steer prices toward their KPIs (business objectives). As pricing reacts dynamically and automatically to a change in customer behavior, discounts can be applied in a more differentiated and intelligent way.
• Data from past sales activities are evaluated to calculate the price elasticity of products. When calculating optimal prices, the software considers sales and profits and all relevant influencing parameters – from competitors’ prices to weather and seasonality. It can also simulate different pricing scenarios and forecast revenue, sales, and profit results for specific targets. Advanced pricing software can affect different strategies for selected categories where, for example, a quick sale is desired.

Predictive pricing

• The new standard in pricing technology is machine learning-based pricing methods, which use advanced algorithms that learn from their results in a (semi-)automated way. These solutions can improve over time, using their continuous learning to find the optimal price points set against a business’ targets.
• Another advantage of machine learning-based pricing applications is that they can consider internal and external data by their algorithms. They can also process more extensive and heterogeneous datasets than previously, more limited technologies.
• The latest generation of advanced dynamic pricing technology, predictive pricing, is further strengthened by its robust forecasting and optimization algorithms. With these, this technology derives even more value from the existing data.

Price communication

How a retailer communicates its prices to consumers (either through the method or the value shared).

Demand forecasting

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• Demand forecasting, a part of predictive analytics, aims to improve business management while meeting critical KPIs by using this data to understand and predict customer demand. By relying on historical sales data, the latest statistical techniques, and algorithms, it is possible to use demand forecasting to estimate future sales.
• As a result, companies can accurately define future service levels and adjust their prices to meet their targets in line with profit and revenue.

Price elasticity

• Price elasticity is an essential factor in determining optimal prices. Price elasticity measures how demand for a product changes after a price adjustment. Price elasticity can be calculated with a mathematical formula to produce a demand function, represented as a demand curve, which shows how often a product is sold at what price. At the same time, the demand function (or demand curve) can be used to determine how the demand for an item changes when the price is adjusted. Accordingly, it is a matter of the price elasticity of demand.
• Most customers and most markets are sensitive to price changes. A price increase usually leads to a decrease in demand because customers do not want to spend more money on a product or service. A price decrease, on the other hand, usually leads to an increase in demand. This demand is called price elasticity because it can fluctuate depending on the price.

Data and influencing factors

Data points used to determine customer willingness to spend, demand, and general help create optimal prices. Data utilized may include:

Internal Data

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• Inventory Levels: This is essential data regarding details about current inventory levels and overall supply. The existing supply is combined (via inventory tracking) with the existing demand, which is a key determinant in how a dynamic pricing software calculates optimal prices in line with the market
• Transactional Data: this includes all transactions, units sold, price history, and conversions. This also provides buyer information and manufacturing or sourcing costs. Any machine learning-based dynamic pricing software will need a company’s sales and transaction data to calculate the demand for each product in your range. This forms the basis for every price decision of the AI. At the minimum, all sales information is necessary, e.g., which items were sold at what price.

External Data

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• Competitor Data: Competitor data can be far-reaching but may include list price, ship price, buy-box price, FBA, out of stocks, geography, and product reviews and ratings. This data can be gathered by crawling (also called ‘scraping’) software which collects the information from publicly available sources. Businesses are becoming increasingly sophisticated in trying to limit the ability of their competitors to gather this data.
• Days of the Week: Days of the week influence consumer demand. Depending on a company’s business model, they will likely see sales go up or down depending on what day it is. Perhaps they’re more of a weekday business or a weekend business. A dynamic pricing strategy can take this data and set prices to increase or decrease over the weekend based on demand for those specific days. Price optimization solutions allow businesses to create custom timeframes for accurately implementing one-time, ongoing, or limited-time price changes.
• Holidays: Upcoming holidays will increase demand for specific items, for example, wrapping paper in advance of Christmas or flowers on Mother’s Day. By utilizing historical transaction data plotted against holiday seasons, retailers can pinpoint what items in their assortment show an increased demand and when. This data helps a dynamic pricing algorithm forecast demand and set an optimal price for those individual items.
• Regional Trends: E-commerce-based retail has the advantage of being able to reach a much wider audience via online-based platforms and marketing. However, localized factors and conditions may influence demand from different regional or geographic segments depending on what is occurring in their area. For instance, one region may celebrate a festival or event, driving demand for specific products. Using data to measure these regional variances can help create pricing strategies and distinctions down to the regional level if desired.
• Weather and Seasonal Data: Weather can affect sales, both as a broader pattern as well as of certain products. For example, good weather is bad for online retail, while people will stay home and shop online if the weather outside is not pleasant. From a product perspective, when the temperature increases, consumers will start searching for standing fans and are more likely to buy them (e.g., higher probability of conversion). Another case in which weather data could be valuable is as winter approaches. When the temperature decreases, the search volume for skis will increase. Temperature and weather forecast data can help predict demand and optimize prices accordingly.

Retail pricing 

E-commerce

E-commerce is a retail based via online channels, such as a website or other online retail platforms.

Omnichannel

Omnichannel is retail that spans both online/e-commerce and stationary/physical locations.

B2B: business to business retail

Unlike consumer goods, this may be specialized products, parts, components, software, or other goods and services which are sold between businesses, not direct to consumers.

DTC or direct-to-consumer retail

A brand markets its products and sells them via its channels, not third-party retailers. Manufacturing may or may not be done by a third party.

Private label

Private label products are manufactured by a third party, then branded by a retailer, and sold under its label and via its retail channels.

Technology Leadership

7Learnings bolsters retailers’ business operations with industry-leading price optimization tools that give teams the insights they need to make better, more impactful business decisions.

Machine Learning

• Deep learning: In deep learning, computers learn via neural networks. The systems do not need to be trained by humans or be trained less intensively. Deep learning systems can also process more significant amounts of data.
• Unlike traditional machine learning, deep learning systems can process unstructured data, i.e., speech and non-categorized images, text, and video. The barrier to entry for deep learning is thus lower, and the time required to maintain the system is much less. At the same time, the quality of predictions and recommendations is much more precise and accurate than in traditional ML systems.
• While machine learning delivers good results in pattern recognition, deep learning plays to its strengths in predictions and recommendations for action. This is also a focus of AI research. After all, smart applications that prevent people from making wrong decisions and support them in making better strategic decisions offer more excellent added value than simple AI data evaluation.

Artificial Intelligence

Artificial Intelligence, or AI for short, is not uniformly defined. Since artificial intelligence is an interdisciplinary research field of computer science, cognitive science, and engineering, definitions of AI vary depending on the field. The following general definition has proven helpful for practical application: Artificial intelligence refers to computers, robots, or other machines that mimic human intelligence and exhibit intelligent behaviors.

In commerce, AI is used for applications such as supporting management decisions, predictive maintenance of machines, chatbots in customer support, optimization of delivery routes, price optimization, and more.

Machine learning involves developing algorithms to solve specific problems and feeding them with huge data sets. The larger the database, the better the systems recognize patterns and the more precise results they can deliver or the more accurate recommendations and predictions they can make.

Typically, the systems need to be trained by humans to learn when they have mismatched/evaluated data and to improve their efficiency. In some cases, the systems also learn from the results they produce and adapt their algorithm themselves. Typical use cases include image recognition or automated stock buying for portfolio optimization.

The distinction between AI, machine learning, and deep learning

• Artificial intelligence describes all technologies that allow systems to act intelligently. Simple methods such as if-then rules or decision trees can be used, but also more complex methods such as machine learning algorithms or deep learning.
• In machine learning, IT systems are programmed with the help of large amounts of sample data or empirical values to carry out a specific process better and better because they optimize their decision criteria.
• Deep learning, in turn, is a form of machine learning in which computers learn independently from their experience with the help of artificial neural networks.

Optimization technology

• Rules-based dynamic pricing: In rule-based pricing, retailers store individual pricing rules in an application to ensure profit margins. Usually, these rules are formulated according to the following scheme: if X happens, adjust the price by Y or keep the price at level X compared to variable Y. The application crawls data sources several times a day (e.g., Google Shopping, comparison platforms, competitors’ online stores) to change prices if the market data should require it.
• Rule-based price optimization quickly becomes very complex. The more extensive the data range and the more numerous the rules, the more difficult it is for managers to keep track of whether the rules are still fundamentally correct or whether the instructions contradict or block each other. This is all the more true because many companies still manage their rules in Excel and do not use user-friendly software.
• Machine learning-based dynamic pricing: Machine learning-based pricing tools use algorithms that learn from their results in a (semi-)automated way. These applications improve over time at the optimal price point for the business, finding the sweet spot between “too cheap” and “too expensive.” The latest machine learning applications consider internal and external data in their algorithms. They can process much larger and more heterogeneous datasets than previous technologies.
• Machine learning algorithms calculate price elasticity, measuring how demand will change due to changes in general conditions. The software then adjusts the prices in the online store accordingly. These tools can also determine which products are remarkably stable in terms of demand, making them suitable for margin optimization, or which play a critical role in overall sales and should only be adjusted cautiously.
• Based on price elasticity, machine learning algorithms provide recommendations for price optimization. Retailers do not need to set any rules, and there is no need for regular manual monitoring. The application recognizes which factors are relevant and in what form and changes its price rules accordingly. As a result, companies can make the most of their profit potential.
• Dynamic pricing software: software, often machine learning based, aids in implementing dynamic price management.
• Intelligent algorithms: these algorithms learn on training data to predict the price effect on sales, revenue, and profit. Based on that forecast, one can run optimizations to reach business targets. This algorithm and its logic of prediction are not explicitly programmed. The algorithm continuously learns from new data.
• Predictive pricing: 7Learnings takes its price optimization solution one step further. We use price elasticity and forecasting algorithms to predict the effect of price changes on KPIs defined by the business. Managers can control prices for the entire assortment intuitively and based on targets. We call this predictive approach pricing.
• Forecasting + forecasting algorithms:  With predictive pricing, online retailers benefit from all the advantages of machine learning, supplemented by accurate forecasts. This is because the applications evaluate current data and anticipate its development so that price adjustments are made earlier than with other available methods. Whereas such future scenarios and their effects previously had to be simulated manually, the management effort of doing so is now reduced, while the quality of the price optimization increases.
• However, users do not have to rely blindly on the algorithm’s price recommendations, as they always retain control. Managers can vary their targets and simulate how resulting price adjustments will affect all relevant KPIs. Completely manual price optimizations also remain possible.
• Goal-driven price steering (target steering): Predictive pricing turns the previous process of pricing around: retailers no longer need to set up a complex set of rules for pricing in hopes of optimizing their KPIs. Instead, they define their goals, and the technology determines which prices will help them achieve them as quickly as possible.

Simplicity 

7Learnings supports our clients with simple, intuitive solutions that save them time and effort, streamlining their business operations and helping them grow and profit. Simplicity is the result of the combination of our expertise and technology leadership.

Automation

Automation is a term for technology applications where human input is minimized. This includes business process automation (BPA), IT automation, personal applications such as home automation, and more.

The most complex level of automation is artificial intelligence (AI) automation. Adding AI means that machines can “learn” and make decisions based on past situations they have encountered and analyzed. For example, virtual assistants can reduce costs in customer service while empowering customers and human agents, creating an optimal customer service experience.

Benefits

• Higher profitability: Our price optimization software increases retail revenues and profits by up to 10%. See tangible benefits in a matter of weeks – not months or years.
• The highest level of automation: Our intelligent software takes the guesswork out of pricing and reduces manual effort. With a few clicks, you can forecast the impact of pricing changes on business goals. It’s the most intuitive price steering method!
• Intuitive target steering: Set your revenue or profit target to optimize pricing using our software. Our software calculates the prices that will maximize your business goals within minutes. Using our platform, you can easily adjust for different business scenarios to assess the impact of different targets. Price setting has never been more straightforward or intuitive.
• Forecast pricing impact: Gain control and security by forecasting pricing decisions before they go live, running different pricing scenarios, and refining your retail pricing strategy.
• Cross-optimize marketing activities: 7Learnings’ innovative technology doesn’t stop at price optimization – it can also optimize discounts and SEA spending for you. This allows for powerful cross-marketing optimization capabilities. These tools allow you to gauge what strategy supports the most growth and profitability: increasing or reducing your discounts, coupon rates, and SEA spending.
• Work with industry experts: Our data scientists have vast experience in retail pricing and understand the unique challenges and needs of the industry.