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In-depth Guide to Price Intelligence for Profitable Growth in 2024

E-commerce enabled buyers to reach prices from multiple providers in real time. This forces e-commerce players to provide competitive prices to win new customers. However, competitive prices also impact profitability. Therefore, e-commerce players need to optimize their prices to manage this trade-off successfully and grow in a sustainable manner.

Price intelligence is a data-driven strategy used by firms to monitor their competitor’s prices and adjust their own prices in response to maintain a competitive advantage. In this article, we’ll explore price intelligence and how it can help businesses.

Process of price intelligence

Price intelligence involves four major strategies:

1)  Identify competitors

Knowing your business’ competitors is beneficial for not only price optimization but also learning from their strategies. Competitors are businesses that serve your target market. They can include:

  • Online or physical businesses
  • Businesses that sell perfect substitutes or similar products

2)  Identify pricing dynamics

Pricing dynamics are important to understand to optimize pricing levels. Factors include:

  • How your customers are likely to react to price changes (i.e. price responsiveness)
  • Relationship between different products. Some products are complementary and their prices may be correlated.

3)  Data extraction

  • Once the list of competitors has been finalized, the next step is to extract all their pricing data. This is often done using algorithms or with data collection services.
  • Data collected should be updated in real time because prices are often changing and missing on these opportunities to strategize your pricing can affect your profitability.
  • Collect data on price responsiveness and on any promotions or discounts being offered by your competitors, such as free shipping.
  • Capturing the market’s reaction to these changes can help you in deciding how the same market would react if you made similar changes.
  • You can only be successful if you also gather all the data and traffic on your own website as well to keep a check on preferences of your customers and build a perceived value of your product.

4) Pricing Analysis

  • Make sure to also keep historical data and look for any trends in your competitor’s data as this would help you determine which pricing technique to use at which time. For instance, some firms often offer special discounts during Christmas.
  • Machine learning models can use these trends in the data to learn the pattern and perform prediction analysis. It can use new information to predict new demands or any changes in trends which can help in setting prices.
  • The price data collected can be used to determine the initial price range of a product. Within this range, firms can also find the best and the worst prices for their products. They can continue to revise the prices until the market responds favourably. 

Benefits of price intelligence

Price intelligence can help businesses achieve maximized profitable growth when used with other pricing and automation techniques, such as:

  • Automated Reporting Functions: Keeping an eye on all the changes in data in real time can be tedious, very demanding, and also slow firms’ response time. Automated reporting functions can be built into the price monitoring system to alert the firms of any changes in their competitors’ prices and promotional activities.
  • Tier-Based Pricing: For better customer experience or to extract the most consumer surplus from the market, firms can use tier-based pricing. This is done by dividing the market into segments, each with a slightly different variant of the product and different prices. If these tiers are priced intelligently, it may enable customers to substitute away from competitors who have a volume-based single pricing rule.
  • Dynamic Pricing: Also referred to as algorithmic pricing, this type of pricing is fully automated where the algorithm places an optimal price on the product, taking into account data on the competitors’ prices, stocks and market trends. This can be beneficial if the firm is dealing with hundreds or thousands of products on a daily basis. For instance, Amazon changes its products’ prices every ten minutes which has helped its profits by 25%.
  • Price Elasticity: Price elasticity of demand refers to the responsiveness in the quantity demanded by customers when there is a small change in price. Often, products with close and high-quality substitutes are more price elastic than products which are either necessities or have fewer substitutes as it increases consumer dependency on them. Price intelligence can help firms figure out the responsiveness to their products as compared to that of their competitors and enable them to price accordingly.

Since price intelligence involves extracting data, which is publicly available on businesses’ websites, it is legal in most countries as long as the data is not used to violate someone’s privacy. However, companies should be careful about how they use the data for pricing as there are some forms which break the anti-trust laws of most countries, such as those discussed below:

  • Predatory Pricing: This method involves reducing pricing lower than your competitors to an extent that it drives other firms outside of the market. Because this method reduces competition and enables the formation of a monopoly, it is illegal and hence care needs to be taken when pricing products lower than the competitors.


Source: University of British Columbia (UBC)

  • Unlawful Price Discrimination: Though price discrimination is mostly legal, in some cases it violates the anti-trust laws and hence care needs to be taken. If you provide a different price to only a certain customer (or a set of customers) without any promotions or discounting policies, when no such discount is being offered at the same time to all the customers, you are discriminating unlawfully and breaking the law.
  • Dumping: This technique is when firms export their products and sell them at a lower price in the international market than the price in their domestic markets. Officials at times consider this a case of predatory pricing. It is illegal under WTO rules if it harms the producers in the exported market.

Case studies

Amazon

Amazon uses big data to intelligently price its products. It changes its product prices 2.5 million times a day through analysis of customers’ shopping data, competitor’s data and its stocks and inventory. It is also reported that Amazon increases prices of certain lesser-known products to artificially make essentials and commonly used products appear cheaper and attract customers.

Walmart

Walmart is known for its low prices. It also uses real time data to improve its pricing and sales. It has been reported that Walmart used real-time data to improve efficiency in its meat aisle which increased sales by 30%.

Further Reading:

If you have other questions about price intelligence, we can help:

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This article was drafted by former AIMultiple industry analyst Rijja Younus.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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