Dynamic pricing your strongest profitability lever

Dynamic pricing your strongest profitability lever

Sellers used to set the price for a product or service based on a manual analysis of the cost, demand, supply or competition. Without sophisticated algorithms, two pricing strategies were common:

  • Premium Pricing: Premium pricing is where companies set the price higher than average competitive price. The key factor for the success of this strategy is differentiation. Premium pricing effectively works when the product has a unique feature that differentiates it from similar products in the market and has a competitive advantage.
  • Penetration Pricing: Penetration pricing is basically setting the price relatively lower than the market competition. Companies use this pricing strategy to raise brand awareness and increase customer loyalty. Initially, penetration pricing may cause revenue loss but the main goal of this strategy is market penetration.

Profit maximization is not always possible with both strategies. At premium price level, demand would be low. Even if you have a high demand for penetration pricing, the price will remain low.  What if you can cover all the price segments and respond faster to demand fluctuations in the market? This is possible with dynamic pricing.

What is Dynamic Pricing?

Dynamic pricing, also called surge pricing, demand pricing, real-time pricing or algorithmic pricing is where the price is flexible based on demand, supply, competition price, subsidiary product prices. Price may even change from customer to customer based on their purchase habits. Dynamic pricing enables suppliers to be more flexible and adjusts prices to be more personalized.

How Does Dynamic Pricing Work?

Dynamic prices are determined by rules based or self-improving algorithms which take into account numerous variables to set the best price for that specific product for that customer at that time. These are some of the variables used to make pricing decisions:

  • Supply
    • Stock levels
    • Current cost
    • Future cost predictions
  • Demand
  • Data about specific customer
    • Demographic data like age, gender, current location and permanent residence, income
    • Device-specific data like device brand and model. For example iPhone users tend to spend more than Android users
    • Behavioral data including
      • Customer’s spending habits: How much did customers spend in the past for similar products
      • Customer’s willingness to search for good prices
  • Competition’s prices
  • Substitute products’ prices including products sold by both company and its competitors

After knowing all of this, extensive machine training is required to build a successful dynamic pricing model.

Different Modules of Dynamic Pricing

Because of the complexity of dynamic pricing, different modules are sometimes used for different product categories and market response to manage complexity.

Image credit: McKinsey&Company

Long tail module

This module is for new products or long-tail products with little or no historical data. Main challenge for this module is to use product attributes to match products with little purchase data with products that have rich purchase data so prices can be informed by rich data.

A US retailer with more than two million range of products customized its long tail module algorithm. To build the long-tail module, company gathered a rich set of data for its 100,000 top-selling SKUs including competitor prices, data on customer behavior, product attributes and descriptions, and online metrics. Developers then worked with category managers to create attribute similarity scores and leveraged rich data of popular products to price products in the long-tail. Pilot resulted in 3% increase in both revenue and margin .

Elasticity module

Elasticity module calculates the impact of price on demand considering seasonality and cannibalization.

A leading Asian e-commerce player built an elasticity module based on a multi-factor algorithm that drew on ten terabytes of company’s transaction records. Data included product price, substitute price, promotions, inventory levels, seasonality, and competitors’ estimated sales volumes. Though price recommendations were generated real time, category managers made the final pricing decisions. Pilot led to an increase of 10% in gross margin and 3% in GMV.

Key Value Items (KVI )module

Key value items are popular items whose prices consumers tend to remember more than other items. KVI module aims to manage consumer price perception by ensuring that items that strongly impact customer’s price perception are appropriately priced.

This is important for resellers like grocery companies. Because they are not selling their own products, they need to make sure that customers see them as the lowest cost option. A leading European nonfood retailer built a sophisticated KVI module statistically scoring each item’s importance to consumer price perception on a scale of 0 to 100. This scale guided pricing decisions and company was willing to lose more on KVIs to retain and improve the customer price perception about their company.

Competitive-response module

This module leverages granular pricing data from competitors and impact of those prices on company’s customers to react to competitors prices real-time.

Though this is a relatively simple mechanism, two competitive-response modules competing with one another can create quite unexpected results like asking $23.6M for a book! Two 3rd party Amazon merchants had dynamic pricing models. While first merchant’s system aimed to sell its book at a price 27% more than the second merchant, that merchant dynamically sets its price to 1% less than the first merchant. Predictably, the price of the book skyrocketed at every iteration of the algorithms. This is why including price data in your dashboards make sense.

Omnichannel module

Companies manage prices between channels both for price discrimination and also to encourage customers to visit less costly channels. Omnichannel modules ensures that prices in different channels are coordinated.

For more details on these models you can see a McKinsey report with more examples.

What are the Benefits of Dynamic Pricing?

Dynamic pricing is the strongest profitability lever. 1% increase in prices will result in 10% improvement in profit for a business with 10% profit margin.  Machine learning based dynamic pricing systems have clear advantages when compared to manual pricing

  • More precise, SKU level prices
  • Faster response to demand fluctuations
  • Price changes take into account more factors including customer’s price perception, leading to long terms increases in sales or profits

Especially for a large company, dynamic pricing is one of the few approaches that can lead to quick results and make the responsible team heroes. We explained why dynamic pricing is so important for large companies in detail.

Where does Dynamic Pricing appear?

Airlines

Airlines are the earliest adopters of dynamic .  A ticket for the exact same flight with the same destination and at the same date can have a number of different prices for different customers. Because airline sales moved online earlier than other categories and because airlines are expected to charge different prices for the same ticket bought on different days, it was easy and acceptable for airlines to move to dynamic pricing.

e-Commerce

Retailers, especially e-commerce companies like Amazon and eBay use dynamic pricing for personalized pricing. If you consistently buy from Amazon or another e-commerce website, prices will be higher. Algorithms calculate the loyalty level of each customer and set the price lower if a person is a newcomer.

Dynamic pricing is now used for almost every product and service. From the price of a concert ticket to the price of a hotel booking is calculated by algorithms. Even Uber is using surge pricing.

Pitfalls and how to avoid them

It makes sense to have human supervision on the pricing policies.

#DeleteUber. Need I say more? Uber normally uses surge pricing to increase ride prices if number of available drivers are not enough to satisfy demand. This increases drivers’ incentives to stay on the road and allows Uber to offer a better service to its customers in terms of vehicle availability compared to traditional taxis. However, Uber riders were not happy when Uber continued its surge pricing at a time when protesters were flocking to JFK airport to protest Trump’s immigration ban.

In the 10 months since #DeleteUber started trending in Jan 2017, Uber still has not recovered its market share in numerous markets. Machines are currently blind to socio-political developments and therefore their actions can seem tone-deaf to many.

How to choose the best pricing optimization solution?

As online retailing grows, dynamic pricing became more important. Major players in the market acquired companies that specialize in algorithmic pricing or outsourced the technology to have a competitive advantage. Working with a vendor that has experience in your industry, you can set up a competitive dynamic pricing solution within weeks.

Absolute must features for pricing optimization software

  • Analyzing profitability
  • Automated price management
  • Forecasting upcoming price trends
  • Customer analysis for personalized pricing
  • Market analysis for price competition
  • Sufficiency to adapt different situations and changes

Be aware of your needs and constraints

  • Software-as-a-Service vs in-house systems: Cloud-based solutions may reduce the risk of data loss but increase the cost.
  • Compatible with existing systems: To get the best results, software should be able to integrate with existing company systems like CRM or ERP to get most accurate data.
  • Price: Some have freemium options where you have access to few features and have to pay for complete service. If you are looking for a long-term solution, subscription method would fit best.

Some dynamic pricing vendors

We have data on 2000+ vendors and here are a few dynamic pricing vendors. This is by no means a complete list.

5Analytics: Analyze each customer’s historical purchase data by applying machine learning to provide personalized prices.

Antuit: Antuit brings a level of price optimization analytics, insights and predictability to the rest of the retailing world.

Import.io: Import.io provides daily or monthly reports showing what products your competition has added or removed, pricing information including changes, and stock levels.

As appliedAI.com we provide the most comprehensive public list of pricing optimization vendors. And if you are short on time and want to work with experts who can suggest you the most suitable vendors for free, just let us know.

For more information and insight about dynamic pricing and pricing optimization solution providers, visit our website.

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