Dynamic pricing disruption: how to stand out in the trend

Whether your company is an e-commerce retailer, a brick-and-mortar retailer, or a manufacturer with a direct-to-consumer strategy dynamic pricing can help your business to be more responsive in ever-changing market conditions.

If your company is not leveraging dynamic price optimization, there is a high chance it’s losing out on better margins, higher profits and better deals to your customers against the competition.

There are several reasons why dynamic pricing is no longer a wish but a necessity:

• Customers tend to compare prices more frequently, with popular price comparison tools
• Leading marketplaces and tech-savvy retailers already use dynamic pricing. A good example of that is Amazon, which changes its prices every 10 minutes, resulting in over 2.5 million product price changes a day
• Customer expectations are rising and customers want to be rewarded for loyalty with personalized offers
• Massive amounts of data is generated from internal and external sources, and companies not leveraging this data risk being outcompeted

This article will help you to understand what dynamic pricing is, how it works and how your company can leverage dynamic pricing to drive growth and achieve operational excellence.

What is dynamic pricing?

Dynamic pricing is a process by which companies adjust their prices in real-time based on the current supply and demand in the market, inventory levels, and competition dynamics. Dynamic pricing works by maximizing expected revenue or profit for any given product.

The basic idea behind it is that during high-demand, low-competition and low-stock periods prices can be raised, while low-demand, high-competition and high-stock periods call for a reduction in price. Static pricing is the opposite of that:

What is the goal of dynamic pricing?

At it’s core, the goal of dynamic pricing is to ensure that the company is able to respond to current and expected market dynamics with a competitive and attractive offer to the customer. However, the strategy of the pricing solution can be tailored to your business objective: market penetration, staying price leader or successfully launching new products.

What data do I need to make it work?

The two key ingredients to a successful implementation of dynamic pricing solution is well-defined pricing strategy and high quality, high quantity data. A list of relevant data includes:

Internal data

• Transactions data: what products were sold at what price in the past. This is a key ingredient to identify the basic relationship between prices and sales. For online retailers, this can further include online shopping behaviour
• Inventory data: current & expected inventory levels to estimate the supply side
• Product data: price-related attributes such as cost, base price, and minimum advertising price. This data is used for setting boundaries of price optimization. It can also include non-pricing attributes such as color, size, stock level, expiration date or target sales date
• Additional internal data: promotion periods, discount policies

External data

• External competitor data: prices of competitors for similar products, public reviews and ratings of their products
• Seasonality data: this includes holidays in a specific country/region
• Market trends: global, regional and local trends that have an impact on the demand or supply of the product

How does dynamic pricing work?

There are multiple approaches to dynamic pricing:

• Rule-based: business rules are used to come up with a price (e.g. price 15% above cost). It doesn’t always take into account market dynamics
• Demand based: estimating demand response to price change. Demand elasticity is estimated and used to suggest an optimal price. It’s a more data-driven approach but may be isolated from other factors
• Competition based: this is when price is matched or set to undercut key competitor prices. This approach is more responsive in nature.
• Price optimization: this approach takes multiple pricing drivers into account: historical and forecasted demand, price elasticity, competition dynamics, stock levels, and uses optimization techniques to come up with an optimal price suggestion

ML based price optimization approach consists of the following steps:

• Historical data on sales and prices is used to identify dependencies and build a demand function
• An effect  of change in price on sales is predicted using identified dependencies
• Then, price is further optimized using taking additional pricing and non-pricing factors into account
• Algorithm is continuously re-run with new data to adapt to reality

Below is an example of end-to-end dynamic pricing with several sub-modules:

We can use ML to handle special cases like:

• New products: pricing products that have a short sales history or no history at all. Machine learning identifies similar products in a company or competitors assortment and suggests a price, based on key product characteristics
• Identifying Key Value Items (KVIs): consumers base their perception of the brand by comparing a few key products in the assortment. Machine learning helps to identify highest impact products from all the products sold. Those products should be priced optimially
• Personalized offers: based on shopping history ML helps to give customers targeted offers at special prices to lower churn and increase customer loyalty. This is specifically related to promotional pricing

What are the benefits of dynamic pricing?

There are two types of benefits that your company can get from dynamic pricing:

• efficiencies arising from automation of costly and time-consuming manual pricing
• increases in key KPIs arising due to smarter pricing

These include:

• Increase in profit margins
• Better competitive positioning/ market share
• Higher profits
• Lower churn
• Better consumer experience
• More efficient use of inventory
• Increasing customer loyalty

What are the steps to take to implement?

To drive benefits from dynamic pricing, you can follow a 5 step approach:

1. Defining the business objective: dynamic pricing can serve multiple, sometimes opposing objectives and its important to define those at the start
2. Setting high-level pricing strategy: do you want to be known as the cheapest retailer in the market, or you want to keep sustainable margins over the long-term? Setting goals goal will inform which solutions to apply
3. Identifying the boundaries: this is a specific step, where you focus on boundaries of a dynamic pricing solution, e.g. capping frequency of price changes or capping the change range for certain strategic products
4. Collecting available data: ensuring data availability at an organization in order to perform the optimization
5. Implementation and monitoring: verifying the pricing suggestions and monitoring the impact of the pricing decisions on the business

About Agnicio

Is your company interested in leveraging dynamic pricing to achieve any of the above objectives? Do you need a partner that helps you to take right steps in advanced analytics and retail intelligence?

Connect to Agnicio team, leader in demand sensing and supply chain forecasting, to create a plan for dynamic pricing implementation at your company. Contact us here or reach out to Agnicio founder and CEO on linkedin to kick-off your data-driven journey

Keywords: dynamic pricing, advanced analytics, machine learning, demand sensing

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