July 8, 2021

Machine Learning Helps Companies Optimize Prices

Knowing the best price for a product or service can be hard to judge. It takes a thorough examination of many factors, such as historical demand and competition. To find the optimal price, you must really understand how your customers will react to a price change.

What is pricing optimization?

Pricing optimization is the process by which the ideal price point for an item or service is chosen. By using market and consumer data, a balance can be achieved between value and profit.

How does price elasticity of demand affect price optimization?

Price elasticity is a measurement for changes in product or service demand versus changes in price.

Price elasticity of demand = % change in quantity demanded / % change in price

If demand stays the same when the price changes, the product or service is considered inelastic. However, if the demand decreases after a price change, it’s then considered elastic.

Inelastic products or services are staple items, such as bread and gas. Elastic products or services are generally luxuries or desired items, such as clothes or jewelry. Shifting prices can affect demand for luxury items and prices should be carefully considered.

What are some use cases for price optimization?

  • Optimize prices.
  • Know what price to use to best meet consumer expectations in response to supply and demand.
  • Monetize unused pricing power.
  • Change inelastic SKUs with the Point of Sale Price Elasticity Model to calculate the best prices.
  • Grow margins with price increases.
  • By analyzing demographics and competitors, know how much and when to increase prices.

What is the best approach to optimize prices?

Machine learning can help companies accurately pinpoint the right price based on many data points. The price recommendations are very precise. Such as they can tell you what to price each SKU (Stock Keeping Unit), not just a general category.

“Setting optimal prices is one of the most difficult tasks even for retail veterans. With the continuous digitalization of our world, data is becoming more available and the number of relevant pricing factors is increasing. People, contrary to machines, are slower, prone to making errors, and simply lack capabilities to consider all factors at once. This calls for the next logical step — automated, ML-driven price optimization tools.”[1]

ElectrifAi’s pre-built machine learning model, Pricing Optimization, has proven to be very effective at recommending the most accurate prices. The model uses a three-pronged approach to optimize prices and monetize unused pricing power. It does so by identifying inelastic SKUs and transactional data to grow margins with price increases.

What are some technical highlights of the Pricing Optimization model?

  • Point of Sale Price Elasticity model calculates price sensitivity of each store, department, and SKU as necessary.
  • Customer Behavior Analysis analyzes transaction-level basket contents/size and shopping patterns.
  • Demographics and Competitor Intensity Analysis linked with account sales/margins.
  • Multidimensional Clustering to generate price recommendations at account, category, and SKU levels based on price sensitivity modeling.

What data sources and features are used in the Pricing Optimization model?

  • The continuous pricing recommender leverages sparse data to shed light on previously unknown patterns in customer demand, price elasticity, promotion sensitivity, demographics, and competitor activity.
  • The model calculates price sensitivity of each store, department, and SKU based on customer spend, promotions used, items bought, departments shopped, etc.
  • The model recommends prices based on recent historical patterns, competition, product groups, seasonality.
  • Based on the model price sensitivity, stores eligible for price increase are selected and recommendations are calculated at the SKU, department, or store level by comparing to other anchor stores within the cluster.

What data is used to run the Pricing Optimization model?

  • Customer data
  • Point of Sale records
  • Loyalty database
  • Marketing efforts
  • Product hierarchy
  • Promotion usage
  • Store data
  • Store attributes
  • Customer service
  • External data
  • Geo-economics
  • Competitor intensity
  • Demographics data

How does all this data tie together?

The Pricing Optimization model outputs recommendations on which SKUs to increase price versus reduce price at each store. This makes it easier to precisely target the right price without having to manually dig through thousands of data points.

Has the Pricing Optimization model been used in the real world?

ElectrifAi’s pre-built Pricing Optimization machine learning model has been extremely effective at solving real business problems. Previous usage of the model achieved:

  • $50 million incremental annual margin achieved.
  • Quantitatively rigorous, scalable pricing procedure implemented.
  • Gained insight into how customers react to price changes.
  • Acquired deeper understanding into customer shopping behavior and the local business environment for each store, which can inform other business decisions.

What’s the next step to take?

Decide how you would like to proceed to gain the benefits of machine learning for your business. You could start your own data science team to build a machine learning model from scratch. But that takes a lot of money and time that could otherwise be strategically used elsewhere in the business.

And why put money and time towards something that may not work? After months or even years, the model may fail to produce actionable insights.

ElectrifAi’s pre-built machine learning models, however, have been used in the real world and can quickly produce results. Start optimizing your prices now rather than waiting for a solution to appear.

If you are ready to begin your machine learning journey, contact us for a custom demo today!

[1] Koptelov, A. (2020, July 13). Pricing Optimization With Machine Learning: Is it Worth it? https://www.mytotalretail.com/article/pricing-optimization-with-machine-learning-is-it-worth-it/.

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