Pricing Optimization with ML
Pricing optimization is the process of pricing goods and services to meet the set objectives such as maximizing profits, getting new customers, clearing certain items, etc., by considering various pricing factors such as competitor pricing, customer demands, market conditions, and customer profiles.
- Successful price optimization is about finding the sweet spot between valuable and lucrative.
- This balance can significantly impact your sales, customer satisfaction, revenue, and achievable growth objectives.
- To conduct price optimization, first understand the customers and the business.
- Understanding the pricing strategies and concepts is also essential for setting the best price for your products.
To optimize prices, you need the following information:
- Customer survey and behavior data
- Demographic and psychographic data
- Geographical market specifics
- Historical sales data
- Operating costs
- Demond fluctuations
- Competitive advantages and concerns
- Lifetime value and churn data.
- You also need to understand and speculate on customer reactions to price changes.
- Price elasticity measures how a change in consumption of a commodity relates to a change in price.
- If demand stays the same when its price changes, the commodity is inelastic; if demand decreases when it fluctuates, it is considered elastic.
- An inelastic product or service is less sensitive to price changes, while an elastic product or service can see considerable changes in demand when prices shift.
- This information helps show how customers are likely to react to price changes.
Choosing the best price for your products and services is all about understanding why the customers choose you over your competitors, understanding what features the customers want, their values, and market and industry trends. B2B pricing is different from B2C pricing. Similarly, travel is different in pricing from retail and food markets. To come up with the best price, the following steps are essential.
- Understand and use the available data.
Both quantitative and qualitative data are necessary to carry out a good rice optimization process. The data will help figure out how much customers would be willing to pay for a given product.
- Quantitative data, in this case, includes demographic data, inventory, supply and demand, sales metrics, churn rate, product feature, and price sensitivity.
- Qualitative data entails customer surveys data. This data involves how and why the customer prefers your product over the competitors’ products, the perceived values, sales processes, loyalty program, discounts, promotions, and other information on how the customer feels about your current prices.
- Define goals and constraints.
The first objective of price optimization is always to increase the revenue earned. However, price optimization can also improve customer loyalty, upselling, and attract new customers. Other reasons for price optimizations include increasing the perceived value of your products or hitting a certain sales quota.
2. Know the value metric
It all comes to first understanding what customers value about our product. The value metrics of your product are how and what you charge for your product or service. Selling a product is more likely to be priced per unit while selling service will involve pricing based on specific features of the service.
3. Create pricing tiers
From your data, divide the customer into segments that align with the value metrics. Most subscription services offer pricing based on customer segments, with each tier providing additional features compared to the preceding tier. Ach tier is priced differently based on the additional value it provides to the customers.
4. Continuous monitoring and data collection
Have a mechanism to collect data to ensure that the products’ value aligns with the customer needs and pricing expectations. Since Price optimization is not a one-off activity, it is constantly changing and needs to consciously be optimized. Revisiting the prices now and then is critical to determine whether it is still the optimal price, and it helps accomplish the set goals. Use the collected data to reevaluate the pricing strategies. Have a price-changing strategy that does not fluctuate very often or too quickly to avoid disappointing and turning off potential customers.
Pricing strategy models.
- Pricing strategy models are methods used to set the best prices for products or services.
- Each pricing strategy has its advantages and drawbacks and where they are best applicable based on the industry or the business.
Common pricing strategy models include:
- Captive product pricing involves one-time low pricing for a product to attract a large volume of customers to a one-time purchase. However, only the main product is lowly priced, and the accessories (captives) are normally priced. The main product would require the captive item to function.
- Cost-plus pricing — setting the price based on the cost of production and profit margins. If a product’s cost of production is $10, the strategy would involve adding the revenue on top of this cost and pricing the items, say, at $15.
- Loss leader pricing entails selecting one product or service to be sold at a loss to the retailer to get customers in the door. The other items are sold at profitable prices.
- Competition pricing — setting prices as a competitive strategy to beat the competitors.
- Dynamic pricing — setting very flexible prices for items.
- Freemium pricing is a practice of offering the basic set of services for free and enhanced features for a fee.
- Value-based pricing is based on customer perception of value or the products or services offered.
- Demand pricing — this pricing strategy considers the demand fluctuations to adjust the prices of the products or services.
- Price skimming -Setting a high price for a product during the introduction and lowering it over time. Helps in recovering initial costs of a good and service before competition enters the market.
- Penetration pricing- where pricing is set low to increase product and service adoption. It helps capture a large market share before competitors can enter the market or as a new way to enter a market saturated.
Pricing optimization models.
- Optimization models are related to math-based programs. These models rely on data related to the demand, price levels, costs, inventory, customer behavior, and other factors to recommend prices that maximize profits.
- Machine learning and artificial intelligence are good tools used in pricing optimization to determine the best prices based on numerous factors.
Machine learning can help set the initial prices, discounts, or promotional prices of your product or services.
- To use machine learning for pricing optimization:-
- Select a modeling tool based on the features available, data analysis, and the intended outcomes
- Round up data such as the pricing and promotions, competitors’ prices, inventory, seasonal and geographical considerations, fixed and variable costs, and customer attributes/ demographics.
- Set rules and confirm the pricing goals.
- Input data and run the models
- Go through the result with the pricing team to make sure everyone agrees with them and the next strategy.
- Track the results and collect new data based on the optimized price to further optimize the prices.
Price optimization software.
- For B2Bs, it is vital to use tools with elasticity-based pricing since such businesses have a hard time sourcing data on customer behaviors, price sensitivity, and customer segments due to the lower volume sales of products and services.
- B2C companies need tools that help figure out how sensitive customers are to price changes. Such attributes as historical customer data, customer segments, behavior profiles, and price sensitivity are important to consider.
- Examples of software to use in price optimization include:
Machine learning techniques for pricing optimization problems
Demand forecasting or prediction would entail using ML techniques such as regression-based models, sequence model LTSM and time series models such as ARIMA. These can be sued to predict future demands for products based on historical data or market trends, thereby helping retailers develop pricing strategies that could maximize revenue while minimizing risks on demands.
Multiple regression modeling and optimization — in problems such as sales vs. pricing problems, one can first train a multiple regression model to get pricing coefficients and use these pricing coefficients with linear regression models to solve pricing optimization problems such as revenue maximization while minimizing discount levels.
In the coming week, we will look into a machine learning model that tries to solve a pricing optimization problem.