Launchpad.ai
INTELLIGENT PRICE OPTIMIZATION
Launchpad's pricing and promotional AI systems can take the guesswork out of your campaign strategies revealing revenue opportunities that are overlooked.
dynamic pricing solutions
Launchpad Price Optimizer
Quickly Evaluate Price Strategies
The Launchpad Price Optimizer dashboard allows you to quickly see various pricing strategies the model has run and their outputs as well as a demand vs. margin overview.
Price Exploration in a few Clicks
Using the price exploration tool, you can quickly investigate different promotional strategies for your products using the trained AI model built from your data.
Dynamic Margin Optimization
When viewing the results of an analysis performed by the AI model, you can see all the different potential outcomes different pricing strategies will have on demand and volume. This includes a selection of outcomes the AI has evaluated as best performers.
Additionally, you can further explore how various pricing strategies will effect overall sales, including predictions on SKU cannibalization and unit sales impact.
Step-by-Step Analysis Setup
For each pricing strategy analysis you want to explore, Launchpad's Price Optimizer solution will take you through a step-by-step wizard that captures the need information to run a new report.
Price Optimizer Capabilities
With Launchpad's Price Optimizer, your team can simulate pricing strategies and outcomes using real sales data from your business.
Price Optimizer's interactive dashboard allows you to select the best price performers and display a detailed breakdown of the demand change, loss opportunity and excessive inventory.
AI Prediction for Price Sensitivity and Demand
Launchpad's Price Optimizer uses a surrogate model architecture that finds the optimal price for maximizing both the demand and margin of a set of products. This architecture is comprised of 2 models: a Price Sensitivity model for demand prediction, and an Optimizer for efficiently exploring the vast search space of all sets of prices for all products within a retailer's catalog.
Demand Optimization
Price Optimizer captures the relationship between price and demand given the current site context, delivering reliable price scenario predictions.
Multi-Variable Prediction Support
Price Optimizer can generate a 30-day forward price prediction with ~90% accuracy based on a number of features including: product attributes, market spending, active promotion details and historical performance.
Fast and Highly Scalable
Price Optimizer is able scale to high dimensions efficiently and is able to parallelize easily, allowing it to search vast price/product combination spaces. Price Optimizer has been tested with exponentiation of 200 to the 20th power, delivering results in roughly two hours.
Price Conflict Minimization
Price Optimizer performs a global search, enabling it to know how changes in price for one product may affect the demand for another, minimizing sales cannibalization between products.
Frequently asked questions
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What are the requirements to use the pricing model?
To produce initial results, the model needs 2-3 years of historical purchases, promotion calendar and historical discounts, product data, traffic data and inventory.
What can we do if we don't have all the inventory data needed?
Since accurate inventory data can be tricky, we’ve found a way to curb that issue by looking at the out of stock date and summing purchases before that.
Can we use the price optimization algorithm on multiple products?
What are the categories of features used by the model?
The features are primarily divided into 4 families. Macro-inherent features are date-dependent features that aid in capturing seasonality, such as US-holidays or promotional calendar; Macro-historical features describe the past-year performance, such as traffic and sales; Micro-inherent features such as product specifications and pricing; and Micro-historical features which enable the model to understand product sell-through in various time frames.
Can the pricing optimization model handle a large catalog of products at scale?
In short, Yes, our model will easily handle a larger catalog. In our implemented solution for one of our larger retail customers, there were 200 products with a price search range of $20 in $1 increments. That’s a space of 200 raised to the 20th power possible configurations.
However, even if you have a larger product catalog, we’ve got you covered as the runtime of the model has been highly optimized using techniques like directed search and early stopping.
However, even if you have a larger product catalog, we’ve got you covered as the runtime of the model has been highly optimized using techniques like directed search and early stopping.
The Next Big Idea is In Your Data
Contact us to learn how we can help you create new solutions and insights to deliver next generation experiences and grow your business.
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