Pricing with MachineLearning Practice

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About TryolabsWho we areTryolabs is a Machine Learning (ML) focused consulting and development firm. We mainly support business in their Machine Learning journey by helping them identify good opportunities, implement proofs of concepts, deploy intro production, ensure the adoption and hiring. Since our foundation in 2010, we have measured success by the impact of automation and optimization in our customers.

Track record

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In particular, the Pricing with ML practice was created after identifying and developing price automation/price optimization projects for different retailers.

This practice aims to help forward-thinking retailers to understand, in a data-driven fashion, the impact of using ML to improve pricing processes and decisions, as well as helping in the development and deployment of a novel solution.

With a solid but customizable services process, our pricing projects have demonstrated to produce high savings and relevant gross margin improvements.

Pricing withMachine Learning

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Our Pricing PracticePricing with Machine Learning fits into the wave of data-driven pricing strategies that are revolutionizing how retail operates.

Price Automation

Take the guesswork out of pricing using multiple data points. Reduce operational costs eliminating manual pricing process.

Optimization

Maximize profit through future product demand estimation. Optimize your gross margin, sales volumes, sales velocity and promotion.

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Price Automationwith Retailer CoLeading public marketplace in the fashion industry, with over $1B in transactions per year and 100k unique SKUs processed every month.

SituationThe pricing process is extremely time-consuming and is neither cost-effective nor consistent.

What we did• Set up automatic pricing for more than

half of the products in the catalog. Pricing algorithm leveraged images of products, descriptions and similar products among other features.

• Optimization models to determine the best price for a given product, at a given time, to maximize revenue in development.

Use case I

68%

5%

of the catalog is automatically priced.

increase in average price of item sold, sustaining sales velocity.

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Price optimizationwith Lux Retail CoBrick & mortar luxury retailer with +9,000 employees and 160M customers per year.

SituationThe pricing process is suboptimal in terms of revenue consistency and cost of operations. The intention is to incorporate price optimization algorithms to deploy in their brick and mortar stores through an accurate demand forecasting.

What we did• Demand forecasting models for 110 SKUs

using various data sources.

• Deployed weekly prices to 110 SKUs in one store, benchmarking with control store.

PhasesData analysis & cleaning, machine learning modeling, price exploration, and price optimization.

Use case II

28%

Stock replenishment

average gross margin gain for SKUs during rollout.

forecast to limit out-of-stocks.

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What makes us unique

Successfully built accurate demand forecasting models and price optimization solutions for lead retailers.

Ability to tackle the problem end-to-end: from data analysis to deployment, rollout and interactive dashboards.

A team of data scientists, machine learning engineers, economists and full-stack developers interacting with a business team on the client front.

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Phases: from PoC to ProductionDoing changes in pricing is risky, therefore we suggest a Proof of Concept / Proof of Value as a first stage. As soon as this phase provides promising results, the rollout phase is planned accordingly.

For the PoC phase the following are the standard phases:

Data cleaning& analysis (3 - 5 weeks)

Machine Learning Modeling(4 - 6 weeks)

Deployment, experimentation &

exploitation(5 - 10 weeks)

How to start?Let’s start by assessing your sales data to understand the automation/optimization potential.

Contact us!

hello@tryolabs.com