Transparent pricing optimization

Holocene | Pricing optimization

Unlock the power of Pricing on Amazon

Profit and Loss optimization powered by Machine Learning

Demo dashboard

14-days free trial

How It Works

1. Analyze

Analyze the impact of historical prices on Profit and Loss performance

2. Train

Apply Machine Learning to understand pricing trends and market dynamics

3. Predict

Predict top performing prices for confident Profit and Loss optimization

1. Analyze
Holocene looks at how past sales, competition, advertising, production costs, and seasonal trends affect your profits and losses.
2. Train
Past data goes into the algorithm that finds trends, events, and how prices affect shifts in market demand.
3. Predict
With the daily infusion of new data, the algorithm continually refines its predictive model, enabling the application of prices that reliably enhance profit and loss outcomes.

How the Machine Learning algorithm works

Different prices. Different performance

Analyze numbers with rigour.
Take actions with confidence.

Traditional profit and loss (P&L) statements focus on overall figures, while unit economics look at individual products. But on Amazon, where things change very fast, the real clues to improving your financial results lie in understanding pricing. Machine learning algorithms can give you deeper insights into how pricing affects your financial performance.

Pic: Profit optimization scenario

Traditional Profit and Loss statement:

Shows the average price for products over a reporting period. Revenue, costs, and profits, all based on average prices.

Enhanced Profit and Loss statement:

Highlights the best-performing price for a product. This version points out how adjusting prices improved revenue, reduced costs, or increased profits compared to the traditional approach.

Optimize with Machine Learning

Analyze numbers with rigour.
Take actions with confidence.

Machine Learning leverages algorithms and statistical models to analyze and interpret patterns in data, thereby learning and adapting over time. As the volume of data input into these algorithms increases, their accuracy in predicting pricing outcomes improves, leading to more reliable forecasts.