Estimated Sales Conversion Rate

Introduction

The Estimated Sales Conversion Rate metric offers valuable insights into product performance in the market. It indicates the likelihood of a customer purchasing a product while browsing its listing and is calculated in-house for all tracked Amazon products.

Purpose and Value

The Estimated Sales Conversion Rate helps businesses understand how effectively their product listings convert potential customers into buyers.

Use Cases

Here are some practical applications of the Estimated Sales Conversion Rate:

Improve Low-Performing Products:
- Identify products with low conversion rates by sorting the data.
- Analyze the data to understand underperformance, possibly using the Listing Quality Score.
- Adjust the product’s listing, packaging, pricing, or marketing strategy to improve conversion rates.
Optimize Pricing Strategy:
- Analyze how pricing affects conversion rates.
- Experiment with different pricing strategies for low-performing products.
- Set optimal prices to maximize revenue and profitability.
Target Competitors’ Audience:
- Identify competitors using the Competitors dataset.
- Target competitors with lower Estimated Sales Conversion Rates (SCR).
- Advertise on these competitor products to attract their audience.

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You can cross-analyze the Estimated Sales Conversion Rate data with the Listing Quality Analysis data to understand the correlation between listing quality and sales performance.

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Data Model

Your DataHawk-powered Snowflake or BigQuery database includes a table for the Estimated Sales Conversion Rate under the PRODUCT schema.

Schema	Table	About	Refresh Rate	Primary Key
PRODUCT	PRODUCT_ESTIMATED_SALES_CONVERSION_RATE	Displays the estimated sales conversion rate of each tracked product	Daily	product_key, observation_date

Technical Specifications

Interpreting the Estimated Sales Conversion Rate

The Estimated Sales Conversion Rate ranges from 0 to 1, with an average of 10% (0.1). For example, a rate of 0.5 indicates that 1 out of 2 users visiting the listing will likely purchase the product. The table includes lower and upper values for added clarity.

Calculation Method

The Estimated Sales Conversion Rate is computed using an in-house AI algorithm that evaluates key product features such as fair pricing, high-quality product information, fast delivery, positive reviews, and good ratings. This algorithm estimates the purchase probability with an average error margin of approximately 5%.

Considerations

The dataset is updated daily with new entries for each tracked product.