Predict

Predict the pricing of a product based on specified attributes.

POST

/price/predict

Example

cURL

curl --request POST \
  --url https://api.bencha.io/v1/price/predict \
  --header 'Authorization: Bearer {api_key}' \
  --header 'content-type: application/json' \
  --data '{
    "query": {
      "brand": "newbie",
      "category": "tops",
      "target": "mixed"
    }
  }'

Body

JSON

Query attributes

Field	Type	Required	Default	Description
`brand`	string	No	-	Brand name or identifier
`category`	string	No	-	Category name or identifier
`condition`	string	No	-	The condition of the product.
`demography`	string	No	-	The targeted demographic.
`target`	string	No	-	The pricing target.
`method`	string	No	-	The pricing method used.
`status`	string	No	-	The listing status of the item.

Brand and category

If an identifier is provided for the brand or category, it will be used directly in the prediction. If not, the API will perform a search based on the provided brand or category name through the /search/brands or /search/categories endpoints.

Condition

💡

The condition of an item is often highly subjective, and different sellers or buyers may evaluate it differently. This subjectivity can have an adverse effect on the pricing, as it may lead to misalignment between the perceived and actual condition of the product. If you are unsure about the condition, take a look at fill_mode for more general values.

Condition values high and low are only available when using spot-2-turbo model.

Defines the state or quality of the product being priced. You can select from the following options (best to worse):

Condition	Description
`high`	Represents top-tier conditions
`low`	Represents lower-tier conditions `good`, `fair` and `poor`, indicating signs of use and potential functional degradation.
`pristine`	Unused, in brand-new condition.
`mint`	Like new, with no visible defects or signs of use.
`near_mint`	Almost new, with very minor signs of wear.
`excellent`	Almost perfect condition, minimal signs of use.
`very_good`	Lightly used, with minor wear and tear.
`good`	Regular signs of wear, but overall functional and intact.
`fair`	Noticeable wear but still functional.
`poor`	Heavily used, with significant signs of wear and tear.

Demography

Indicates the target demographic for the product. Choose from:

Demography	Description
`women`	Designed for women.
`men`	Designed for men.
`unisex`	Suitable for all genders.
`boys`	Targeted towards boys.
`girls`	Targeted towards girls.
`children`	Suitable for all children.

Target

Specifies the pricing segment you’re aiming for in your predictions. Select from the following:

Target	Description
`affordable`	Priced for affordability, focusing on lower-range estimates.
`mixed`	A balanced approach between affordable and premium pricing, providing moderate estimates.
`premium`	High-end pricing for luxury or exclusive items, reflecting higher potential outcomes.

For more information on how these targets relate to data handling, refer to the fill_mode section.

Method

Describes the sales method used for the product:

Method	Description
`consignment`	Products being sold through consignment.
`p2p`	Products being sold on peer-to-peer marketplaces.

Status

Represents the listing status of the product:

Status	Description
`listed`	Products currently listed for sale.
`sold`	Products that have been sold.

Additional Parameters

Currency

All prices are in EUR and will be converted to the provided currency using the latest exchange rates, updated atleast every 12 hours.

Specifies the currency in which the predicted price will be returned. The value should be a valid ISO 4217 currency code, e.g. "SEK" for Swedish Krona.

Model

The model parameter specifies the model to be used for generating price predictions. Select from one of our various pre-trained models tailored for different scenarios or datasets.

Selectable Models:

Model Name	Description
`spot-1-turbo`	A quick model designed for fast predictions, ideal for real-time applications.
`spot-2-turbo`	An updated version of `spot-1-turbo`, offering improved performance and accuracy for most use cases.

Fine Tuning

Fine-tuning allows customization to the behavior of the model, ensuring that it aligns more closely with specific use cases or market conditions. By adjusting certain parameters, you can enhance the model’s performance, leading to more accurate and reliable predictions.

Fill Mode

💡

Choosing the right fill mode affects how the model interprets and predicts based on incomplete data, so it is important to select the option that best fits your application needs.

The fill mode parameter determines how the model handles missing or undesirable values during the prediction process. The default value is mixed and the available options are:

Fill Mode	Description
`premium`	Fills missing values with estimates that reflect higher potential outcomes. Suitable for more optimistic scenarios.
`mixed`	Uses a balanced approach to fill missing values, providing an estimate that represents an average outcome.
`affordable`	Fills missing values with lower-range estimates. Designed for situations where quick predictions and cost-effectiveness are prioritized.

Tolerance

The tolerance parameter is only available when using the spot-2-turbo model.

The tolerance parameter controls the level of adaptability in the model’s suggestions. It determines how much the model can adjust to the input data, offering flexibility for different use cases.

Tolerance	Description
`high`	The model applies significant adjustments.
`medium`	The model applies moderate adjustments.
`low` (default)	The model makes minimal adjustments.

Biases

The biases parameters allows for adjustment to the model’s predictions, helping to mitigate the impact of possible outliers or skewed data distributions.

Field	Type	Required	Default	Description
`bias_correction_factor`	number	No	0.5	The factor at which to impact the prediction (`0-1`). A smaller value means smaller impact on the prediction, and vice versa.
`bias_correction_threshold`	number	No	0.95	Predictions below this threshold will be adjusted (`0-1`).

Response

JSON

Categories Bencha.io ↗