3.3.3 Marginal Incremental Discount (MID)

Updated by Andrea Squatrito

Marginal Incremental Discount (MID)

The Marginal Incremental Discount (MID) is a discount mechanism designed to encourage buyers to purchase additional datasets within the same data bundle when there are efficiencies in extracting multiple datasets from a single website. This discount applies when a seller identifies cost synergies in extracting data across multiple geographies or schemas for the same website, allowing them to offer a reduced price for each additional dataset in the bundle.

How Does MID Work?

The MID is available to sellers who acknowledge that adding extra datasets from the same website comes at a lower incremental cost due to shared resources, such as the initial setup and maintenance of the scraper. This discount incentivizes buyers to create larger data bundles by reducing the price of additional datasets, making it a strategic tool for sellers to increase bundle sales.

Here’s an example:

Imagine a seller has developed a scraper for an e-commerce website that tracks product information. The primary cost was in building and configuring the scraper. Once created, running this scraper across multiple countries or regions incurs relatively low additional cost, as the scraper can easily adapt to each location’s content. In this case, the seller can apply an MID to each additional country in the bundle, lowering the total cost for buyers purchasing data across multiple geographies.

How MID is Applied

The MID applies to a single contract when included in a data bundle, provided these conditions are met:

  • Same Seller: The datasets in the bundle must all be from the same seller.
  • Same Website: The datasets must be from the same website, with variations only in geography or schema.
  • Seller-Activated: The seller has the option to activate MID, and it is fully at their discretion. Sellers can select discount options of 30%, 60%, or 90% on additional datasets within the bundle.

This discount structure allows sellers to adjust the price to reflect the marginal cost of adding extra datasets, thus making it attractive for buyers to purchase multiple datasets in a bundle.

Benefits of MID

  • Increased Bundle Sales: By making additional datasets more affordable within a bundle, MID encourages buyers to include more datasets from the same website in their purchase.
  • Efficient Cost Allocation: MID allows sellers to share the cost savings associated with multi-geography or multi-schema data extraction with buyers, reflecting the lower incremental costs for each added dataset.
  • Flexible Discounting: Sellers have the flexibility to choose from three discount levels, allowing them to tailor the discount according to the specific efficiencies they experience with each dataset.

Example of MID in Action

Suppose a seller offers product data for a retail website across five countries. If the base price for each country dataset is 100 EUR, but the seller applies a 60% MID on additional countries, the bundle price would be calculated as follows:

  • First Dataset (Base Price): 100 EUR
  • Each Additional Dataset (60% MID): 40 EUR

If a buyer creates a bundle including all five countries, they would pay:

  • Total Bundle Price: 100 EUR + (4 x 40 EUR) = 260 EUR

Without MID, the same bundle would cost 500 EUR. By using MID, the seller incentivizes the buyer to purchase all five datasets while still covering the incremental cost of additional data.

In Summary

  • Marginal Incremental Discount (MID): A seller-defined discount that reduces the price of additional datasets in a bundle from the same website.
  • Criteria for Application: MID applies to datasets from the same seller and website, and it’s activated solely at the seller's discretion.
  • Discount Options: Sellers can choose from 30%, 60%, or 90% discounts, depending on the efficiencies gained from adding additional datasets.

The MID helps sellers on Data Boutique offer competitive bundle prices, encouraging larger data purchases while efficiently reflecting the shared costs of extracting multiple datasets from the same source.

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