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Dropshipping, Dropshipping, Product Inventory Managment, Product Inventory Managment

Make your datasets as complete as possible

Today from Doodat.io we present the third in our series: the six ways to manage your product information and help your customers in their purchases. This time we’ll talk about how to make your datasets as complete as possible to achieve the desired conversions.

Today, nine out of ten shopping cart abandonments are caused by poor product information.

Even more surprising: 40% of fashion purchases are returned, and in electronics, this figure is 15%. With an average cost of return per package of $20, profits are decreasing.

Buyers return items because they do not meet their expectations. Some even anticipate possible disappointment and order two or more product variations, because they simply don’t feel completely confident with their decision.

There’s a powerful way to minimize returns: by answering all the questions buyers may have (even before they have them). The more complete your product information is, the more likely buyers are to choose wisely (so they don’t have to return products).

Studies show that full product pages perform better. Buyers want as much information about a given product as possible. The main elements that help buyers decide if a product is right for them are:

  • Good quality images.
  • View a selected product in the color of choice.
  • View a product in a model / in use / in a room environment
  • Alternative views
  • A zoom function
  • Ratings / reviews of people who have already purchased the product
  • Similar products

The following are the essential action points to obtain more complete product data:

  • Identify which descriptions are needed for each category (weight, size, shape, color, material, etc.).
  • Define a person or team that is responsible for the quality of the data in this master record.
  • Use a PIM tool that can handle different data sources and formats, so you can extract the best information from your system (e.g., existing data sets and industry standards).
  • Automate your data quality processes.
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