Only exact and comprehensive product descriptions help to sell a product. That’s why efficiently collecting, enhancing and organising product data is essential for market success.
The importance of data quality is growing with the increasing market share of online retail. A simple comparison illustrates this:
Imagine that you buy a new pullover. If you’re wandering down shopping streets and looking for your new pullover in different shops, you don’t need detailed product data to make your purchase decision. You can ask for advice, touch the garment of your choice, inspect the material, check the colour with your own eyes and try the pullover on to see if it fits.
But if you’re sitting on your couch instead and browsing through different online shops looking for your new pullover, the product data – and above all data quality – is extremely important. After all, no one is by your side to advise you. You can’t touch, feel and try on any products. You have to rely on what the product says about itself. If this information is more accurate and thorough, if the product’s story is more inspiring, and if the visual design is more attractive and authentic, you will be more inclined to add the item of your choice to the digital basket.
This brief excursion into the experience of buying a pullover shows you how important the quality of product data can be. It is a differentiating characteristic that can determine the success of online retailers and set them apart from the competition.
In this context, clear rules and guidelines for data governance should also be established. For example, these rules should define who is able to handle product master data in what form and how this must be carried out. These internal rules for data governance can also be easily mirrored in PIM systems with a comprehensive work flow management.
Given this background, it is clear why excellent data quality should be a decisive factor when introducing your PIM system. Set high standards in this regard from the start. If data quality is insufficient, this can hurt your market position and cause revenues to drop. Better data quality reduces your costs and increases the chance of a positive purchase decision, which means more business. Accomplishing this is not so much a Herculean task as the result of consistently following basic rules – for example: Setting unified standards for the description of products, variants, technical attributes and functional characteristics. Using consistent terminology. Clear and logical organisation by product groups or other relevant categories.