Product information management (PIM) is a crucial part of digital commerce, as it involves the collection, management, and distribution of product data to different channels and platforms. Bad data in PIM can lead to bad decisions both internally and externally, as it can affect product development, marketing, and customer experience. Here are some examples and research data to illustrate this:
- Inefficient Product Development: Bad data in PIM can lead to inefficient product development processes, where teams waste time and resources working with inaccurate or incomplete product information. According to a study by PwC, 43% of organizations reported that data quality issues were a significant barrier to efficient product development. For example, if a product manager relies on outdated or inconsistent data to make decisions about a new product feature, it may result in unnecessary delays, revisions, or missed opportunities.
- Poor Customer Experience: Bad data in PIM can also lead to poor customer experience, as customers may receive inaccurate or misleading product information. According to a survey by Salsify, 87% of customers say that product content is important in their purchase decision, and 50% have returned a product because it did not match the product description. For example, if a retailer publishes incorrect product dimensions or images, customers may receive a product that is not as expected, leading to frustration and negative reviews.
- Lost Revenue: Bad data in PIM can also lead to lost revenue, as companies may miss opportunities to sell their products due to inaccurate or incomplete data. According to a study by Gartner, poor data quality is a primary reason for 40% of all business initiatives failing to achieve their targeted benefits. For example, if a retailer fails to publish up-to-date pricing information or product availability, they may lose sales to competitors who have better data management processes.
- Wasted Resources: Bad data in PIM can also lead to wasted resources, as teams may spend time and money trying to fix data issues that could have been prevented. According to a survey by Experian, data quality issues cost US businesses an average of $15 million per year. For example, if a company needs to manually correct hundreds of product records due to inconsistent data formats, they may waste valuable employee time that could have been spent on more strategic tasks.
- Biased data: Data can also be biased, based on the methods used to collect it or the sources that it comes from. For example, if a company relies solely on customer feedback from a particular demographic group, they may miss valuable insights from other customer segments. This can result in a product that only appeals to a narrow audience, leading to limited sales and revenue.
In conclusion, bad data in PIM can have serious consequences for digital commerce, leading to inefficient product development, poor customer experience, lost revenue, and wasted resources. To avoid these problems, companies must invest in robust data management processes, including data curation, creation, enrichment, and publishing, to ensure high-quality and accurate product information across all channels and platforms. By doing so, companies can make informed decisions and drive growth in digital commerce.