Retailers are using artificial intelligence to target ads and improve product descriptions to reduce return rates and increase profits.

  • Retailers are using AI to reduce return rates for online purchases
  • AI tools are being used to improve product descriptions and recommendations
  • Ads are being targeted towards consumers less likely to return products
  • Returns are a costly problem for online retailers
  • Some retailers are offering discounts or charging for returns
  • AI has been used to manage returns and improve recommendations for a while
  • New applications of AI are being experimented with to reduce returns
  • AI is helping retailers make the most of consumer data to tackle returns

Retailers are turning to artificial intelligence (AI) to combat the costly problem of returns for online purchases. AI tools are being used to sharpen product descriptions and recommendations, as well as target ads towards consumers who are less likely to return products. Returns can be a significant burden for online retailers, with processing costs accounting for a percentage of overall sales. Some retailers have implemented analog solutions, such as offering discounts to customers who agree not to return their purchases. Others have used AI to manage returns and improve recommendations. However, retailers are now experimenting with new applications of AI to further reduce returns. For example, one retailer worked with Google and a marketing agency to develop a machine-learning system that predicts the likelihood of returns based on customer data. This system is then used to target search ads more precisely. Another retailer used AI sentiment-analysis tools to improve product descriptions and reduce confusion over key elements that often lead to returns. By combining these new descriptions with behavioral data, the retailer was able to target ads more effectively and reduce return rates. Despite the challenges, retailers are increasingly relying on AI to make the most of their consumer data and tackle the issue of returns.

Public Companies: Perry Ellis (Unknown), H&M (Unknown)
Private Companies: Acorn-i, Dress the Population, goTRG, Omoda, DEPT, Google
Key People: James Poll (Chief Technology Officer at Acorn-i), Jan Baan (Chief Executive at Omoda), Claire Leon (Co-founder at Acorn-i), Brian Kalms (Partner and Managing Director at AlixPartners), Brad Herndon (Partner at PwC)

Factuality Level: 8
Justification: The article provides information about how fashion brands are using AI to reduce return rates on online purchases. It includes quotes from industry experts and examples of companies implementing AI solutions. The information is specific and relevant to the topic, and there is no obvious bias or opinion presented as fact. However, the article could provide more data and evidence to support the claims made.

Noise Level: 7
Justification: The article provides information on how fashion brands are using AI to reduce return rates. It includes examples of companies implementing AI solutions and the impact it has had on their returns. The article also mentions the challenges retailers face in managing returns and the potential of AI to address these issues. However, the article lacks in-depth analysis and scientific rigor. It mainly focuses on the use of AI in reducing returns without exploring other potential solutions or discussing the broader implications of this trend.

Financial Relevance: Yes
Financial Markets Impacted: The article does not provide specific information about financial markets or companies impacted.

Presence of Extreme Event: No
Nature of Extreme Event: No
Impact Rating of the Extreme Event: No
Justification: The article discusses how fashion brands are using artificial intelligence to reduce return rates for their products sold online. While this is a relevant topic for the retail industry, it does not pertain to extreme events or specific financial market impacts.

Reported publicly: www.wsj.com