Retail & E-Commerce
What Drives Consumers to Abandon Purchases
Report summary
Sales are lost to indecision as much as they’re lost to competitors.
Shopping can be hard. With myriad options for what to buy and where to buy it, the journey to buy a new pair of jeans or a household appliance can be daunting.
Sometimes consumers just choose not to buy anything at all. To understand what specifically causes a non-decision decision, we set out to analyze the factors that influence conversion and non-conversion from shoppers planned purchases.
Based on a survey of 4,404 U.S. adults and a random forest-based drivers analysis, we determined the causes of shopping journey abandonment, as well as the most helpful actions consumers take to overcome indecision.
Key Takeaways
- Abandoned purchases are common across categories. Home furnishings have the highest rate of abandonment, with nearly 40% of consumers dropping intended purchases due to factors like affordability and style uncertainty.
- Consumer uncertainty inhibits many purchases. Uncertainty about style, return policies and general concerns about if a product will work for a consumer contribute to shoppers opting out of the decision entirely.
- Getting into a store helps overcomes indecision. Among shoppers who had a tough time choosing, getting offline and into a store was a consistent solution that increases likelihood of purchase.
- Online shoppers face more complexity. Shoppers who choose to go online generally rate the shopping experience as more complex. Shoppers who describe their purchase as essential cut through that complexity by shopping in store more.
Data Downloads
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Methodology
The data in this report draws from a survey conducted Jun.16-18, 2025, among 4,404 U.S. adults, with a margin of error of +/- 1 percentage point.
The data on pages 7-11 used a random forest-based drivers analysis. Drivers analysis (also known as “key driver analysis” or “derived importance analysis”) helps us understand which brand associations, behaviors, or audience characteristics are most associated with predicting an outcome of interest. Random forest drivers uses a machine learning algorithm known as a random forest to predict outcomes based on splits of predictor variables. Only factors with statistical significance are shown.
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