Shoppers expect personalized experiences, which means that retailers must learn how to understand shoppers on an individual level, specifically by analyzing shopping behaviors. Sampling is a shopping behavior that has become increasingly common in the digital age, where free returns and next day shipping exist.
Types of Samplers
Shopping online limits the ability for shoppers to physically try on products. Due to this, many shoppers tend to purchase the same product in multiple sizes with the intention of returning one, or all, of the products. Thisbehavioris known as size sampling.
Size samplingcan be broken down further into order sampling and sequential sampling. Overall, size samplers tend to comprise5%or lower of total shoppers across retailers.
Order samplingis within one order, and is often the most obvious to spot. A shopper may purchase multiples of a product within a single transaction.
Sequential samplingoccurs when a shopper has multiple orders within a defined time period, which is usually about 60 days. These instances have the same style in different sizes in each order.
Super samplerspurchase multiples in style AND size, and is a behavior exhibited by nearly15%of shoppers across retailers. These shoppers tend to have the highest average net sales and highest returns.
The Impact of Samplers
For shoppers, sampling is convenient because it allows the shopper to try every variation of a specific product in terms of color and size at once, as if they were in-store trying on multiples. Add in the convenience of free returns, and this behavior is basically a no-brainer in replicating the in-store dressing room experience.
Shoppers who lack confidence on either the size or style (color) they want, fall into the allure of sampling. This means they will likely have to return part —or all— of a single order if none of the options work. Size samplers show a 50–66% purchase return rate, style samplers show have a purchase return rate of 30% and color samplers have a 25% return rate.
For retailers, the behavior poses increased costs and a less-than-optimal shopping experience. Sequential sampling increases the cost of returns for the retailer, as they need to cover the cost of the likely return of part —or all— of a single order. Plus, their shoppers are likely to be frustrated if they need to order multiples in order to gain confidence in the fit, style, or size of a product.
How to Identify Samplers
Retailers and brands can identify these shoppers through order analysis: is the shopper buying multiples of the same style in different sizes? Different colors?
Typically, multi-brand retailers experience the lowest percentage of samplers which is around25%. This behavior is likely due to the lower price point, and therefore less “risk” associated with purchasing an incorrect style or size.
Sampling behavior is much more prominent at higher end retailers where the price point is higher. The percentage of sampling is around85%, likely because of the higher “risk” involved in purchasing a more expensive product and having the style, size, color etc. be undesirable for the shopper.
Leading retailers and brands are implementing technologies that track this behavior before the shopper goes to purchase.
By implementing an AI and machine-learningpersonalization platform, retailers can give their shoppers the tools to feel confident when selecting a style or size.
Data-driven personalization platforms can help shoppers find the best styles and sizes for them, increasing the likelihood that they will love and keep their purchases. These platforms can also help to alleviate size sampling for shoppers who are lacking confidence during their session - the shopper can receive a personalized recommendation for which size to purchase, based on his or her personal preferences and shopping history.
Understanding shopping behaviorsis no easy feat. Many leading retailers are prioritizing data analysis to learn more about the constantly evolving shopper and optimize the shopping experience..