I don’t know about you, but when I go to a store, I usually end up leaving with more than what was originally on my list. Brick and mortars are designed this way, to entice customers to pick up strategically placed items that catch your eye. On the other hand, when I buy something online, I almost never purchase supplementary items.
Through the boom in online retailing, shopping behavior has shifted from an open-ended activity to a targeted pursuit of a specific item available at the lowest available price. One survey of online grocery shoppers found that 29% of respondents feel that they make far fewer impulse purchases online than in store.
It’s clear that consumers are more frugal in their online purchases, however, there is an opportunity for online retailers to leverage Revenue Management strategies to combat this trend.
1) Product Recommendations. This is the most common practice, and already in place at some retailers (think about Amazon’s “customers who bought X also bought Y” recommendations). Often when users select an item or go to check out, they will be shown a page that highlights other related items to buy. These recommendations are generally based off of a market basket analysis identifying what items are most often purchased together. It typically does not factor in any customer-specific insights, thus most or all customers selecting an item will see the same recommendation. While this can certainly be a good start, it does not take into consideration information available to make a more compelling offer.
2) Targeted Personalized Promotions. Many retailers rely heavily on promotions to drive sales volume and combat competitive pressure. However, the majority of promotional activity is highly generic, with a retailer offering a set discount on a given item to all customers for a defined period of time. Alternatively, some retailers appear to take a more targeted approach by emailing incentives to customers following a purchase to incent a repeat visit (a noble goal!). Both approaches have merit, but neither seeks to increase the customer’s basket size at the point of primary engagement – at checkout. If these promotional investments were redirected into targeted personalized offers at the point of checkout, the likelihood of a growing basket size would increase. A combination of market basket analysis and promotion analytics can guide the optimal product / price offers to organically grow revenue.
3) Dynamic Bundling. Dynamic bundling is an analytics-driven capability that presents relevant and specific packaged deals to consumers. This strategy proactively pushes products that are complementary to a consumer’s planned purchase, based not only on the product being purchased but also customer and situation-specific inputs. Thus, overall, these recommendations yield higher conversation rates as they are more likely to apply and appeal to the customer at that specific time. The bundled offer – informed by sophisticated customer segmentation and the product / promotion analytics described above – can be priced at the sum of its parts, or offered at a discount to further incent purchase. Either way, the goal of migrating the customer from a one to multi item shopping basket is achieved.
Online retailers regularly squander the potential to drive larger basket sizes by making more compelling offers to its customers. The data to inform these decisions already exists; you just need the right analytics to guide the targeted recommendations. Companies need to change the way they analyze their data and consider implementing targeted and personalized promotions and bundles that can combat the shrinking online shopping basket size and lead to organic revenue growth.