Impulse purchases account for a large amount of revenue that retailers can’t afford to miss out on. In fact, impulse buying accounts for nearly 80% of purchases
. Impulse buying is when a shopper purchases something that was not originally on their shopping list – whether it’s adding chips to their grocery cart or buying shoes on sale that go really well with the dress they just purchased. When retailers drive impulse purchasing, they increase both basket size and sales.
Impulse purchasing accounts for a lot of revenue. According to a survey by CreditCards.com:
- 5 in 6 Americans say they have made impulse buys
- 84% of poll respondents say they’ve made an impulse purchase at some time
- 1/3 of consumers who make more than $75K a year have made an impulse purchase of $1,000 or more.
Traditional retailers employ a number of strategies to generate impulse purchases. These include a transaction building strategy, where the retailer strategically places complementary products next to each other; need recognition, where you display products relevant to a target market’s needs and interests; and cross-merchandising, where a retailer suggests products that work well together.
But capturing impulse purchases is largely the domain of brick and mortar stores. The same study shows that nearly 8 in 10 impulse purchases are made in a physical store. The challenge facing today’s online retailers is how they can increase impulse purchasing when customers shop on their sites.
Amazon has mastered the art of deploying some of these strategies – particularly serving up complementary products, displaying products that target a customer’s need, and suggesting products that go together. Netflix does this as well, although not in the context of impulse purchases and/or increasing the customer’s basket. By using customer personalization journeys, Amazon and Netflix have demonstrated how powerful recommendation engines can be.
In the case of Amazon, the retailer (or to more accurately describe the business, the distributor), has developed algorithms based on the items in the customer’s shopping cart, items the customer has rated and liked, and what other customers have viewed and purchased. According to a recent article on MarTechAdvisor, 35% of all Amazon’s sales are “estimated to be generated by the recommendation engine.”
Netflix relies on its recommendation engine to increase viewership and lower customer churn. But custom-built, AI-based recommendation engines do not come cheap. The same article in MarTechAdvisor states “Realizing the importance of having the best recommendation engine, Netflix puts a lot of effort into its algorithm. Updates to the algorithms are researched and tested by a team of over 70 engineers. In 2009, Netflix offered a $1 million prize in an open competition to any research team which could improve on the efficiency of their algorithms.”
Retailers and distributors who do not have the deep pockets to invest in a custom-built, multi-million-dollar development effort might feel they can’t make use of a recommendation engine. Not only is there significant up-front cost, but given the nature of AI, machine learning and predictive analytics, there needs to be an on-going commitment to development efforts. One can’t build a recommendation engine and then let it sit idle. It must be continually improved to leverage the advancements in the field.
But then there’s the potential for increased purchases and capturing those impulse buys. Retailers need to ask themselves not only if that investment in a custom system will pay off (likely it will), but also will it pay off in a timeframe short enough to make a difference for the retailer?
Read more blog posts on customer analytics:
Net Promoter Scores and Customer Outcomes
Using Customer Analytics to Solve Critical Business Challenges