Using dynamic content to recommend the next action customers should take

Dynamic recommendations are highly relevant marketing communications that suggest the next action customers or prospective customers should take based on insights such as demographics or browse and purchase history.

Brands offer many dynamic recommendations that may sound familiar. Just a few include:

• Next steps—suggest complementary items; for instance, a tie to go with the purchase of a button down shirt
• People who bought x, also bought y—share other products that people who bought the same item also purchased
• Similar products—let shoppers know about items like their past orders
• Best-sellers—highlight the most popular products

1. They’re tried-and-true. Amazon is one of the most well-known brands using them, and other leading companies have hopped on the bandwagon over the past decade or so. Vendors like IBM have cited a 40-50% increase in engagement and 10-12% boost in sales for clients through the automated and personalized messages.
2. Shoppers approve: 72% say that personalization makes it easier to find products they want to buy.
3. Their enhanced relevance can improve consumers’ perception of brands, thus encouraging incremental and repeat purchases. Dynamic recommendations can prove especially successful for brands that offer many products since they help minimize choice overload. Research reveals that in some cases, shoppers that have fewer choices are six times more likely to make a purchase.

There’s an array of data marketers can use to determine what to recommend for specific customers. For simpler dynamic recommendations, marketers can take into consideration general characteristics such as gender, age, and location (for instance, mountain versus city dwellers). More complex recommendations can utilize factors like:

• customers’ expressed preferences—specific brands or product categories; a sporting goods store could send an athlete who signed up for soccer communications a “Next steps” email offering socks that could be worn with shin guards she just purchased
• historical data—customer email engagement and purchase activity, or lack thereof, over time; someone who recently purchased a golf polo could receive a “People who bought this, also bought” message with complementary pants
• contextual information—recent on-site behavior, including shopping cart or browse abandonment; a retailer could recommend handbag “Best sellers” to a woman who checked out purses online