The combination of data-driven machine learning and in-house merchandising expertise to drive product recommendations and personalize the customer experience across the various customer touch points has major benefits to your bottom line.
Recommendations are everywhere, from eCommerce sites to Netflix to Spotify to even news sites. They seek to predict the "rating" or "preference" a user would give to an item and help the users discover other products they would like to buy. You can think of them as product pitches to engage the user by offering a personalized customer experience.
Amazon estimates that about 35% of its sales come from recommendations. Netflix estimated that their recommendations influence about 80% of hours streamed.
And Monetate found that:
According to Kibo, “The brands that offer ‘personalized commerce’ not only see higher ROI, they are in the best position to quickly pivot their business to meet changing customer behaviors, and deliver personalized messaging to accompany it.”
This ability to pivot was vital in 2020 when the COVID-19 pandemic changed customer preferences drastically. In the scramble, brands have inadvertently offered out-of-stock products, dramatically increased delivery times, sent out tone-deaf messages, and run unfortunate promotions that point to untimely or sold-out items. Companies that could adapt quickly to the shopper’s COVID-19 behavior thrived while others were forced to layoff and cut costs.
Use the power of both data-driven machine learning and in-house merchandising expertise to drive product recommendations and personalize the customer experience across the various customer touch points.
Let’s explore some types of recommendations, ranging from the easiest to set up to the toughest.
It’s important to think of AI as Augmented Intelligence, not Artificial Intelligence. Gartner defines Augmented Intelligence as “a design pattern for a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making, and new experiences.”
The most straightforward recommendations use the "wisdom of the crowds" or “social proof.”
They have titles like “bestseller,” “trending,” and “most-viewed,” but the taglines change based on the creativity of the design team. These recommendations use basic analytics data like purchases, views, and clicks to rank the products in the catalog. The taglines change based on the creativity of the design team.
These have provided the highest uplift across important metrics such as CTRs and conversions in repeated studies. If your catalog and purchase data is small, this type of recommendation may be all you need. However, if you have a larger store, then you will want to leverage AI-powered product recommendations.
Merchandiser curation works well in cases when there is not enough historical data:
Machine learning can uncover unknown relationships and deliver a unique, personalized experience for each user. It uses inputs such as:
They can also consider other sources of information such as local time of day, local weather, device, location. You can augment this with data further with information from other channels like physical stores - purchases of products, products bought together, store traffic patterns etc. Machine learning allows you to scale up the personalized experience for the users as users and catalog grow.
An extreme example of this is the Discover Weekly playlist from Spotify. Spotify delivers a playlist called Discover Weekly to over 200 million Spotify users every Monday. The playlist delivers a unique set of 30 songs they've never heard on Spotify before. This allows Spotify to delight the listener every week, but it also reduces Spotify’s licensing costs by highlighting non-popular songs (which are typically cheaper). Retailers can leverage a similar approach to reduce long-tail inventory and non-seasonal inventory.
Of course, the best approach of all is a hybrid one that blends recommendations from each of these three types based on the customer context.
One of our clients had plenty of insightful customer data. They wanted to use that to deliver personalization for even anonymous users in a way that increased conversation rates.
Our team of experts created an analytics engine that processed both past data and real-time data. It predicted purchases for both anonymous and new customers. It then identified top products and the categories from which customers were likely to purchase.
Learn more about this particular case here.
Place different types of recommendations as the customer moves along the marketing funnel from awareness to purchase.
For example, bestsellers are the most successful on the home page and category pages, while the product page should have related or complementary products.
Here are some specific suggestions for each type of page:
Leverage recommendations across all your channels. Email recommendations are one of the best performing product recommendations channels. The product recommendations can be leveraged across different types of emails - welcome, abandoned cart, order confirmation and more.
Not sure where to begin with product recommendations? Reach out to the team of experts at Object Edge today.
Plus, check out some additional recommended reading:
