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Customer Experience and Design

Want to Provide Amazon-Level Personalization? Now You Can.

CI/CD in Databricks

Building customer satisfaction is crucial to winning satisfied, loyal, long-term customers. Customers appreciate when merchants remember them by name, or make recommendations based on their interests and past interactions. This gets tricky in eCommerce. People will “window shop” or browse leisurely for an hour in a store, but online customers who know exactly what they’re buying want to get to checkout as quickly and as easily as possible. While a salesperson or shop owner can build a relationship with customers in person over time, the approach must be very different online. 

The Amazon Effect: Marketing for Impulse Buys

In brick-and-mortar stores, product placement is key. You want customers to make impulse buys, which tend to be small, inexpensive, and fulfill a need or desire. Think batteries to go with your new smoke alarm, or lightbulbs for the lamp you’re about to purchase. Placing similar or related products near these items can make them more attractive, and using discounts and promotions to draw attention is effective as well.
But how can you provide this same experience in eCommerce? Amazon is a prime example of a site that focuses on personalizing the customer experience. Much of Amazon’s success has been attributed to its use of AI to learn and predict each customer’s unique shopping behaviors over time in an effort to present better product recommendations in the future. 
The “Frequently Bought Together” section featured on each Amazon product page has been effective. It recommends products that could be beneficial to the user that pair with the item they’re considering. For example, if a customer is looking to purchase a cell phone, the recommendations might include a phone case and an additional charger. Another Amazon technique is to present the “Customers Who Bought This Also Bought” section, which uses a larger data set and lists products that shoppers just like them chose to purchase. Seeing products that other customers trust and reviewed positively serves to make people feel better about their own purchase. By presenting customers with these bundles of complementary products, Amazon has vastly expanded its upselling potential, and it’s been reported that these recommendations are responsible for up to 35% of Amazon’s sales.

Personalizing Your Web Experiences with Magento

Providing this kind of tailored buying experience is crucial in modern eCommerce, and only serves to become more powerful as you grow stronger relationships with your customers. When a customer makes their first purchase from a site, there is no real history to base product suggestions on, and customers will automatically be presented with promotional products or top sellers. But recommendations improve over time. As a purchase history builds, your site’s AI algorithms will continue to fine-tune the recommendations described above to make for a more personalized experience for repeat customers. These algorithms will provide recommendations based on how a customer interacts with your site. The searches they did, content and images they clicked on, products and categories browsed, and more are all accounted for. 
So how can you create this kind of deep personalization on your own site? At Magento Imagine 2019, it was unveiled that Product Recommendations Powered by Adobe Sensei is set to be worked into Magento Commerce. Sensei is Adobe’s Artificial Intelligence and machine learning technology that leverages data and insights to drive relevance and personalized experiences.
Sensei Powered Magento Recommendations will:

  • Gather and analyze shopper behavior
  • Provide highly personalized product recommendations
  • Increase sales by adding the recommendations to key pages
  • Define exclusion categories, to account for inventory, price levels, and other business situations
  • Allow custom product widgets to be placed on different page types and placements on those pages
  • Allow choice of algorithms to power the products displayed
  • Measure performance metrics to gauge the impact of recommendations

To get your personalization process up and running in Magento, you can start with the following:

  • Configure the module in Magento, and determine desired styling
  • Create a Recommendation and give it a name
  • Select a Page Type (Home, Category, Product, Cart, for example)
  • Select a Recommendation Type (Most Popular, Top Sellers, for example)
  • Choose placement for the Recommendation in relation to other Recommendations and content on the page
  • Pick the number of products to display and set a label for the storefront

Contact us to learn more about how others have benefitted from product recommendations, and how you can be part of the Early Access Program for Magento Commerce Product Recommendations in 2020.

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