Ensuring Einstein product recommendations were displayed in the desired product slots.
Management of inventory threshold checks.
Leveraging developer tools, product recommendations were verified in newly created content slots in the product details page.
Site preferences were configured and tested with different inventory thresholds as per the minimum orderable logic.
Customers were invited to discover more with strategically placed recommendations for new and relevant products.
Reduced chances of customers leaving the site through not having a specific color/size variant in stock.
As a leading American clothing brand aiming to become the world’s most sought-after lifestyle label, staying ahead of industry trends is imperative. They aim to create the most desirable lifestyle brands in the world and make them one of the highest performing brand groups in the fashion sector.
Our Client has created a foundation to drive sustainable, profitable and brand-accretive growth through their five growth drivers. This all comes to life through their people, who power their growth drivers by embodying the company values – Individuality, Partnership, Passion, Integrity, and Accountability – which define who they are and who they aspire to be.
Our client integrated Einstein AI recommendations to deliver a more personalized shopping experience which resulted in an increase in Average Order Value (AOV) and minimized the manual effort needed to create curated looks for shoppers. This successful implementation, coupled with thorough quality assurance, significantly boosted revenue and was instrumental in the growth of the company’s digital commerce business.
To achieve this, Qualitest successfully validated Salesforce Einstein product recommendations for luxury shopping brands in multiple content slots throughout various pages.
Products were intelligently suggested by Salesforce B2C Commerce Einstein which leveraged machine learning capabilities to analyse customer behavior, purchase history, and product association. However, the process of testing Einstein recommendations posed several challenges, including the necessity for diverse datasets and extensive testing. Through several configurations and threshold checks, it was guaranteed that recommendations appeared on targeted pages according to searched categories, visited product pages, and recently viewed products.
Personalized recommendations therefore encouraged users to buy more and to improve the percentage of visitors who make a purchase. Thus, increasing Conversion Rates and Enhanced Customer Experience lead to significant growth in cart size, and higher conversion rates directly translated to increased revenue
Leveraging developer tools, product recommendations were verified and were available in newly created content slots in product details page. The type of data recommendation and products list were also confirmed, and errors were reported and analyzed if a product was listed but not available in the slot.
To control the display of products based on the number of units available at a color variant level site preferences were configured in BM and different inventory thresholds were tested as per the minimum orderable logic. For example, if there are no in-stock products via Einstein product recommendations, it was ensured that empty containers were not shown.
The following was rigorously tested:
Einstein reporting depends on activity tracking to generate data, so key reporting was verified via events, viewReco & clickReco, to ensure they were firing each time recommendations are clicked.
Testing Einstein product recommendations for our Client’s eCommerce site gave them several key benefits including:
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