Learn About Einstein for Commerce
After completing this unit, you’ll be able to:
- Explain what Einstein for Commerce is.
- Describe the features and capabilities of Einstein for Commerce.
Einstein for Commerce features are directly embedded into Salesforce B2C Commerce to delight shoppers, make merchants more productive, and most importantly—grow revenue. This is all achieved through AI and machine learning, using their own wealth of commerce data.
Einstein for Commerce Features
Einstein powers these key B2C Commerce features.
- Einstein Product Recommendations
- Einstein Predictive Sort
- Einstein Search Recommendations
- Einstein Commerce Insights
- Einstein Search Dictionaries
First, let’s take a look at the Einstein experiences that are oriented toward the shopper.
Einstein Product Recommendations
Product Recommendations uses machine learning to make suggestions personalized to a consumer’s shopping experience. Whether a shopper is a guest or known—that is, signed in to an account—these recommendations suggest the most relevant products throughout a consumer’s shopping journey to keep them engaged on the website, and help them browse more efficiently.Merchants can choose from multiple recommendation types. They can use traditional recommendations that show like-for-like products, for example, where a shopper sees blue shoes recommended on a product detail page for another pair of blue shoes.
Einstein Product Recommendations present shoppers with the right products at the right time. They encourage more purchases and maximize a brand’s revenue. But they do something else that’s just as important, if less quantifiable. They offer each shopper a delightful shopping experience through personalized—perhaps even thoughtful—attention to their interests, based on the items they themselves have shown interest in. And happy shoppers are good for business.
Einstein Product Recommendations is available for B2C Commerce shoppers for free, and is turned on at the shopper's request.
Einstein Predictive Sort
With Einstein Predictive Sort, brands can automatically personalize the order of products shown on each search results or category page. This means products most relevant to a shopper show up first based on their past browsing and buying behavior. Predictive Sort makes the web experience more enjoyable since shoppers don’t have to scroll through as many products and pages to find items of interest.
It’s even more impactful for shoppers using a mobile device. Mobile screens typically show fewer products per page, and shoppers don’t like to click and scroll through several pages to find what they’re looking for. Einstein Predictive Sort is a simple way to make the mobile shopping experience faster and more seamless.
Einstein Search Recommendations
Einstein Search Recommendations powers personalized type ahead search guidance for each individual shopper on site. This Google-ifys the brand’s site search so that shoppers are automatically guided to the best search terms for them.For example, if one shopper starts to type the letter s, she might see the word sandals autocomplete for her. This is based on her past shopping and browsing history. If another shopper types the letter s, he might see the word sneakers autocomplete based on his history.
Search Recommendations applies algorithms to B2C Commerce data to identify which search results are the most relevant to each shopper, even before they enter a full search. Brands can be confident that their shoppers are exploring the site on the quickest path to conversion. This isn’t just type ahead search—It’s intelligent, personalized type-ahead search.Next, let’s look at some tools designed specifically for the merchandiser!
Einstein Commerce Insights
Einstein Commerce Insights empowers merchandisers to interpret purchasing behavior using a powerful shopping basket analysis dashboard. Merchandisers can choose key items in their inventory and learn which products shoppers most commonly purchase along with them. Merchandisers can drill into data on specific products based on date range, and gain insight into metrics such as product-specific sales and top “co-purchase” categories.
Einstein for Commerce consumes key commerce data sources, such as browse and purchase history, directly from B2C Commerce. Unlike third-party personalization technologies, Einstein’s machine-learning algorithms require no system integrations to access and analyze the rich data set contained within B2C Commerce. Einstein for Commerce and its machine learning algorithms are the magic behind the personalized experience that brands can give each shopper. Einstein lets brands offer a more relevant experience across channels, without requiring them to invest heavily in additional technology or operations.
Einstein Search Dictionaries
Have you ever searched for an item on a site and gotten zero results? Maybe you spelled the word wrong or there were no items that matched the specific words you were searching for, such as "cobalt." Well, that's what this feature helps fix!
Einstein Search Dictionaries consumes all the site searches and surface terms that are used in searches, but not yet in the retailer’s keyword list. It then makes recommendations to merchandisers, for example, synonyms that can be added to the list. Before, if retailers wanted to find missing search terms and pick the synonyms list to add them to, they had to sift through a long spreadsheet and guess which one they wanted.
Einstein Search Dictionaries analyzes data across B2C Commerce to find relationships between search terms, and then recommends the synonym list to add them to. For example, a shopper searching for “mauve sweater” might not get any results. But Einstein Search Dictionaries, seeing a potential relationship—mauve being sort of pink—will recommend adding “mauve” to the synonym list for “pink” and “purple.” Now, a shopper looking for a "mauve sweater" might see exactly what they want. The benefits for shoppers and retailers are clear. Shoppers aren’t disappointed, coming away empty-handed when they can’t find the stuff they want. Retailers benefit in multiple ways: by not losing a sale to a shopper who can’t find what's actually in stock, and by reducing some of their workload so they can focus on other important site merchandising tasks.
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