What Do B2B Customers Expect From the eCommerce Recommendation Engine?
In our last article on B2B personalization, we extensively discussed the differences between the customers’ expectations and their respective shopping journeys in B2C and B2B. Customers in retail and business make purchases for different reasons, and their buying behaviors are motivated by different factors. Since recommendations are part and parcel of personalization, we’ll essentially go over some of the key differences already mentioned in the previous piece. As a refresher, consider the following table that compares personalization in B2B ecommerce and B2C:
|Origin of a demand||An individual buyer||Resource planning for the entire organization|
|Decision-makers||An individual buyer who places an order||Multiple people with different roles affect resource planning. An individual who creates an order is typically not a decision-maker.|
|Perception of eCommerce experience||Entertainment||Business application that helps people do their jobs more efficiently.|
|Business account||An individual buyer’s account||A company that comprises multiple users with different roles and requires internal user and permissions management. One user can relate to several accounts.|
|Catalog, prices||Generic or based on rule-based or AI personalization||Contract-based pricing and catalog management|
|Recommendation’s goal||Get a bigger share of a customer's budget||Help users optimize and place orders more efficiently (faster, accurately)|
|Order processing||Ideally, one-click purchasing||Complicated scenarios: quotes, internal order approval workflow, etc.|
|Payments||Online real-time payments||Postpaid processing model (invoicing), balances, credit limits, bank transfer|
|Shipping||A product is delivered to a single destination point; timelines are pre-defined by a seller.||Multiple fulfillment centers and destination points; timelines are discussed and agreed upon by both parties.|
|Integration between a seller and buyer||All interaction between a seller and a buyer happens within the confines of the available UX/UI||A buyer’s digital ecosystem can be integrated with a seller’s eCommerce solution|
What is a recommendation engine?
Wikipedia defines a recommendation system as a subclass of an information filtering system that predicts a user’s preference for an item. The engine analyzes huge volumes of data to provide users and businesses with personalized content and services according to their needs, preferences, tastes, and goals.
Recommendation engine goals in B2B
While in B2C, the main purpose of a recommendation engine is to provide users with as many relevant products as possible to maximize seller’s profit, in B2B the goal is distinctively different: to help users accurately and quickly fulfill their jobs. The answer to such disparity lies in users’ perception of their eCommerce shopping experiences. While for an e-tailer customer, shopping is an enjoyable experience, for a business customer, it’s a job responsibility. Because of different end goals, recommender systems in B2B and B2C offer differing functionalities, which we will discuss below.
Recommendation engines functionality in B2B
The complexity of products and services in B2B requires assistant guidance in their correct assembly. Therefore, recommendation engines need to suggest items based on their compatibility. Your B2B ecommerce platform should provide cross-category recommendations that curate relevant product bundles and suggest complementary parts and accessories without the need for manual search. The so-called “wisdom of crowds” data shall be employed to surface similar products when the searched items are out of stock.
Suppose, however, that two similar products fulfill a customer’s need to an equal degree, but one of them is priced higher. In that case, recommending the most profitable item makes perfect sense. Therefore, by suggesting the most relevant and compatible products, sellers don’t necessarily have to forego increasing revenue – they, in fact, can do both.
Another important component of a recommendation system in B2B is based on previous browsing and order history. Memory-based collaborative filtering builds correlations of products based on the client’s historical records and then adopts such correlations to predict future interests. Thanks to the recurrent nature of business operations and contractual obligations, it’s relatively easy to predict future orders. By carefully analyzing data of previous purchases (which are mostly driven by customers’ sales forecasts or production plans), you can predict your customers’ demand, sometimes even more efficiently than the customers themselves.
If there’s not enough data for a particular customer, recommendation systems usually base their predictions on the data for customers with similar purchase history (or equivalent industry profile). There are various types of calculation formulas in recommendation engines that determine the degree of customer similarity. One of them is based on creating the purchase vectors that establish corresponding coordinates between customers (such as the purchase of similar products, the total number of purchased items, the average amount spent on shopping, and so forth).
Completed auto-reorder forms or auto-recommended frequently purchased items, at the re-buy interval right for each business unit, is essential for good customer experience and retention.
Although out of scope of what is typically referred to as recommendations, the B2B ecommerce platform needs to respect account-specific catalogs, contractual price commitments, user specifications, and compliance norms. In short, business customers, upon logging into their accounts, shall see the prices and products contractually agreed on with the seller (something we have discussed at length in the previous article on personalization).
Recommendation system challenges in B2B
While in the B2C space, recommendation engines have already been used very successfully – think Amazon and Alibaba as the brightest examples of impressive implementations – the thriving use cases in B2B are still scarce. The main challenges of recommendation systems in B2B are the lack of sufficient data, especially for nascent companies with no or limited previous business interaction, resulting in difficult integrations and challenging scalability.
The complex structure of organizations may require different business units and geographical areas have varying sets of recommendations.
Moreover, the models currently available on the market have not yet achieved a high level of accuracy. They are occasionally error-prone, which, however, will be remedied, over time and through a gradual increase in the data available for model training.
Virto Commerce B2B eCommerce platform value proposition
For your B2B ecommerce platform to successfully utilize product and marketing recommendations and forecasts from predictive analytical engines, it needs to easily integrate with third party services. Because of its API-based nature, Virto Commerce can seamlessly integrate and interact with multiple third party systems. Our B2B ecommerce solution can also help you effectively translate the engines’ findings to your platform in the required form: for example, as an editable auto-reorder form with frequently purchased items or a suggestion toolbox on the customer’s dashboard.
Virto Commerce is a headless ecommerce platform that can help extend your ecommerce solution with an unlimited number of channels, each with its unique customer experience.
Furthermore, Virto’s flexible account structure with varying access levels and customizable permissions, robust pricing and catalog management modules guarantee that you can offer the first-rate personalized service to your B2B customers.
The success of your B2B ecommerce endeavor might be a click away – schedule a 30-min demo now to boost your B2B and B2C sales.