Headless, composable architecture creates a critical flexibility when building engaging and adaptable product search and discovery interfaces that ensure conversions and increased transactions and ordering. In this article, we discuss how these 3 ingredients, or steps – headless/composable, search/discovery, conversion/engagement – function for a modern, competitive B2B business.
Gartner predicts that “By 2023, organizations that have adopted a composable approach will outpace the competition by 80% in the speed of new feature implementation.”
Headless architecture allows freedom and flexibility by removing the link between front-end and back-end. Site content and UI elements can be instantly changed with no effect on the back-end infrastructure. The modern API-first approach offers internal teams (product, marketing, and merchandising) to iterate online omnichannel experiences and test new strategies with high agility and scalability.
B2B Industry leaders, such as Dell Technologies adopt headless, API-first architecture for their ecommerce platforms. The benefits of the headless/composable approach compared to legacy monolithic solutions are highly impactful for any organization:
Composable architecture enables B2B companies to implement an omnichannel sales model, which has proven to be more effective than traditional approaches. Recent research published by McKinsey identified omnichannel as a “critically important fixture for B2B sales globally”, with 83 percent of B2B leaders declaring it as a more successful way to prospect and secure new business than traditional, “face-to-face only” sales approaches.
Moreover, the omnichannel model pattern holds true across multiple geographies, showing enthusiastic adoption trends by B2B companies globally.
According to Forrester, 92% of B2B purchases start with search. Additionally, Baymard Institute’s recent research found that “Product Type” searches (e.g., “boy’s hoodie”, “sandals”) are almost always the first interaction of users with a site. Product type queries help shape users’ early impressions of what’s available. It is critical to get search relevance configured right to provide relevant results even when user queries don’t match a site’s precise category descriptions. When search results give users the impression of limited or undesired inventory, the risk of abandonment increases significantly.
Note: For the cases where pricing is dynamic and product prices change frequently, a pricing engine solution should offer real-time pricing data for any pricing complexity level.
What are some of the elements to increase conversions and user engagement?
A decision process of choosing the right search and discovery engine for digital transformation of a B2B commerce company:
Performance, reliability, scalability, and flexibility are the four essential benefits a search and discovery engine should provide to the B2B commerce organization. Open source solutions such as Elastic and Solr are highly resource intensive, requiring a significant amount of engineering resources to maintain and update. While open source solutions appear to be “free”, their implementation and maintenance are costly and time consuming. These difficulties pile up on top of additional challenges, such as lack of transparency regarding the search results ranking algorithms. On the other side of the spectrum are the easy to implement solutions, which are limited in flexibility and scalability, due to their reliance on open source solutions and a cookie-cutter architecture and design. Those solutions suffer from opaque ranking algorithms limitations and are known for their unpredictability when faced with the need to implement modifications or customizations to the relevance ranking of the search results. These will present a significant disadvantage for any B2B commerce company, which are likely to be in need of multiple product modifications. Algolia offers customers a wide range of customizations, to ensure that the product meets each B2B business’ unique requirements and offers the scalability and easy replication from one platform to multiple others.
Tanya Herman
Product ManagerPowered by Algolia AI Recommendations
Tanya Herman
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