What Is Headless Search? Improve Experiences, Simplify App Dev, Enable AI

Microservice architectures, serverless frameworks, low-code development platforms, oh my! What used to be a choice between two and three-tiered web architectures now has several distinct options, and in large technology departments, chances are the architects and senior developers debate and defend their preferred approaches.

Today, I’m going to simplify headless architectures, an approach that’s been around for many years but tends to get less coverage.

There are headless CMS, headless ecommerce platforms, and headless CRM, but headless search may be the most useful – because enterprise search and search embedded in customer experience and support functions are primary capabilities for many businesses. 

Optimizing Search Experiences is a Challenge for IT Leaders 

Implementing enterprise search, search embedded in customer experiences, and search for customer service functions can be one of the trickiest technology development programs. Consider the following variables that go into selecting platforms and architectures:

• Many businesses have at least three primary use cases: enterprise digital workplaces, customer experiences, and customer service, with a fourth being online shopping. Each has different business goals and end-user personas.
• Each use case likely has several implementations with different requirements, for example,  enterprise search capabilities may be used in multiple businesses and departments with different content sources, taxonomies, and business needs. A business may have B2B and B2C ecommerce stores with several content management systems. Individual business units likely have different customer support tools and workflows where well-tuned search can improve servicing customers.
• Each use case often has multiple content sources, content types, systems of origin, and file types. Converting the unstructured content into the relationships, taxonomies, and categories has specific business requirements and nuances that factor into optimized experiences.

CIO and IT leaders get the idea, and they’re the ones holding the bag and paying to support multiple search technologies added and developed over the years to support individual use cases. Recent research shows that businesses have at least three types of search applications while 62 percent manage multiple indexes for different applications.

And only 13 percent report excelling at enterprise search. Ouch. 

Why are Headless Architectures Vital for Search Experiences

Headless search enables CIOs to have their cake and eat it too. It means that the CIO and the chief digital officer can say ”yes” to support world-class search experiences without having to select and support multiple business-specific search platforms. 

In simple terms, headless means you own and develop the experience while the platform provides robust capabilities and easy-to-use APIs to access them. Instead of configuring the user interface, the development team has full control to optimize the experience. And because headless architectures are API driven, the development team can code front-end experiences in .Net, Java, Angular, React, PHP, or other web development platforms.

Let’s say you have several experiences all tied to the same search capability; a B2B portal, a B2C storefront, a customer support platform, and a UI for business managers. Tech leaders can say “yes!” to optimize experiences because the headless search platform enables the dev team to build front-ends and embed them in the UIs for each end-user segment. That might be embedding the customer support search into Salesforce, developing the business interface in Sitecore, and developing custom UIs to support the B2B and B2C experiences.

Another important use case today is supporting the digital workplace and hybrid work. Instead of a one-size-fits-all portal experience to find relevant information, dev teams can tailor experiences to a department’s needs. And they can pick the right tool to develop the job, with some experiences built with low-code search tools and others built with pro-code headless architectures. Agile development teams can also tailor search experiences for different geographies, languages, and compliance factors.

Leapfrog from Poor Experiences to AI-powered Intelligent Search

Headless search platforms enable CIO to consolidate legacy search platforms, sunset search index engines, and create centers of excellence to build and support optimized end-user experiences. And then, the innovation is truly enabled when these dev teams tap into a search platform’s AI and machine learning capabilities.

Reviewing the research, 56 percent report challenges in report ranking, and 46 percent struggle to provide natural language query interfaces. Once you are on a single platform, it’s much easier for dev teams to learn capabilities and implement personalized, relevant search capabilities across multiple use cases.

• What can AI and machine learning enable? Here are five AI-enabled search capabilities:
• Relevance tuning based on machine learning and relying less on hard-coded rules and heuristics
•  Recommendations based on the content’s taxonomy and usage patterns
• Query suggestions, especially where there are ambiguities in how end-users enter search terms
• Dynamic navigation, which is especially important when searching sparse data sets
• User profiling, which uses behavioral data and can help personalize experiences

Being able to say “yes” should be a goal for CIOs and CDOs leading digital transformation programs, especially those aiming to improve customer experience and enable digital workplaces. Headless and low-code intelligent search is a primary transformation platform, especially when it enables technology consolidations, skillset development, and delivering world-class capabilities. 

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The views and opinions expressed herein are those of the author and do not necessarily represent the views and opinions of Coveo.