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
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.
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
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
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
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
User profiling, which uses behavioral data and can help personalize
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
You can download a
production version to try
for free for 30 days.
This post is brought to you by Coveo.
The views and opinions expressed herein are those of the author and do not
necessarily represent the views and opinions of Coveo.