Intelligent search powers ecommerce websites, site search engines, portals to
support hybrid working, and customer support workflows. We’re going from the
early days of keyword search boxes to today’s natural language query
interfaces. The intelligence will progress from today’s automatic relevance
tuning machine learning algorithms onto tomorrow’s pre-trained models and
This was the nature of my conversation with Paul Nelson, managing partner at
Accenture, and Ciro Greco, vice president of AI at Coveo, during a
webinar covering insights on intelligent search in driving digital
During the webinar, we covered several essential questions around intelligent
search, how progressive organizations use them to compete for customers, and
how evolving algorithms improve end-user experiences.
The Future of Intelligent Search in Customer Experiences
Should you upgrade the website or improve the ecommerce store and leave the
search experience to basic keyword searching and category-based navigation?
Greco believes that customer experience is more than an assembly of
technologies. He says, “I like to think of a website as an organism, so you
don’t want to optimize one organ and not the other, and they have to work in
Nelson provides some insights into what it means for a customer-facing website
to “work in harmony.” He focuses on the external information and data sources
that can enrich the experience. “Intelligent search is not just for ecommerce
– but for everything and is about including information from anywhere you can
get it,” says Nelson.
He then lists a mix of internal data sources such as customer support
platforms and external sources of product information that provide context and
richer experiences during the buying process.
My point of view: Experiences need to achieve loyal buyers on ecommerce
sites, frequent readers in media, and enthusiasts for all brands. Product
managers can think of intelligent search experiences as bringing external
information to their users, which will drive key user behaviors like growing
repeat visits and increasing the number of buyers.
Hybrid Work Is the Future of Employee Experiences
One of my questions for these experts was to better understand a key finding
from recent research. The research shows
85 percent of companies increased their investment in enterprise search
over the last 12 months, with almost 50 percent labeling it significant or
Nelson shared his insights on why intelligent search is a critical capability
to support hybrid work. “People are dealing with the digital view of the
organization and discovering that search is a critical component to get people
to the content and the knowledge. Because you can’t just bump into somebody in
that hallway and for someone to introduce you to the right expert.”
Greco also reminds everyone why enterprise search is challenging to implement.
“There’s a lot less behavioral data to infer context, so you have to
capitalize on the knowledge encoded from many company data sources.”
My point of view: With more organizations committing to hybrid working
and trying to derisk the impacts of the great resignation, IT leaders must
find ways to deliver
enterprise search experiences without the tech complexities. IT leaders should focus on loading, cleansing, and enriching data
sources so that an
intelligent search platform
can rank the results and optimize the experience. IT can accelerate developing
department-specific experiences and integrations with
low-code search capabilities
headless search architectures
when implementing more advanced experiences.
Want Intelligent Search? Focus on the Data!
Should you focus on the machine learning models when upgrading search to
support the digital workforce, improve customer experiences, or enhance
customer support capabilities?
Nelson has a very simple recommendation that should be the basis of search
implementations. He asks, “What is the goal of search? “ and then answers his
question with, “It’s to reduce the distance between the user and the
information that they need.”
Greco follows with a simple recommendation. “Put the data in one place,”
implying that IT leaders should focus on integrating, cleansing, and joining
data sources and optimally structuring it for search. He goes on to say,
“Not understanding small things about data management and data engineering
torpedoes a whole bunch of stuff that you do later on.”
So even though a lot of today’s media and interest is around the algorithms,
Greco believes most leaders should invest their efforts in aggregating data
and improving data quality. Greco says, “The marginal gain that an enterprise
has adopting machine learning-based applications is spending time and
resources in making the data good. Capitalizing on whatever downstream
application you want to build on top of this data, being that BI or machine
learning model, is going to be way so much better with cleaner and enriched
And Nelson warns listeners around pursuing what may look like an easy path.
“Installing an open source search engine and making it work for you, that’s a
long road,” Nelson states.
My point of view: The insight here is that intelligent search requires
product managers, UX designers, and technology architects to focus on the
inputs (the data!) and the outputs (the experience!). In other words,
avoid a do-it-yourself to the models and algorithms
because these can be bought, managed, and upgraded with an
intelligent search and recommendation system. The future of search is in pre-trained models developed with billions of
parameters, but the business team must own the data and manage the
Watch the webinar
to learn more insights on the present and future of intelligent search in
driving digital transformation.
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.