The Who: Call in the Search Excellence Team
Accenture's internal IT organization, also known as the CIO Group, is made up of nearly 8,000 people who support the company's over 400K employees – everyone from call center and application outsourcing employees to consultants on the line. The group is charged with working on a range of applications, including an internal portal, a knowledge platform, a collaboration platform, and enterprise search.
To address the need to modernize their search platform, Accenture created a 25-person Search Excellence Team within the CIO Group. Team members based in Manila and Chicago were charged with focusing on not only the technology of search, but also the ongoing improvement and processes of search.
The What: Replacing Black Boxes with Search Visibility
Search has historically been viewed as an experience leaving Accenture users wanting better. People knew that information was there, but since they weren't always able to find it efficiently, they ended up frequently abandoning searches. Employees were frustrated and annual surveys showed search as one of the least favored applications provided by Accenture to its employees.
Although the Search Excellence team knew that users were dissatisfied, they lacked the visibility and tools to measure and influence search rankings. "Black box" solutions failed to provide them with the information they needed (like why certain results were popping to the top) to make improvements. They made content-side updates, but with 50 content sources and many millions of items, it was time consuming to test and implement changes.
"We wanted to introduce a service that brought our search capability to another level," said Chip Allen, senior manager at Accenture. "It had to be seamless, easy to find what you're looking for and just a much better process overall."
The Why: Search Engine Flexibility Fuels Speed, Continuous Improvement
In July 2016, 10 months after the original proof of concept, the Search Excellence team broke new ground, introducing a continuous improvement approach to search through the innovative use of open source search technology coupled with custom extensions and tooling. Accenture chose Elasticsearch because it offered transparency into search results and a flexible query language.
Now they could open up the hood and tune the engine the way they wanted it to work.
"The number one thing Elastic did was give us visibility into the search engine," said Chip. "We were able to get deep insight into the rationale of why certain results were showing up to better optimize and deliver search results."
Today, the team can monitor which search terms may not be getting the expected results and figure out why: is there a gap in the content available, or is it a matter of content not appearing in searches?
For example, when the team identified that searches for "Fitbit" were trending, but yielding no results, they realized that employees were looking for the company's new fitness tracker program, "Accenture Active." They were able to easily enrich the results, even though "Fitbit" doesn't appear anywhere on the site.
Every time the team learns something new, they can quickly understand how the current relevancy is preventing the right result, propose and test changes, and quickly put the refined model into production.
The How: Optimizing Data to Generate the Accurate Search Results
In addition to the core Elasticsearch engine, the Accenture team added layers to improve, normalize and enrich the metadata across multiple data sources. The resulting solution includes five main components: a crawler, enrichment layer, Elasticsearch search engine, user presentation, and reporting and analytics.