Candidate Search Redesign

Avram Baskin Senior User Experience Designer Kenexa, an IBM Company

BrassRing & Candidate Search Overview

  BrassRing   Kenexa BrassRing is a talent acquisition product used by global

companies to manage all aspects of the talent acquisition process, from sourcing to hiring

  Candidate Search   Candidate search is used by recruiters to search the clients’

BrassRing data base for candidates that match the requirements for open job requisitions

Client Interviews   I conducted a series of remote client interviews   Each interview was with representative users from a

single client company   The groups generally consisted of one or two technical

administrators and approximately seven recruiters   The companies were from a mix of industries, but the mix

was not necessarily representative of all of Kenexa’s clients

Conclusions From the Interviews   It was known that clients were dissatisfied with search   The initial assumption was that an improved user

interface that facilitated search would increase client satisfaction

  User research revealed that, while a new user interface was part of the solution, the real problem was that the search engine didn’t provide reliable results and that clients had lost confidence in the search functionality

  In order to find “good” candidates, recruiters had to be experts in Boolean search, which is not part of a typical recruiter’s skill set   The current keyword search does not support natural language

search and does not recognize context

  In addition, recruiters wanted the ability to weight the importance of different search fields and to be able to include and exclude values within the same search field   One service that was mentioned was See More, which includes

a switch to indicate a field is “nice to have” or “required”

  Recruiters have no way of knowing if the search is going to produce an empty result set without invoking the search and proceeding to the results screen

  There is no way to exclude a value within a field   For example, include candidates with a Master’s degree, but

exclude candidates with a Master’s degree and a PhD

Search Requirements   The following search requirements are based on the user

research and my knowledge of the features that facilitate a good search experience   Natural Language Search

  The system should support searches in the users’ own, natural words, i.e. no Boolean searches required (but Boolean searches should be supported). For example, ‘goldman sachs employees with an mba and experience with derivative securities”. In other words, the user should not even have to know what Boolien search is.

  Autocomplete Search Suggestions   As a search term is being entered, the search engine should display

search suggestions that are related to the entered search. As additional characters are entered the search engine should update the list of suggested searches.

  Provide Reference Results   Example: if the user enters “map of interaction designers near

lexington ma” the system displays the results on a map   Example: if the user enters “recruiter notes for applicants to req

32104”, the system displays the requested notes

  Recognize Context   Don’t just match keywords, recognize context. For example,

differentiate between people who have experience working at Kenexa and people who have experience with Kenexa 2xBrassRing

  Search and Aggregate Multiple File Types   Be able to search across multiple file formats and aggregate results

from multiple file formats.

  Support Multi-language Search

  Suggest Related Searches/Queries   After the search is executed the search engine should propose search

recommendations as part of the results that are in some way similar to the entered search. For example, if you search for “interaction design experience near lexington, ma” the recommendations might be:   human factors experience near lexington, ma   user experience design experience near lexington, ma   information architecture experience near lexington, ma   usability specialist experience near lexington, ma

  Be able to Indicate Relative Importance of Multiple Search Criteria (BIAS)

  Be able to Indicate Aggregate Relevance of the Entire Result Set to the Search Criteria

  Be able to Indicate Relevance of Each Search Result to the Search Criteria

  Highlight Text Related to the Search Term   In the results, highlight text that is related to the search term by

pattern recognition one-to-one matching.

  Full Text Similarity Search   A block of text ranging from a phrase to a complete candidate record

can be submitted and the system can find matching results.

  Concept Search   If the user searches for “interaction designers” the results might

include ux designers, information architects, and human factors engineers. Likewise, if the search is for “attorneys”, the results would include results with the title “lawyer”. The conceptual relationships can be specified in a taxonomy, with statistical co-occurrence, or similar techniques.

  Ontology-based Search   The search engine understands inter-entity relationships. So, if the

user searched for “candidates matching skills that an interaction designer has” the results would include relevant results and highlight the relevant skills.

  Do You Mean   Provide “do you mean” functionality (similar to Google’s “did you

mean” functionality) if, for example, the user searches for “gldmn sachs” instead of “goldman sachs”. The spelling suggestion should display before the search is executed.

  Tagging (automatic and manual)   I’m thinking of a visible list of tags or a tag cloud.

Current Workflow & Screens

Video

Current Search   The current search is a parametric search

  User enters search criteria on the search screen, presses a search button, and the system displays the result set on a separate screen

  The user has no way of knowing if they are going to receive an empty result set before executing the search

  “For this reason, parametric search has largely fallen out of favor and been replaced by faceted navigation” (Hearst, 2009)

  Required Fields   Certain search fields were hard coded as recorded fields and

always appear on the search screen   There does not appear to be a business related use case for doing

this

Current Search Screen

Current Results Screen

Configurability   In the current system, the fields on the search screen and

the results screen are configurable by the recruiters and this is a requirement for the new design

  The configuration is limited, and the process for changing both screens requires multiple clicks and the user must interact with pop-ups and new screens to accomplish the tasks

Redesigned Workflow & Screens

New Search Screen   A search screen is still required to “set the table” for the

faceted search   The search screen provides an enhanced parametric

search experience   The enhancements are described on the following screen

shot

Search Screen

Key word search supports natural language search, context recognition, and other search elements specified in the search requirements section. Boolean search is still supported.

The result set indicator provides an indication in real time that the selected search will (or will not) provide a non empty result set.

This slider control allows the user to weight the importance of one facet relative to other facets.

Enhanced location facet allows users to specify a search radius around the selected locations.

New switch design for selecting all or individual values within a multi-select facet.

Enhanced single-select radio button selection facilitates selection of values.

Enhanced facet design allows values within a facet to be included and excluded.

Cleaner, reorganized left oriented menu structure.

Labels positioned above fields and facets facilitates visual scanning.

Enhanced multi-select checkbox selection facilitates selection and de-selection of values without inadvertently deselecting all selections.

Facet values that exceed a maximum width or maximum number of characters are truncated with an ellipses. The full value displays on mouse-over.

New Results Screen   The redesigned results screen uses faceted search to

refine the search results   Faceted search allows users to move through large

information spaces in a flexible manner without feeling lost (Hearst, 2002)

  Faceted search exposes hierarchical faceted metadata, which both guides the user toward possible choices and organizes the results of keyword searches (Hearst, 2002)

  Faceted search facilitates the users’ innate process for searching for, finding, and consuming information (Card and Pirolli, 1999)

Faceted Search Results Screen

Faceted Search: Overview “Ribbon” provides access points to invoke additional functionality.

Depending on the configuration, results can provide a more compact view of data for each candidate.

Easy access to up to three levels of sorting.

Keyword search supports natural language search, context recognition, and Boolean search, including support for searching in specific fields.

Faceted Search: Adding & Removing Facets

•  All facets are expand/collapse panels. •  Facets can be added and removed using

drag & drop or point & click. •  Facets in the Filter Your Results list can be

repositioned using drag & drop or point & click.

•  A facet can be configured to include and exclude values.

Faceted Search: Adding Tags

•  Tags can be applied to all results or to one or more selected results.

•  Tags can be viewed in a cloud or alphabetically in a list.

•  Note: I’m not recommending the design of the pictured tag cloud; this is just a screen shot I found on Google images.

Faceted Search: Adding or Editing a Layout (1)

•  New layouts can be created on the fly. •  The form for adding or editing a layout

displays as a modal window over the currently displayed screen.

•  Fields can be added or removed from the new layout by drag & drop or cut and paste.

•  Fields can be a text field (like degree), an icon (like forms), or an open ended text entry field.

•  Cells can be split or merged, new cells can be added and removed, and new rows can be added.

•  The idea is to visualize the result set, up to the entire database, in 3D space.

•  The user can interact with the visualization to select candidates and refine the search results.

•  Color, size, and position indicate relevance.

•  Note: I’m not recommending the design of this visualization; this is a screen shot from Google Images that comes close to what I’m thinking of.

References   Hearst, M., 2002. Finding the flow in web site search.

Communications of the ACM, 45:9, 42-49.   Hearst, M., 2009. Search User Interfaces. Cambridge

University Press.   Pirolli, P. and Card, S., 1999. Information foraging.

Psychological Review, 106, 643-675.