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HR TECH: ADVANCED CANDIDATE SEARCH
(A sample product design project adapted for my portfolio)
INTRODUCTION
We’re looking to develop a new feature for our businesses called "advanced search".
The intention is to allow...
HOW DO
CURRENT
CUSTOMER
S FEEL?
RESEARCH DESIGN
We will begin by considering the level of time and budget for research to ensure that we
build a product t...
RESEARCH DESIGN (continued)
Analytics on Current Use: What do our current analytics capabilities tell us about filter
util...
...BUT WITHOUT REAL USERS TO
SPEAK WITH
...we will need to create the sample user:
Gus
Age: 27
Hometown: NYC
Company: IvyG...
SAMPLE PERSONA
Gus is recruiting for an academic research paper company of 50+ employees based
in Hong Kong and abroad. Th...
GUS USES LINKEDIN RECRUITER
Features: over 20 filters, save searches + notifs,
activity-based, lite and full versions
Stre...
SAMPLE PERSONA (continued)
What Gus likes and dislikes:
● He liked the headshots on LinkedIn and uploaded writing projects...
GUS AND OUR PLATFORM
Observations from our client visit:
● Gus frequently re-opens the mega-dropdown of filters to toggle ...
WHAT ARE
WE
MISSING?
MORE SAMPLE PERSONAE
Let’s imagine we heard from Gus and also:
1 - Sarah, who recruits one-day volunteers for an education...
WHAT’D WE LEARN?
Our customer research reveals the following insights:
● Many of our recruiters are not HR professionals a...
SO WHAT
CAN WE ADD
TO THE
SEARCH?
FEATURE SUGGESTION 1
ADDITIONAL FIELDS
● REQUIREMENTS:
○ Presence of writing samples, volunteer experience, test scores as...
FEATURE SUGGESTION 2
MATCH SCORE
● REQUIREMENTS:
○ Implement a 1-100 match score for candidates whose properties we can ad...
FEATURE SUGGESTION 3
SAVED SEARCHES / NOTIFICATIONS
● REQUIREMENTS:
○ User can save their advanced search
○ User can estab...
FEATURE SUGGESTION 4
SAVE CANDIDATES
● REQUIREMENTS:
○ User can mark candidates for a short list (before clicking ‘invite ...
DESIGN CONSIDERATIONS
LOADING INDICATOR:
● With more advanced search (may) come longer load times. Consider a ‘loading’ ic...
DESIGN CONSIDERATIONS (cont.)
FORM DESIGN:
● What types of forms guide our filtering?
○ Testing shows that search boxes ma...
WHAT DOES
ADVANCED
SEARCH
LOOK LIKE?
A SEARCH FUNCTION...
● May be a set of filters and select boxes (current search)
● May be able to interpret natural langua...
MONSTER POWER RESUME SEARCH
Features: contextual search (human-guided
machine learning), match score
Strengths: simplified...
ANGEL LIST
Features: open-ended keyword search,
8 filters, incorporates ‘looking for’
Strengths: simplicity, narrowed cand...
STREETEASY - INSPIRING SEARCH
Not a competitor, but a site with a similarly feature-heavy dataset and functional
advanced ...
STREETEASY - INSPIRING SEARCH
Strengths (cont.):
Hierarchical radioboxes / multi select for full control
and smart time-sa...
TIMELINE
AND
KANBAN
BOARD
TIMELINE CONSIDERATIONS
We want to budget time for three key types of testing:
● Feasibility Testing (can the team create ...
SAMPLE TIMELINE
MEASURING
SUCCESS
SAMPLE BOARD
To organize our project and its goals
SAMPLE METRICS TO LIVE BY
We will host a few simple graphs measuring the above in a shared, real-time dashboard such as Ta...
SAMPLE METRICS TO LIVE BY (p2)
We will host a few simple graphs measuring the above in a shared, real-time dashboard such ...
CHECKING BACK IN WITH GUS
Gus is now able to create saved advanced searches with different combinations of
high-match, hig...
FUTURE
RESEARCH
FUTURE RESEARCH
Down the line, there are more ways we can grow our search’s power. Ideas include:
Resource Center: A poten...
FUTURE RESEARCH (continued)
Program Keywords: We can master an understanding of one-of-a-kind major names
like “Media, Cul...
THANKS
FOR
READING!
MAX BRAWER
MAX.BRAWER@GMAIL.COM
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Product Research: Advanced Talent Search

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A deck adapted for an interview/portfolio setting where I walk through how a product manager/designer would build a new search tool for an HR Tech platform

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Product Research: Advanced Talent Search

  1. 1. HR TECH: ADVANCED CANDIDATE SEARCH (A sample product design project adapted for my portfolio)
  2. 2. INTRODUCTION We’re looking to develop a new feature for our businesses called "advanced search". The intention is to allow businesses to do highly targeted lookups of students We need to ask: ● What are the features and functionality that would stand out? ● How would we get across the features and functionality to the dev team? ● How do we quantify success? What are our core KPI’s and benchmarks?
  3. 3. HOW DO CURRENT CUSTOMER S FEEL?
  4. 4. RESEARCH DESIGN We will begin by considering the level of time and budget for research to ensure that we build a product the user’s want, not what we think they want. Customer Visit: First, observe real users attempting to hire. Observe, understand process and watch for pain points. Follow up with discussion of what they couldn’t accomplish. Customer Service: Review past year of client requests and customer service inquiries to identify user stories that require further study and client input on search. ...continued on next page
  5. 5. RESEARCH DESIGN (continued) Analytics on Current Use: What do our current analytics capabilities tell us about filter utilization and outcomes? Design & Engineering Feedback: How is our database optimized for advanced search? What requirements would we need to consider when adding functionality? What functionality do the designers and engineers have in mind?
  6. 6. ...BUT WITHOUT REAL USERS TO SPEAK WITH ...we will need to create the sample user: Gus Age: 27 Hometown: NYC Company: IvyGate Tenure: 4 years Position: Team lead
  7. 7. SAMPLE PERSONA Gus is recruiting for an academic research paper company of 50+ employees based in Hong Kong and abroad. The company needs native english speakers to edit papers submitted to international journals. He is looking for part and full-time employees among students and recent grads who show strong writing acumen and a liberal-arts background to flexibly understand papers on multiple subjects. They should be in New York schools or residents of New York. Gus comes from academia and is new to recruiting. He is spending averse. He found us through a friend after browsing LinkedIn & Monster’s pricing structure and will be trying us out.
  8. 8. GUS USES LINKEDIN RECRUITER Features: over 20 filters, save searches + notifs, activity-based, lite and full versions Strengths: can keyword search any component, low-cost, taps into user gen content (e.g., Notes) Weaknesses: limited by networks, limited to LinkedIn content types
  9. 9. SAMPLE PERSONA (continued) What Gus likes and dislikes: ● He liked the headshots on LinkedIn and uploaded writing projects, which he skims ● He likes keeping a saved search + notifications from LinkedIn because he needs to consider part-time openings at proactively ● He has trouble confirming that his college candidates will be in NYC consistently ● He notices that many college students don’t maintain detailed profiles or share writing work that is relevant to him
  10. 10. GUS AND OUR PLATFORM Observations from our client visit: ● Gus frequently re-opens the mega-dropdown of filters to toggle items on and off ● He writes down or screencaptures names to remember who stood out (but is not yet ready to send invites) ● He tries to click and add to the school search and wishes aloud that he could multi-select a list of schools ● He filters but does not see reason to utilize the sort function
  11. 11. WHAT ARE WE MISSING?
  12. 12. MORE SAMPLE PERSONAE Let’s imagine we heard from Gus and also: 1 - Sarah, who recruits one-day volunteers for an educational nonprofit 2 - Mark, a customer care manager at Uber seeking drivers with communication skills 3 - Emma, a Google HR recruiter who is assessing the “googly-ness” of our candidates but does not plan on sending invites yet And we identified these insights:
  13. 13. WHAT’D WE LEARN? Our customer research reveals the following insights: ● Many of our recruiters are not HR professionals and lack a clear understanding of what to look for in college candidates (tl;dr they wing it) ● Users wanted to carefully weigh their options across multiple advanced searches and re-sorts to create an overall “match” score ● A diverse mix of extracurriculars, non-profit experience, and published work showed initiative and was valued beyond grades
  14. 14. SO WHAT CAN WE ADD TO THE SEARCH?
  15. 15. FEATURE SUGGESTION 1 ADDITIONAL FIELDS ● REQUIREMENTS: ○ Presence of writing samples, volunteer experience, test scores as search levers ● CONSIDERATIONS: ○ For these to be searchable, they must exist in student data ■ Action: Feasibility discussion with B2C product team ○ Is our database suited to handle these additional criteria? ■ Action: Feasibility discussion with engineering ○ Does this bias students who signed up in the past? ■ Action: Consult with customer service teams about recontacting/updating options
  16. 16. FEATURE SUGGESTION 2 MATCH SCORE ● REQUIREMENTS: ○ Implement a 1-100 match score for candidates whose properties we can adjust over time (or this value can be hidden, but sorted upon) ● CONSIDERATIONS: ○ What features go into this score and how do we dynamically weight features? ■ Action: Plan for ongoing testing with engineering, ensure we capture analytics needed to build this ELO score (similar to OKCupid, Tinder) ○ What messaging do we put out around this score? How do we encourage students to think about it? ■ Action: Consult with customer service and marketing teams ■ Action: A/B testing plan of different inputs/weights
  17. 17. FEATURE SUGGESTION 3 SAVED SEARCHES / NOTIFICATIONS ● REQUIREMENTS: ○ User can save their advanced search ○ User can establish notifications for new student signups who meet the search criteria ● CONSIDERATIONS: ○ What defines a ‘new match’? ■ Action: Ensure we synchronize with profile completion goals and match score ○ What do notifications look like? How can users adjust their content and frequency? ■ Action: Consult with customer service and marketing teams
  18. 18. FEATURE SUGGESTION 4 SAVE CANDIDATES ● REQUIREMENTS: ○ User can mark candidates for a short list (before clicking ‘invite to apply’) ● CONSIDERATIONS: ○ How does this appear graphically? Is a new list or page needed for saved results? ■ Action: Consult with design and engineering on feasibility ○ What happens to candidates who go off the market? ■ Action: Consult with design and engineering on options
  19. 19. DESIGN CONSIDERATIONS LOADING INDICATOR: ● With more advanced search (may) come longer load times. Consider a ‘loading’ icon while search is running queries BREADCRUMBS: ● Assess with designers if navigational aids will be possible or helpful to detail the chosen filters (as with Amazon
  20. 20. DESIGN CONSIDERATIONS (cont.) FORM DESIGN: ● What types of forms guide our filtering? ○ Testing shows that search boxes may need hint text or revised form type (see next section for inspiration) ○ Filter dropdown may need to expand to fit new criteria, assess fit ICONS ON CARDS (INDICATORS): ● With advanced criteria it may become impractical to fit blue icons for all items and “yes/no.” Recommendation: blue icon present only for completed criteria
  21. 21. WHAT DOES ADVANCED SEARCH LOOK LIKE?
  22. 22. A SEARCH FUNCTION... ● May be a set of filters and select boxes (current search) ● May be able to interpret natural language (e.g., Facebook) ● May privately contain advanced features not visible to the end user (e.g., Amazon rankings, Google page rankings) Besides LinkedIn, we can find inspiration in...
  23. 23. MONSTER POWER RESUME SEARCH Features: contextual search (human-guided machine learning), match score Strengths: simplified match score (1-10), semantic comprehension of keywords, built-in resource center Weaknesses: charges by resume views (not outcomes), antiquated design, busy UI
  24. 24. ANGEL LIST Features: open-ended keyword search, 8 filters, incorporates ‘looking for’ Strengths: simplicity, narrowed candidate universe, modern interface, solicits candidate input Weaknesses: open to bias, dual-modes of search input may confuse
  25. 25. STREETEASY - INSPIRING SEARCH Not a competitor, but a site with a similarly feature-heavy dataset and functional advanced search to match Strengths: Gives user ownership over complex data / features Basic / Full toggle to keep search lightweight Caters to market idiosyncrasy (e.g., public transit)
  26. 26. STREETEASY - INSPIRING SEARCH Strengths (cont.): Hierarchical radioboxes / multi select for full control and smart time-saving efficiencies Searches, when saved with one click, become daily email notifications Weaknesses: Low contrast in visual design (no color or weight changes) Lacks levels of accordion functionality (all or nothing toggle, advanced search is overwhelmingly long) Mobile optimization uses space clumsily on desktop
  27. 27. TIMELINE AND KANBAN BOARD
  28. 28. TIMELINE CONSIDERATIONS We want to budget time for three key types of testing: ● Feasibility Testing (can the team create the prototype we’ve developed) ● Usability Testing (are users able to advanced search with ease) ● Value Testing (did targeted search add value) We also consider: ● Client deadlines ● University schedules ● Product marketing timelines/revenue goals
  29. 29. SAMPLE TIMELINE
  30. 30. MEASURING SUCCESS
  31. 31. SAMPLE BOARD To organize our project and its goals
  32. 32. SAMPLE METRICS TO LIVE BY We will host a few simple graphs measuring the above in a shared, real-time dashboard such as Tableau ● Utilization: ○ # of saved searches ○ Avg. # of filters activated per search ■ Invites Sent (cut by # of filters activated to find candidate, see ‘branch links’) ○ Drop off rate: what % of searchers close or leave the page at each step (initialize, search, re-sort, re-filter, save search, add to short-list, invite to apply) ○ Hires + match rate (find the efficacy of match rate in predicting a hire) ○ Time on search page (though we need to analyze / uncover if we want this lower or higher ● Branch Links ○ What combination of filters lead to an invite? A hire? Opportunity to analyze through links
  33. 33. SAMPLE METRICS TO LIVE BY (p2) We will host a few simple graphs measuring the above in a shared, real-time dashboard such as Tableau ● Net Promoter Score ○ Measured before and after rollout, also compared between user clusters who search simple or advanced. Also gather NPS on Linkedin/Monster as benchmark ● Alignment with guiding principles: ○ Did we improve KPIs that were previously important to the team? And do we feel we have honored the values of our company as a whole?
  34. 34. CHECKING BACK IN WITH GUS Gus is now able to create saved advanced searches with different combinations of high-match, high-GPA students with writing samples from 3 popular NYC-area universities. One for part-time, one for full-time. He checks his weekly update email for new hits to the searches and periodically adds top matches to his saved lists. He invites a batch to apply every couple days until his new openings are met. He’s glad he can stay on top of a candidate pool of smart, motivated students without having to scramble each time he needs to recruit. He’s thankful we made this part of his job so much less time consuming.
  35. 35. FUTURE RESEARCH
  36. 36. FUTURE RESEARCH Down the line, there are more ways we can grow our search’s power. Ideas include: Resource Center: A potential form of product marketing would be to build college-student hiring resources that highlight our search tools Student-centric view of ‘location’: Career sites typically search for within X miles of a city. For a student, we could master a special blend of college location and permanent residence to guide employers to students who will be in the right place
  37. 37. FUTURE RESEARCH (continued) Program Keywords: We can master an understanding of one-of-a-kind major names like “Media, Culture, Communications” (vs. “Psychology”) to break down the info barrier between employer and student and cluster programs in the aims of an effective match score algorithm Mobile: More advanced searches may not translate well to mobile apps / mobile browser. Down the line we would consider which components would carry over (e.g., saved advanced searches, conducted on PC, can have results viewed on mobile)
  38. 38. THANKS FOR READING! MAX BRAWER MAX.BRAWER@GMAIL.COM

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