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THE CONSULTING SIDE: why is manual > automated? (Push, not pull (Follow…
THE CONSULTING SIDE: why is manual > automated?
Push, not pull
Zero additional effort to initiate search
Remove double cognitive cost
Start with rec and treat onboarding as first feedback
Rec first, invite later
No repetitive efforts to get to the same results
Saved filters, or a smarter version of sticky filters
Email with "Here are the candidates that you've selected" - for your reference
Well-defined "search" object that can be revisited
Integrate collections with shortlists and promote heavier use
No cognitive fatigue - just tailored candidates (the problem "goes away")
Customized Drip emails with net-new high-potential matches
Language adaptions in emails (forget "invite", replace by "which candidates do you want to talk to?")
Pro
Collect filter information, feedback (and all actions - scheduling, etc.) via call or "personal" email
Candidates (in theory) all willing to talk
Matching questions
Visible matching score + matching reasons (Geekhunter has it)
Pro with pre-called candidates
Follow-up burden on the provider(all channels are game)
Process of calling after every rec
Create scarcity in the follow-up call
Structured 4-week onboarding (ZP)
Make "collecting feedback" and "learning" the point of follow-ups
What candidates did you like? Can I pull the trigger for you?
Never the same mistake twice (continuous improvement of lists)
Make every shortlist learn
Make feedback richer
Structured info for AMs/RMs to fill out during feedback calls
Company-wide preference criteria
Fully customizable criteria
No "MECE" lock
More flexible taxonomy
Custom filters populated by logical expressions on existing filters or keywords
Custom filters from logical expressions on keywords
Create "candidates persona" through assisted filter (Zenprospect)
Every bit of info is fair game
Customizable filters
Richer profiles through MATCHING QUESTIONS
Full linkedin profile download via Chrome plugin (legal if consent given)
Standardized "crowdsourced questions" used to fill out profile
Enriching via matching questions
"Companies_sectors" table
Product RMs that work for the filters feature only
"Companies_sizes" table
Custom definitions by company (same "filter", different criteria)
Human layer to translate filter (initial chat)
Behavioral training of filters
Saved search becomes filter item
Company-wide vs. employer-specific preferences
Mass "hide" candidates with yellow flags
Modify profile based on which company is looking
Verification and disambiguation are tailored
Thorough checklist (FAQ) for each career/focus, delivered through pre-round calls, matching questions and/or automated and anonymous crowdsourced questions
Fix our experiences fields
Verify experience fields
Simple checks and alerts for ops check (all candidates)
Priority companies trigger ops checks to disambiguate info before approval (generates a better first impression)
Late-stage information collection is part of the package
Companies can ask for more info before invite
Companies can ask for more info before proposal
Companies can prepare custom list of questions to be sent to all potential matches (incorporate into early-auction experience)
Dynamic push of candidates based on late-stage info collection (after initial shortlist) - also possible for cross-company candidate re-utilization
Virtually unbounded inventory
All sources are fair game
In-house SWAT team: Linkedin search, ApInfo search, community search, once liquidity is low
Reactivation based on demand (broad)
Real world version of the "Liquidity map" integrated to marketing
Internalize virtually infinite portfolio: Owner-less profile based on Chrome extension (used by RMs only)
Invite to ownerless profile triggers Linkedin message + call
Unbounded marginal cost curve (cost translated into TTH)
Build SMC view (marketing team)
Decision of what channels to trigger based on expected client LTV
Trigger higher SMC sources once a few scarcity thresholds are hit
Create team of product RMs focused on acquisition efficiency
Alternatively, create 3 main information-collection pillars for RMs (sourcing cost, filters, Pro info)
Higher TTH is acceptable because it's base case
Collect "difficulty" index data during onboarding
Present expected SLA to companies based on "difficulty" index
Lower TTH expectations from the get go
Modulate RM help based on difficulty + LTV
Optimize for a number of iterations per search (RM level) that is > 2
Known model legitimizes failures and cost
The world is ok with process being a black box
Prioritize hard-to-find roles and test recruiter-like approach there (not for traditional positions we can serve)
Stop saying that candidates are not interviewed
Stop detailing the process
It's not easy, so it's naturally expensive
Stop selling the ease of use
Increase examples and/or mentions of "manual work" being done
Create the image of "your personal recruiter" for the first 3 months
Cognitive comfort in the well-known translates into engagement
Create the Revelo CV
Export CVs to email (individual or wishlist) + links to profile
Response to rec (email) becomes invites
Expedite Drip integration and collection-based recs (automation)
Why sell process as something different? Sell just the "AI".