Before you start
Qualifying LPs is the step between "I have a database" and "I have a list I should actually email." Skipping it is the single most common reason outreach underperforms — not because the LPs weren't real, but because they were never a plausible fit in the first place. This works on any LP database, including Altura Data's, but the same filtering logic applies whether the raw list came from a purchased database, a referral network, or manual research.
What you need before starting: a raw list of LP contacts with at minimum investor type, AUM range, stage focus, sector focus, and geography per contact — the five fields this whole process filters on. A list of names and emails alone isn't enough to qualify against.
Step-by-step
1. Confirm investor type matches your fund structure
What: Filter out any contact whose investor type doesn't structurally fit what you're raising — a sovereign wealth fund and a family office invest through very different processes, even at similar check sizes.
Why: Investor type determines the entire sales process — timeline, diligence depth, decision-maker structure. Getting this wrong wastes the most time.
Common mistake: Treating "institutional investor" as one category. A pension fund's process looks nothing like a family office's.
How Altura Data helps: Every contact is tagged by investor type — endowment, pension, sovereign wealth fund, fund-of-funds, family office, or VC-as-LP — as a filterable field, not something you infer from a firm name.
2. Filter by AUM range against your fund's actual size
What: Cross-reference each LP's AUM range against typical allocation percentages for a fund your size.
Why: Large institutions often have minimum check sizes or diligence requirements that a Fund I can't meet — targeting them wastes a conversation that was avoidable from the data alone.
Common mistake: Assuming bigger AUM always means a better prospect. A $50B fund's minimum ticket may be 10x your entire fund size.
How Altura Data helps: AUM range is a structured field per contact — filter directly rather than researching each firm individually.
3. Match check size to what you can realistically absorb
What: Filter for LPs whose typical check size fits comfortably within your fund's target allocation per LP.
Why: A check that's too large concentrates your cap table risk; one that's too small may not be worth the diligence time on either side.
Common mistake: Chasing the largest possible checks instead of the best-fitting ones.
How Altura Data helps: Check-size and stage-focus fields let you filter for LPs whose typical allocation matches your target range before you draft a single email.
4. Confirm stage focus matches your fund's stage
What: Filter for LPs whose stated stage mandate includes your fund's stage — seed, early, growth, or multi-stage.
Why: An LP with no mandate for your stage is a near-automatic pass, regardless of how well everything else matches.
Common mistake: Assuming a generalist-sounding LP will consider any stage. Most have real, often internally enforced, stage boundaries.
How Altura Data helps: Stage focus is a structured field, filterable alongside AUM and check size rather than requiring a separate research pass per contact.
5. Filter by sector focus if your fund has one
What: If your fund has a sector thesis, filter for LPs whose sector focus overlaps — or who are explicitly generalist.
Why: A sector-specialist LP outside your thesis is a low-probability conversation regardless of everything else lining up.
Common mistake: Skipping this filter for a generalist fund and missing that some "generalist" LPs actually have unstated sector leanings visible in their portfolio.
How Altura Data helps: Sector focus is tracked per contact where known, letting sector-focused funds filter directly instead of researching portfolio composition firm by firm.
6. Check geography against your fund's mandate and your own reach
What: Filter for LPs in geographies your fund is actually structured to accept capital from, and where you have some capacity for relationship-building — time zone, travel, existing network.
Why: Cross-border LP relationships add real friction — tax structuring, currency, travel — worth factoring in before outreach, not after a promising first call.
Common mistake: Casting a maximally wide geographic net without weighing the added complexity of managing relationships across many jurisdictions simultaneously.
How Altura Data helps: Geography and a cross-border flag are both structured fields, so you can deliberately scope a raise to specific regions or explicitly include cross-border LPs, rather than discovering the mix after the list is built.
7. Build the qualified shortlist and rank by fit
What: After filtering on all five dimensions, rank what's left by overall fit rather than treating every qualifying contact as equal priority.
Why: A shortlist of 100–200 well-matched LPs, prioritized, converts at a meaningfully higher rate than an unranked list of the same size.
Common mistake: Stopping at "qualified" without a second pass for relative fit — treating a borderline match the same as a near-perfect one.
How Altura Data helps: Because every filter dimension is a structured field, ranking by how many criteria a contact matches (not just pass/fail) is a straightforward CRM or spreadsheet exercise once the data is exported.
Example workflow
A Fund I manager raising a $30M generalist seed fund: start with the full Standard LP Pack (5,800+ contacts), filter to investor type = family office or fund-of-funds (institutional LPs typically pass on unproven Fund I managers), AUM range under $2B (larger institutions rarely write Fund I checks), stage focus includes seed, and geography = home region plus 2–3 target expansion markets. That filtering pass alone typically reduces a multi-thousand-contact database to a few hundred genuinely plausible conversations — the actual outreach list.
Tools needed
- A structured LP contact database with the five filter fields above — not just names and emails.
- A spreadsheet or CRM that supports multi-field filtering (Airtable, HubSpot, Affinity, or a plain spreadsheet all work).
- A clear, written definition of your own fund profile (size, stage, sector, structure) to filter against — write this down before opening the database, not while filtering.
Quality checks
- Does every contact on the final shortlist match on investor type, AUM, check size, stage, and geography — not just 2 or 3 of the 5?
- Is the shortlist ranked, not just filtered — do you know which 20 conversations to prioritize first?
- Have you sanity-checked a random sample of 10–15 contacts against public information, not just trusted the filters blindly?
Common Mistakes
- Filtering on only one or two dimensions. Investor type alone, or AUM alone, misses real mismatches the other fields would have caught.
- Treating "qualified" as binary rather than a spectrum. A borderline match and a near-perfect match are not the same priority.
- Skipping mandate and sector fit for a "generalist" LP. Many self-described generalists have real, visible leanings in their actual portfolio.
- Qualifying once and never revisiting. LP mandates and AUM change; a list qualified a year ago needs a refresh, not blind reuse.
FAQ
What does it mean to "qualify" an LP before outreach?
Qualifying an LP means checking that its investor type, AUM range, check size, stage focus, sector focus, and geography plausibly match your fund before you spend time on outreach — the same filtering a sales team applies to leads before calling them, applied to fundraising.
What AUM range should I target as an emerging manager?
There's no universal number — it depends on your fund size and the LP's typical allocation percentage per fund. As a starting heuristic, an LP with $50M–$500M AUM is more likely to write a check sized for a Fund I than a $10B+ institution, which typically has minimum check sizes and diligence requirements built for larger, established managers.
How many qualified LPs do I actually need for a raise?
Most successful raises close from a relatively small number of genuinely well-matched conversations — often well under 100 — not a mass campaign of thousands. A tightly qualified shortlist of 100–200 contacts, properly researched and personalized, consistently outperforms a much larger unfiltered list.
Should I qualify by mandate before or after checking AUM?
Mandate first, then AUM and check size. An LP with a large AUM but no mandate for your stage or sector is a worse fit than a smaller LP whose mandate matches exactly — mandate mismatch is the fastest way to waste an outreach conversation.
Start With Structured, Filterable LP Data
Every contact tagged by investor type, AUM range, check size, stage, sector, and geography. Get the Standard LP Pack → — 5,800+ verified contacts, $487 one-time.