The Self-Sourced Expert Panel: Question Grid to Cleared, Booked Calls

This playbook is for strategy teams, talent-intelligence analysts, and operators who need 8 to 20 qualified people on the phone this month and want to recruit them directly instead of paying expert-network markup. Those people are former competitor staff, customers, channel partners, and suppliers. The method runs in order: list of research questions, quota grid, sourcing, cooling-off and conflict screen, imposter check, booking, and a stop rule for each cell. Each stage says what to do, roughly how long it takes, and what done looks like.

When self-sourcing beats an expert network

Self-sourcing beats a network when you have two to three weeks, a team willing to own its clearance policy, and the discipline to over-recruit. The money case is simple: experts get roughly 30 to 40% of what networks charge clients, so most of a network fee is markup.

At the largest networks a client pays around $1,000 to $2,500 per hour, while the expert on the line might get $300. Across the industry, client prices run $700 to $1,500 per hour, and subscriptions run $20K to $150K per year. The table compares published B2B honorarium benchmarks with network client prices by seniority band.

The 12-call row is my own arithmetic: twelve calls at a $300 honorarium against twelve calls at $1,000 to $2,500. Self-sourcing comes out roughly 3 to 8 times cheaper, before you count your own staff time.

In exchange, you take on the work the network was doing for you:

• Show rates. On cold LinkedIn outreach, 30 to 45% of booked people don't show. Through networks it is 5 to 10%.
• Completion. Do-it-yourself recruiting gets 50 to 70% of recruits through to a completed call. Verified panels get 70 to 85%.
• Vetting. Cooling-off checks, conflict screens, and identity checks become steps you carry out yourself, not promises in a vendor's compliance deck.

Setting the expert interview honorarium

Choose a band for each cell before outreach and write it down. Early-career experts commonly earn $75 to $150 per hour and experienced professionals $250 to $300. Senior specialists can command over $1,000 per hour. I have found no published rate band for regulated specialists. If your study needs them, ask your compliance team what your sector allows before you name a number.

How many expert interviews a market study needs

A narrow study of a homogeneous group reaches saturation within 9 to 17 interviews, according to a peer-reviewed review of 23 studies. A panel drawn from several vantage points usually spreads about 14 to 22 calls across its cells, and each cell's quota is a starting estimate, not a finish line. Saturation is the point where more interviews stop producing new codes, meaning new themes or facts in your notes.

The anchor is Hennink and Kaiser's 2022 review in Social Science & Medicine. Code saturation, which means hearing the full range of issues, came at about 9 interviews. Meaning saturation, which means understanding those issues well enough to explain them, took 16 to 24. Another summary puts the median at 12 to 13. Two common rules of thumb have no evidence behind them. Nobody has tested "3 to 5 per population", and I know of no published source that calls 10 or more interviews overkill.

Commercial due diligence guides written by practitioners split a panel like this:

• Customers: 5 to 8
• Former competitor staff: 4 to 6
• Industry experts: 3 to 5
• Channel partners: 2 to 3

Published per-study totals run 10 to 30 expert interviews. One customer workstream example pairs 15 to 30 interviews with 100 to 200 survey responses. Note the gap: a cell quota of 4 to 6 sits below the 9 to 17 range. That is fine for narrow factual questions answered from one vantage point. It is not enough for "why" questions. Push the cells that carry your hardest questions toward the top of their band.

Building the question grid

The question grid is a table with your research questions as rows and vantage cells as columns. A vantage cell is one population that can answer from direct experience. Mark one primary cell for each question, and a second cell to cross-check where you can. Suppliers are missing from the split above. Add them as a cell when a question depends on input volumes, lead times, or capacity.

STUDY: <market or competitor>
PERIOD STUDIED: <start month> to <end month>
COOLING-OFF FLOOR: 6 months from departure (see clearance policy)

Question                          | Customers | Ex-competitor | Channel | Suppliers
Q1 <question>                     | P         | T             |         |
Q2 <question>                     |           | P             | T       |
Q3 <question>                     | T         |               | P       |
Q4 <question>                     |           | T             |         | P

Honorarium band                   | <$>       | <$>           | <$>     | <$>
Starting quota                    | 5-8       | 4-6           | 2-3     | <set>
Booking target (quota x 1.3-1.5)  | <n>       | <n>           | <n>     | <n>
Backfill geography if cell is thin| <region>  | <region>      | <region>| <region>
Saturation stop                   | close after <n> consecutive calls with no new codes

The clearance policy: cooling-off period and banned categories

Before you source anyone, write a one-page clearance policy with a cooling-off window of at least 6 months since the candidate left the company you are studying. No industry body sets that number, so your team has to pick it and record why.

The FISD Alternative Data Council publishes an Expert Network Research Provider DDQ, a due-diligence questionnaire buyers send to networks. It defines an employee expert as one "currently employed by, or has within a specified cooling off" period been employed by the target. It leaves the length of that period to each network and asks networks which categories of expert they bar. Published policies keep landing on 6 months:

• Integrity Research. Its best practices bar consulting about any company the expert works for or left within the past six months.
• Hyperion. This asset manager's buy-side policy runs a 6-month employment check.
• One expert network. It describes its rule as "within the last six months - sometimes longer."

Six months is a floor, not a universal rule. Some categories have their own rules:

• Officers and directors must not consult while an IPO, merger, or tender offer is pending.
• Former senior US government employees face a one-year statutory cooling-off under 18 U.S.C. 207(c).
• Windows by seniority, function, or sector, such as a longer window for former finance or strategy staff, are not established in any public source. If you set one, write the reasoning into the policy.

The policy contains:

• The cooling-off window and the date you measure it from: the departure date, not the date the profile switched to freelance
• Banned categories: current employees of the target, former employees inside the window, people in a material relationship with the target, officers or directors while a deal is pending, and anyone under a statutory window
• Prohibited topics: confidential information, material non-public information (MNPI, meaning non-public information that could move a security's price), and anything about a current employer
• The pre-clearance procedure: topics approved before the call, whether calls are chaperoned or recorded, and a do-not-contact list
• A cap on calls per expert. One network's example is a maximum of 5 calls with an expert in any given year.
• Record retention. Integrity Research recommends a minimum of three years.

How much of this binds you depends on who you are. The SEC's April 26, 2022 examinations risk alert flagged investment advisers that failed to track and log expert calls, review detailed call notes, and review staff trading in similar-industry securities. Section 204A applies to registered and unregistered advisers alike. Those are obligations on advisers. Whether any of them bind a corporate strategy team is not publicly established, and Regulation FD falls outside the sources behind this guide. Ask counsel. The stakes are real: in the Primary Global Research case, the SEC alleged more than $30 million in illicit profits.

The procedure: from question grid to booked calls

Eight steps, run in order, take a team from a list of research questions to cleared, booked calls. Outreach takes longest, at one to two weeks. Only that figure comes from a published benchmark. The other durations are my own working estimates, so time your first run and replace them.

• Step 1: half a day to a day
• Step 2: about a day
• Step 3: one to two days
• Steps 4 and 5: per candidate, as the list fills
• Step 6: one to two weeks of manual LinkedIn outreach at 5 to 15% response

Sourcing and ranking candidates by relevance, not seniority

Rank candidates on relevance: the role they held, how much their tenure overlaps the period you are studying, and when they left. Seniority filters miss exactly the people most likely to take an expert call, because many of them now call themselves consultants.

In Refolk's index of professional profiles, the consultant pool looks very different from one market to the next:

The shares come from samples of 25 profiles, so treat them as directional only. Many of the profiles that did not hold the exact title held CEO or founder titles at the same time. That matters for screening, and it has two consequences for sourcing:

1. Rebuild seniority from prior roles. In Refolk's index, adding a CXO or VP seniority filter to the US consultant pool returned 0 results. The people are there, but the tag is not.
2. Plan for thin UK cells. The UK pool is about 9 times smaller than the US pool. A niche UK sector cell may run out of candidates before it reaches saturation, so name a backfill geography in the grid on day one.

The four relevance criteria

• Role. Shows they worked in the function your question is about. It misleads when a consultant headline hides the original role or a past title was inflated. Read the job history, not the headline.
• Tenure overlap. Shows they were inside the company during the period you are studying. It misleads when the overlap exists only on paper, for example in a different division or region from the one your question covers.
• Departure date. Shows whether they clear the window and how fresh their knowledge is. It misleads when you take the freelance switch date for the departure date. A renamed career can make someone look gone when they still hold an operating role.
• Specific exposure. Shows they handled the exact thing you are asking about: a supplier category, a product line, a sales channel. It misleads when it is just a keyword added to a freshly rewritten profile.

Most of a sourcing day goes into turning those four criteria into one search across several sources. In Refolk you describe the population in plain English: role, former employer, departure window, current status, and country. You get people back from LinkedIn, GitHub, and the open web, then rank them against the matrix. Writing the departure window into the request applies the cooling-off floor at the search stage, so fewer people fail the screen later.

Funnel math: how many contacts per booked call

At published reply and conversion rates, budget 27 to 200 manual LinkedIn contacts for every booked call, and book 30 to 50% more calls than each cell's quota. Nobody has published reply rates for paid expert calls with former employees, so treat these as general B2B research benchmarks and log your own.

A worked example: a former-competitor cell with a quota of 5 needs 7 to 8 booked slots once you add a 30 to 50% buffer. At manual LinkedIn rates, that is roughly 190 to 1,600 contacts. At the InMail example's rate, it is about 53 to 60. The spread is why you work warm introductions first and why the ranked list has to be long before outreach starts. A niche cell may not contain 190 eligible people at all. A published rule of thumb sets the no-show floor: at a 30% no-show rate, you must "recruit and schedule four participants to complete three interviews".

Hi <first name>,

I'm running a short research study on <market or topic> covering <period>, and your time at <former company> as <role> looks directly relevant.

Would you take a paid 45-minute call in the next two weeks? The honorarium is <amount> for the call.

On scope: I won't ask for anything confidential about any current or former employer. I'll send the topics in writing beforehand so you can flag anything you'd rather not discuss. If you left <former company> less than <window> ago, I can't include you this time, but I'd value a conversation later.

If you're open to it, reply with two times that suit you and I'll send a short screening form.

<your name>
<team, company>

Log every touch as a row in the outreach log: date, channel, cell, candidate, message version, reply, booked slot, honorarium, and the ID of the matching clearance row. That log is how you replace the benchmark rates with your own after the first study.

How to verify an expert is who they claim to be

Compare the profile with earlier versions of itself, from a search-engine cache or a web archive, and confirm at least one past role from a source off the platform. A government-ID badge proves a real person holds the account. It does not prove the career written on it.

The one documented first-person case comes from an expert network that caught computer-science undergraduates posing as senior alumni of leading tech companies. The pattern:

• Both LinkedIn accounts were verified with government IDs
• The profiles had been updated within weeks, with photos that looked AI-generated
• A recent switch to freelance, with keyword-rich profiles
• Dense connections to expert-network staff
• Posting history that began only 4 weeks earlier

What exposed them was a search engine's cache of their old LinkedIn pages. It showed student profiles from before the claimed 30+ years of experience appeared.

What each signal is worth on its own:

• Edit recency. A profile rewritten in the past few weeks is a reason to check its history, not a verdict. Real people rename their careers to earn consulting income.
• Freelance switch date. Tells you when the consultant label appeared. A switch in the last few weeks combined with a dense, keyword-heavy history raises the priority of the history check.
• Photo provenance. A photo that looks generated is suggestive, never conclusive. Treat it as a reason to dig.
• Connections to expert-network staff. Real experts connect with network recruiters too. Density on its own proves only that the person is chasing calls.
• Cached or archived history. The strongest signal you have. If a claimed career is missing from an earlier version of the profile, reject the candidate.
• Off-platform corroboration. One past role confirmed somewhere else, such as a conference speaker page, a patent, a press release, or a public commit history, is what turns a plausible profile into a verified one.

An ID badge proves the name on the account, never the career written underneath it.

The FISD questionnaire, at item 4.27, asks networks what controls they use to catch expert misrepresentation during a live call. When you self-source, the screen call is that control. Ask one specific fact an insider would know without thinking, such as a reporting line, an internal system name, or the order of a launch. Then listen for answers that sound recited rather than remembered.

How this goes wrong: failure modes and false positives

Most bad panels come from one of seven failures. The usual pattern is trusting a credential that proves less than it seems, applying a window too bluntly, or closing a cell too early. Each failure below has a check you can run before the call.

1. Treating an ID-verified badge as proof. It looks like a verified badge on a polished senior history. In the documented case, profiles with that badge carried invented careers. Check: compare cached or archived versions, and confirm one past role off the platform.
2. Reading a recent "independent consultant" switch as a clean exit. A real career may simply have been renamed to earn expert-network income. In Refolk's index samples, people holding CEO or founder titles alongside the consultant title were common. Check: departure date against the window, plus current board seats and operating roles.
3. Ranking by seniority. Seniority filters miss consultants entirely, as the 0 above shows. A former VP who left 4 years ago knows less about your period than a manager who left 7 months ago. Check: tenure overlap with the period you are studying.
4. Applying one 6-month window everywhere. Officers and directors are barred while a deal is pending, and former senior government staff have a statutory one-year window under 18 U.S.C. 207(c). Check: run the category-specific rules before the generic window.
5. Declaring saturation on a mixed cell. The 9 to 17 range assumes a homogeneous group. Cross-site work across geographies has needed 20 to 40 interviews. Check: if a cell mixes regions, company sizes, or buyer types, split it and track saturation in each half.
6. "Professional experts." These are the regulars "who circulate through every network" and repeat the same talking points. They pass every identity check because they are real. Check: one specific factual question at the screen call, and a note of which experts you have already used.
7. Booking without over-recruiting. At a 30% no-show rate you need four booked to complete three. Cold LinkedIn runs 30 to 45%. Check: each cell's booked count equals its quota times 1.3 to 1.5 before you stop outreach.

Closing cells and keeping the panel current

Close a cell when consecutive calls add no new codes. Keep the outreach log, clearance rows, attestations, and call notes for at least three years. Hennink suggests starting near the low end of the 9 to 17 range and monitoring a new-information threshold, the approach described by Guest and colleagues in 2020.

A stop rule you can run:

• Code each call within a day, while the notes are fresh.
• Count new codes per call in each cell.
• Close the cell when the number of consecutive calls set in your grid adds nothing new. Fix that number before the first call, so you are not choosing the stop after the fact.
• If a cell keeps producing new codes past its quota, suspect a mixed population and split it.
• If a cell runs out of eligible people first, which is likely in niche UK sectors, backfill from the adjacent geography named in the grid and record the substitution.

For the backfill, rerun the same plain-English request in Refolk with the neighbouring geography instead of rebuilding the search, then send the new names through the same clearance and imposter screens.

To keep the method current, remember that the cooling-off length is policy, not law, outside the statutory categories. The FISD questionnaire is at version 1.0. Re-read it when it is revised, and have compliance confirm your window at least once a year. When you go back to an expert from an earlier panel, rerun the clearance screen. People change employers and join boards, and the per-expert cap in your policy counts across studies, not within one.