31 meetings in 16 weeks, and a precise map of who actually buys corporate travel in India.

The client wanted meetings with the people who own corporate travel at India's large enterprises. The brief named the HR Manager as the buyer. The calls pointed elsewhere, so we rebuilt the persona set inside the first month, held 9 meetings a month on a list that kept shrinking, and worked the reachable market to 96% coverage. Then we measured where it ends.

Cold calling · Persona rebuild · Data waterfall · India enterprise

• 3,074 outreach attempts in 16 weeks

• 707 live conversations, 23% connect

• 31 meetings booked, 100% phone-sourced

• 72 open pipeline records

• 96% of the reachable market worked

At a glance

• Client: Corporate travel management company, part of a larger group

• Sector: Corporate travel management: air, hotels, MICE, Forex & Visa, duty of care, self-booking tool

• Market: India, all regions · 501 to 10,000 employees · 3 crore+ annual air spend

• Engagement: 16 weeks · 24 Jun to 9 Oct 2026 · ongoing

• Channels: Cold calling (primary) · LinkedIn · Email

• Stack: Clay · Apollo · RocketReach · SignalHire · ZoomInfo

• Services: Plug-in SDR team · ICP & persona engineering · List building & enrichment · Appointment setting

Result

What sixteen weeks delivered.

• 31 meetings booked, 16 already held. The client has qualified 6 so far, with estimated annual air spend of up to 10 crore, and more are under review.

• Efficiency rising while supply fell. Bookings were held at 9 a month through July, August and September as attempts dropped from 1,014 to 884, which took the rate from 0.89 to 1.02 meetings per 100 attempts.

• A buyer map built from 707 real conversations. The HR Manager came out. Travel, admin, procurement and finance titles went in, each added because someone holding it answered the spend question on a live call.

• A measured ceiling, not a guess. The list went from 15,108 contacts to 1,672 callable contacts across 1,030 accounts. With 96% of them worked, the next phase is already mapped.

A replacement sale, sold by phone.

The client is an India-based corporate travel management company within a larger group. It runs the full corporate travel stack for 600+ large, mid-sized and SME clients through 470 trained travel professionals and a self-booking tool that plugs into expense and HRMS portals. It reports 99% client retention.

Managed corporate travel is nearly always a replacement sale. The companies big enough to need it usually already have a TMC, an agency or a booking portal, and those contracts run on annual cycles. Who owns the decision also changes from one company to the next. The client needed a steady flow of conversations with whoever holds the travel budget at India's mid-to-large enterprises, and it needed them on a list that never touched an account its own team was already working.

Meetings with the travel owner, at companies that spend enough to matter.

The goal was qualified meetings with the person who owns or influences corporate travel procurement. Raw dials and contacts didn't count. Every account the client was already working was off-limits, across the whole company and not just the named contact.

The qualification bar

• Geography: India, all regions, with no city or state restriction.

• Size: 501 to 10,000 employees.

• Spend: 3 crore or more in annual travel, confirmed in conversation.

• Industries: pharma, manufacturing, chemicals, medical devices, biotech, IT services, software. Government and real estate out.

• Exclusions: every existing client account, removed at account level.

• Owner: the meeting is with the person who owns or influences travel.

The buyer had no fixed title, and the filters left little room.

There were two problems. First, travel ownership in Indian enterprises is scattered. Depending on the company it sits with HR, admin, facilities, a travel desk, procurement or finance, so the HR Manager named in the brief was a reasonable guess that turned out to be wrong.

Across the engagement, 438 of 707 live conversations reached someone who did not own or influence travel. Second, the filters were tight once stacked: India only, 501+ employees, a 3 crore spend bar and account-wide exclusions, all on back-office personas whose direct dials are thin in every commercial database. The work was to find the real buyer quickly and then make every reachable number count.

What moved in sixteen weeks

Before

• List built to the brief on paper: 15,108 contacts

• Buyer assumed to be the HR Manager

• Sub-500-employee companies in scope despite the 3 crore bar

• Exclusions matched contact by contact

• No read on which persona or channel reaches the travel owner

• No measure of how much of the market can be phoned

After

• Persona set rebuilt on live-call evidence: travel, admin, procurement, finance

• Size floor at 501 employees, applied on evidence

• Exclusions applied account-wide, with every colleague behind an excluded contact removed

• Phone confirmed as the converting channel, sourcing all 31 meetings

• Callable market measured at 1,672 contacts across 1,030 accounts, 96% worked

• 31 meetings, 16 held, 72 open pipeline records

Three systems working against one shared list.

• 01 Evidence-led persona engine. The list started exactly to the brief and was then rebuilt on what the calls showed. After about 700 dials and 140 conversations, two segments came out (HR Managers, and companies under 500 employees) and the titles whose holders could actually answer the spend question went in: Travel Manager, Travel Coordinator, Admin and Administration Head, HR Director, and Procurement Head, Director and Manager, with CFO, Head of Finance and Facilities Manager as secondary routes to the budget. Clay · Apollo.

• 02 Six-provider phone waterfall. Clay on Apollo built the base. RocketReach, SignalHire and others then ran in sequence, each one taking only what the one before it missed, which took phone coverage to about 1,500. A negotiated ZoomInfo batch then went after the last few hundred hard cases and returned 241 more records. The final pool was 1,672 callable contacts across 1,030 accounts.

• 03 Three channels, one scoreboard. Cold calling, made under the client's name, ran as the appointment engine. LinkedIn ran senior-to-senior from a client leader's own profile, leadership tier first. Email ran at volume to test whether a different channel could reach the people the phone could not. All three worked the same list, so the results could be compared like for like.

The list, filter by filter

Every filter defensible. Together, they leave 11% of the market.

• 15,108 Original TAM, onboarding filters as given

• − 6,181 HR Manager titles out, could not name the travel owner

• − 1,014 Size band applied, 501 to 10,000 employees

• ~3,600 Working personas added: travel, admin, procurement, finance

• − 2,450 Client exclusions applied, whole accounts and not just contacts

• 1,672 Has a phone number, after six data providers

1,672 callable contacts across 1,030 accounts: 11% of the market we started with. No provider returned a number for the other 778.

Built to brief, rebuilt on evidence, worked to the edge.

• Phase 01 — Built to brief. Late June to mid July, test the brief on live calls. The list was built strictly to the onboarding form: 15,108 contacts. In the first learning period, about 700 dials produced roughly 140 live conversations, and two patterns were impossible to miss. HR Managers couldn't answer the travel-spend question or say who could, and sub-500-employee companies almost never cleared 3 crore. Outcome: first meetings on the board, two filters proven wrong.

• Phase 02 — Rebuilt. Mid July to August, let the calls name the buyer. HR Managers and sub-500 companies came out, and the titles whose holders answered the spend question went in. Exclusions moved to account level when the client confirmed that an excluded contact means the whole account. The six-provider waterfall then took the list to 1,672 callable contacts. Outcome: a working persona set, 9 meetings in July.

• Phase 03 — Worked to the edge. August to early October, convert harder as supply narrows. Bookings held at 9 a month as attempts fell. The coverage ceiling was flagged to the client in mid-August, and written proposals for the next phase followed in September. By 9 October, 96% of the reachable list had been worked, at an average of 1.9 attempts per contact. Outcome: 9 meetings a month, next phase mapped.

Execution overview

• Duration: 16 weeks, 24 Jun to 9 Oct 2026

• Market: India, all regions, 501 to 10,000 employees

• Channels: Cold calling (primary) · LinkedIn · Email

• Audience: Travel, admin, procurement and finance owners

One channel converted, and the data shows which.

Across 16 weeks the programme logged 3,074 call attempts, 707 live conversations and 31 booked meetings, alongside 258 LinkedIn invitations and 2,084 emails against the same list. Taken channel by channel, the results are clear.

• Cold calling — 3,074 attempts, 23% connect. Conversations 707. Meetings 31. The appointment engine, sourcing every meeting. It also works through the list fastest.

• LinkedIn — 258 invitations, 30% accepted. Messages 130. Replies 1. The senior-to-senior request gets accepted (77 of 258), but conversations don't follow.

• Email — 2,084 sends, 0.4% reply. Replies 9. Positive 1. Delivered fine but drew no usable signal. Deliverability is not the constraint here.

Meetings booked by month, on a list that was shrinking

• June — 1 meeting, 57 attempts, ramp-up

• July — 9 meetings, 1,014 attempts, 0.89 per 100

• August — 9 meetings, 914 attempts, 0.98 per 100

• September — 9 meetings, 884 attempts, 1.02 per 100

• October — 3 meetings, 205 attempts, to 9 Oct. A part-month sample.

Attempts fell 13% from July to September while meetings held flat, so each 100 dials booked more than the month before.

Open pipeline: 72 records, coded

• 37 already contracted: an incumbent TMC, agency or booking portal is in place, several signed within the last three months.

• 35 parked, not lost: they asked us to return on a named date (next quarter, January, February, April 2027).

• 14 below the bar: the right persona with real interest, but 1 to 2 crore in annual air spend.

Categories overlap: one account often sits in two. The serviceable market is narrower than the qualified one.

The pivot that kept the engine producing

The onboarding brief assumed the HR Manager owned travel. Within a month, live calls showed otherwise: HR Managers couldn't say what the company spent on air travel or who did. We took them out and rebuilt the persona set around the titles that answered the spend question on real calls: Travel Manager, Admin Head and Procurement Head first, with CFO and Head of Finance as routes to the budget. Every meeting since has come from that rebuilt set.

A shrinking list, a steady calendar.

Impact.

• A steady flow of travel-owner meetings: 9 a month for three straight months, on a list that shrank the whole time.

• A buyer map the client can reuse, built from 707 real conversations rather than a title library.

• A measured ceiling. The client knows exactly how much of its agreed market can be phoned, so the next phase is aimed by data.

• A warm re-entry calendar: 35 accounts gave dates to come back, running into 2027.

Key takeaways.

• Title doesn't tell you who owns travel. The spend question does: ask it, and listen to who can answer.

• Qualified isn't the same as serviceable. A company can pass every filter and still have signed with an incumbent in August.

• When three channels flatten together on the same list, the list is the variable, not the channel.

• The calls carry the client's name, so re-dialling a worked list spends their goodwill. Coverage has to be managed as carefully as volume.

Follow the calls, hold the filters, flag the ceiling early.

Blueberg treated the onboarding brief as a starting hypothesis and let live conversations correct it inside the first month. The filters the client set were held strictly, account-wide exclusions included, so no live deal of theirs was ever double-touched. A six-provider data waterfall squeezed out phone coverage that no single source could reach, and all three channels ran against one list, so the results gave a clean read. When the numbers showed the list would not last to year end, that went to the client in month two with options attached, well before the list actually ran out.

What's next: same motion, new demand, none of it needing new data.

The next phase points the same SDR and the same motion at demand already inside the client. First come dormant and closed-lost accounts, where spend and persona are already known, which adds four to six weeks of capacity. Next come accounts the client is actively pursuing that don't have a demo yet: Blueberg books the meeting and the client's account executives run it. Third is upsell and cross-sell into existing clients, under a qualification bar built for services beyond air. The provider sweep keeps running underneath and should add another 50 to 100 accounts, and the 35 parked accounts come back on their own dates from January.

In summary: right buyer, right channel, a market measured to its edge.

Sixteen weeks in, the client knows who owns travel in the companies it sells to, knows the phone is the channel that books them, and knows exactly how much of the agreed market is left. It also has a next phase built on demand it already owns.

The numbers

• 31 Meetings booked

• 707 Live conversations

• 72 Open pipeline

• 1.02 Meetings per 100 attempts (Sept)

• 96% Reachable list worked