outreach
How to Cross-Reference LinkedIn Export Data With Telegram Usernames for CIS Prospects
LinkedIn export data mostly falls flat for CIS prospects. Here's how to cross-reference it with phone-based lookups to get working Telegram usernames instead.
You exported 2,000 contacts from LinkedIn Sales Navigator, tagged "Russia" and "Kazakhstan," ready to run an outreach sequence. Then you realize half those profiles are inactive, most don't list a phone number, and almost none of your target decision-makers actually use LinkedIn to check messages. In the CIS, LinkedIn is often a parked resume, not a live inbox — and your carefully built list is dead weight.
This is the core problem with CIS prospecting off Western data sources: LinkedIn simply isn't where the conversation happens there. Telegram is. So the real task isn't "export more LinkedIn data" — it's converting what you already have into Telegram usernames you can actually message.
Why doesn't LinkedIn export data work well for CIS prospects?
LinkedIn penetration among business decision-makers in Russia and the CIS is low compared to Telegram, which functions as the default professional and personal communication layer across the region. A LinkedIn export typically gives you a name, job title, company, and sometimes an email — but rarely a working Telegram handle, because LinkedIn has no field for it. Without a phone number or username to anchor the match, you're stuck guessing.
The fix is a bridge step: take whatever contact data you can extract from the LinkedIn export (name, company, sometimes phone from a Sales Navigator enrichment add-on) and run it through a phone-to-Telegram conversion process. That's the actual cross-reference — not matching names directly, but matching phone numbers.
What's the best way to turn LinkedIn contacts into Telegram usernames?
If your LinkedIn export already includes phone numbers (common when it's enriched via Apollo, Clay, or a similar tool), you can convert those numbers directly. CRMChat includes a Phone Number to Telegram Username Converter that takes a list of phone numbers and returns matching Telegram usernames, with an average enrichment rate of 50% for CIS, India, and MENA regions — compared to just 30% for the EU, UK, and Americas. That regional gap is exactly why this workflow is worth running for CIS lists specifically.
But most raw LinkedIn exports don't include phone numbers at all — they're the one field LinkedIn rarely surfaces. That's where cross-referencing against a business registry becomes the stronger path for CIS, since registries (unlike LinkedIn) are a primary, structured source of founder and director phone numbers in the region.
How do you fill the phone number gap when LinkedIn doesn't have one?
For CIS markets, the more reliable starting point isn't LinkedIn at all — it's a business registry. Registries list verified founders and directors by company, filterable by industry code (OKVED) and revenue, which gives you a far cleaner match to "decision-maker" than a LinkedIn job title ever will.
Here's the workflow that gets you from company data to a working Telegram handle:
Pull company data from a registry provider like DataNewton, filtered by industry and revenue, and get an API key for integration.
Connect an AI assistant (Claude or ChatGPT) to DataNewton via that API key so it can query companies on your behalf.
Connect the same AI assistant to CRMChat using your CRMChat API key from Settings → API Keys.
Run the lookup sequence — instruct the assistant to find each company's founder through CRMChat's contact-lookup bot, @crmchatcontactbot.
Pull the founder's phone number from the bot's results.
Convert phone to Telegram username using CRMChat's Instruments → Phone → Telegram tool, then normalize everything into a clean CSV with Username and First Name columns.
CRMChat automates the contact-lookup and phone-to-Telegram conversion steps of this chain, so you're not manually checking each founder's phone number or testing conversions one by one. The result is a CSV ready to load into an outreach sequence, matching the format CRMChat expects for campaigns. For a deeper walkthrough of this exact setup, see the guide on identifying the right decision maker at a CIS company.
Can you still use your existing LinkedIn export in this process?
Yes — treat it as a filter, not the source of truth. Your LinkedIn export is useful for confirming which companies and names are worth prioritizing (titles, seniority, company size signals). Run those company names through the registry-based lookup above to get verified phone numbers and Telegram usernames, rather than trying to match LinkedIn names directly against Telegram, which has no public directory to search against.
Extract company names and target titles from your LinkedIn export as a priority list.
Match those companies against registry data filtered by the same industry/revenue criteria.
Pull founder or decision-maker phone numbers via the contact-lookup bot.
Convert phone numbers to Telegram usernames using the Phone → Telegram tool.
Normalize the output into one CSV before loading it into your outreach tool.
This way your LinkedIn research isn't wasted — it just gets re-routed through a channel that actually converts for the CIS.
What if you need a broader list, not just named decision-makers?
The registry-to-phone-to-Telegram path is best when you're targeting specific named decision-makers. If you instead need volume — a wider pool of prospects in a given industry or niche — group parsing is the complementary move. CRMChat's Telegram Group Parser extracts member lists from public groups and communities, which is useful for building out a broader audience once your core decision-maker list is in place. Group parsing success rates run 20-30% of total members for groups with private member lists (those active in the last 6 months), and processing takes up to 24 hours.
Once you have both lists — verified decision-makers from the registry path, and a broader pool from parsing — you can layer them into a single Telegram-first martech stack and run tiered outreach: direct, personalized sequences to the named contacts, and a lighter-touch sequence to the broader list.
What should your final CSV look like before you start outreach?
Regardless of which path fed your list, normalize everything into one format before sending. CRMChat expects CSV files with clear headers, specifically Username and First Name columns, for outreach campaigns.
Deduplicate entries pulled from multiple sources (LinkedIn, registry, group parsing).
Drop any row missing a Telegram username — it's not usable for outreach regardless of how complete the rest of the data is.
Keep source tagging (registry vs. parsed group) so you can measure reply rates by source later.
Check account warmup status before sending — a fresh or flagged account will tank your results no matter how good the list is.
Once your list is clean, you can push it straight into a sequence via the CRMChat API or run it directly through the dashboard. Full setup details for each tool in this workflow are in the CRMChat Help Center.
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