outreach
Best Martech Tools for Building CIS Decision-Maker Contact Lists
There's no LinkedIn for Russia and the CIS. Here's the martech stack that actually finds verified decision-maker contacts — registries, phone lookup, and Telegram enrichment.
You've got a target list of 200 companies in Russia or Kazakhstan. You search LinkedIn for their founders. Nothing. Maybe a dead profile from 2015 with no activity since. Your entire outreach motion just hit a wall that doesn't exist in the US or EU market.
That's the core problem with CIS B2B prospecting: there is no single professional network where decision-makers keep their contact info updated. LinkedIn adoption is thin, especially post-2022. So the data you need — who runs the company, how to reach them — is scattered across business registries, phone directories, and Telegram, and none of those sources talk to each other by default.
What tools actually build verified CIS decision-maker lists?
The stack that works combines three specific layers: a business-registry provider filtered by industry code and revenue (like DataNewton), a contact-lookup tool that resolves founder names to phone numbers, and a phone-to-Telegram enrichment tool that converts those numbers into messaging-ready usernames. On CIS numbers specifically, phone-to-Telegram enrichment rates run around 50% — roughly double the 30% rate you see on EU, UK, or US numbers, because Telegram penetration is simply higher in that region.
That 50% number matters because it tells you the ceiling on any CIS list-building effort: for every 100 phone numbers you resolve from a registry, expect about 50 usable Telegram contacts back. Plan your registry pull volume accordingly — if you need 100 real contacts, pull data on at least 200 companies.
The core workflow: registry data to Telegram username
Here's the sequence that actually gets you from "I know the industry I want" to "I have a CSV of Telegram usernames ready for outreach." It's a six-step chain, and the key insight is that an AI assistant (Claude or ChatGPT) can orchestrate the whole thing once it's connected to the right APIs.
Pull companies from a business registry. Use a provider like DataNewton to filter by OKVED industry code and revenue band. Grab an API key from their documentation.
Connect your AI assistant to the registry API. Feed it your DataNewton API key so it can query companies on your behalf, filtered exactly the way you specify.
Connect the same assistant to CRMChat. Your CRMChat API key lives in Settings → API Keys. This lets the AI drive CRMChat's contact-lookup tools directly.
Run the founder lookup. The AI sequences through CRMChat's contact-lookup bot to find the founder or decision-maker tied to each company returned in step one.
Retrieve the phone number. Each match returns a direct line to the person who actually makes buying decisions — not a generic info@ inbox.
Convert the number to a Telegram username. Phone-to-Telegram enrichment turns that number into a contact you can actually message.
CRMChat automates the phone-to-Telegram conversion step that turns a static contact database into an active outreach list — you feed it numbers, it hands back usernames ready to import into a sequence. That single step is usually the bottleneck teams get stuck on when they try to build this manually.
Registry data alone isn't enough — why decision-maker lookup matters
A lot of teams stop at step one. They buy a registry export, get 5,000 company names and generic contact emails, and call it a lead list. That's not a decision-maker list — it's a company list. The person answering info@company.ru is rarely the founder.
CRMChat includes contact-lookup tools that resolve a company record to the actual founder's phone number, which is the piece registries alone don't give you. This is the difference between "we have their address" and "we have their direct line." If you're prospecting Russian manufacturing plants specifically, the same registry-to-founder logic applies — see how to find decision-makers at Russian manufacturing plants without LinkedIn for a sector-specific walkthrough.
Should you use group parsing instead of decision-maker lookup?
Group parsing has a place, but it's not the primary tool for decision-maker targeting — treat it as a secondary, broader-reach method. Extracting member lists from public Telegram groups (industry channels, conference chats, niche communities) gives you volume and works well when you're chasing a broad audience rather than a named list of founders and executives.
The tradeoff: group parsing gets you people who are active in a space, not necessarily the person who signs the check. For a targeted decision-maker list, the registry-to-phone-to-Telegram chain outlined above is the more precise approach. Use group parsing to supplement volume once your core decision-maker list is built, not as your primary sourcing method.
What should your final contact list actually contain?
Before you push any list into an outreach sequence, check it has these fields — missing any one of them kills your personalization and your reply rate:
Company name and industry code — pulled straight from the registry filter, confirms you're targeting the right vertical.
Founder or decision-maker name — not a department, an actual named person.
Direct phone number — resolved via contact lookup, not a switchboard number.
Telegram username — the enriched contact you'll actually message.
Revenue band or company size — lets you segment outreach messaging by company scale.
Once that CSV is built, don't skip account prep. Cold-messaging a fresh batch of decision-maker contacts from an unwarmed Telegram account is the fastest way to get flagged before your campaign even starts — see how to set up a Telegram Business account for B2B outreach in CIS and CRMChat's Telegram account warmup guide before you send anything.
Is there a pre-built option instead of building lists from scratch?
If your target market is Web3 rather than general CIS B2B, you don't need to run the full workflow at all. CRMChat's Web3 decision-makers database ships 7,000+ verified Telegram contacts sourced from real conference attendance — Devconnect, Token2049, Korea Blockchain Week — organized by role and niche, delivered as CSV or Google Sheets with monthly updates.
For non-Web3 CIS verticals, though, the registry-plus-lookup workflow above is still your best bet since there's no pre-built equivalent at that granularity yet. Teams running this at scale often plug it straight into the CRMChat API so the AI-assistant orchestration runs on a schedule instead of manually per batch.
Common mistakes when building CIS contact lists
Skipping the founder-resolution step. A company list is not a decision-maker list — always resolve to a named person.
Under-pulling registry volume. Since roughly half your numbers won't enrich to Telegram, pull at least 2x the contact count you actually need.
Messaging from a cold account. Warm the account for 10-14 days before sending outreach to a freshly built list — see the reasons Telegram restricts accounts used for cold outreach.
Ignoring revenue/industry filters. Unfiltered registry pulls waste enrichment budget on companies that were never a fit.
Check CRMChat's Help Center for the full step-by-step setup on connecting DataNewton and CRMChat to an AI assistant, including exact prompt structures for each stage of the lookup sequence.
