Your channel is flooding your pipeline with junk.
Your forecast is off by ±42% — and now you’ll know why.
This is the first benchmark built for telecoms and IoT sales motions — not generic SaaS. You’ll see your channel vs direct conversion rates, deal cycle economics, device-based pricing advantage and forecast accuracy compared against 700+ telecoms and connectivity companies. Your score is also your AI readiness score — the number your board will ask about next. Score yourself in ten minutes. Find the one fix that matters most — before you spend another quarter on AI forecasting tools that can’t see through your channel.
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What you’ll know after 20 minutes
This report gives you six things. Each one answers a question no SaaS benchmark can touch — because telecoms and IoT sales motions are structurally different. Including the AI question your board is about to ask.
Which of your channel partners are sending you junk
Channel pipeline converts at 9%. Direct converts at 26%. But most telecoms companies can’t tell the difference in their CRM. AI lead scoring on top of this just prioritises the junk faster. The report shows the conversion rate by partner tier — so you can see which partners are helping and which are polluting your forecast.
Why your forecast is off by ±42% — and what to do about it
Telecoms forecast variance is the highest of any tech subsector. The main driver is channel-sourced pipeline that nobody qualified. AI forecasting tools can’t fix what the CRM was never told to track. The report shows the partner tiering model that cuts variance by 15–20 points.
How much revenue your pricing model is leaving behind
Device-based pricing delivers 128% NRR. Per-seat delivers 104%. That’s 24 points of expansion revenue that grows on its own as your customers deploy more devices. And the 2026 AI pricing wave is about to lock the category in. The report shows which model wins — and why most telecoms companies haven’t switched.
Your score — and your AI readiness
Not SaaS scoring. This self-assessment is built for channel-heavy motions, 11-month deal cycles and multi-year contracts. Your total out of 30 is also your AI readiness score. Below 20, your AI tools don’t have the foundation they need. Finish it in ten minutes.
A real company that boosted revenue 34% by cutting channel partners
An $8M ARR IoT platform had an 8% win rate. The VP Sales wanted to fire 4 of 6 partners, hire direct reps, and buy an AI lead scoring tool. The real problem? 62% of channel pipeline had no budget holder. They kept 4 partners, exited 2, and revenue rose 34%. No new hires. No AI tool.
Why IoT companies get 30% higher multiples than traditional telco software
IoT hits 7.6× EV/Revenue versus 5.8× for traditional telecoms software. Acquirers now read that premium as the AI-native premium: only IoT platforms with clean device telemetry and real architecture can deliver edge AI that actually works. The report shows which metrics drive the gap.
Five things this report will change about how you think
Preview of what’s inside. Each finding points to a fix you can act on — not just a number to stare at.
Your channel is the main reason your forecast is wrong
Telecoms forecast variance averages ±42%. The highest of any tech subsector. The main cause? Channel partners logging opportunities they have no ability or intent to close. It inflates your coverage ratio and makes your forecast meaningless. AI forecasting tools can’t fix this — the source quality data was never in the CRM to begin with. Companies that tier partners by quality and weight pipeline by tier cut variance by 15–20 points.
Direct deals close 3× better than channel — and you’re spending 68% on channel
Direct enterprise leads convert at 26%. Channel converts at 9%. Yet most telecoms companies put 68% of GTM budget into channel programmes. This isn’t an argument for killing channel. It’s an argument for knowing the conversion rate by source — so you can invest where deals actually close. AI scoring can’t tell you where to invest if the CRM doesn’t track source quality.
IoT companies earn 30% higher multiples — and it’s now an AI-native question
IoT hits 7.6× EV/Revenue. Traditional telco software hits 5.8×. The difference is device-based pricing. When a customer rolls out from 500 devices to 50,000, revenue grows automatically. No upsell call needed. And acquirers now read that premium as the AI-native premium: IoT platforms with clean device telemetry can deliver edge AI and predictive maintenance. Traditional telco software can’t. The report shows what that pricing architecture looks like.
94% retention is the best in tech — and it’s hiding a problem
Telecoms has the highest retention of any subsector. Multi-year contracts and deep integration make switching almost impossible. But high retention masks whether you’re acquiring the right customers. If your top 3 accounts are 40% of revenue and one leaves, retention didn’t save you. AI churn prediction can’t protect you from concentration risk — it can only tell you when the damage is already landing. The report shows how to spot the concentration risk.
Your L2O score is also your AI readiness score
87% of companies missed forecast in 2025 despite record AI spend. 48% say their revenue data isn’t AI-ready. 67% don’t trust their own numbers. For telecoms, it is worse — channel-logged pipeline and multi-party CRM data make the problem structurally worse. AI can only see what’s in your CRM. It cannot see inside your partners’ systems. The single number that tells you whether AI will work on top of your CRM is your Lead-to-Order score. Below 20 out of 30, AI amplifies the chaos. Above 22, it multiplies what’s already working. That’s the answer your board is looking for.
Normally £495. Free for a limited time. No sales call required. AI readiness scored at the same time.
This is why benchmarks matter
A real telecoms company. A real problem everyone misdiagnosed. The report would have shown them the answer in ten minutes — before they fired partners, hired reps and bought an AI tool that couldn’t fix the channel.
Win rate at 8%. The VP Sales wanted to fire most of the channel partners.
Overall win rate 8%. Channel-sourced deals closing at just 6%. The VP Sales proposed terminating 4 of 6 channel partners, hiring 2 direct AEs to replace the lost pipeline, and buying an AI lead scoring tool to catch bad deals earlier.
Fired most partners. Hired direct reps. Bolted AI lead scoring on top of the CRM. Replaced bad pipeline with no pipeline — and burned 6 months ramping new hires. The AI would have scored the same channel junk — just faster — and with no visibility into the partner systems where the junk actually lived.
Nobody could tell the difference between partners bringing real enterprise IoT deployment opportunities and partners logging junk to meet co-marketing obligations. 62% of channel pipeline had no identified budget holder or deployment timeline.
Added a partner qualification gate: deployment timeline and budget authority required before deals entered pipeline. Kept 4 partners. Exited 2. Pipeline volume dropped 45%. No new hires. No AI tool. Just better partner rules underneath the CRM they already had.
Result: Win rate rose from 6% to 19% on channel deals. Total closed revenue increased 34%. Fewer leads. Fewer partners. No new headcount. And now — when they do add AI — it will multiply something that actually works.
Score yourself in 10 minutes
Built for telecoms and IoT — not SaaS averages. These are the six questions. If you can’t answer them clearly, that’s the gap. Most telecoms companies between $5M and $50M score 10–16 out of 30. Your total is also your AI readiness score — below 20, your AI tools don’t have the foundation they need.
D1 Signal Architecture
Can you tell direct enterprise leads from channel-sourced pipeline? What share comes from each — and what’s the conversion rate difference? AI intent tools can’t see source quality the CRM never tracked.
D2 Pipeline Structure
What’s your channel pipeline contamination rate? Can you tell which partners send qualified deals versus ones logging junk to hit co-marketing targets? AI can’t see into partner systems — if you can’t, it can’t.
D3 Conversion Mechanics
Is your quota set against telecoms benchmarks or SaaS averages? Are your pipeline coverage targets adjusted for 11-month enterprise cycles?
D4 Pricing Realisation
Are you pricing per device, per connection, usage-based or per seat? Do you know which model drives the highest NRR — before the 2026 AI pricing wave locks your model in?
D5 Retention & Expansion
How much of your NRR is automatic — growing as device fleets scale — versus needing a sales motion? What happens to revenue if one of your top 3 accounts leaves?
D6 Process Discipline
What’s your forecast variance over the last four quarters? Can you separate deal lumpiness and channel timing from your baseline accuracy? Can you answer the board’s AI question with a number?
What to do after you read the report
“The report shows where telecoms companies like yours score. The assessment shows what it’s costing yours — and whether your AI spend has a foundation to deliver.”— Michael Williamson · Lead-to-Order Architect · Platform-Independent · 25 years including O2/Teléfonica, Vodafone & Helvar (industrial IoT)
Six dimensions. Your own data. Every gap costed. AI readiness included. Delivered in five working days.
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