The 5 GTM Problems That Appear Right After Series A

After raising a Series A, five GTM problems appear almost immediately: hiring reps before building process, broken lead handoffs, untrustworthy CRM data, unreliable forecasting, and misaligned team metrics. Identifying them early is the difference between scaling revenue and scaling chaos.

Series A is supposed to be the moment everything accelerates. The money is in the bank, the team is growing, and the pressure to hit aggressive targets feels both exciting and urgent.

But for most B2B startups, the first six months after Series A are when the go-to-market motion starts cracking. Not because the product is wrong or the market is bad — but because the informal systems that worked with five people collapse the moment you try to scale them.

The problems below are predictable. They show up in nearly every post-Series A startup we work with. The good news: they’re fixable, especially if you catch them early.

Problem 1: Hiring Reps Before Building a Repeatable Process

This is the most common and most expensive mistake.

The board wants growth. The CEO hires three sales reps in month one. But there’s no documented sales process, no clear deal stages, and no playbook for how the founder was closing deals before.

Why This Happens

Founder-led sales is intuitive. The founder knows the product deeply, reads buyers well, and navigates objections instinctively. None of that is written down.

When new reps start, they inherit a CRM with inconsistent data, a pipeline with vague stages, and a comp plan tied to targets that were set based on founder performance — not a scalable process.

What Breaks

  • Reps ramp slowly because there’s nothing to ramp on
  • Pipeline metrics become meaningless because everyone defines stages differently
  • The founder becomes a bottleneck, pulled into every deal to “help close”
  • Quota attainment drops below 50%, and the instinct is to hire more reps

How to Fix It

Before hiring your first post-Series A rep, document three things:

  1. Deal stages with exit criteria — What has to happen before a deal moves forward?
  2. Qualification framework — What makes a lead worth pursuing? What disqualifies one?
  3. Sales playbook — Key objections, competitive positioning, discovery questions, and demo flow

This doesn’t need to be perfect. It needs to exist. A basic playbook that gets refined is infinitely better than no playbook at all.

If your sales process still relies on founder instinct, RevOps should be your first operational hire — not your fifth.

Problem 2: Unclear Lead Handoffs Between Marketing and Sales

At the seed stage, everyone sits in the same room. A lead comes in, someone follows up. There’s no handoff because there’s no distance between teams.

After Series A, marketing starts running campaigns, SDRs start qualifying, and AEs start expecting a certain quality of meeting. Suddenly, the distance between “lead created” and “deal opened” is full of gaps.

Why This Happens

  • No agreed definition of MQL (Marketing Qualified Lead) or SQL (Sales Qualified Lead)
  • No lead routing rules — leads sit unassigned or get claimed randomly
  • No SLAs for follow-up speed
  • Marketing measures leads generated; sales measures pipeline created. The gap between the two is invisible.

What Breaks

  • Leads go cold because nobody follows up within the first hour
  • Sales blames marketing for “bad leads”; marketing blames sales for “not working the leads”
  • Conversion rates drop, but nobody agrees on where the problem is
  • The CEO mediates instead of selling

How to Fix It

Define the handoff in writing. Specifically:

  • MQL criteria — What actions or attributes make a lead marketing-qualified? (e.g., demo request from ICP company, content download + pricing page visit)
  • SQL criteria — What qualifies a lead to enter the sales pipeline? (e.g., budget confirmed, decision-maker identified, timeline under 90 days)
  • Routing rules — Who gets which leads, based on geography, segment, or deal size?
  • SLAs — SDRs respond within 1 hour. AEs schedule discovery within 48 hours of SQL handoff.

Track handoff conversion weekly. If MQL-to-SQL conversion drops below 20%, the definitions need adjusting — not the team.

Problem 3: CRM Data Nobody Trusts

By the time Series A closes, the CRM is usually a mess. Half the contacts are outdated, deal amounts are guesses, close dates have been pushed three times, and custom fields are either empty or inconsistent.

This doesn’t matter much when the founder is the only seller. It matters enormously when three new reps, a marketing team, and a board all need to make decisions based on that data.

Why This Happens

  • No data entry standards — reps log what they want, when they want
  • Too many custom fields created without a plan
  • No validation rules or required fields at stage transitions
  • The CRM was set up quickly at founding and never cleaned

What Breaks

  • Forecasts are unreliable because the underlying data is wrong
  • Reports contradict each other depending on how they’re built
  • Leadership loses trust in the numbers and goes back to asking reps directly
  • Board meetings become debates about data accuracy instead of strategic discussions about growth

How to Fix It

You don’t need a CRM overhaul. You need hygiene enforced at the point of data entry.

  1. Required fields at stage transitions — A deal can’t move from Discovery to Proposal without an amount, close date, and decision-maker logged
  2. Standardised pick lists — No free-text fields for things like lead source, industry, or loss reason
  3. Weekly pipeline reviews — Not to interrogate reps, but to catch stale deals and incorrect data early
  4. Automated alerts — Flag deals with no activity for 14+ days, or close dates that have passed

If your CRM isn’t set up for clean data, fixing the architecture now prevents a painful migration later.

Problem 4: Forecasting Based on Gut Feel, Not Pipeline Data

Pre-Series A, forecasting is informal. The founder knows every deal personally and can estimate the quarter with reasonable accuracy.

Post-Series A, the pipeline grows, new reps manage their own deals, and the CEO can no longer hold every opportunity in their head. But the forecasting method doesn’t change — it’s still based on rep confidence and gut feel.

Why This Happens

  • Deal stages don’t have clear exit criteria, so stage-based forecasting is unreliable
  • Historical conversion rates don’t exist because the CRM hasn’t been tracking them accurately
  • Reps are optimistic by nature — they overweight deals that feel good
  • There’s no forecasting methodology (weighted pipeline, commit categories, etc.)

What Breaks

  • The board gets a different number every meeting
  • Revenue misses create trust issues with investors
  • Hiring plans get built on forecasts that don’t materialise
  • Cash runway decisions are made with unreliable data

If your forecast is consistently wrong, the issue is almost always process, not people.

How to Fix It

Implement a three-tier forecast:

  1. Committed — Verbal agreement, procurement in process, close date within 30 days
  2. Best case — Strong champion, budget confirmed, active evaluation
  3. Pipeline — Qualified opportunity, still in discovery or proposal stage

Then track forecast accuracy monthly. Compare what you predicted to what actually closed. Over two to three quarters, you’ll build the historical conversion data that makes forecasting reliable.

Don’t expect accuracy overnight. Expect improvement quarter over quarter — and show that improvement to your board.

Problem 5: Misaligned Metrics Across Teams

Marketing celebrates a record month of MQLs. Sales says pipeline is thin. CS reports churn is rising. Everyone is hitting their own targets, but revenue isn’t growing.

This is the alignment problem, and it appears in nearly every post-Series A startup that hasn’t built a shared operating model.

Why This Happens

  • Each team sets its own KPIs without connecting them to a shared revenue goal
  • Marketing optimises for volume (leads), sales optimises for revenue (closed-won), CS optimises for retention — but nobody owns the full funnel
  • There’s no single dashboard or report that shows the complete journey from lead to revenue to renewal
  • RevOps doesn’t exist yet, so no one is responsible for cross-team alignment

What Breaks

  • Teams optimise locally at the expense of the whole
  • Marketing generates leads that never convert because they’re outside ICP
  • Sales closes deals that churn within six months because qualification was too loose
  • The CEO spends time refereeing between departments instead of leading strategy

How to Fix It

Build a shared metrics framework that connects every team to revenue:

  • Marketing — Pipeline generated (not just MQLs), pipeline-to-revenue conversion rate
  • Sales — Win rate, average deal size, sales cycle length, forecast accuracy
  • CS — Net revenue retention, expansion revenue, time to value
  • RevOps — Funnel conversion rates stage by stage, data quality scores, SLA compliance

The key metric every team should track: pipeline-to-revenue conversion by source and segment. This single metric forces alignment because it spans the entire funnel.

Review these metrics together in a weekly or bi-weekly GTM standup. When marketing and sales look at the same funnel data in the same room, alignment happens naturally.

The Common Thread: Process Before People

All five problems share a root cause — scaling people before scaling process.

Series A funding creates urgency to hire. But hiring into a broken GTM motion doesn’t fix the motion — it amplifies the dysfunction. Three reps running three different processes is worse than one founder running an imperfect but consistent one.

The sequence that works:

  1. Document — Write down what’s working and what isn’t
  2. Design — Build the process, handoffs, and metrics framework
  3. Implement — Set up the CRM, automation, and reporting to support the design
  4. Hire — Bring in reps who can execute a defined process
  5. Optimise — Use data to refine, not reinvent

This is exactly what RevOps enables — the operational infrastructure that turns a founder-led GTM motion into a scalable revenue engine.

When to Bring in RevOps After Series A

The best time to invest in RevOps is within 90 days of closing your Series A. Not after the first bad quarter. Not after the third missed forecast.

You don’t necessarily need a full-time hire. A fractional RevOps partner can design the foundation — process, data model, reporting, and handoffs — in 60-90 days and hand it off to your team.

What matters is that someone owns the operational layer between strategy and execution. Without that, every team builds their own version of the truth, and the CEO becomes the only integration point.

If you’re approaching or past Series A and recognise these problems, get in touch with Altura. We help B2B startups build the GTM infrastructure that turns funding into predictable revenue.

Frequently Asked Questions

What GTM problems appear after Series A?

The five most common GTM problems after Series A are: hiring sales reps before building a repeatable process, unclear lead handoffs between marketing and sales, CRM data that no one trusts, forecasting that relies on gut feel instead of pipeline data, and misaligned metrics across teams. These problems are predictable and fixable with the right RevOps foundation.

When should a startup invest in RevOps after Series A?

Immediately. Series A is the inflection point where informal GTM processes break. Investing in RevOps — even fractionally — within the first 90 days of funding prevents the compounding operational debt that becomes expensive to fix later. A fractional RevOps partner can design the foundation in 60-90 days.

Why do startups struggle with forecasting after Series A?

Post-Series A, boards expect accurate revenue forecasts, but most startups still forecast based on gut feel and rep optimism. Without clean CRM data, defined deal stages with exit criteria, and historical conversion rates, forecasts remain unreliable. Implementing a three-tier forecast (committed, best case, pipeline) and tracking accuracy monthly solves this over two to three quarters.

How do you fix lead handoff problems at a Series A startup?

Define clear MQL and SQL criteria that both marketing and sales agree on. Implement lead routing rules in your CRM, set SLAs for follow-up times, and track handoff conversion rates weekly so you can identify and fix breakdowns quickly. If MQL-to-SQL conversion drops below 20%, adjust the definitions rather than blaming the team.