From good to great: create a loop to level up your Allbound strategy

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Here’s the truth: No Allbound strategy stays “great” forever. What works today can (and will) get stale. Buyer behavior shifts. Inboxes get noisier. Competitors copy your playbook. And that killer CTA you crafted six months ago? Feels like background noise now.

That’s why the best growth teams don’t set their strategy once. They test. Learn. Adapt. And do it on repeat.

They treat improvement not as a project but as a mindset.

A/B testing: the secret behind top performing campaign

When we say “continuous testing,” we’re not just talking about A/B subject lines. We mean:

Experimenting with team structures
Tuning copy mid-sequence
Rethinking your funnel based on user behavior
Stress-testing your targeting assumptions

Because here’s the thing: Your market is a moving target. If you don’t adjust, you miss.

Even the smartest A/B test will fail if your segment is off. Testing without clean segmentation is like optimizing a message no one should’ve received in the first place.

Why testing isn’t optional:

The environment constantly shifts: What worked six months ago might be noise today.
Buyer behavior evolves fast: Messaging fatigue is real. So are changing pain points.
Competitors adapt: If you’re not iterating, someone else is—and they’ll steal your edge.
“Hacks” die quickly: LinkedIn tricks, cold email formulas, inbox subject trends… all have a shelf life.
Macroeconomics impact conversion: What your ICP prioritized last year may be irrelevant post-budget cuts.

What continuous testing unlocks:

More intelligent resource allocation: No more guessing where to invest. Let the data point the way.
Message-market fit in real-time: Stop relying on gut instinct. Start adjusting based on feedback.
Sharper targeting: Run micro-experiments on segments to see who’s converting.
Conversion process optimization: Every step, from lead magnet CTAs to demo handoffs, can be tuned.
Risk reduction: Test slightly before scaling. Catch bad ideas before they eat the pipeline.
Hyper-personalization: Not just “FirstName” variables—real, segment-level personalization based on behavior.

The teams that win are the ones that learn faster than their competitors. That means testing isn’t a side project. It’s the culture.

What are the solutions to iterate on your sales strategy?

1. A/B Testing, but make it smart

This is your day-to-day optimizer. But A/B testing isn’t about swapping button colors or changing “Download” to “Get.” That’s not a strategy—that’s UX cosplay. Real A/B testing focuses on substantive variation:

Messaging angles
Copy tone (authority vs. empathy)
Sequence structure
Pain point positioning
CTA strength

Run tests like a product team would:

Form a hypothesis (“CTAs that mention ROI will convert better than feature-based CTAs”)
Launch a clean, controlled test
Let the data run: statistical significance > gut feel
Roll out the winner, archive the loser
Repeat

A/B testing is only as good as what you’re testing. No one cares if your button is blue or green if the value prop is weak.

2. Cohort Analysis: slice the funnel, find the signal

This is where testing gets strategic. Cohort analysis means grouping leads based on a shared trait and tracking their behavior over time. Want to know which channels bring high-LTV leads? Which ICP segments convert faster? Which campaigns nurture best-fit customers? Cohorts give you that clarity.

Common cohort setups:

By acquisition date (e.g., pre/post-product launch)
By channel (LinkedIn vs. outbound email vs. Pay-Per-Click)
By firmographic segment (company size, industry, region)

What to measure per cohort:

Lead-to-opportunity conversion rate
Sales cycle length
Average deal size
Upsell rate
Retention or churn

Cohort analysis answers every CMO’s question: “Are we attracting the right customers—or just the ones who click?”

3. The Sales Lab: your R&D space for sales experimentation

This is your innovation playground—a dedicated space (team + process) for testing bigger bets, such as new strategies, pricing, sequencing models, and even sales motion shifts.

Think of it as R&D for revenue.

The Sales Lab process:

Form your hypothesis: “What if we move personalization before the value prop in LinkedIn messages?”
Design your experiment: Clear variables, tight scope, clean cohort.
Test at a small scale: 50–100 leads max. Don’t burn the list.
Measure and analyze: Use La Growth Machine (LGM) + CRM to track touchpoints, replies, and deal impact.
Scale or iterate: Does it work? Systematize it. If not? Kill it and move on.

You can test:

Sales messaging/frameworks
Cold call or meeting structure
Pricing strategy/offer positioning
Tool adoption or channel mix

Real talk: This requires budget and team bandwidth. But if you want a competitive edge, you can’t just optimize—you must invent.

A/B Testing with LGM: Step-by-step guide

How to set up A/B testing in LGM

1. Choose the element to test

Rule #1: Test one variable at a time. Otherwise, you won’t know what moved the needle.

LGM gives you options to test:

Email subject lines (your attention trigger)
Email body content (your pitch engine)
LinkedIn messages (your conversation starters)
LinkedIn voice notes (your human touch at scale)
LinkedIn connection request (your first impression, make it count)

Use Spintax to test advanced variations at scale. Its allows for micro-variations inside the same step (for example: {Hi|Hello|Hey there}). Even better: generate it with LGM’s built-in GPT assistant and let the AI do the heavy lifting.

2. Define your objective (Like you mean it)

Before you click “A/B,” know what you’re solving for. Are you optimizing:

Open rates? → Tweak subject lines
Click-through rates? → Focus on your CTA or layout
Reply rates? → Hone in on tone, length, or personalization

Set your primary KPI in the LGM dashboard. This is what you’ll optimize against, everything else is noise.

3. Build your variations in the sequence editor

Use the A/B toggle inside any step of your sequence. LGM makes it frictionless:

Keep structural consistency between variations
Test bold changes, not just “Hi” vs “Hello”

Use Spintax to test messaging without creating bloated sequences.

4. Launch the A/B test in your LGM campaign

LGM handles distribution randomization for you, so you don’t need to do manual split testing or shuffle your lists in Excel.

Ensure your audience segment is large and clean enough to detect meaningful differences.
Use custom filters or pre-built LGM segments to define your target precisely.

Pro tip: Keep audience profiles consistent between tests; ICP skew will ruin your results.

5. Monitor performance in real-time

As soon as your test goes live, the data starts flowing.

Track performance directly in your LGM dashboard:

Open rate per variation
Click rate
Reply rate
Lead conversion (if integrated with CRM)

LGM also automatically tracks statistical significance; there is no need to run chi-square tests like in 2008.

6. Analyze. Learn. Scale.

Once results are in, go to Dashboard – Channel Statistics

Here’s where you are:

Identify the winner
Document insights
Apply learnings to future sequences (or spin up a clone in seconds)

Don’t just stop at one win. Build a feedback loop. Every winning test becomes your new baseline. Then, you test again.

A/B test ideas to boost your Allbound conversion rate

Let’s go beyond emails.

Ads and landing pages

Page titles: First impression, significant impact
Layout: Clear visual hierarchy drives trust
Visuals: Test image type, placement, contrast

Copy and offers

CTAs: Button copy, placement, tone
Lead magnet format: Webinar vs. white paper vs. checklist
Headline messaging: Feature-driven vs. outcome-driven

Use LGM’s AI to generate variant-ready copy optimized for clarity, tone, and conversion.

Outbound emails

Subject line: Curiosity vs. direct value
Body content: Length, structure, tone
Sender Name: Personal vs. branded
Follow-up timing: Does Day 3 outperform Day 5?

The best part? LGM lets you test up to 20+ variables per sequence while keeping everything automated, balanced, and trackable.

How to analyze A/B test results (without jumping to conclusions)

Conversions per variation: Who took action?

Start with the raw numbers. How many people converted to Version A vs. Version B?

Whether you measure clicks, form fills, or replies, total conversions give you directional insight into which variant is performing better.

But don’t confuse totals with performance. Raw conversions will always skew if twice as many people see one version.

That’s why your next step matters even more.

Conversion rate: Who converted proportionally?

This is the real test of performance: how efficiently each version turned attention into action.

Formula: (Number of conversions ÷ Number of impressions or sends) × 100

A variant with a 9% conversion rate is better than one with a 7% conversion rate, even if it got fewer total conversions. The margin doesn’t have to be massive.

A 1–2% uplift at scale can drive serious compounding growth when sending to thousands.

Percentage improvement: How much better was the winner?

Now quantify it.

Formula: ((Winning version rate − Losing version rate) ÷ Losing version rate) × 100

Example:

Variant A: 6.8%
Variant B: 8.2%

That’s a 20.6% performance improvement.

You don’t need a 2x lift to call it progress. Sometimes, a 7% edge is all it takes to justify a strategy shift — or a new copywriting direction.

Statistical significance: Can you trust the result?

This is where things get real. Just because Version B outperformed doesn’t mean it’ll keep outperforming.

Without statistical significance, you’re guessing.

Good news: LGM handles this for you automatically. Once your sample size is large enough, LGM will tell you whether the test passed the confidence threshold, and whether you’re seeing a real result or a lucky spike.

If it’s not significant? Run a more extensive test, tweak your segment, or simplify the variable you’re testing.

Segmentation: Who did it work for?

Now, go deeper. Even if one version “wins” overall, the real story often lives in the audience breakdown.

Use LGM’s filters or your CRM data to analyze performance by:

Persona or job title
Company size or industry
Region or language
New vs. returning leads
Cold vs. warm outreach

You might discover:

Version A works better for mid-market prospects
Version B crushed it in EMEA but tanked in the U.S.
Shorter messages win with founders, but longer ones convert better with RevOps.

These insights inform future sequences and shape your messaging by segment, where the real Allbound advantage kicks in.

What to do next: Refine, launch, repeat.

A/B testing isn’t a one-off growth tactic. It’s a feedback loop.

Once you’ve got a clear winner, apply the learning:

Turn the winning copy into your new default
Use it as a base for the next test
Build segment-specific versions based on what you learned
Share the data with Sales to sync up messaging

This is how testing turns into strategic acceleration, better emails, faster learning, sharper segmentation, and smarter sequences over time.

Takeaways

Allbound isn’t static. Your market shifts—so your strategy needs to evolve with it. The teams that win don’t just have a great playbook and learn faster than everyone else. And for that, continuous testing isn’t optional—it’s cultural.

A/B testing works best when you test bold, meaningful variables: think messaging angles, not button colors.
Use cohort analysis to go beyond surface-level insights and understand what drives performance.
Build a Sales Lab: a structured space for high-impact experiments on messaging, channel mix, or pricing.
LGM makes A/B testing seamless: from Spintax-powered variations to real-time analytics and statistical significance.
Smart testing beats guessing; it always defines a hypothesis, measures it cleanly, and lets data lead the way.

Don’t stop at one win. Every test is a step forward. Apply, iterate, and scale what works.
The real advantage? Learn faster than your competitors. That’s how you win in Allbound.