Growth hacking strategies and techniques to try

Growth hacking in a B2B context is a disciplined process for identifying a growth constraint, testing a specific fix at small scale, and scaling only what the evidence supports.

It can be hard to know where to start when testing growth hacking strategies and tactics. But these 11 ideas are designed to give you somewhere to start – and tips on how to measure and understand if those experiments are worth repeating.

1. Prioritize outbound using website intent signals

Hypothesis: An outbound sequence triggered by repeat visits or high-intent page views will produce higher connect and meeting rates than a static, list-based sequence covering the same accounts.

Suited to: Sales development teams with an existing outbound motion and a visitor identification or intent data source.

Implementation:

• Define what counts as a high-intent signal, for example, a repeat visit to pricing or product pages within a set window.
• Split a comparable set of target accounts into an intent-triggered group and a standard sequence control group.
• Keep messaging and cadence otherwise consistent between groups.
• Track connect rate, meeting rate and opportunity rate for both groups over the same period.

Metrics: Connect rate, meeting rate, opportunity rate; guardrail on unsubscribe or opt-out rate.

Example: A mid-market software vendor triggers outreach when a named account visits its pricing page twice within a week, and compares results against its standard weekly outbound list.

If it fails: The intent signal is not predictive for this product category, and the team should look for a different trigger or accept that outbound performs similarly regardless of on-site behaviour.

If it succeeds: Extend the trigger logic across more of the target account list and consider surfacing the same signal directly to account executives, not only the SDR team.

2. Build industry-specific landing-page variants

Hypothesis: A landing page tailored to the language, use cases and proof points of one vertical will convert a higher proportion of qualified visitors than a generic version of the same page.

Suited to: Teams with at least two or three verticals large enough to justify dedicated content, and enough traffic to compare results meaningfully.

Implementation:

• Choose the one or two verticals with the highest existing pipeline value.
• Rewrite headline, use cases and proof points for that audience while keeping traffic source and offer consistent.
• Route a comparable share of relevant traffic to the generic and variant pages.
• Measure qualified conversion, not total form fills, since a more specific page may reduce volume while improving fit.

Metrics: Qualified conversion rate and downstream opportunity rate; guardrail on total lead volume so a genuine drop is visible.

Example: A logistics software provider builds a page addressing cold-chain compliance for food and beverage buyers, distinct from its general product page.

If it fails: The vertical version converts no better than the generic page, suggesting the vertical is not yet distinct enough in buyer needs to warrant separate content.

If it succeeds: Extend the same approach to the next-highest-value vertical and route relevant paid and outbound traffic to the new page by default.

3. Test a self-serve calculator or assessment

Hypothesis: Turning a question prospects usually ask a salesperson into an interactive, self-serve tool will capture qualified contacts earlier in the buying process.

Suited to: Businesses where prospects routinely ask a quantifiable question, such as expected savings, headcount impact or return on investment.

Implementation:

• Identify the single most common quantifiable question raised in early sales conversations.
• Build a simple calculator or assessment that answers it using a small number of inputs.
• Capture contact details either before or after the result, and test both approaches.
• Route completions with sales-relevant results into the appropriate follow-up sequence.

Metrics: Completion rate, contact capture rate, downstream qualified pipeline and assisted conversion.

Example: A cybersecurity vendor builds a short assessment estimating a prospect’s exposure based on company size and industry, then offers a personalised follow-up.

If it fails: Completion is high but few results lead to sales engagement, suggesting the tool attracts curiosity rather than genuine buying intent.

If it succeeds: Feature the tool more prominently in outbound and paid campaigns, and give sales visibility into individual results ahead of outreach.

4. Test gated versus ungated high-intent content

Hypothesis: Removing the form from one piece of genuinely high-intent content will reduce recorded lead volume but improve the quality and progression rate of the leads that remain, once combined with visitor-level account data.

Suited to: Teams with at least one asset that reliably attracts late-stage, high-intent readers, and a way to identify the companies reading it even when ungated.

Implementation:

• Select one high-intent asset, such as a buyer’s guide or comparison document.
• Publish an ungated version for a defined test period, using visitor identification to see which companies are engaging with it.
• Compare recorded lead volume, sales follow-up rate and eventual opportunity rate against the gated version’s historical performance.
• Brief sales on how to act on account-level engagement rather than waiting for a form submission.

Metrics: Lead volume, sales follow-up rate and qualified pipeline; do not judge the test on form fills alone.

Example: A financial services platform ungates its category comparison guide and relies on visitor identification to flag which target accounts are reading it.

If it fails: Pipeline quality does not improve, and the business loses a genuine source of contact-level lead volume it depended on.

If it succeeds: Apply the ungated approach to a wider set of late-stage content and adjust sales processes to work from account-level signals as standard practice.

5. Create a customer or partner referral loop

Hypothesis: Existing customers or partners who receive a clear, proportionate incentive will refer a measurable number of new opportunities, without materially damaging the quality of the accounts referred.

Suited to: Businesses with a customer base that has genuine reasons to advocate, and the ability to track referral source through to pipeline.

Implementation:

• Define eligibility clearly — who can refer, and what counts as a qualifying introduction.
• Choose an incentive, recognition mechanism or shared-value offer proportionate to deal size.
• Set up tracking so referred accounts can be followed from introduction through to opportunity and close.
• Build in safeguards, such as a minimum fit check, to prevent low-quality referrals being submitted purely for reward.

Metrics: Referral volume, referral-to-opportunity conversion rate, and average deal quality of referred accounts versus other sources.

Example: A B2B SaaS company offers existing customers account credit for referrals that convert to a paid contract, tracked through a dedicated referral code.

If it fails: Referral volume is low or quality is poor, suggesting the incentive or the ask is not well matched to how customers naturally advocate for the product.

If it succeeds: Formalise the loop into a permanent partner or customer advocacy programme with clearer tiers and ongoing promotion.

6. Turn content engagement into a sales trigger

Hypothesis: A defined combination of page depth, topic and recency of engagement will identify accounts more likely to respond to outreach than a randomly selected control group.

Suited to: Teams with enough website traffic and account-level visibility to define engagement patterns with confidence.

Implementation:

• Define a combination of signals — for example, three or more product pages viewed within seven days.
• Route accounts meeting that combination into a defined outreach workflow.
• Hold back a comparable control group receiving standard prospecting instead.
• Compare response, meeting and opportunity rates between the triggered and control groups.

Metrics: Response rate, meeting rate, opportunity rate; guardrail on total outreach volume to keep the comparison fair.

Example: A manufacturing software vendor triggers a personal outreach sequence when a target account views three or more solution pages within a week.

If it fails: The signal combination does not outperform standard prospecting, indicating the threshold needs adjusting or the signal is not a reliable indicator of intent for this audience.

If it succeeds: Refine the signal definition, then build it into the sales team’s standard daily workflow rather than treating it as a one-off test.

7. Personalize returning-visitor journeys

Hypothesis: Showing relevant content, proof points or calls to action to a returning visitor from a known target account will improve engagement compared with a generic experience, without requiring the visitor to identify themselves.

Suited to: Teams with an account-based approach, a defined list of target accounts, and clear internal agreement on privacy and consent requirements.

Implementation:

• Confirm the legal basis and consent requirements for any personalisation approach before building it.
• Define a small number of account segments and the content or proof point each should see.
• Build the personalised experience for a limited set of pages rather than the whole site initially.
• Compare engagement and downstream conversion between personalised and standard visitor journeys.

Metrics: Time on page, return visit rate, and downstream pipeline engagement from the personalised segment.

Example: An enterprise software vendor shows industry-specific case studies to returning visitors from named target accounts in the professional services sector.

If it fails: Personalisation shows no measurable lift, suggesting the content differentiation is not significant enough to change visitor behaviour.

If it succeeds: Expand personalisation to additional segments and pages, and feed the same segmentation logic into sales messaging.

8. Repurpose one expert asset across the funnel

Hypothesis: A single well-researched asset, such as a webinar or original research report, will produce a measurably greater commercial return when repurposed across search content, sales enablement and nurture than when published once and left alone.

Suited to: Teams that already invest in original research, webinars or expert interviews but currently publish them once.

Implementation:

• Select one strong asset with genuine expert input or original data.
• Break it into search-optimised articles, short clips, one sales enablement one-pager and a nurture sequence.
• Track engagement and pipeline influence across each derivative piece, not only the original asset.
• Compare total commercial result against the resource invested in repurposing.

Metrics: Combined pipeline influence across derivative content, not reach or views of any single piece in isolation.

Example: A revenue operations vendor turns one customer research report into three articles, a sales one-pager and a six-part nurture sequence.

If it fails: Derivative content generates engagement but little measurable pipeline influence, suggesting distribution or topic relevance needs revisiting rather than format.

If it succeeds: Build repurposing into the standard process for every future research or webinar asset rather than treating it as a one-off.

9. Run a lapsed-account reactivation experiment

Hypothesis: Combining historical relationship data with current intent signals will identify a set of lapsed accounts worth re-engaging, with a higher response rate than a generic reactivation campaign.

Suited to: Teams with a meaningful base of past customers, trial users or lost opportunities, and access to current visitor or intent data.

Implementation:

• Segment lapsed accounts by reason for lapsing — for example, budget, timing or a lost competitive deal.
• Cross-reference the segment against current website or intent activity to find renewed interest.
• Design outreach and messaging specific to why the account lapsed, not a generic win-back message.
• Track re-engagement, meeting rate and, where relevant, recovered revenue.

Metrics: Re-engagement rate, opportunity creation rate, recovered or new revenue from the segment.

Example: A marketing technology vendor cross-references accounts that churned over a year ago against renewed pricing-page activity, then reaches out with messaging addressing the original reason for churn.

If it fails: Renewed activity does not translate into meaningful re-engagement, suggesting the original reason for lapsing has not genuinely changed.

If it succeeds: Build a standing process that regularly cross-references lapsed accounts against intent data, rather than running it as a single campaign.

10. Create decision-stage comparison content

Hypothesis: Content that directly answers late-stage evaluation questions, including alternatives, integrations and implementation, will attract more engagement from target accounts and produce more assisted opportunities than top-of-funnel content alone.

Suited to: Teams competing in categories where buyers actively compare vendors before a sales conversation.

Implementation:

• Identify the specific comparison and implementation questions buyers raise late in the sales process.
• Build content that answers those questions directly and fairly, including genuine trade-offs.
• Use visitor identification to see which target accounts engage with this content ahead of a sales conversation.
• Track assisted opportunities where this content appears in the account’s engagement history before close.

Metrics: Engagement from target accounts specifically, plus assisted opportunity rate rather than raw page views.

Example: A cloud infrastructure vendor publishes a direct, evidence-based comparison against its two most common competitors, including where each option is genuinely stronger.

If it fails: The content attracts traffic but shows no measurable link to assisted opportunities, suggesting the questions addressed are not the ones actually influencing the buying decision.

If it succeeds: Expand the format to cover additional competitors or evaluation criteria and brief sales to reference it directly in late-stage conversations.

11. Test a coordinated phone, email and LinkedIn sequence

Hypothesis: A coordinated sequence across phone, email and LinkedIn will produce a higher meeting rate than the same audience and offer delivered through a single channel alone.

Suited to: Sales development teams with the capacity to execute a multichannel cadence consistently across a test period.

Implementation:

• Hold the target audience, offer and overall volume constant between the test and control groups.
• Design a coordinated sequence combining calls, email and LinkedIn touchpoints over a defined period.
• Run a single-channel control sequence, typically email alone, against a comparable account list.
• Track conversations, meetings booked, and unsubscribe or objection rates for both groups.

Metrics: Meetings booked and conversation rate as primary metrics; unsubscribe and objection rate as guardrails.

Example: An HR technology vendor tests a coordinated four-week sequence across phone, email and LinkedIn against a single-channel email sequence to the same account list.

If it fails: The coordinated sequence produces more activity but no meaningful increase in meetings, suggesting channel coordination is not the constraint for this audience.

If it succeeds: Standardise the coordinated sequence as the default outbound motion and test further refinements to timing and sequencing order.

A one-page experiment template

Running eleven experiments well depends less on any single tactic than on tracking them consistently. A simple one-page template covering hypothesis, audience, metric, duration, result and decision keeps every test comparable and makes the documentation step of the growth hacking process easy to maintain, rather than something that happens only for the experiments that succeed.

Where this connects to demand you already have

Several of the strategies above depend on the same underlying capability: knowing which companies are already showing interest before they fill in a form. Most B2B growth experiments focus on creating new demand, but businesses often have target accounts actively researching them right now, unrecognised because no one has converted on a form yet.

Lead Forensics identifies those companies from website activity and gives sales teams the account-level detail to act on it.