The Science and Craft of User Acquisition & Growth

At some point, a subscription business has an uncomfortable meeting. Customer acquisition looks healthy in one report. Finance is less enthusiastic. Product has questions about the people arriving. Customer support has been answering the same question for three months.

Everyone has data. Somehow, they appear to be working at four different companies.

This is where growth leadership earns its keep. A good growth leader can follow the entire relationship: the problem someone is trying to solve, the promise that gets their attention, the experience that makes the promise believable, the decision to pay and the reasons to stay. Then they can connect that relationship to the company’s money, people and ability to deliver.

For consumer subscription products, that responsibility is especially demanding. Customers can arrive in a hurry, compare alternatives in seconds and stop paying when the product stops earning its place in their lives. A clever campaign might introduce the relationship. It cannot do the relationship’s job indefinitely.

I approach this work with a scientist’s respect for evidence and a creative person’s interest in what makes people care. My background in research, psychology, data, writing, storytelling and speaking influences how I think about growth: the explanation needs to survive scrutiny, and the experience needs to make sense to an actual human being.

A surprising number of marketing problems become clearer when both requirements are enforced.

Evidence meets imagination

Scientific rigor and creative judgment belong in the same chair.

My scientific training is in Earth and Environmental Science. Research teaches you to ask what was measured, what was missed, which assumptions hold and how confident you should be. A model is useful because it helps explain something. It does not become true because the chart is attractive.

That habit transfers directly to growth. An apparent improvement in acquisition cost might come from a better campaign. It might also come from seasonality, a change in attribution, a different audience or a temporary discount. Before committing more money, I want to understand which explanation the evidence supports.

My psychology background adds a different question: what was happening in the customer’s life when they made that decision? Someone downloading a planning app before a major life change is not necessarily motivated by the same thing as someone trying to repair an established routine. Their clicks may look similar. Their needs may be miles apart.

As a bestselling author, award-winning storyteller, frequent podcast guest and speaker, I have learned to pay close attention to language, timing and the way an idea unfolds. My humor columns have built a large social following, and appearing as a guest on podcasts gives me another way to connect with an audience. Both depend on understanding what people recognize, care about and want to share. The detail you emphasize changes what someone understands first. Leave everything equally loud and you have not created clarity; you have created a meeting with no chairperson.

I am decent at sketching on a Wacom tablet, too. A quick sketch can help explain an idea; my main creative work happens through words, stories and conversations. For a current example of the technology, the Wacom MovinkPad Pro 14 shows how those drawing tools have evolved.

Consider an onboarding screen for a family organization product. The analytical task is to locate the step where people leave and separate technical failure from confusion. The creative task is to explain the next step in language a tired person can understand, show the value before asking for unnecessary effort and make the interface feel manageable. The scientific task returns when we test whether that change helped the right people reach a useful outcome.

That is an example of how I would apply my background, not a claim about a particular client result. The same combination can guide a paid-social concept, a product walkthrough, a subscription offer or a board presentation. I have written more about the connection in my work on data, creativity and storytelling.

The creative work makes a better possibility visible. The analytical work helps us decide whether it deserves to become the plan.

Every growth leader needs both capabilities, whether they bring them personally or deliberately build them into the team. Scientific discipline without curiosity about people produces very precise irrelevance. Creative conviction without measurement can spend a remarkable amount of money defending a hunch.

Paid acquisition needs a creative point of view and a financial stopping rule.

Paid search, paid social and programmatic solve different problems. They should not be judged as if they were three buttons on the same vending machine.

Search engine marketing can capture explicit intent. I would examine the search terms, separate branded from nonbranded demand, match the promise to the destination and follow conversion into paid retention. Branded search deserves particular care: some of those people already wanted the company. Attribution alone does not tell us how many needed an ad to get there.

Paid social often has to help someone recognize a problem before they actively search for a solution. Creative strategy therefore deserves real research. I want distinct concepts built around customer situations, objections and benefits. Twelve crops of one image do not constitute a testing program.

For a household planning subscription, one concept might show the relief of everyone knowing the plan. Another might demonstrate how quickly a recurring task gets organized. Those are different hypotheses about value. Each needs an honest product demonstration, a coherent destination and a downstream success measure.

Programmatic needs a clear reason to exist in the mix, enough measurement discipline to evaluate it and scrutiny of inventory, frequency, placement quality, invalid traffic and brand suitability. Cheap reach can be useful. Cheap reach to people who cannot or will not become customers is a procurement achievement, not a growth achievement.

Across channels, I want consistent campaign naming, conversion-event quality, creative learning records, pacing controls and cohort reporting. Optimization should move toward meaningful outcomes as signal volume allows. Training a system to find cheap registrations can be appropriate for an early diagnostic test; letting registrations remain the final goal after discovering they do not convert is a management problem.

Budget decisions should consider creative fatigue, auction changes, marginal CAC, contribution and cash constraints. A disciplined team has room to explore, funding for proven work and a reserve for opportunities or corrections. The proportions depend on the business. A neat allocation formula cannot replace a current understanding of the opportunity.

The creative director and the analyst should be talking before the campaign launches. That conversation improves the hypothesis, the measurement plan and the chances that a winning execution teaches us something reusable.

The app-store page is part of the product’s promise.

App Store Optimization, or ASO, combines discoverability with persuasion. The leader needs to understand search language, positioning, screenshots, previews, localization, review themes and the match between the store experience and what happens after installation.

I would begin by reading the listing as a prospective customer. Can I tell what the product helps me do? Is the first screenshot communicating a benefit or displaying a crowded interface? Does the language fit the audience? Are the strongest claims supported by the experience?

Apple’s product page optimization supports testing assets such as icons, screenshots and app previews. Its custom product pages allow different presentations of an app for different audiences or features. These are related tools with different jobs: testing a presentation and matching a presentation to an audience should not be confused.

On Android, Google Play store listing experiments support testing graphics and localized text. Platform definitions and experiment behavior differ, so I would review results within each platform before combining them into an executive view.

A successful listing test should lead to a second question: did the extra downloads become activated, retained customers? A dramatic screenshot can attract the wrong expectation. A more specific screenshot can reduce casual installs while improving paying-customer quality. The install conversion rate alone cannot settle that tradeoff.

Localization should include the customer’s context, not merely translated words. Expectations about household roles, pricing, support and examples may differ. The corresponding onboarding, payment and support experience must be ready for the audience the listing brings in.

Reviews are also a source of product and messaging research. Repeated confusion about billing, setup or a missing capability deserves an owner. A prettier store page will not repair the issue that keeps earning the same disappointed review.

Referrals work when the other person has a reason to care.

A satisfied customer is not automatically an active advocate. They may love the product and have no desire to discuss it publicly. They may want to recommend it privately. They may need an easy way to include someone who shares the task.

Those are different behaviors. A useful referral strategy starts by understanding the social situation, then choosing the mechanism. A shareable plan, a gift, an invitation to collaborate and a public testimonial do not create the same experience or the same economics.

In a family product, inviting a partner can improve the existing household’s experience without creating a new paying household. Count the invited person as a collaborator if that is what they are. Otherwise, the company can report impressive user acquisition while acquiring no additional customer revenue.

The viral coefficient needs a denominator and a clock.

A simple viral coefficient, or k-factor, can be modeled as invitations per eligible user multiplied by the recipient conversion rate, with a defined conversion event and time window. If 1,000 eligible users send 600 unique invitations and 20% of recipients become new activated users, the observed result is 120 activated users, or k = 0.12 for that cohort and window.

Illustrative example. This is an activation-based coefficient, not a paying-customer coefficient or an estimate of causal lift.

I would break the mechanism into its parts: exposure to the opportunity, share participation, invitations per sharer, recipient arrival, signup, activation, payment and retention. An improvement can fail at any handoff. A strong invitation rate is less exciting when the link is confusing or the recipient already has an account.

Cycle time matters. A referral loop that repeats in a week behaves differently from one that repeats in six months. Deduplication matters. So do fraud, incentives, channel overlap and whether referred users actually refer others. A coefficient above one in a selected group is not a promise of indefinite exponential growth; the reachable audience and the behavior change as the product expands.

Viral lift should mean the additional acquisition produced by the mechanism compared with a credible counterfactual. Observed referrals include people who might have joined anyway. Where feasible, a holdout can help estimate the extra contribution, with an experimental design that accounts for people influencing one another.

A referral loop and a network effect are different.

A referral loop brings additional users through existing users. A network effect means the product becomes more valuable as relevant participation increases. A discount for recommending a solo-use app can generate referrals without a network effect. A shared household tool may have a local network effect inside the household without becoming more useful because strangers across the country joined.

That distinction changes the roadmap. One opportunity needs a better invitation or incentive. Another needs a better shared experience. Both can be worthwhile. Neither benefits from being described as the other.

The renewal is where the original promise gets reviewed.

Consumer subscriptions can face a difficult combination: low-friction signup, high expectations, discretionary spending and easy comparison. Some products also serve a life stage with a natural endpoint. A customer who completes that stage is different from a customer who leaves because the experience failed.

I would separate those reasons before choosing a response. Lifecycle communication can help someone discover a relevant capability. It cannot manufacture an ongoing need that no longer exists. Sometimes the best outcome is a satisfied departure, a future return or a recommendation.

Useful lifecycle work begins with the customer’s state: new and not yet activated, active and learning, approaching renewal, struggling with payment, recently disengaged or ready for another relevant use case. Messages should help with the next useful action. Email, push and in-app prompts need coordination, consent and frequency discipline. Three teams each sending a “gentle reminder” can become a percussion section.

Pricing and paywall experiments should assess value communication, conversion, refunds, contribution and later retention together. An annual discount may increase cash collection while reducing contribution per month. An aggressive paywall may lift early payment while reducing the pool of users who experience enough value to stay or refer.

Payment failure deserves separate attention from voluntary churn. So do app reliability, support quality and the experience of changing or ending a subscription. Making cancellation frustrating may delay an event in the dashboard while damaging the relationship the company worked so hard to earn.

For wellness, education and family-oriented products, the tone of growth work is especially important. People may arrive anxious, tired or vulnerable. Clear expectations and responsible claims can be commercially strong choices. Fear-based creative may win a click while making the product harder to trust.

Retention is shared work. Growth should bring cohort evidence and customer insight to Product, Support and Finance, then help determine which causes can be addressed. It should not become the department that sends more notifications every time the retention curve disappoints someone.

Fast experimentation needs patience in exactly the right places.

Moving quickly is useful when the team can trust what it learns. Running many tests with unclear hypotheses, broken exposure logging or premature stopping creates activity and a collection of unreliable opinions.

Before launch, I want the team to specify the eligible population, randomization unit, primary metric, minimum effect worth acting on, sample requirements, duration, guardrails and decision rule. Choose the unit carefully: an individual-user experiment may be inappropriate when household members share the treatment or referral recipients influence one another.

Check assignment and exposure before interpreting outcomes. Microsoft Research’s work on sample ratio mismatch explains why an unexpected imbalance between experimental groups can signal problems that invalidate a result. The first question is whether the test ran correctly.

Set a stopping approach appropriate to the statistical method. Repeatedly looking at an ordinary fixed-horizon test and ending it the first time it becomes significant can inflate false positives. Valid sequential methods can support ongoing decisions, but they need to be chosen deliberately. A dashboard refreshing every hour is not itself a sequential testing method.

Multiple variants and multiple metrics add opportunities to find a lucky result. A team should distinguish its preselected primary outcome from exploratory findings, report uncertainty and avoid turning an interesting subgroup into a universal conclusion.

Here is the kind of experiment brief I would want to read.

A change from 20% activation to 22% is a two-percentage-point increase and a 10% relative lift. Both descriptions are mathematically correct. Good reporting states which one it means and whether the uncertainty allows a useful conclusion.

Low traffic limits what can be learned from small effects. In that situation, I would favor larger, well-reasoned changes, customer interviews, usability research and careful analysis over a sprawling multivariate test that will finish around the time the company retires.

The test backlog should include meaningful questions about creative concepts, audience fit, onboarding, pricing presentation, referrals and product value. Document failures as well as wins. A result that prevents six months of unproductive development can be an excellent return on a small experiment.

Making the organization capable

A growth leader builds a team that can disagree productively.

Managing senior channel leaders is different from personally operating every campaign. Directors need clear outcomes, budget boundaries, access to evidence and room to exercise judgment. They also need coaching that goes beyond “the number should be higher.”

I would expect paid, organic, lifecycle, creative and growth specialists to bring strong domain opinions. The leadership task is to connect them to a common customer and economic model. A paid leader should understand why an editorial investment matters. An organic leader should understand the company’s cash constraints. Neither needs to become the other’s assistant.

When a growth leader reports to Brand Marketing, the relationship should strengthen both functions. Brand provides a coherent promise and standards for how the company shows up. Growth contributes customer response, commercial evidence and learning from product behavior. A short-term conversion gain that undermines trust needs executive scrutiny; so does a brand program whose intended business contribution nobody can articulate.

The Growth Pod needs protected capacity. Product, Engineering, Analytics and UX cannot be treated as an unlimited internal agency for marketing requests. Agree on an outcome, a planning horizon, tradeoffs and the amount of work the team can actually deliver.

A useful operating rhythm leaves time to do the work.

I would use a weekly review for material changes, experiments, customer signals and decisions. A monthly discussion would examine cohorts, investment allocation, forecasts and capacity. Quarterly planning would revisit strategic assumptions, channel concentration and the product opportunities that deserve a larger commitment. Incidents and major performance changes should not wait for a calendar invitation.

Those meetings need a decision record: what we decided, the evidence, the owner, the constraint and when we revisit it. A status update can often be read. The conversation should be reserved for the parts requiring judgment.

Mentoring includes helping leaders present uncertainty clearly, distinguish controllable issues from external changes, develop successors and disagree without protecting territory. The team should be rewarded for improving the business, including identifying that its own channel deserves less money for a while.

This is also where technology, marketing and talent planning intersect. Sometimes the next constraint is a missing skill. Sometimes it is unclear authority. Hiring an additional specialist will not repair a decision nobody is allowed to make.

A credible growth story explains what happened after the impressive number.

“We grew tenfold” invites useful follow-up questions. From what starting point? Over what period? At what cost? What changed in retention, contribution and the product? Which parts did the leader own, and what did the rest of the team contribute?

When assessing a growth initiative, I want the before and after, the baseline dates, the cohort definitions, the investment, the uncertainty and the durability of the result. Revenue that doubled while acquisition spending quadrupled is a different story from revenue that doubled through stronger activation and retention. Either can be explainable. They should not be presented as interchangeable.

I apply the same standard to my own work: connect the contribution to the actual problem and available evidence. Scientific training, creative experience and technical fluency are useful because of how they affect decisions. A list of tools or credentials is not a substitute for demonstrating that reasoning.

The best growth leaders can explain an outcome to Finance without hiding the assumptions, discuss the implementation with Engineering, challenge the creative thoughtfully and hear a customer describe a problem without immediately turning it into a campaign.

That combination is valuable across consumer apps, education, wellness, family products and other subscription businesses. The details change with the market and the customer. The responsibility remains recognizable: earn attention honestly, deliver value early, invest with discipline and give people a reason to stay.