How to choose a growth strategy that fits your business

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A target is not a strategy

"Grow by 20%" describes an outcome. "Post daily" describes an activity. Neither explains why the right customers will choose you or how the business will fulfil the promise. A useful strategy connects a customer problem to a commercially sustainable response. It also makes exclusions explicit: customers you will not chase, promises you cannot support and projects that must wait.

Start with a period that represents your trading cycle. Trace suitable enquiries through decision, delivery, payment and the next relevant opportunity. Do not average away important differences between customer groups. A profitable repeat service and a heavily discounted one-off project may need completely different plans.

Separate the symptom from the decision

"Not enough revenue" is a symptom. It does not tell you whether to advertise, improve quoting, adjust the offer or repair delivery. List the evidence beside each explanation. Customer research should include what people actually need and what alternatives they consider, not only what the business would like to sell.

Use three evidence labels: observed, inferred and unknown. An observed fact might be that 12 quotes have no agreed next action. An inference is that follow-up is causing lost sales. Whether those customers would have bought remains unknown. This distinction lets you test the explanation without pretending to have diagnosed the whole business.

Five useful strategic options

For a practical starting point, look at five options: improve penetration of an existing customer group, improve conversion of suitable demand, earn relevant repeat work, test an adjacent offer or market, and remove a delivery constraint. It is a shortlist, not a formula every business has to follow. Each option should name a customer, a commercial logic and a capability requirement.

Compare options using evidence strength, likely contribution, delivery load, reversibility and time to learn. Do not turn subjective ratings into a score that looks more exact than the evidence allows. A low-evidence opportunity with a large imagined market should not beat a smaller, testable opportunity merely because its forecast has more zeros.

Worked example (illustrative): the owner who wanted more leads

A design business receives 60 enquiries in a month. Thirty fit its offer. Twelve receive proposals and four buy. Delivery can accommodate six more projects. The owner proposes doubling advertising.

The counts reveal several questions, not one automatic answer. Why did 18 suitable enquiries never reach a proposal? Were they unaffordable, badly timed or simply unanswered? The business reviews those records and finds eight waiting for an owner-approved scope. It tests a standard discovery checklist and a delegated approval limit on the next ten suitable enquiries.

The goal is not "convert everyone". It is to reduce avoidable waiting while maintaining fit and contribution. The team records response time, proposal progression and reasons for decline. If faster proposals produce low-margin work or scope disputes, the apparent improvement is not enough.

Make the choice reversible where possible

Write the experiment as: "For this customer group, we believe this change will improve this measure because of this evidence." Add a maximum budget, an owner, a review date and a condition that stops the test. Keep an unchanged comparison where practical, and record other changes that could affect the result.

A sensible first result can be deciding not to expand. It can also be discovering that acquisition really is the constraint. The discipline is choosing from evidence, rather than assuming that every slow-growth business needs the same intervention.