Salespuzzle121 Use Cases

The commercial problems we are asked to solve.

Four representative examples of how Salespuzzle121 works: the problem as it is usually described, what the analysis actually finds, what changes, and what the organisation is left with.

These are representative examples, not named customer case studies. They describe patterns we see repeatedly, written so you can recognise your own situation in them.

Use case 01 · Commercial Engine

The business that grew faster than its commercial operation.

A B2B technology company has reached approximately £8m revenue. It has marketing, sales, customer success, CRM, marketing automation and extensive reporting. Growth has stalled.

Marketing believes it generates enough leads. Sales believes lead quality is poor. Customer success sees expansion opportunities that sales does not know about. Management does not trust the forecast.
The problem

Four functions, four explanations

Each explanation holds up on its own terms, which is precisely why the argument never resolves. The business is being examined function by function.

The analysis

One journey, examined end to end

Salespuzzle121 reviews the complete commercial journey rather than treating these as isolated problems, and discovers:

  • ICP definition is too broad
  • Qualification differs between salespeople
  • Marketing and sales use different definitions of a qualified opportunity
  • CRM stages do not represent the actual buying journey
  • Customer expansion is not systematically identified
The intervention

Redesigned around one journey

The commercial engine is redesigned around a common customer journey, shared definitions, clearer qualification, meaningful metrics and connected data.

The outcome

One commercial system

The organisation starts operating as one commercial system rather than several disconnected functions.

Commercial Engine

Where the whole commercial journey is examined as one system: strategy, ICP, demand, sales, customer growth, people, process, data, CRM and management visibility.

Use case 02 · Territory & Sales Organisation

Is it really a salesperson problem?

A company employs eight salespeople. Three consistently achieve target. Three fluctuate around 70–90%. Two are significantly below target. Management is considering replacing the bottom performers.

The problem

A clear table, an obvious conclusion

The performance data supports the decision. What it does not show is whether the eight people were given comparable opportunity in the first place.

The analysis

Opportunity before performance

Salespuzzle121 analyses CRM performance, customers, prospects, ICP concentration, account allocation, historical conversion, territory potential and Sales DNA, and discovers:

  • One high performer holds one of the strongest existing-customer territories and generates substantial expansion revenue
  • One struggling salesperson predominantly holds net-new accounts in a poorly penetrated market
  • Another has strong complex-deal and relationship capabilities but a territory requiring intensive new-logo prospecting
The intervention

Deployment, not dismissal

Territory and account allocation are redesigned around opportunity and capability, and Territory DNA × Sales DNA matching is introduced.

The outcome

Four problems, told apart

  • Territory problem
  • Capability problem
  • Coverage problem
  • Performance problem
The issue is not simply salesperson performance. It is deployment.

Territory & Sales Organisation

Territory potential, coverage, capacity and account allocation — and the Territory DNA × Sales DNA work that establishes whether the right person holds the right opportunity.

Use case 03 · Sales Performance

Plenty of pipeline. Not enough revenue.

A company consistently carries £4m–£5m of pipeline against a £1.5m target. Pipeline coverage appears healthy. Yet forecast is repeatedly missed.

The problem

Healthy coverage, missed number

The shortfall is explained each quarter by a different exception, which is usually the clearest sign that the explanation is somewhere else entirely.

The analysis

The opportunities, not the total

Salespuzzle121 analyses the underlying opportunities rather than simply looking at pipeline value, and discovers:

  • Opportunities enter pipeline too early
  • Decision authority is unclear
  • Several opportunities depend on one contact
  • Close dates are salesperson-generated rather than customer-driven
  • Proposals are produced before sufficient qualification
The intervention

Qualification, power and checkpoints

Stronger qualification, contact-power analysis, deal checkpoints and management disciplines are introduced. Pipeline initially becomes smaller — but management can finally trust it.

The outcome

Better revenue, smaller numbers

The organisation focuses on better revenue rather than bigger CRM numbers.

The problem is not insufficient pipeline. The pipeline is overstated.

Sales Performance

What happens between first engagement and closed revenue — qualification, contact power, deal momentum, conversion, forecasting and the management disciplines around them.

Use case 04 · AI Build for Commercial

“We know we should be doing more with AI.”

A £20m B2B organisation has CRM, marketing automation, Microsoft 365, website analytics and years of customer and opportunity data. Management has experimented with AI. Employees use general AI tools, but there is no coherent commercial AI strategy and little measurable impact.

The problem

Activity without direction

Individual experimentation is widespread and mostly useful to the individual. None of it has changed a commercial number, and there is no basis for deciding what to do next.

The analysis

Thirty opportunities, scored

Salespuzzle121 maps the complete commercial journey and identifies more than 30 potential AI applications. Rather than attempting everything, each is scored against commercial impact, feasibility, data readiness and implementation effort.

The intervention

Four priorities, designed and built

  • AI account researcher
  • Opportunity-risk agent
  • Account-expansion agent
  • Management intelligence assistant

The design establishes how these interact with the existing stack, and what data and controls they require.

The outcome

AI with a job description

AI performs specific valuable jobs inside the commercial engine rather than simply being another technology employees have access to.

AI Build for Commercial

Finding where AI genuinely improves a commercial process, prioritising it honestly, designing it against your systems and building what is worth building.

Talk to us

Recognise one of these?

Most commercial problems arrive described as something else. Tell us how yours is currently explained inside the business, and we will tell you where we would look first.

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