Practice 01

Strategy, growth and value creation

Where to play, how to win and where value is created — decided with evidence and kept live as the context moves. This practice sets the direction the other two practices work from.

The standard: business decisions with the analytical rigor of the global strategy firms.

Business strategy and dynamic planning

Strategic plan — where to play and how to win, with prioritized bets
Cause-and-effect map — how the strategy turns into KPIs, targets and owners
Scenario planning — alternative strategies pre-built by scenario, with measurable triggers
Value creation plan — the levers that raise enterprise value, sequenced and measured
Strategy execution control — the plan reviewed and recalibrated on a management cadence

Growth and portfolio

Growth diagnostic — where the company stands and why it is not growing faster
Customer segmentation and targeting
Portfolio strategy — what to push, fix or discontinue

Pricing and monetization

Pricing and margin diagnostic — value leaking through prices, discounts and terms
Price architecture — price lists, tiers, bundles, versioning
Monetization models — recurrence, subscription, services layered on product
Commercial policy — discounts and terms with clear rules
Launch pricing for new products or markets

Strategic marketing and go-to-market

Positioning and value proposition — of the offer and of the brand, decided with evidence
Strategic marketing plan — objectives, mix, annual budget and the return expected of each channel
Market-entry strategy — country, segment or channel
Channel design — direct, partners, digital
Sales playbook — process, pipeline and qualification criteria

Commercial effectiveness

Marketing and sales audit — what returns and what does not, with evidence
CRM and pipeline implementation
Commercial KPIs and sales forecasting
Ongoing sales enablement

Pricing decisions here are grounded in the customer analytics DBNG Digital builds, and every plan runs on the numbers Corporate finance produces — reviewed every month in the decision meeting, on the same data and AI-agent layer.

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