How We Work With You

Data is everywhere and nowhere.

• Different teams report different numbers.
• Critical data lives in spreadsheets and tools.
• Definitions drift quarter-to-quarter.
• Reporting must be rebuilt frequently.

You’ve started centralizing data, but it’s still fragile.

• Partial warehouse or central store exists.
• Pipelines break or need manual intervention.
• An emerging metric list exists but is incomplete.
• New use cases require ad-hoc pipelines.

Data flows are predictable, but not yet optimized.

• Core tables are documented.
• Ownership is emerging.
• Dashboards refresh reliably (with occasional surprises).
• New sources can be added without breaking everything.

Data is wired into operations.

• Operational tools consume shared models.
• Access is governed and role-based.
• Data quality is monitored proactively.
• Processes rely on accurate refresh cycles.

Data is discoverable, reusable, and valued.

• Key datasets have owners and SLAs.
• Teams treat data as a product with a roadmap.
• Users can self-serve confidently.
• Impact of data investments is measurable.

Reporting is ad-hoc and disputed.

• Spreadsheets and exports power most answers.
• KPIs vary by team or meeting.
• Analyst time is spent rebuilding slides, not insight.
• Experiments and models are rare or one-off.

You have dashboards, but they’re mostly rearview mirrors.

• Central reporting exists but is mostly static.
• Segmentation is manual and slow to update.
• Stakeholders queue requests with a small analytics team.
• Insights rarely change how teams operate.

You can ask why and explore drivers confidently.

• Cohorts, funnels, and root-cause views are standard.
• Source-of-truth datasets are documented.
• Self-serve works for common questions.
• Analysts focus on patterns, not plumbing.

Models guide planning and prioritization.

• Forecasts and scoring models run on governed data.
• Pipelines exist for feature creation and retraining.
• Business teams use model outputs in workflows.
• Model coverage is focused on a few critical journeys.

Analytics is embedded into decisions and automation.

• Experiments, rules, and models sit side-by-side.
• Recommendations and nudges adapt in real time.
• Human oversight is built into high-impact flows.
• Impact measurement is continuous, not quarterly.

Small team, building product-market fit.

• Founders and early hires wear every hat.
• Minimal process; decisions happen in real time.
• Data is anecdotal or pulled directly from tools.
• Success is defined by momentum, not consistency.

Demand is growing and repeatability matters.

• Hiring accelerates; roles start to specialize.
• First playbooks emerge for sales, product, and ops.
• Systems are patched together to support growth.
• Metrics focus on acquisition and activation.

You’re building durable systems and governance.

• Core processes are documented and measured.
• Budgets and planning cadences are established.
• Data stewardship and access rules take shape.
• Standard tools replace one-off solutions.

New products, markets, or regions are coming online.

• Shared services support multiple teams.
• Localized needs create variants of core processes.
• Partnerships and acquisitions add new data sources.
• Portfolio-level metrics gain importance.

You operate as a portfolio with intentional variety.

• Scenario planning and capital allocation drive priorities.
• Playbooks are reused and adapted across lines of business.
• Autonomy is granted within clear accountability models.
• Strategic bets are measured against long-term value.

Tools are disconnected and execution is inconsistent.

• Each team uses its own tools with little integration.
• Audiences and segments are rebuilt separately in each platform.
• Reporting is manual and stitched together from exports.
• First-party data capture is incidental, not intentional.

Some standardization and automation emerges.

• Key platforms (CRM, email, analytics) are defined.
• Basic lifecycle programs exist (welcome, nurture, simple winback).
• Integrations are configured but fragile.
• First-party data practices improve but are not fully governed.

Data, audiences and lifecycle programs shared by channels.

• Core lifecycle journeys run across multiple channels.
• Audiences and segments are defined once and reused across tools.
• Data flows between systems are documented and reliable.
• Personalization is rule-based and consistently applied.

Journeys adapt based on behaviour, signals, and logic.

• Targeting and routing use behavioural and model-driven signals.
• Orchestration guides channel timing and messaging.
• Personalization rules are governed and reused across journeys.
• 1st data is reliable and ready for automated use.

Experiences are consistent, personalized and optimized.

• Activation is driven by real-time decisions and trusted data.
• Journeys adapt using governed models and business rules.
• New channels and use cases plug into existing patterns.
• Teams share a common experience roadmap.