Product Vision and AI Roadmap

Context

A growth-stage MarTech company running a giveaway and promotions platform for brands. The product worked, campaigns ran, but the team was at an inflection point: they had traction and needed to decide what the product becomes next. No structured roadmap, no pricing model that scaled with customer value, and no framework for evaluating which AI capabilities to build versus skip.

Three-month engagement. Solo consultant, working directly with the founder.

The Problem

Three gaps, each blocking the next stage of growth:

No product vision connecting features to outcomes. The platform had features, but no coherent story about where it was headed. AI was on the table, but "add AI" isn't a strategy. The team needed to know which AI capabilities would actually move their metrics, and in what order.

Pricing didn't reflect value delivered. Flat or loosely structured tiers meant high-value customers paid roughly the same as low-value ones. No mechanism to capture more revenue as campaigns scaled, no way for customers to self-select into the right plan.

No systematic way to track market position. Competitors were launching features, Reddit communities were discussing alternatives, and the team was relying on ad hoc awareness. No early warning system, no structured competitive intelligence.

Approach

Vision and roadmap first, features second. Started by mapping the Jobs to Be Done across the customer base. What are brands actually hiring this platform to do? That shaped the roadmap into a sequence of capability layers rather than a feature backlog. AI capabilities, including a consumer profile portal, predictive lead scoring, and ROI forecasting dashboards, were slotted where they solved real customer problems, not where they sounded impressive.

Pricing architecture tied to value metrics. Built an 11-tier pricing model where each tier corresponded to a distinct level of campaign complexity and expected ROI. The logic: customers running 50 campaigns a year with attribution tracking need fundamentally different tooling than someone running 3 giveaways on Instagram. Pricing should reflect that, and customers should be able to see themselves in a tier without a sales call.

Alongside the tiers, built an ROI calculator supporting 3 scenario types so customers could model expected returns before committing. The goal was reducing sales friction by letting the product make the case.

Metrics tree for internal decision-making. Created a metrics tree connecting campaign-level performance indicators to business outcomes. This gave the team a shared language for prioritization: if a feature doesn't move something on the tree, it's not a priority this quarter.

Competitive intelligence via automation. Prototyped an n8n workflow tracking 4 relevant subreddits for competitor mentions, feature launches, and sentiment shifts. Target was 20+ actionable alerts per week. Not a dashboard to stare at, but a notification system that surfaces what matters and ignores what doesn't.

Results

• 11-tier pricing architecture replacing a flat structure, designed to scale revenue with customer value
• ROI calculator supporting 3 scenario types for customer self-service and sales enablement
• AI-led product roadmap with sequenced capabilities mapped to customer jobs, not technology trends
• Metrics tree connecting campaign performance to business KPIs
• n8n competitive intelligence prototype tracking 4 subreddits with a target of 20 alerts/week

The founder's feedback: "He brought exceptional clarity, organization, and strategic focus to the process, and made complex initiatives feel cohesive and achievable. He was able to bridge product strategy, market fit, and analytics and connected big-picture goals with practical next steps."

What Transferred

The pattern here applies to any platform at the "we have traction, now what?" stage. Most early-stage companies add features reactively. The shift that matters is connecting every product decision to a value metric the customer cares about, and pricing in a way that captures it. AI capabilities follow the same logic: build what moves the metrics tree, not what demos well.