Strategy

Strategy Phase: From Understanding to Decisions

The strategy phase turns research data into concrete decisions: how to position, what to offer, at what price. All artifacts in this phase build on the results of the research phase — segments, Job Statements, pain map.

Competitive Landscape

Methodology: Consideration Set + Non-consumption

In Product DNA, competitive analysis is built not on features, but on the Consideration Set — the set of solutions the user actually considers before purchasing. This includes:

Consideration Set: Buyer's Mental Model

The Consideration Set forms before the trigger, not after. By the time the user starts actively searching, they already know 2-3 options. Marketing must enter the Consideration Set in advance — through content, recommendations, SEO.

Analysis Quality Criteria

• Competitors are evaluated by Jobs, not by features
• For each competitor, it's noted which Jobs they solve well, poorly, or don't solve
• Non-consumption is included as a full competitor
• Vocabulary from interviews (DP11: "what they told others") is used for descriptions

Positioning

Methodology: April Dunford + Product DNA Job Language

Positioning defines your product's place in the target audience's mind. AI generates positioning using the April Dunford framework, enhanced with Product DNA:

Positioning Formula

"For [target segment] who [situation/trigger], [product] is a [category] that [key differentiator]. Unlike [alternatives], we [unique value]."

Value Proposition (Value Proposition Canvas)

Methodology: Value Proposition Canvas + JTBD Fit

The value proposition is built as a Value Proposition Canvas adapted for Product DNA. Both sides of the Canvas must match — this IS Product-Market Fit:

Fit Score

AI calculates a Fit Score (0-100%) — the degree of alignment between Value Map and Customer Profile. The Fit Score consists of:

• Pain-Reliever fit: how many of the top-10 pains are covered by pain relievers
• Gain-Creator fit: how many desired outcomes are covered by gain creators
• Job-Product fit: how well product features correspond to Core Jobs

Switch Formula in the Value Proposition

The value proposition must deliver ≥ 2x value compared to the current solution. This is the loss aversion rule from neuroscience: losses feel ~2x stronger than gains (Kahneman). If your product is only slightly better — the user won't switch, because the pain of transition (Inertia + Anxiety) will outweigh the benefit.

Offer Bank

Methodology: 5 Awareness Levels + Job Language

The Offer Bank generates advertising propositions adapted to the buyer's awareness level. Each offer is tied to a specific pain and Job Statement.

Offer Types in the Bank

• Main headline — for landing pages and ads (in Core Job language)
• Subheadline — value expansion through desired outcome
• CTA — call to action (tied to a micro job)
• Social proof — review formulations (from DP11)
• Objection handlers — responses to Anxiety from the Switch formula
• Activating Knowledge — phrases that update the Consideration Set

Activating Knowledge

Activating Knowledge — 3-5 phrases that change the user's mental model and trigger a Switch WITH YOUR product. Extracted from DP11 (what early users told others) and search queries for orientational jobs.

Pricing Model

Methodology: Three Pricing Dimensions

AI analyzes pricing through a synthesis of three frameworks. The right price lies at the intersection of all three:

Pricing Models

WTP Estimation (Willingness to Pay)

WTP is extracted from several sources:

• DP16 (Budget/WTP signal): Direct signal from interviews — "how much do you pay now?"
• Competitor prices: Set an anchor in the Consideration Set
• Cost of the problem: If the problem costs the client $1,000/month → WTP = $50-300/month
• Pre-order test: The strongest signal — real money before the product exists

Simplicity Rule