Need Attractiveness Scorecard

Purpose

Quantitatively evaluate the intrinsic desirability of a validated need — independent of organisational fit — across four dimensions: intensity, prevalence, growth trajectory, and urgency. Produces a composite score (0–12) informing portfolio prioritisation and the P/D/D recommendation.

When to use

A1.4 Step~6, alongside the Strategic Fit Scorecard. The two scorecards answer different questions: Fit asks “Should we solve this?” Attractiveness asks “Is this worth solving?”

Output

Need Attractiveness Scorecard (table) with composite score and threshold interpretation.

Inputs

• A1.2 validation data: pain intensity ratings, prevalence estimates, user segment data
• A1.3 success criteria: consequence severity
• External data: industry trend reports, market research, demographic projections

Procedure

1. Score each dimension (0–3). itemize
2. Intensity (weight 30%): 0 = minimal pain (<3/10); 1 = mild (3–5/10); 2 = moderate (5–7/10); 3 = severe (>7/10, high consequences).
3. Prevalence (weight 25%): 0 = rare (<10% of segment); 1 = uncommon (10–30%); 2 = common (30–60%); 3 = widespread (>60%).
4. Growth trajectory (weight 30%): 0 = declining (need shrinking); 1 = stable (flat); 2 = growing (5–15%/year); 3 = rapidly growing (>15%/year or strong structural tailwind).
5. Urgency (weight 15%): 0 = evergreen (no timing pressure); 1 = mild (gradual market evolution); 2 = moderate (competitive window 12–24 months); 3 = acute (regulatory deadline, closing window <12 months). itemize
6. Cite evidence per dimension. Intensity: A1.2 pain ratings + A1.3 consequences. Prevalence: A1.2 segmentation data + external validation. Growth: credible analyst reports or demographic data (quantitative preferred). Urgency: competitive timing evidence, regulatory deadlines, or window-of-opportunity analysis.
7. Calculate weighted total. Maximum: 12.0.
8. Apply thresholds. ≥9: HIGH attractiveness (strong Pursue signal). 6–8: MODERATE (contingent on strategic fit and white space). <6: LOW (strong Decline signal).

Quality criteria

• Intensity and prevalence grounded in A1.2 data
• Growth claims supported by external sources (not extrapolation from small samples)
• Urgency justified by specific deadline or window (not “feels urgent”)

Common errors

• Prevalence extrapolation — projecting from unrepresentative A1.2 sample to general population
• Wishful growth — assuming exponential when evidence shows linear or stable
• Intensity inflation — A1.2 recruited extreme users; broader population is less intense
• False urgency — “competitive window closing” without evidence of competitor activity

Theoretical basis

Outcome-Driven Innovation (Ulwick, 2005) for importance-based need scoring; Real Options Theory (McGrath, 1999) for urgency as option value.