Willingness to Pay isn’t just about pricing — it’s about knowing who would buy, at what spend, and why. CMOs and consultants need fast answers to size a market or brief a launch.
Panels take weeks and often miss behavior. With only your target segment and product brief, we estimate who’s concerned, who’s likely to buy, and how much they’d spend monthly — scored, sourced, and ready to activate.
Define your target segment and product characteristics
We match population data: size, demographics, and context
Behavioral scores are derived from apps, queries, and relevant signals
Outputs include buyer share, purchase blockers, and expected monthly spend
This segment is activated with a blend of trusted sources and your own inputs
AI reasoning
Web intelligence
User Input
Estimated buyer share per segment
Monthly spend range, with blockers and notes
Behavioral scoring with supporting signals
Transparent, exportable output for sizing or prioritization
One-click access to segment-level insight — no panel, no black box.
Your questions on this segment, answered
Your questions on this segment, answered
What does this Magic Segment actually deliver?
It estimates what share of a defined segment is likely to buy your product, and how much they would plausibly spend per month — based on behavior, health context, and profile.
How can I use the output in planning or activation?
You can size your addressable market, stress-test pricing, and brief teams with confidence — whether you’re launching, repositioning, or prioritizing segments.
Can I run it for different target segments?
Yes — you can define multiple segments by age, gender, location, or socio-economic code, and compare buyer share and spend across them.
How often is the model updated?
There’s no fixed model — we rebuild the output based on the latest data each time you run it. Freshness and confidence vary with each request, and are always visible.
What type of data sources are used?
We use open and web-accessible data: census tables, app stores, product listings, user forums, search patterns, and behavioral studies. The value isn’t in the raw data — it’s in how we structure it around your segment and product, with transparent logic and confidence scoring.
Can I use this instead of a pricing panel?
Not exactly — this doesn’t replace a pricing panel, but it offers a faster, behavior-based view of intent and spend. Ideal for testing positioning before launching panels.
Does this work without any prior sales data?
Yes. This is designed for early-stage sizing — you don’t need transactions or market share to run it.
What kind of product briefs can I use?
You can run this for any B2C product or service — from health and beauty to food, mobility, or consumer tech — as long as you can describe its use, audience, and price point.
How do you estimate who’s likely to buy?
We match behavioral and contextual signals — like app usage, search patterns, and profile traits — to identify which segments show relevant interest for your product type.
Is the monthly spend based on declared data?
No — it’s not survey-based. Spend is inferred from behavior, needs, and segment characteristics, not from stated preferences.
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"urbanicity_confidence", "Data_Type": "INTEGER", "Sample_Value": "92", "Description": "Confidence score (0-100) for urbanicity"}, {"Variable": "segment_population", "Data_Type": "INTEGER", "Sample_Value": "27000", "Description": "Estimated number of people in the segment"}, {"Variable": "segment_population_confidence", "Data_Type": "INTEGER", "Sample_Value": "95", "Description": "Confidence score for segment population estimate"}, {"Variable": "overweight_pct", "Data_Type": "DECIMAL", "Sample_Value": "52.3", "Description": "Estimated % of population with overweight or obesity"}, {"Variable": "overweight_pct_confidence", "Data_Type": "INTEGER", "Sample_Value": "93", "Description": "Confidence score for overweight percentage"}, {"Variable": "supplement_usage_pct", "Data_Type": "DECIMAL", "Sample_Value": "64.0", "Description": "% of population consuming food supplements"}, {"Variable": "supplement_usage_pct_confidence", "Data_Type": "INTEGER", "Sample_Value": "90", "Description": "Confidence score for supplement usage"}, {"Variable": "health_app_penetration_pct", "Data_Type": "DECIMAL", "Sample_Value": "62.0", "Description": "% of population using digital health or nutrition apps"}, {"Variable": "health_app_penetration_pct_confidence", "Data_Type": "INTEGER", "Sample_Value": "89", "Description": "Confidence score for health app usage"}, {"Variable": "health_app_usage_level", "Data_Type": "ENUM", "Sample_Value": "High", "Description": "Frequency or depth of health app usage"}, {"Variable": "health_app_usage_level_confidence", "Data_Type": "INTEGER", "Sample_Value": "88", "Description": "Confidence score for health app usage level"}, {"Variable": "main_supplement_motivation", "Data_Type": "VARCHAR", "Sample_Value": "Energy / Muscles", "Description": "Dominant consumer motivation for supplement use"}, {"Variable": "main_supplement_motivation_confidence", "Data_Type": "INTEGER", "Sample_Value": "85", "Description": "Confidence score for motivation value"}, {"Variable": "purchase_channel_dominant", "Data_Type": "ENUM", "Sample_Value": "E-commerce", "Description": "Primary channel for supplement purchases"}, {"Variable": "purchase_channel_dominant_confidence", "Data_Type": "INTEGER", "Sample_Value": "92", "Description": "Confidence score for dominant channel"}, {"Variable": "price_sensitivity_level", "Data_Type": "ENUM", "Sample_Value": "Moderate", "Description": "Price sensitivity for supplement products"}, {"Variable": "price_sensitivity_level_confidence", "Data_Type": "INTEGER", "Sample_Value": "90", "Description": "Confidence score for price sensitivity"}, {"Variable": "premiumization_potential", "Data_Type": "ENUM", "Sample_Value": "High", "Description": "Propensity to trade up to premium products"}, {"Variable": "premiumization_potential_confidence", "Data_Type": "INTEGER", "Sample_Value": "87", "Description": "Confidence score for premiumization potential"}, {"Variable": "glp1_behavior_score", "Data_Type": "INTEGER", "Sample_Value": "5", "Description": "Composite score (0-5) for GLP-1 adoption likelihood"}, {"Variable": "glp1_behavior_score_confidence", "Data_Type": "INTEGER", "Sample_Value": "95", "Description": "Confidence score for GLP-1 behavior score"}, {"Variable": "glp1_usage_pct", "Data_Type": "DECIMAL", "Sample_Value": "4.8", "Description": "% of population likely to use GLP-1 based on score"}, {"Variable": "glp1_usage_pct_confidence", "Data_Type": "INTEGER", "Sample_Value": "90", "Description": "Confidence score for GLP-1 usage %"}, {"Variable": "side_effects_pct", "Data_Type": "DECIMAL", "Sample_Value": "70.0", "Description": "Estimated % of GLP-1 users with side effects"}, {"Variable": "side_effects_pct_confidence", "Data_Type": "INTEGER", "Sample_Value": "90", "Description": "Confidence score for side effect %"}, {"Variable": "purchase_intent_pct", "Data_Type": "DECIMAL", "Sample_Value": "62.0", "Description": "% of segment with high intent to buy GLP-1 Support"}, {"Variable": "purchase_intent_pct_confidence", "Data_Type": "INTEGER", "Sample_Value": "88", "Description": "Confidence score for purchase intent"}, {"Variable": "wtp_range_eur", "Data_Type": "VARCHAR", "Sample_Value": "€25–30", "Description": "Estimated monthly Willingness to Pay range"}, {"Variable": "wtp_range_eur_confidence", "Data_Type": "INTEGER", "Sample_Value": "91", "Description": "Confidence score for WTP range"}, {"Variable": "purchase_blocker", "Data_Type": "ENUM", "Sample_Value": "Cost", "Description": "Main friction to purchase (Cost, Trust, Saturation, Access, Awareness, Other)"}, {"Variable": "purchase_blocker_confidence", "Data_Type": "INTEGER", "Sample_Value": "90", "Description": "Confidence score for blocker label"} ] }
