Predictive Price Sensitivity Ranking.

Standard marketing tools make a huge mistake: they assume anyone who uses a discount is a bargain hunter. In reality, a high-value customer using a 10% referral code is NOT price-sensitive—they are just taking a convenient discount. Meanwhile, a customer who only buys during 40% clearance sales is a risk to your profit margins. This algorithm fixes this issue. By comparing every customer's spend size and discount frequency, we reveal their true willingness to pay, letting you offer discounts only when they are absolutely necessary to secure a sale.

Instead of complex averages, we rank all customers relative to one another. This keeps the data accurate even if a small group of high-spending customers generates most of your sales.

• Vector A (Spend Size): We check the customer's average order value. High spenders get ranked higher.
• Vector B (Discount Habits): We track how often the customer uses coupons. Frequent coupon users get ranked lower.
• Weighting: Discount habits get 70% of the weight because coupon usage is a much stronger predictor of discount dependency than overall spend size.

Running these calculations on millions of rows in standard database systems causes massive delays. Bliz.cc runs this logic instantly at the network edge as soon as a visitor lands on your page, updating their score and profile in milliseconds without page lag.

# Easy Logic:
# 1. Rank customers by spend size (1 to 100)
# 2. Rank customers by discount habits (1 to 100)
# 3. Blended Score = (Discount Rank * 0.70) + (Spend Rank * 0.30)
# 4. High Score = Premium Buyer (No promo needed)
# 5. Low Score = Bargain Hunter (Needs coupon to buy)

Optimizing your discounts directly recovers lost profits. Stop giving away money to customers who are already standing at the checkout with their credit card in hand.