x + by + cz + d = 0 a (f(x + T) = f(x), ∀ x ∈ IR) (f(−x) = f(x), ∀ x ∈ D) −x + 2y + z + t = −1 2x + 2y + z + 4t = 0 C = {z = x + iy x, y ∈ IR} z = −1 + i nα = θ + 2πk

Response Prediction

Acquiring a new customer costs much more than retaining an existing one. Satisfying and protecting the customer base is essential to the profitability of a business, but it is not always easy to manage. In fact, competitor acquisition campaigns are often aggressive and, having a very large customer base, it is complex to keep satisfaction levels monitored and recognize the risk of churn. At the same time, it is necessary to plan customer retention campaigns in a targeted way that optimizes the use of time and resources.

  • Description and benefits
  • Application examples
High precision
Decreased churn rate
More effective retention campaigns

Application examples of Response Prediction

Products
Utilities
Services

Operating logic

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z = −1 + i nα = θ + 2πk u = ρ (cosα + isinα) f(x) = 3x − 2 z = r (cosθ + isinθ) ax + by + cz + d = 0 a (f(x + T) = f(x), ∀ x ∈ IR) (f(−x) = f(x), ∀ x ∈ D) 2x + 2y + z + 4t = 0 [x0 −r, x0 + r] (cosnα + isinnα) = (cosα + isinα)n z = −1 + i nα = θ + 2πk u = ρ (cosα + isinα)

The solution for Mission Bambini