Page 10 - Telecom Reseller MarApr 2016

10 Telecom Reseller March/April 2016

ALMIYA continued from page 3

e Benefactors of Propensity Scoring:
(identi ed below) to accommodate aws in creates an analytical model to nd the cause- Covariate adjustment using the propensity
Marketing campaigns’ cost-to-bene t ratio available empirical data missed by traditional and-e ect relationships by combining behavior score: When the outcome variable is regressed
e ectiveness is transformed by precisely targeting techniques due to issues such as selection bias patterns spread across multiple sources to create on an indicator variable denoting behavior status
customers who are likely to accept a cross-selling and confounding factors that could result in accurate predictions.
and the estimated propensity score.
or upselling o er rather than the traditional and completely erroneous conclusions. By adjusting ere are four common methods to achieve Computing propensity scores on large datasets
expensive broad-based blanket marketing.
for sampling issues, noise or other limitations in a propensity score, each providing a di erent that are discontinuous and noisy with large
Fraud prevention is instituted during a available data, this technique yields signi cantly way to summarize covariate data into unbiased number of covariates can be challenging. Genetic
telecommunications provider most vulnerable better results in predicting outcomes in real life information of value, including:
algorithms are well suited for such a class of
time - at Point of Sale (POS). Within seconds, scenarios. is overcomes issues with traditional Propensity score matching: Entails forming problems due to their inherent robustness in
scoring can immediately determine if a customer methods such as regression adjustment to matched sets of behavior and untreated data that complex tness landscapes. e evolutionary
applying for services is a returning bad debt account for e ects of confounding and data shares a similar value of the propensity score.
nature of genetic algorithms and intrinsically
customer, fraudster, or credit risk. It’s critical to selection.
Strati cation (or sub-classi cation) on the parallel approach to optimization lends well
know right then how to move forward with the Propensity scoring on empirical data
propensity score: Involves stratifying data to problems when solution space is vast for
customer - by either denying service or o ering when enriched with customer, operational, into mutually exclusive subsets based on their traditional algorithms.
an alternate plan. is type of fraud prevention geographic and vendor data allows for more of estimated propensity score.
e use of a propensity scoring method using
translates in both maximizing revenue and an accurate assessment of the desired outcome Inverse probability of treatment weighting genetic algorithms for matching is most e ective
minimizing risk.
scores that rank-order every customer for any (IPTW) using the propensity score: Uses weights in capturing subtle behavioral in uences on
action of interest. When integrated with lateral based on the propensity score to create a sample desirable outcomes in the telecommunications
WHAT IS PROPENSITY SCORING?
information sources such as purchases and in which the distribution of measured baseline industry typically characterized by large and
Propensity scoring incorporates methods
renewal history, propensity scoring combines and
covariates is independent of the behavior.
noisy data sets. It addresses the limitations with
available empirical data and accurately detects
the e ects of covariates spread across varied data
sources.
As a superior method for complex predictive
modeling problems, propensity scores with Shopping around for a new
genetic algorithms nd applicability across a wide
range of problems and can easily be retrained for
speci c populations and problem details. New
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or speci c models can be created using the same
technology and drawing on experience across
multiple industries. When paired with traditional Let’s Chat...
regression models, or as a standalone analysis,
propensity scoring provides a proven additional
li on the accuracy of scores generated.

HOW TO USE PROPENSITY
SCORING
Propensity scoring can be a powerful tool in
telecommunications when meshed into day-to-
day operations. at means as new information
becomes available, scores are dynamically

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