G-STAT Next Best Action Solution
Automatic Cross/Up-Sell Modeling and Deployment
G-STAT Next Best Action (NBA), a r-based big data predictive
analytics software, provides a unique and revolutionary
approach to the development, deployment and update of
cross-selling, up-selling, acquisition and next best offer or
action models.
G-STAT NBA enables companies’ marketing analysts to face
their cross-sell, up-sell and personalized recommendations
challenges by using a powerful, automated big data
predictive analytics application which can help them:
1.Identify high-potential customers for every product or
service sold by the company, based on automatic data
mining processes for customer behavior analysis;
2.Estimate the impact of a customer’s acceptance of an
offer to purchase a product on the revenues of the
company from each customer in order to avoid revenue
cannibalization;
3.Identify the next best offers or actions for each customer,
out of possibly hundreds and thousands of products or
actions sold or offered by the company.
G-STAT NBA automatically performs all the steps that are
being done today manually by data scientists who are
using any predictive analytics tool, for cross-sell or upsell modeling of all the products sold by the company, by
different customer segments, regardless of whether there
are dozens or hundreds or thousands.
G-STAT NBO automatically provides the following:
•Data extraction;
•Sampling of customers who bought or did not buy the
product;
•Variables selection;
•Machine learning & Regression modeling for predicting
propensity to buy;
•Machine learning & Regression modeling for predicting the
impact of accepting the offer on the company’s revenues
from each customer in order to avoid revenue attrition;
•In-sample, out-of-sample and out-of-time validation for each
of the models, even with potentially thousands of models;
•Deployment of the scoring process on periodic basis.
G-STAT automatic modeling platform saves months of
statistical work, while its prediction results are up to 70%
better than those of manually-developed models using any
predictive analytics tool.
Multi-SegmentModeling Technique
Because all the modeling and deployment phases
are automated, G-STAT Multi-Segment-Modeling
technology can be used to obtain better lifts
as compared to manually-developed models.
The Multi-Segment-Modeling Technique can:
• Segment the population for a specific product
or service the company wants to model into
20 to50 sub-segments using G-STAT NBA;
• Develop automated, separate prediction
models for the same product for each of the
sub-segments selected using G-STAT NBA;
• Gather scores from each sub-segment’s
model into one overall score list and rank
the customers in that list into percentiles;
• Compare the lifts in the top percentiles within
the ranked list based on the scores of many subsegments to the lifts among a ranked list based
on one model of the entire population or on only
a few segments of it, as is done manually today;
• Benefit users as comparisons show that lifts
among lists based on scores coming from ~50
models on sub-segments are higher by 10%70% compared to lifts within lists based on
scores coming from one model of the whole
of the population or of only a few segments.
Advantages of G-STAT NBA
The core differentiators of G-STAT NBA compared to a
classic predictive analytics project using any predictive
analytics tool include the following advantages:
•Provides a whole business solution for personalized
cross-sell and up-sell recommendations, unlike a data
mining R&D environment which all predictive analytics
vendors provide and require professional services of
data scientists and business intelligence experts for data
management and modeling;
•Results in better lifts, higher response rates and increased
revenues from campaigns due to the Multi-SegmentModeling technique’s ability to model 50 times more
models by sub-segments for each product;
•Dramatically decreases the time required for
development and deployment of cross-sell and upsell models of up to thousands of models from weeks
or months to just hours, while still getting higher lifts
as compared to manually developed models by data
scientists;
•Updates the models more frequently, providing weekly
or monthly updates of all models instead of yearly
updates, which are commonly done today. This results
in better predictions and campaign response rates;
•Offers a friendly graphic user interface for the marketing
staff which requires no statistical-analytical know-how
whatsoever. G-STAT NBA automatically performs all
complex ETL and statistical processes by clicking without
using a line of code;
•Integrates with conventional campaign management
environments;
•Integrates with other G-STAT applications and solutions,
such as G-STAT Customer Retention Optimization (CRO)
for automatic churn prediction modeling or G-STAT
Lifetime Value Optimization (LTV) for automatic lifetime
value prediction and customer segmentation. As a result,
a company can achieve an integrated, one-stop-shop
for customer-centric business analytics models.
G-STAT Differentiators Table
Characteristics of developing and
deploying 500 cross-sell and up-sell
models using predictive analytics tools
Characteristics of developing and
deploying 500 cross-sell and up-sell
models using G-STAT NBA
Months of development
Hours of development
Months of deployment
Automatic deployment
Updates done once a year
most of the time
Updated usually once a week
or once a month
Models built on whole population
Different models for each product are
built based on customer segments
Operated by data scientists and
SQL experts
Operated by marketing analysts with
no statistical know-how
Predicts propensity to buy
Predicts both propensity to buy and
the impact of purchases on revenues
Reliable predictions
Even better results:
up to 50% higher lifts
Using G-STAT NBA
results in
• Higher lifts, which lead
to higher response rates
and increased revenues by
millions of dollars, proving
a ROI after only one or two
campaigns;
• Increased productivity of
the analytical team, who
can develop, deploy and
update 100 times more
models using the same
resources;
• Enhancements of existing
predictive analytics and
campaign management
tools.
G-STAT Analytics Solutions Headquarters: 6 Granit St., Petah Tikva 4951405, Israel
Tel: +972-77-8011500 | Fax: +972-77-8011511 | Email: [email protected]
www.g-stat.software
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