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Transcript
Where Big Data Analytics meets
Business Analytics
The G-STAT Platform for Big Data Analytics
G-STAT Analytics Solutions is a leader in developing and implementing big data predictive analytics software solutions
for customer-centric marketing. G-STAT Analytics Solutions was founded in 2011 as a spin-off of G-STAT, a leading
integration company with over 150 consultants, mainly data scientists, specializing in analytical and predictive analytics
projects in the fields of customer-centric marketing and risk management.
The creation of G-STAT platform was based on a deep
understanding of B2C companies’ business analytics
challenges and on the ambition to solve the primary pain
points in predictive analytics project implementation arising
from the use of the predictive analytics tools currently on
the market. The problems included:
•A lengthy time-to-market for implementation of such
projects;
•The need for constant updating of the predictive
analytics models to maintain their relevancy;
•The difficulty in allocating resources in an organization to
enable data scientists and business intelligence experts
to develop and continually update various business
models.
The G-STAT platform enables automatic development,
deployment and update of cross-sell and up-sell,
acquisition, next best offer, churn prediction and lifetime
value prediction models, including all data extraction, data
preparation modeling, validation and deployment phases.
Using the G-STAT platform, companies can produce 50 to
100 times more propensity and revenue impact models,
doing so in only a few hours instead of weeks and months.
By using the patent-pending G-STAT Multi-SegmentModeling technology for developing and deploying
propensity models on customer segments rather than
on whole populations, our customers enjoy significantly
higher model lifts, up to 50%, in comparison to manuallydeveloped models using any predictive analytics tool. This
leads to higher response rates in their outbound and
inbound campaigns, incremental revenues from their
targeted campaigns and clear and measurable ROI after
only one or two campaigns.
G-STAT Analytics Solutions’
unique and revolutionary
approach to the
development, deployment
and update of customercentric big data predictive
analytics models helps B2C
companies, such as banks,
telecom companies and
retailers maximize their
revenues from customers.
G-STAT Analytics Solutions
The G-STAT platform is a package of revolutionary software solutions that provide actionable insights and recommendations
for the proper management of an organization’s customer-based, automated predictive analytics processes:
G-STAT Next Best Action (NBA): Automatic
cross-sell or up-sell modeling of dozens to thousands of
models by customer segments for all products sold by the
company, targeted for:
–– Outbound campaigns: Identifies the target population
for every product, based on customers’ propensity to
buy the product and the projected revenues for the
company from the customers who accept the offer and
buy the product;
–– Inbound campaigns: Identifies the next products for
each customer based upon his or her propensity to buy
or accept an offer and predicting the impact of buying
the product on the projected revenues of the company
from the customer.
G-STAT Customers Retention Optimization (CRO):
Automatic development and deployment of dozens of
churn prediction models by customer segments for
different business lines, identification of high churn risk
customers, by different business lines or products, and
recommendations on the optimal retention offer for
maximizing each customer’s lifetime value;
G-STAT Lifetime Value Optimization (LTV): Automatic
development and deployment of dozens of lifetime value
models by different customer segments which can:
–– Predict each customer's revenues or value in the coming
months or years;
–– simulate the impact that different actions will have on
each customer's LTV and identify in advance which
actions may be taken to maximize each customer's LTV;
G-STAT Credit Risk Analyzer (CRA): Rapid credit
scoring modeling and recommendations for additional
credit levels that can be given to each customer based
on his or her credit risk.
G-STAT Business Solutions
Leading companies are using the G-STAT platform as part of their deployment of end-to-end business solutions
in the following areas:
Finance
Increasing revenues from outbound and inbound campaigns: Increasing campaign revenues
by 50% to 200%, using automatically built, data-mining-based potential lists for every product and
service sold by the company, in contrast to manually developed models using existing data mining tools;
Increasing credit cards usage: Increasing the use of a bank’s credit cards in business and stores
which were recommended to customers as personalized promotions. The G-STAT platform is used
for automatic modeling of thousands of promotions with personalized recommendations for each
customer for promotions in specific stores in his or her geographical area, based on statistical analysis
of the customer’s financial behavior and past cards transactions;
Telecom
Increasing prepaid recharges: Recommending to each subscriber the personalized retention or crosssell offer which will motivate him or her to recharge and increase his or her overall recharges over time;
Recommending a personalized retention offer to each postpaid subscriber who calls the retention
team, which will retain the customer while decreasing the revenues attrition;
Recommending each postpaid subscriber identified with low churn risk on the best next offer
which he or she has the highest probability of accepting and which will increase revenues;
Retail
Personalized promotions and coupons allocation: Increase the net sales of fidelity program
members by up to 5% by modeling thousands of propensity models for predicting each customer’s
propensity to buy each of the products sold by the chain and recommending the right personalized
cross-sell or up-sell promotions for each customer which will increase net sales.
G-STAT’s Advantages
The G-STAT platform has several unique characteristics which make it the preferred choice
for companies wishing to streamline their customer-centric statistical modeling operations
and to increase their targeted campaign revenues:
•Using G-STAT’s automatic modeling capabilities, our
customers can develop and deploy up to 100 times
more models by customer segments for different
products than they do today, thereby improving lifts
and response rates of inbound and outbound campaigns
and witnessing a ROI after only one or two campaigns;
•Because modeling and deployment are completely
automatic, our customers update their models every
month, instead of every 12 months on average, as
is usually done today. This leads to much improved
predictions and higher lifts and campaign response rates;
•Operating the G-STAT platform does not require any
statistical or SQL experience. All data mining processes,
including data preparation and deployment, are done
automatically by clicking, not coding;
•When using the G-STAT platform, companies experience
a 50 to 100 time increase in the productivity of
marketing analysts, since they can develop and deploy
hundreds of models in a single day instead of only a
few models a month;
•In-memory processing and in-DWH scoring capabilities
lead to fast modeling, scoring and deployment of
hundreds of models at large B2C companies with many
millions of customers within only a few hours;
•The G-STAT platform complements classic data mining
environments and integrates with conventional
campaign management tools;
•G-STAT enables real-time scoring through integration
with conventional real-time interaction tools.
G-STAT Platform Architecture
Contact
channels
Web & Social
Media
Big Data
Operational
systems
Users
DWH
(Marketing, Risk, Data
security, …)
G-STAT
Data Model
Campaign
management
G-STAT Big Data Predictive
Analytics Suite
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
© Copyright G-STAT Analytics Solutions 2015