William Tangalos provides marketing analytics consulting for various clients. One project involved developing predictive models to recommend the highest probability products to representatives within 45 seconds of a call. This increased high-speed internet sales by over 25%. Another project reviewed a financial organization's direct marketing program and identified gaps in modeling, testing, and collaboration between teams. Recommendations simplified processes and established shared goals, resulting in improved predictive models and test designs.

1. William Tangalos, Marketing Analytics for Campaign & Product Marketing Solutions
Summary of Select Data Driven Marketing Consulting Projects & Results
Marketing Analytics Project: Next Best Offer Program for Fortune 200 organization;
9-month engagement
Objective:Increase sales by 25%
Challenge:Customer services representatives for this client receive 30,000 calls each month.
During each call, the representative has approximately 45 seconds to make their sales pitch.
The complexity is how can predictive analytics be used to simplify which product offer(s) have
the greatest chance of success out of 200,000 product bundles available.
Marketing Analytics Solution: Developed predictive product preference models for each
existing customer which rank orders the product likelihood need for each product for each
customer. Developed the customer segmentation which the Real-Time Decision Engines uses
to “learn” which segment and product offer will have the greatest sales likelihood. Developed the
process for integrating both the predictive product propensity models with the Decision Engine
to be used in Real Time.
Methodology:
• Using Real Time Decision Engine, created 144 individual customer segments based upon
3,000 customer attributes.
• Analyzed results of 5 different product category purchase models to identify the most useful
customer attributes to utilize to establish the initial Decision Engine customer segmentation.
• Designed, corrected, validated and implemented 18 product propensity models. Within this
model identified 850 customer attributes, determined how to handle missing data, identified the
best model for each of the product holdings, directed the model scoring and validation for each
model and wrote detailed model development and deployment documents for the models.
• Designed the test and hold-out methodology to measure the incremental lift provided by the
marketing platform.
• Trained the project team on how to build their own Product Receptivity models moving
forward.
Result: Increased high speed internet sales by more than 25%;

2. Marketing Analytics Project:Major Financial Organizations Direct, Digital and Predictive
Marketing Program Review;
4 Month Review
Objective:Assess current Direct and Digital Marketing Model practice and deployment. Provide
specific recommendations for model development, campaign application and measurement
improvement.
Challenge: How to identify “if and what” gaps in direct, digital and predictive marketing models
and CLTV model best practices exist and how to communicate resolving these gaps to Client.
Solution: Obtained documentation, campaign reporting and campaign process overview from
limited amount of time working with Client. Discovered the company’s biggest obstacle to
customer-centric marketing was not having one solid view of their customers in a user-friendly
database built for their marketing team goals and objectives. Test design was not set up
properly resulting in the in ability to learn what is the optimal audience, message, offer and offer
channel for each type of customer. Media Mix models were not effective as they included
candidate variables (potential predictors) which were near perfect predictors and rendered the
model results as not useful.
Methodology:
 Reviewed 6 different predictive targeting models, 3 different CLTV models, and 3 Media
Mix Models for different product lines.
 Identified gaps in direct & digital marketing best audience targeting, model usage and
campaign management best practices
 Recommended specific improvements within campaign test design across marketing
channels (mail, email, website)
 Reduced complexity and redundancy of systems and processes.
 Structured appropriate direct marketing test cells to measure incremental value of both
the targeting and the CLTV models
 Established cross department goals which would have the full continuous learning goal
as part of the predictive modelers, marketing and channel managers goals to ensure
closer collaboration between these three work teams which are required for successful
campaigns
Result: Developed new predictive model design, implemented direct marketing test design to
enable properly measuring incremental lift and organizationally formulated joint “goals” between
Predictive Modelers and Direct & Digital Marketers to encourage collaboration and achieving
success. Restructured Media Mix Model design. Simplified database interface and
recommended Life Event Triggering where applicable which was not in place.