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How to Use Data Analytics and Response Models to Reach, Realize, and Retain Customers
 

How to Use Data Analytics and Response Models to Reach, Realize, and Retain Customers

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Caserta Concepts (http://casertaconcepts.com/) and PNT Marketing Services (http://www.pntmarketingservices.com/), a leading provider of Customer Intelligence-based database marketing and analytic ...

Caserta Concepts (http://casertaconcepts.com/) and PNT Marketing Services (http://www.pntmarketingservices.com/), a leading provider of Customer Intelligence-based database marketing and analytic services, discussed how to turn your customer data into increased sales with predictive analytics and response modeling in a recent webinar.

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    How to Use Data Analytics and Response Models to Reach, Realize, and Retain Customers How to Use Data Analytics and Response Models to Reach, Realize, and Retain Customers Presentation Transcript

    • WELCOME! Joe Caserta Founder & President, Caserta Concepts This document is solely for the presentation of confidential PNT information. No part of it may be circulated, quoted, or reproduced for distribution outside the organization to which it was presented without prior written approval from PNT Marketing Services, Inc. This material was used by PNT Marketing Services during an oral presentation; it is not a complete record of the discussion. October 11, 2013
    • Caserta Concepts • Technology services company with expertise in data analysis: › Big Data Analytics › Data Warehousing › Business Intelligence › Strategic Data Ecosystems • Core focus in the following industries: › eCommerce / Retail / Marketing › Financial Services / Insurance › Healthcare / Higher Education • Established in 2001: › Industry recognized work force › Consulting, Writing, Education October 11, 2013
    • Expertise & Offerings Strategic Roadmap/ Assessment/Consulting/ Implementation Big Data Analytics Data Warehousing/ ETL/Data Integration BI/Visualization/ Analytics October 11, 2013
    • Our Strategic Partners Hadoop Distributions Platforms Analytics & BI October 11, 2013
    • Our Clients Finance & Insurance Retail/eCommerce & Manufacturing Education & Services October 11, 2013
    • October 11, 2013 5
    • What is Predictive Analytics October 11, 2013 6
    • Use Cases for Predictive Analytics October 11, 2013 7
    • The Basic Steps Prepare and Sample Data October 11, 2013 8
    • Common Predictive Methods October 11, 2013 9
    • Additional Resources Special Thank You: Jen Underwood phone: 813.435.5344
email: jen www.impactanalytix.com October 11, 2013 10
    • Do you have access to all your customer data? Are you able to quickly and easily derive customer-intelligent insights? Are you using those customer insights to drive more precise lead targeting? Do you use that targeting to direct your marketing dollars to the highest-ROI acquisition campaigns? Are you able to leverage campaign response data to generate better leads faster? Turbo-Charging Lead-Gen and Conversion How to Use Data Analytics and Response Models to Reach, Realize, and Retain Customers. This document is solely for the presentation of confidential PNT information. No part of it may be circulated, quoted, or reproduced for distribution outside the organization to which it was presented without prior written approval from PNT Marketing Services, Inc. This material was used by PNT Marketing Services during an oral presentation; it is not a complete record of the discussion. October 11, 2013
    • Outline • Introduction and background • Methodology • Case study • Q&A/wrap-up October 11, 2013 1
    • PNT Marketing Services Overview October 11, 2013 2
    • About PNT Marketing Services PNT Marketing Services (PNT) is a leading provider of Customer Intelligence-based marketing services (customer databases, insights, and high-ROI marketing actions). PNT helps clients acquire, grow and keep profitable customer relationships. October 11, 2013 3
    • PNT Clients October 11, 2013 4
    • PNT Methodology Step 1: PNT uses a powerful 5step methodology to turbocharge marketing results Step 2: Step 3: Step 4: Step 5: • Diagnostic • Creating the customer-centric marketing and analytic datamart • Generating customer insights • Implementing high-ROI marketing actions driven by customer insights • Measure/track/improve (“virtuous spiral”) October 11, 2013 5
    • Case Study October 11, 2013 6
    • Case Study • Business problem • Insights • Approach • Application of the methodology • Results October 11, 2013 7
    • Case Study: Business Problem PNT Client: › PNT client working with a large for-profit education firm Existing Program Design: › Undifferentiated offers blasted to an undifferentiated but very broad list › Had proven success increasing:    Response Click-through-rate (CTR) Click-to-lead rates Need: › Breakthrough results for even greater lead generation and more sophisticated predictive analytics October 11, 2013 8
    • Cast Study: Insight Diagnosis: › Most programs are 80/20 Prospect Base 20% Responses 80% › If you can find part of that subset you can pick your choice of:   Save money by reducing program size to likely responders – not the issue for this client Increase response by shifting program budget and concentrating funds on differentiated offers and extra engagement targeting likely responders. This strategy is often called strategic engagement which is just jargon for segmentation and offer matching. October 11, 2013 9
    • Case Study: Approach The trick is to find the likely responders and engage them. Here’s how we did it! October 11, 2013 10
    • Case Study: Application of the Methodology All E-mail messages Construct an Analytic Data Warehouse October 11, 2013 All Contacts 12 months of history 11
    • Case Study: Application of the Methodology Data Capture Included Contact Level Data Send Data & Response Data Client provided data – which contacts became leads who enrolled October 11, 2013 Analysis of behavior patterns that influence form-fill Call-center Data 12
    • Case Study: Application of the Methodology Database Analysis Goal: Generate “Quick Hits” to impact lead generation fast Method: 1. A statistical platform, which packages components from R with an easy-to-use interface and ODBC connectivity to the MS SQL Server datamart 2. RFM Model Identify “near-miss” contacts – those with high RFM scores but who had not yet filled out a lead form online – segmented into four distinct sub-groups for maximum targeting effectiveness October 11, 2013 13
    • Case Study: Application of the Methodology Pilot Campaign Created Included special messaging and creative to target each of the four “near-miss” sub-groups “Near-miss” Sub Groups Identified › Near-miss” sub-group 1: Recent and Active › “Near-miss” sub-group 2: Recent and Inactive › “Near-miss” sub-group 3: Not Recent and Active › “Near-miss” sub-group 4: Not Recent and Inactive October 11, 2013 14
    • Case Study: Results Almost 300 incremental leads generated for an ROI of 2.8X (180%) Click-Through Rate Click-to-lead 116% 55% October 11, 2013 15
    • Case Study: Results Refined our master data model (MDM) Used to develop increasingly sophisticated models › Predicted likelihood to open (linear regression) › Predicted likelihood to fill out lead form (linear regression) › Predicted likelihood to enroll within next 60 days (logistic regression) October 11, 2013 16
    • Case Study: Results The Analytic Platform Leveraged to support on-going improvement in response and lead-gen Demographic data reveals key predictors in age and income Reveals key “digital-body language” components in sites visited October 11, 2013 17
    • Case Study: Results • “Likely to open” model based on historic open behavior for pool of contacts; linear regression that predicts open rates • Open model produces a predicted lift of +238.76% for decile 1 vs. 10, and 99.12% for decile 1 vs. random. • Messages to Decile 1 contacts produce almost twice as many opens as a random selection. October 11, 2013 18
    • Case Study: Results • “Likely to fill out lead form” model based on demographics, engagement, digital body language, and prior form-fills; linear regression that predicts lead form fill. • Lead model produces a predicted lift of +315% for decile 1 vs. 10, and 287% for decile 1 vs. random. • Messages to Decile 1 contacts produce almost three times as many leads as a random selection. • An improvement of almost 50% vs. the original “RFM” lead-gen model October 11, 2013 19
    • Case Study: Results • “Likely to enroll in 60 days” model based on demographics, engagement, and digital body language; logistic regression that predicts enrollment • Enrollment model produces a predicted lift of +328.25% for decile 1 vs. 10, and +215.15% for decile 1 vs. random. • Messages to Decile 1 contacts produce more than three times as many enrollments as a random selection. October 11, 2013 20
    • Q&A/Wrap-up • Summary • Q&A • Contact Tony Coretto, Co-CEO Joe Caserta, President PNT Marketing Services, Inc. Caserta Concepts http://www.pntmarketingservices.com http://www.casertaconcepts.com tcoretto@pntmarketingservices.com joe@casertaconcepts.com 914-588-7278 (m) 914-261-3648 (m) October 11, 2013 21