Direct Marketing Analytics with R useR! 2008 Dortmund, Germany - - PowerPoint PPT Presentation

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Direct Marketing Analytics with R useR! 2008 Dortmund, Germany - - PowerPoint PPT Presentation

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Direct Marketing Analytics with R useR! 2008 Dortmund, Germany August, 2008 Jim Porzak, Senior Director of Analytics Responsys, Inc. San Francisco, California Revised Sep08 Outline Introduction What is Direct Marketing (DM)? How

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Direct Marketing Analytics with R

useR! 2008 Dortmund, Germany August, 2008 Jim Porzak, Senior Director of Analytics Responsys, Inc. San Francisco, California

Revised Sep08

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Outline

  • Introduction

– What is Direct Marketing (DM)? – How does “analytics” play a role? – What's Special About DM data & analytics?

  • DM data requirements -> Class Structure
  • Basic DM Metrics
  • Testing
  • Segmentation
  • Modeling
  • Directions & Questions
  • (Appendix with resources & links)
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Introduction

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What is DM?

  • Also know as “direct response marketing.”
  • Characteristics:

– Directed at targeted individuals or demographic – Response is asked for and expected – Tracking of responses back to source – Evaluated by counts and value [€, £, $, ...] – Testing of alternate elements is implicit in DM

  • Elements (in order of importance):
  • 1. List
  • 2. Offer
  • 3. Creative
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Channels used in DM Classical

  • Individual

– Direct Mail

  • Demographic

– Advertisement – TV or Radio – Billboard – Insert

Internet

  • Individual

– Email

  • Demographic

– Banner – Search

  • Paid
  • Free

Remember, all of above ask for a response that is traceable back to source!

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Use Analytics to Answer these Questions

  • Directed to whom?

– Predicting responses

  • Which of list, or part of list?
  • When to send?

– Segmenting population –

  • To use best offer, creative, & channel
  • Evaluated with accepted metrics

– Open definitions are important here. – Use confidence intervals

  • Testing to improve next time around.

– Show significance of results

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So What's So Special?

  • Statistically speaking?

– Not much... – But remember the nature of DM problems:

  • Huge N (typically 104 to 107)
  • Small proportions (often 3% to 0.05% for direct or email)
  • The audience!

– The corporate world – DMers themselves

  • The Data Structure

– Levels of granularity – “Campaign” hierarchies drives testing

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“It's the structure, stupid!”

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The DM Process (Individual) Postal Mail

  • Outbound

– Mail a “piece”

  • Tagged?
  • Inbound

– Recipient responds

  • Return mail
  • Calling 800#
  • Visits

– Web – Physical location

Email

  • Outbound

– Send a “message”

  • Tagged?
  • Inbound

– ISP

  • Bounce
  • Opt-out

– Recipient

  • Open
  • Click
  • (Request or Buy)
  • Opt-out
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Data Elements

  • Details

– The “List” (perhaps with additional data) – Send Events – Response Events

  • Summaries

– Response counts & rates (total & unique) – Simple “cell-level” metrics

  • Campaign Meta-data

– Costs and Values – Time window – Batch or Triggered –

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Class & Method Challenges

  • Detail & Summary classes. ~ straightforward
  • Campaign wise meta-data. ~ straightforward
  • Campaign elements & relations. harder!

– summary, print & plot should be able to understand

a group of campaigns and elements within a campaign.

  • Leverage arules?

From package vignette: arules.pdf

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DMA Modules

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Direct Marketing Metrics

  • Direct Mail

– Response counts & rate – Cost per response (sale, lead, ...)

  • Email

– all above, plus email specific metrics

  • Opt-out, bounce, open, click counts & rates
  • Add unique opens, clicks, responses
  • General

– Campaign ROI – List growth (opt-ins / -outs per time period) – List fatigue

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DM Testing

  • Simple 2-way: A/B, Control/Test
  • Multiple test against control: A/BCD...
  • True MVT

Goal is appropriate analysis done based on campaign meta-data.

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Example A/BC Test

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Segmentation for Targeting

  • Behavior based

– Purchases – Usage

  • Attitudinal

– Preference / Interest Survey

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Purchase Behavior Example

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Purchase Behavior Categories

For executive presentations, we re-draw the segment cells in this way:

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Modeling for List Optimization

  • Model full list to select those recipients with

highest expected response to offer

  • Methods include logistic regression and

machine learning tools like random forest.

  • Supply “model validation” curve (ROC) so

marketer can pick “depth of file” to use based

  • n economics of the offer
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Response Prediction Example

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Future Directions

  • Finalize class structure

– Need to work through more use cases

  • Feel free to send examples!

– Sketch method dependencies

  • Roadmap

– Independent batch campaigns – 2-way & n-way against control – Triggered campaigns – True MVT – Segmentation – Response Modeling

  • On R-Forge: https://r-forge.r-project.org/projects/dma/

– Collaborators welcome!

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Thanks!

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Appendix

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Links & References

  • Books

–

Metrics

  • Davis, Measuring Marketing – 103 Key Metrics Every Marketer Needs,

Wiley, 2007

  • Farris, Bendle, Pfeifer & Reibstein, Marketing Metrics – 50+ Metrics Every

Executive Should Master, Wharton, 3rd printing, 2006.

–

Marketing

  • Libey & Pickering, RFM & Beyond, MeritDirect Press, 2005.
  • Alan Tapp, Principles of Direct and Database Marketing, 3rd Edition, Pearson,

2005.

  • A. M. Hughes, Strategic Database Marketing, 3rd Edition, McGraw-Hill, 2006.
  • Links

–

Related Talks on www.porzak.com/JimArchive/

–

dma on R-Forge https://r-forge.r-project.org/projects/dma/

–

Responsys.com Resource Center

–

Direct Marketing Association International Resources

–

Email Experience Council Home

–

EmailLabs: Glossary, Benchmark Data

–

use R Group of San Francisco Bay Area http://ia.meetup.com/67/