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Chapter 2 - Drilling Down

2009-01-02T10:36:00.000+07:00

Chapter 2

Customer Profile or Customer Model?

Many people think using your customer data for marketing
efforts is about creating a customer "profile." It's a hot
topic. Everybody wants to do it. But what is a customer
profile? Here are 2 kinds of customer profiles:

* Customer is married, has children, lives in an upscale
neighborhood, and reads Time magazine

* Customer visited the web site or business every day for
2 months, but has not visited at all in the past 2 weeks

The first profile is demographic, a set of characteristics.
The second profile is behavior-based, involving what the
customer is actually doing. It's about customer activity.

Which seems more important to you?

They're both important in their own ways. For someone
selling advertising, or deciding on content for a website,
the first profile could be important, because it defines
the market for ad sales and provides clues to editorial
direction. These are important considerations in
attracting customers and generating revenue in the first
stages of an online project.

The second profile is about action, behavior, and for
anybody concerned about what his or her customers are doing,
is more important than the first. Will they visit again?
Will they buy again? These are the questions answered by
looking at behavior. Customer behavior is a much stronger
predictor of your future relationship with a customer than
demographic information ever will be. You have to look at
the data, the record of their behavior, and it will tell
you things. It will tell you "I'm not satisfied." It
will tell you "I want to buy more, give me a push."
It will tell you "I think your service is awful."

I'd argue the second type of profile is more important
longer term, because if the customer stops buying from
or visiting the site, you're not going to have much of a
chance to serve up the customized pages or ads based on
any "profile" given to you. You could customize the heck
out of the site based on demographics or self-reported
survey data but customers would never see the results if
they never come back. So for the long haul, if you had
to choose the more important profile, the profile based
on action and behavior would be more critical to you than
a demographic one. Customer behavior profiling is critical
to a company interested in selling more to current
customers while at the same time reducing costs.

Marketers who use data often talk about "customer modeling"
instead of customer profiling. Modeling is kind of like
profiling, but it is action oriented. Models are not about
a static state, like "Customer is 50 years old." Models
are about action over time, like "If this customer does not
make a purchase in the next 30 days, they are unlikely to
come back and make any further purchases."

It sounds so mystical, and it is. To see a mathematical
model predict customer behavior is astonishing, to say the
least. The model says, "Do this to these people and they
will likely do this." The marketer or service provider goes
out and does what the model says, and like magic, a good
bunch of the customers do exactly what the model said they
would. It works like a charm - usually.

Building heavy-duty models is expensive, because it
requires an awesome amount of talent and experience.
There are many mathematical techniques used to build models,
each with their own pitfalls and gotchas. Success depends
a lot on the type of business, the kinds of data available,
and the experience of the modeler / analyst in building
models for a particular business.

What is a model? Simply, it looks at customers who are
engaging in a certain behavior and tries to find a
commonality in them. The marketer might say to the
modeler, "Here's a list of our very best customers,
and here's a list of our former best customers. Is
there any behavioral signal a best customer gives before
they stop being a customer? What does the data say to you?"

So here's what's in it for you, what this book is about.
You can do your own models, based on the decades of
experience Data-Driven marketers and service provider
have already invested. And while they won't be as good as
the heavy-duty models done by Ph.D. analysts, they'll be
pretty darn good. Plus, they will help you increase
profits while cutting marketing and service costs. This
book will show you how to do it, with just a spreadsheet.

Ph.D. not required.

By the way, once you figure out your behavioral models,
you can use them in combination with demographics and
characteristics to produce an even richer picture of the
customer. But the behavior comes first, because it is
behavior you want to influence. Knowing the following
about a customer is not very actionable; there is not
much you can do with this information:

* Customer is married, has children, lives in an
upscale neighborhood, and reads Time magazine

But if you add behavior to this demographic profile:

* Customers who are married, have children, live in
upscale neighborhoods, and read Time magazine
appear to be disappointed with our site, because a
high proportion of them haven't visited the site
in the last 30 days

you can start deciding what (if anything) you want to
do about it, because you know these customers are
engaging in a specific behavior.

The combination of behavior and demographics can be
very powerful indeed. But without the behavior,
demographic characteristics don't tell you much.
You will learn how to use both in building your
models. First we'll talk about customer behavior,
and then add customer demographics later on.

Chapter 1 - Drilling Down

2009-01-01T22:43:00.001+07:00

Chapter 1

Welcome to Drilling Down, your resource for High ROI Customer Marketing Tactics!

Hi there! Jim Novo here. Thanks for subscribing to the Drilling Down
newsletter. We'll be talking about High ROI Customer Marketing
models and methods driven by customer data tracking and analysis.

I will show you how to go beyond using simple customer demographics to
start looking at past customer behavior, the most reliable source of
data for predicting the potential value of a customer and their
likelihood to remain a customer. When you nail down these behavioral
metrics and start measuring their trends, you will be able to:

* Identify customer segments with the highest future potential
* Focus on growing the profitability of middle potential customers
* Stop wasting resources acquiring and marketing to low potential
customers

Once you identify these groups, you can manage their value by:

* Emphasizing the ads, media, and products creating long term
high potential value customers and downplaying ones that don't

* Creating High ROI marketing programs that maximize customer
value by increasing sales while lowering expenses

* Predicting when best customers are about to leave you and
reacting with customer retention and save-a-customer programs

* Quantifying the profitability of marketing and operational
initiatives by linking them to potential customer value

The newsletter will provide background and descriptions of
techniques usable by both small and large businesses - it's
the thought process that's important, not whether you use
a spreadsheet or a rules-based CRM engine to manage your
customer data. The newsletter and the web site together will
provide you a roadmap for implementing some of the best techniques
CRM has to offer - without all the costs. This "CRM-Lite" approach
allows you to test out the most powerful CRM and database marketing
ideas using existing resources - low hanging fruit; High ROI.

These techniques are based on the work I have been doing for over
20 years for clients like Media One (cable), MBNA (credit cards),
Home Shopping Network (TV shopping/catalog/web), CBS/SportsLine (web),
Cellular One / Cingular (cellular), Barnes & Noble (on and offline),
Tupperware (consumer), SteelTorch Software (retail analytics) and
Retek Direct (multi-channel retail order management and distribution).

Table of Contents - Complete Book

2008-12-31T15:42:00.001+07:00

Table of Contents - Complete Book

Preface
Introduction
About Jim Novo
Chapter 1 Jonesin' for Some ROI
Chapter 2 Customer Profile or Customer Model?
Chapter 3 Data-Driven Marketing and Service Drivers
Chapter 4 Customer Marketing Basics
Chapter 5 Customer Marketing Strategy: Friction Model

Latency Metric Toolkit

Chapter 6 Trip Wire Marketing
Chapter 7 The Hair Salon Example
Chapter 8 The B2B Software Example
Chapter 9 Turning Latency Data into Profits

** Your E-mailed Chapters Will End Here **

Recency Metric Toolkit

Chapter 10 Predictive Marketing
Chapter 11 The Ad Spending Example
Chapter 12 Turning Recency Data into Profits
Chapter 13 The Online Retail Example

RFM Scoring Toolkit

Chapter 14 Cash Flow Marketing
Chapter 15 A Tweak for Interactive Customers
Chapter 16 No Customer Database?
How to Set Up a Spreadsheet to Score Customers
Chapter 17 How to Score Your Customers
Chapter 18 The Commerce and Content Examples:
Turning Scoring Data into Profits
Chapter 19 Case Study: Non-Profit Scores
192% Increase in ROI using RFM Model

Advanced Data-Driven Marketing Toolkit

Chapter 20 Using Customer Characteristics & Multiple Scores
Chapter 21 Customer LifeCycles: Tracking Scores Over Time
Chapter 22 Customer LifeCycle Grids:
High Performance Behavior-based Modeling
Chapter 23 Straight Talk on LifeTime Value
Chapter 24 Lifetime Value,
I'd Like to Introduce You to the CFO
Chapter 25 Fellow Drillers at Work
Definitions and Background Information
Customer Loyalty and Retention
Customer Segmentation and LifeTime Value
Professional Services
Ad-Supported Content / Subscription Models
Online / Offline Retailing and Catalogs
Distribution / Operations / Channel Management
The ROI of Online Branding Efforts
Chapter 26 Predicting Campaign ROI: Set Up
Chapter 27 Predicting Campaign ROI: The Model
Chapter 28 Predicting Campaign ROI: Fine Tuning
Chapter 29 Expense and Revenue You May Not be Capturing:
Subsidy Costs and Halo Effects
Chapter 30 Some Final Thoughts: Seasonality, CRM,
Behavioral Inertia, Data-Driven Program Outlines
APPENDIX: Software Download and ReadMe

source : "drillingdown"