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As the relationship changes from casual browser to interested prospect and then to active
buyer, your Web site can collect a whole host of personal preferences, plans, penchants, and
peccadilloes. You need merely ask. Coupling that information with offline data from surveys
and commercial databases, you now have a chance to build a fully realized portrait of each
customer. Just how balanced is your portrait?
In the end, you have to decide what granularity of identification will suit you best. Is it
enough to know the following about a certain type of visitor?:
• Interested in product A
• Works in industry B
• At a company that's size C
• Is moving through the qualification process at a rate of D
Or, in order to sell more, faster, at a higher margin, does it add significantly to the bottom line
if you also know the following about her?:
• A shopping type E (adventurous explorer)
• Reviews the F section with a frequency rating of G
• Wears size H shoes
After all, just how valuable is it that you know which customers like green marsh-mallow
moons in their Rice Chex?
While the sheer quantity of data elements is the most significant factor in your personal
profile depth scoring, each element must be weighted according to its value. A customer
identification number has no weight at all because one is indistinguishable from the next. The
usual information collected about a customer (name, address, phone number) is critical but
carries a low weight because it is not actionable.
Types of Customer Information
Actionable information pertains to a customer's predilections, purchasing history, and
declared interests. Be sure you properly weigh implicit, explicit, and factual information:
• Implicit: He looked at those pages so he must be interested in these items.
• Explicit: He filled out a survey and told us he was interested in these items.
• Fact: He looked at those pages and bought these items.
Web site visitors tell you explicit information, and you derive implicit information. For
instance, a customer can say he likes reading biographies and wants Amazon.com to email
notifications about famous figures in European history. But if he buys books about dogs,
Amazon knows what to put on his recommendation list. Watching what customers actually do
is far more revealing than reading what they say. And it's revealing in ways that don't
necessarily make sense.
Suppose the database shows that visitors who are shopping for electric razors are also buying
personal CD players, or that visitors who read the detailed specifications of the surface mining
and construction equipment are seldom interested in extended warranty information. Are
these the sorts of correlations marketing mavens are going to come up with in brainstorming
meetings? No. They don't make any sense, but they're true. So now the marketing mavens
have a new datapoint to work with, and the systems behind the scenes have the ability to act
on the information in real time.
Besides weighing data elements based on whether they are declared or derived, their value
must take into account freshness and results. Knowing the correlation between electric razors
and personal CD players is the first step, using that information is the second, and measuring
the results of that use is the most important.
Data Cleansing
Living up to customer relationship management means ensuring that the information used by
the marketing systems and the customer service representatives is fresh, current, and accurate.
That means bringing data together from many systems and that means figuring out how to get
all that data to look alike.
Data normalization has typically applied to the format of information that is entered into a
system. Does the middle initial carry a period? Does the phone number include parentheses or
dashes? Is the zip code five digits or nine? Is there a hyphen in the middle? But in these days
of CRM, data cleansing goes far beyond punctuation.
Let's assume you have a sales contact management system, an invoicing system, and a
customer care database in each of four divisions. Let's say John Smith sends you an email
from <JohnSmith@Yahoo.com>. Which John Smith is this?
You'll need multiple points of comparison. Maybe <JohnSmith@Yahoo.com> let slip that he
was having trouble with your product while he was in California for the first time. You can
then eliminate all of the John Smiths who live in California. Possibly he mentioned which
product or service of yours he was using. Perhaps he includes his phone number in his email
signature file. That could be the clue you need to identify this John Smith from the twentyseven
others in your database.
Data cleansing focuses on the verification and validation of the information. If all your John
Smiths are formatted the same, you're off to a good start. If none of your John Smith records
have been verified for more than 6 months, their value deteriorates.
I'm trying to clearly depict a set of problems that are neither easy nor quick to solve. If it's
going to cost so much and create such pain, how do you go about measuring the value of all
these possibilities? The question is whether the cost of collecting and processing the
information is worth the value you derive from having the information, less the pain you
cause your customers in its collection.
Personalization Quotient
Dr. Kamran Parsaye, president of Intelligence Ware, Inc. and author of Intelligent Database
Tools and Applications (John Wiley & Sons, 1993) wrote a white paper called "PQ: The
Personalization Quotient of a Website." At the moment, the paper can be found online
(www.kellen.net/ect586/personalization_parsaye.pdf), even though the company Parsaye
worked at when he wrote it (NovuWeb) cannot.
In his paper, Parsaye made a valiant attempt to create "a framework and a theory to measure
how personalized a system is in terms of the Personalization Quotient (PQ) and illustrate how
the theory can be used to improve e-service." The concept of the personalization quotient is
then used to measure how personalized a system really is.
In this paper, Dr. Parsaye differentiates between an impersonal system, which treats everyone
the same way, and a fully personalized system, which adjusts its behavior to specific users.
An impersonal system has a PQ of zero, since it provides the same static response to all users
regardless of their characteristics.
Personalization comes about as a reaction to individual information, and Dr. Parsaye divides
personalization into three areas—customization, individualization and groupcharacterization.
Customization is the oldest and at times the easiest to address. It allows you
to set specific preferences, e.g., the stocks you want to track, the type of news you want to see,
the colors you want set on your screen, etc. Individualization goes beyond this fixed setting
and uses patterns of your own behavior (and not any other user's) to deliver specific content
to you. [For instance,] if you have clicked a lot on finance-related items but not on sports, it
will show you more financial news rather than sports news, without your asking for it. In
group-characterization you receive a recommendation based on the preferences of people
"like" you, e.g., books may be recommended to you based on books ordered by people with
similar interests. Approaches based on collaborative filtering, case-based reasoning, etc.
focus on the group-characterization measure.
• PQ: The Personalization Quotient takes all three of these issues into account.
It has three specific components, PQ1, PQ2 and PQ3, where:
PQ1 measures the system's ability to customize items.
PQ2 measures the system's ability to use individual preferences.
PQ3 measures the system's ability to deal with group-based preferences.
We then measure PQ as the average of these two elements, i.e.:
PQ = (PQ1 + PQ2 + PQ3)/3
Here each PQ1, PQ2 and PQ3 will be a number between 0 and 100. A system with a PQ of
100 is totally personalized, while a system with a PQ of zero is totally impersonal.
Dr. Parsaye then describes creating an ultimate profile of your site visitor:
One way to represent and measure similarity of users and customers is in terms of DNA
strings or attribute vectors.
A DNA string for a Web user is a set string of integers between 0 and 9, e.g., the string
1309735183291. Each integer here shows the relative value of some trait, e.g., scoring an 8
or a 9 on the "sportspage" indicator means that you view many sports-related pages, while a
0 means that you never see such pages at all. Similarly, other integers on the string can tell us
how you visit the site and how you click through on banner advertising—all in relative terms.
Similarly, we can define a DNA string for a Web page by considering the components that
comprise it. For instance, the number of banners and the type of banners.
He concludes by suggesting "An interesting direction for enhancements will be that of
measuring the comparative PQ of two systems."
He then wanders off into a world where only mathematicians dare to tread by slipping into
some serious formulae such as: PQ3(U, P) = 100 / maximum((ä U / ä P), (ä P / ä U)).
But how do we factor in the pain caused to the site visitor who is followed around from page
to page by a cookie and asked for an opinion about whether a woman's life is fulfilled only if
she can provide a happy home for her family? That's where the personalization index comes
in.
Personalization Index
The universe of profile elements is virtually unbounded, covering familiar items such as last
name and business address, technical concepts such as IP address and connection speed, and
domain-specific attributes from pore size (for cosmetics) to lifestyle risk profile (for
insurance). By adding incremental profile information, e-business managers are able to move
prospects and customers through the four stages of e-customer understanding, transforming category 1 anonymous users into the distinct, real-world category 4
individuals.
Collecting information is one thing. Using it in a judicious way is another. The
personalization index (PI) distinguishes just how well you are using the data you are
gathering. The index is a measure of how effectively an e-business is leveraging this customer
data.
If your PI is above 0.75, then you are making the most of the information you are collecting.
That means your efforts are not wasted, nor are those of your customers who are providing the
raw material.
The preceding assumes that you are using a significant number of elements to make a
personalized Web experience. If you are only collecting two elements and using them both,
your PI score may be 1.00, but here it means you are only going so far as market
segmentation rather than personalization—you're only grouping your prospects and customers
into broad categories. While useful, broad categories aren't as powerful as true personalization
based on dozens of attributes.
When you collect more and more elements, you can classify users into more and more
clusters, and broad segmentation moves toward personalization. This is where you start to
foster a customer relationship and turn it into a loyalty relationship, significantly raising the
cost for your customer to switch to another vendor.
If your PI is less than 0.30, then you are collecting more information than you are using. The
good news is that you have a huge untapped reservoir of actionable data about your
customers. The bad news is that the data is lying fallow and probably getting stale fast. You
need to either start using the data you have more effectively or cut down on how much
explicit data you are trying to collect. Most likely, the correct answer is both. You are
spinning your wheels collecting that information, but you are not using it to benefit your
customers, which adversely affects your customers' experience.
That's the greatest downside to a low personalization quotient. All that time and effort that
you force your customers to invest in giving you information is a waste. They get nothing out
of it. Even when the process is simple, such as scanning a key chain fob at the grocery store,
there's still no real value to the customer. Why bother? Why are they being bothered?
At this point, we have finally attracted, navigated, persuaded, and converted that unknown
prospect into a known customer. Can we get that customer to come back? | 
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