This document provides a data dictionary and overview of predictive customer analytics metrics calculated by Loyalty Builders for effective marketing. It defines metrics like Loyalty Score, Risk Score, and Likely Buyer Score that measure customer value, purchase probability, and more. It also outlines Loyalty Segments that group customers into categories such as Loyalists, Nurturers, and Underperformers based on their scores. The goal is to enable marketers to target the right messages and offers to each individual customer.

1. Loyalty Builders Data Dictionary of
Calculated Metrics on Each Customer
Predictive Customer Intelligence for More Effective Marketing
Document Version 1.2 © 2015 Loyalty Builders, Inc. Confidential. All rights reserved.
www.loyaltybuilders.com
Simple, automated, cloud-based
predictive analytics services enabling
marketers to effectively target customers
based on loyalty, value, risk, or product
propensity, and deliver the specific 1:1
product recommendations to each
customer that will lift revenue.

2. Loyalty Builders Marketing Lift Service Technical Overview Ÿ Page 2
© 2016 Loyalty Builders, Inc. All rights reserved.
www.loyaltybuilders.com
Data Dictionary of Calculated Metrics
Predictive Loyalty Metrics for Each Customer:
Metric Type Range Explanation Example Uses
Loyalty
Segment*
Text –
7 values
Loyalist, Nurturer,
Underperformer,
Fader, Win Back,
1&2 Buyer,
Inactive
A segmentation of an entire
customer population based on
customer Value Rank and Risk
Score. Each customer is assigned 1
of 7 designations.
See description below.*
Loyalty Segment is a
combination of Loyalty
Score and Risk Score.
Loyalty
Score
Numeric
(0.0000)
0-100
(each customer
force ranked)
A proprietary measure of
potential lifetime value of each
customer based on timing of
purchases, spending trends, items
purchased, population patterns,
and several other factors.
Loyalty Score is used to
target marketing based on
each customer’s relative
long-term value within the
customer base.
Risk Score Numeric
(0.00000)
0 to 1 The probability that a customer
will not make a purchase before
going “inactive.” The active period
is determined by analysis of inter-
order frequencies among all
customers.
Risk Score is used to win-
back fading customers or
target loyal customers.
Likely Buyer
Score
Numeric
(0.00000)
0 to 1, for each of
10 periods
The probability that a customer
will make a purchase within each
of 10 time horizons, which are
configurable (e.g. 7, 14, 21, 28, 35,
42, 49, 56, 63, and 90 days from
analysis).
Provides guidance on how
much effort, cost, or margin
to invest in a customer.
Example: if a customer has
a 60% probability of
buying, does a discount
make sense? If negligible
probability, should they get
an expensive mailer?
Expected
Value
Currency
($0.00)
$0 to unlimited The expected amount of revenue
from the customer within each of
10 time horizons, which are
configurable (e.g. 7, 14, 21, 28, 35,
42, 49, 56, 63, and 90 days from
analysis).
Indicates whether
expensive marketing
programs, such as print
mailings catalogs, or sales
calls, are justifiable (based
on expected value of future
purchases).
*Further description provided below.
Predictive Product Recommendations for Each Customer:
Metric Type Range Explanation Example Uses
Re-Sell
Probability
Percent
(float)
0 to 1, for each of
10 periods
The probability that a given
customer will purchase each item
they have purchased in the past
within 10 future time horizons,
configurable in days (e.g. 7, 14, 21,
28, 35, 42, 49, 56, 63, and 90 days
from analysis).
Recommendations that
encourage customers to re-
purchase products.
Particularly useful to offer
high probability re-sell
offers to customers with
deteriorating loyalty.
Cross-Sell
Probability
Percent
(float)
0 to 1, for each of
30, 60, and 90
days
The probability that a given
customer will purchase each item
they have never purchased before
in 3 future time horizons,
configurable in days (e.g. 30, 60,
and 90 days from analysis).
Recommendations that
encourage customers to
buy more, different
products. Particularly
useful for increasing value
of more loyal customers by
widening the basket of
products they purchase.

3. Loyalty Builders Marketing Lift Service Technical Overview Ÿ Page 3
© 2016 Loyalty Builders, Inc. All rights reserved.
www.loyaltybuilders.com
Cross-Sell
Rank Score
Numeric
(0.00)
0 to 10, for each
of 30, 60, and 90
days
The potential interest a given
customer has in purchasing each
item never purchased before in 3
future time horizons, configurable
in days (e.g. 30, 60, and 90 days
from analysis) as a ranked score.
This may be more useful
than Cross-Sell Probability
in situations where the
best product offers are
needed, irrespective of the
customer’s loyalty or
current likelihood to buy.
Additional Analytics for Each Customer:
Metric Type Range Explanation Example Uses
Loyalty
Rank
Numeric
(0.0000)
0-100
(each customer
force ranked)
A forced ranking of the Loyalty
Score to indicate each customer’s
loyalty relative to one another.
Value Rank is used to
target marketing spend for
a range of customers based
on their relative value
within the customer base.
Purchase
Delay
Numeric-
Days
(0)
0 to N
Number of expected purchases
missed by a customer, based on
the average inter-order time
between purchases. The delay
period is unique to each customer.
A number of 2 indicates 2 missed
expected purchases based on the
average established pattern.
Used as an early warning to
address customers who are
starting to fall off expected
purchasing pattern.
Category
Score
Numeric
(0)
0 to N
(N = Number of
Product
Categories)
The number of Product Categories
(products as grouped for analysis)
the customer has purchased from
historically (e.g., if the analysis
was run at the SKU level, Category
Score refers to number of SKU’s
purchased).
Used with other metrics to
determine how extensively
to make cross-sell offers.
Retention Numeric-
Days
(0)
0 to unlimited The time, in days, since the first
purchase.
Used to tailor specific
messages, based on
customer seniority.
Recency Numeric-
Days
(0)
0 to N
(N = Inactive
designation)
The time, in days, since the last
purchase.
Used to tailor specific
messages, based on recent
customer activity (or lack
thereof), or better time for
additional
communications.
Number of
Orders
Numeric
(0)
0 to unlimited Total number of orders by a
customer (each order having a
unique transaction date).
Used with other metrics to
determine types and
frequency of offers.
Amount
Spent
Currency
($0.00)
0 to unlimited Three separate metrics, including
the total amount spent by each
customer in the last period (e.g.
month), last 12 months, and
historically over entire data set.
Used with other metrics to
tailor specific messages.
Product Affinities Table:
The Product Affinities Table contains products also purchased by customers when a “Predictor
Product” had been purchased, ranked by likelihood to make the additional product purchase. The file
contains pair-wise product combinations. The first Product ID is the “Predictor Product.” The second
Product ID paired with it is the “Companion Product,” which is identified with its lift value in terms of
likelihood to be purchased by buyers of the Predictor Product. Every pair of Product Category ID’s is
evaluated and all pairs with lift values above a user-defined threshold are included in the file.

4. Loyalty Builders Marketing Lift Service Technical Overview Ÿ Page 4
© 2016 Loyalty Builders, Inc. All rights reserved.
www.loyaltybuilders.com
*Loyalty Segments
A two dimensional segmentation is created for the customer population based on two metrics:
predicted Loyalty Score and Risk Score. Loyalty Segments can also be thought of as “lifecycle stages.”
One- and two-time buyers are not included in the main loyalty segmentation, but are scored for all
metrics. Reports are produced indicating customers in each Loyalty Segment, rolled up from their
individual scores, and identifying customer trends and movements between segments. This is one
way of finding customers to target based on loyalty and lifecycle stage.
Below are the descriptions of the segments:
Loyalists – High value, low-risk customers. These are the best of the best customers who buy most
frequently, most broadly, and spend the most. These customers are usually the best targets for cross-
sell items, as they have brand loyalty and have numerous transactions, which lead to stronger
scoring recommendations. Also, many Loyalists will make a purchase whether they are marketed to
or not, so large discounts are not necessary to move these customers. A strategy that some of our
Subscribers use is to offer a discount only to Loyalists if they include a cross-sell item in their
purchase, thus increasing their total basket size.
Nurturers – Low risk, mid to low-value customers. This group, and Underperformers, are usually the
most interesting groups of customers. Nurturers are buying consistently and periodically, since they
have a low Risk Score. But they are not high value customers, in that the dollars they spend are low
or they may not buy very broadly across categories, or both. Either they are buying at capacity, in
which case they will stay where they are, or they are buying other products from competitors. A little
investigation and some targeted programs could increase the dollars per order for these customers
and increase their value.
Underperformers – High value, mid to high-risk customers. These are customers that used to be
better customers, but recently have fallen off pattern, either by buying less per order, or buying less
frequently, or both. There are many valuable customers in this segment, and reversing their
behavior is essential to increasing revenue. Fortunately, these customers typically have an
©2014EpsilonDataManagement,LLC.Private&Confidential
Customer Migration Heat
Map – December 2015
[Color – 6 Months Ago]
ghlights
Movement to the
top/left is the goal
Large group of Loyalty
that are drifting to
Underperformers
Huge opportunity in the
Underperformer
Segment

5. Loyalty Builders Marketing Lift Service Technical Overview Ÿ Page 5
© 2016 Loyalty Builders, Inc. All rights reserved.
www.loyaltybuilders.com
established purchasing history, so making relevant product offers is easy to do. However, these
customers may need some additional incentive to get back on track, so deeper discounts are
recommended for these customers.
Faders – Low value, mid to high-risk customers. These are customers that never really took off or
established a significant buying pattern. They make sporadic purchases of low dollar amounts, and
they typically buy very narrowly across categories. Other metrics, such as Expected Value, can be
used to determine which customers in this group are worth the cost and effort to save vs. which ones
are not profitable in the long run. Deep discounts and rich offers are usually required to move these
customers, but may not be cost effective.
Win Backs – High-risk customers. These are customers that have not made a purchase in a long time
and have a high likelihood of defecting. These customers can almost be considered as acquisitions
because of the effort involved in regaining their business. Ordering these customers by Value Rank
can give a starting point for a campaign. The advantage of these customers over acquisitions is that
they have some purchasing history, and relevant product offers are possible.
1&2x Buyers – These are customers that have made at most 2 purchases, where a purchase is defined
as a unique transaction date with positive revenue. These customers may not yet have a clear loyalty
segment, and may be separately targeted from other customers in re-sell or cross-sell campaigns.
Inactive Customers – An analysis of the transaction data can be used to determine the most
appropriate cut-off point, in terms of number of days since a purchase, to designate a customer as
“Inactive” (cut-off point can be overridden by the Subscriber if special circumstances exist.) Inactive
customers are generally not worth pursuing, although some customers who formally were high value
at some point before going inactive might be worth some targeted effort.
Loyalty Builders, Inc.
+1.603.610.8800
info@loyaltybuilders.com