How to leverage predictive analytics whitepaper for business

1 HOW TO LEVERAGE PREDICTIVE ANALYTICS IN B2B SALES AND MARKETING
HOW TO LEVERAGE PREDICTIVE
ANALYTICS IN B2B SALES AND MARKETING
Say goodbye to spreadsheets and hello to predictive analytics

TABLE OF CONTENTS
ABOUT MARKETBRIDGE ................................................................................................................. 3
Part I: Why Predictive Analytics? .................................................................................................. 4
Part II: Applications of Predictive Analytics in Consumer Marketing ..................................... 6
Part III: How to Leverage Predictive Analytics ............................................................................ 8
Part IV: A Practical Example of Predictive Analytics in B2B .................................................... 13
Part V: How to Build a Predictive Model .................................................................................... 17
Part VI: The Predictive Analytics Ecosystem ............................................................................. 22
CONCLUSION.................................................................................................................................23
ABOUT THE AUTHOR ....................................................................................................................24
HOW TO LEVERAGE PREDICTIVE
ANALYTICS IN B2B SALES AND MARKETING
Say goodbye to spreadsheets and hello to predictive analytics

ABOUT MARKETBRIDGE
MarketBridge is a leading technology enabled
services firm, providing digital marketing,
sales enablement, and customer analytics
solutions for Fortune 1000 and emerging
growth companies.
We help companies improve sales productivity by increasing
digital customer engagement and building robust customer
analytics engines that focus marketing investments and sales
activity on the right customers, with the right messaging and
solutions, through the right marketing and sales channels.
Our unique RevenueEngines™ and SMART™ Analytics solutions
deliver data-driven digital customer engagement by connecting
marketing and sales to increase pipeline volume, velocity, close
rates and customer loyalty. Our solutions are powered by best-
of-breed technologies including social, marketing automation,
CRM and business intelligence, all of which dramatically improve
revenue performance, cost efficiency and customer experience.
Corporate Website:
www.market-bridge.com
MarketBridge Community:
www.the-digital-bridge.com
Phone:
1-888-GO-TO-MKT
Corporate Headquarters:
4800 Montgomery Lane, 5th Floor
Bethesda, MD, 20814
San Francisco, CA
49 Stevenson St., Suite 660
San Francisco, CA 94105
New York, NY
79 Madison Ave, 3rd Floor
New York, NY 10016

PART 1
Why Predictive Analytics?
Predictive Analytics...
Simply helping us more efficiently
identify and harness patterns in our data

Marketers today are drowning in petabytes of data while sales teams are
overwhelmed with leads and under-armed with tools that can actually make
a difference, much less make a difference in the same quarter.
Marketing is doing their job by getting prospects into the funnel, while sales
leaders are looking carefully at their pipeline and at the calendar. They need
their army of sales reps to hit their targets in the next 90 days, and there
aren’t that many things they can do. Hollering at marketing for more leads
won’t help this quarter’s sales revenue, so they turn their attention to the
bottom of the funnel and try to coach and cajole their reps into closing these
deals as soon as is humanly possible.
But what if there were a way to prevent this bottom of the funnel hustle?
There is indeed a powerful and often underused tool at our disposal.
Predictive analytics is a great tool to help us efficiently identify and harness
patterns in our data. However, predictive analytics holds much more
potential to transform organizations than simply ranking and ordering leads.
When deployed correctly, predictive analytics can support marketing and
enable sales by identifying better, more appropriate leads, thus improving
the overall efficiency of both marketing and sales.
Although predictive analytics is the buzzword of the moment, it’s been
around for a while. In fact, we encounter predictive analytics daily. In the
next chapter we’ll reveal common examples of predictive analytics and
then we’ll dive into how predictive analytics can be applied in B2B sales and
marketing tactics.
PART 1: Why Predictive Analytics?

A most basic throw-back example of predictive analytics is the
direct mail that lands in our mailbox every day.
Direct mail isn’t cheap, so marketers have long used very
predictive data to justify the spend, typically drawing on
demographic attributes like our geography, customer penetration
in our market, marital status, income, credit rating and the value
of our home. Even for shared mail, marketers use predictive
statistics to drive decisions about whether they want to be in your
mailbox or in your geography, at a given time.
While we might not appreciate the bountiful mail we get, we
generally do appreciate the predictive analytics that fuel the
product recommendations from consumer-centric giants like
Amazon and Netflix (unless we’re spending too much of our hard-
earned money because of them).
Marketers at these companies use highly predictive signals like
prior purchase behavior, as well as product details like genre, year,
and popularity of movies to make recommendations that drive
consumers to spend more.
PART 2
Applications of Predictive Analytics in Consumer Marketing

PART 2: Applications of Predictive Analytics in
Consumer Marketing
What isn’t as widely recognized among marketers is how much predictive
analytics drives decisions for email content. Many sophisticated marketers
are using predictive analytics combined with online behavioral data like time
on page, purchase recency, last product purchased and segment learning
to optimize decisions around email content, products, and offers. These
analytics can be tuned to optimize clicks or revenue, profit or value. (Also,
it is critical that the tuning of the analytics and the evaluation of “success”
leverage the same metrics).
Dell is a great example of a company using predictive analytics for email
content. The company makes targeted product recommendations for
consumers based on predictive analytics, in some cases machine-learning
that gets smarter and smarter with every email click, every piece of content
you download, and every minute you spend on a web page. Technology and
automated processes are playing a big role here as many marketers have
a large number of email communications, both scheduled and triggered,
whose effectiveness can be maximized with the right content decisions.
These decisions are also ideally drawing on site behavior and, as such,
require a level of timeliness best achieved through automated processes to
define and apply predictive analytics.
A lesser known application of predictive analytics is targeted display media.
Predictive analytics allows marketers to capture data and make real-time
decisions about targeted ad placement, based on your online behavior.
Marketers are leveraging contextual data to score algorithms in real-time
and make bidding decisions about whether to serve up an ad along with
which complimentary ad, which content, or which offer. Attributes like
which browser we are using, the time of day, where we live and which type
of device we’re using are commonly drawn upon to optimize targeting
decisions for online media.

Taking off our consumer hat and putting on our marketing hat, predictive
analytics helps us as B2B marketers to make a variety of decision types more
effectively. We organize these into three primary categories: who, what, and
where.
Predictive analytics are most commonly applied to decisions about “who”
based on various leading indicators, drawing on data patterns to understand
and harness predictive signals that identify who is likely to engage, buy,
convert, or have a high lifetime value.
As we move from the who, we look to predictive analytics to determine the
next best product that we want to serve up. Is there a right offer, and if so
what is the message we want to use to deliver that product and offer?
Analytics can also help us optimize where to engage. It can help identify the
right outbound media, whether its mass media or precision media like email
or direct mail, sales call (field or inside) and increasingly print and display.
Inbound channel propensity is the final key decision point that often draws
on predictive analytics.
• Do we want to drive customers to a website?
• Are we asking them to call us or request a face-to-face meeting?
These are the most common signals that B2B marketers are using predictive
analytics to make decisions around, and the impact of using predictive data
efficiently can be immediate and profound.
To understand where B2B organizations are most often leveraging predictive
analytics in an ROI positive way, let’s look at a classic marketing and sales
funnel. From Reach at the top of the funnel, all the way down through
Engage, Convert, and Expand.
PART 3
How to Leverage Predictive Analytics

PART 3: How to Leverage Predictive Analytics
Predictive analytics is most commonly used to identify the best
targets and reach them where they’re already engaging and
buying.
• What was the hook or offer that compelled them to engage?
• How are they engaging?
• What content drives them to engage?
Here is where we often clone our best customers and find them
in the marketplace. Generally we’re drawing on firmographics of
current customers—things like revenue, employee count, number
of locations, credit rating, and industry.
We use these firmographics as a bridge across both existing
customers and the pools of prospects we wish to communicate
with, and these attributes serve as the medium through which
we apply predictive analytics to make better decisions about how
we’re going to reach out to them, whether it’s by phone, mail,
email or other digital engagement channel.
Reach
Industry
Revenue
Employees
Credit Rating

Our most successful B2B clients are leveraging predictive signals and data
to prioritize their leads and optimize inside sales time by nurturing the right
leads (who), in the right channels (where), with the right content and tactics
(what).
Many B2B marketers and sales teams are overrun by the sheer volume of
leads and are not spending call time in an optimal way. More importantly,
they’re not leveraging digital engagement to fill the gap. Predictive analytics
can help prioritize your call list and understand:
• Who is ready to engage with us?
• What is their channel preference?
• What signals has this prospect given us that tells us he or she is likely to
engage?
• Is this the right lead for our sales force to contact?
Once we know the prospect is ready to engage, we can then also predict
which messages to which a prospect is most likely to respond favorably.
Marketing Automation platforms collect an abundance of data on campaigns
and marketers often run tests of different messages to see which work best.
This feedback loop allows us to further optimize our tactics to ensure our
prospects engage.
It’s critical that marketers capture and draw on this predictive data to
understand when and how sales and marketing should reach back out to
prospects. Business buyers are smarter and more educated about your
products than ever before, and predictive analytics will help you meet
customers on their terms, wherever they are on their buyer’s journey.
Engage
Channel
Firm Size
Engagement Recency
Promotion

As we move further down the funnel into conversion, we’re leveraging data
signals and predictive analytics to close business with the optimal mix of
channel, product, offer, and message.
The most predictive indicators for closing the sale are often focused on
content marketing. How do we get the right content in front of leads, and
as we engage them, how to make sure we’re highlighting the products that
interest them—so we can get that deal closed now? We’re looking at things
like where leads are engaging and any historic purchase activity if it exists.
Increasingly important to marketers is integrating what we know about a
lead offline with web log data, particularly where leads are authenticating.
Are they consuming gated content? How much time are they spending on a
page? Ideally, we want to tie the different product offerings they’re viewing
online to their personally-identifiable or firm-identifiable information.
If marketing and sales is looking to sell an add-on or a cross-sell,
understanding a customer’s last purchase and other RFM (recency,
frequency, monetary value) characteristics of purchase and browsing
behavior is essential.
Convert
Last Purchase Email Clicks
Lead Source

For most marketers, the ultimate goal is to find, nurture, and
expand customer relationships so that they are as valuable as
possible. As our customers move further into the funnel, their
value naturally grows as does our accuracy in predicting their
lifetime value. As customers move through the funnel, the richness
of data from which we can identify patterns expands into purchase
and engagement activity both on and offline.
Once a prospect becomes a customer, we often have rich data on
how they interact with our products (especially if our product is
software!) which provides us signals to when they’re most likely
to upgrade, products that we should cross-sell to them and also
when they’re at risk of leaving. When you fold in service and other
engagement data beyond sales and marketing (for example calls to
customer support or service) our ability to predict when customers
are at risk becomes even stronger.
Effective lifetime value (LTV) predictions focus on two key
dimensions: your tenure as a customer and the volume of spend
or profit across each time period of your ‘survival’. These are born
from attributes such as how much a customer spends on a first
purchase, their purchase frequency, how they pay, whether they
are adopting your solution or products, if they are engaged in your
web properties, and so on.
Predictive analytics allow us to project a customer’s LTV and
toggle sales, marketing and service spend accordingly to create a
competitive advantage.
Expand
Usage
Purchase Amount
Payment Method

PART 4
A Practical Example of Predictive Analytics in B2B
Here’s an example of how a B2B organization can leverage predictive
analytics to find their perfect customer. Our mock company, ACME
Applications, sells hardware and software to small and medium-sized
businesses. Based on ACME’s offering, current customer profile, and whom
its offer is resonating with, ACME’s perfect customer is Agile Mobile.
If we’re marketers at ACME and want to find more customers like Agile
Mobile, we first want to see what our prospect universe looks like. We have
plenty of top-of-the funnel firmographics and characteristics at our disposal.
ACCOUNT

AGILE
MOBILE,
EST.
2011

OWNER:

ELENA
STARK

LOCATION:

SAN
MATEO,
CA
AND
BANGALORE,
INDIA

EMPLOYEES:

34

INDUSTRY:
PROFESSIONAL,
SCIENTIFIC,
AND
TECHNICAL
SERVICES
(54)

REVENUES:

$10.5

CREDIT
RATING:

A

Agile
Mobile
opened
their
doors
just
over
three
years
ago.

They

specialize
in
mobile
applicaYon
development.

Run
by
Elena
Stark,

the
business
has
grown
reliably
and
steadily
over
Yme.
With
good

margins
and
a
great
ﬁnancial
record
their
credit
raYng
is
strong.

Like
others
in
their
industry,
Elena
is
looking
forward
to
a
strong

future
of
growth.
Example Only: Not a Real Company

As we mine the data, we start to find our ideal range, our customer
sweet spot. We’re successful with small firms with 10-49 employees in the
professional services industry who have been in business for less than 7
years. Our best customers have small to medium square footage, good
credit or no credit history, and revenue of $10-60 million.
Now we need to move from understanding who our perfect customer is to
planning an outbound marketing campaign or building out a target list for
inside sales. We start by trying to understand how many firms out there fit
these categories.
PART 4: A Practical Example of Predictive
Analytics in B2B
Employees Target Industry Time in Business Sq. Footage Credit Rating Revenues
Firms with 10
to 49
employees
Professional
services
Younger
businesses
Small to
medium
square
footage
High credit
…
Or no credit
history Revenues
$10- $60 MM

As we move from having an overarching view of the ideal customer into the
spreadsheet view as we start to plan our campaign, it might look like this.
As you can see below, we have a scalability issue. In this example, given
the criteria, when we combine all the data together there are only 8,448
total firms that meet all of our criteria. This means we don’t have a huge
population to go out and market against. In addition, we have to ask
ourselves, are these really the best targets?
Are there more intelligent ways to think about this not only to improve our
scale, but to differentiate the very best prospects and opportunities from
those that might be less viable for us?
Analytics in B2B
Waterfall counts
… TBD
Employees Industry Age
<10
&
Unknown

11,463,543

Public
&
Non-‐Proﬁts

1,480,867

0-‐2
years

3,324,670

10-‐49

2,040,705

Private
-‐
Goods

5,985,910

3-‐5
years

3,509,974

>49

494,374

Private
-‐
Services

6,531,845

>5
years

7,163,978

15%
47%
25%
Sq. Footage Credit Ra:ng Revenue
<2.5k
&
unknown

5,412,622

Unknown

2,965,757

<$10MM

2,213,793

2.5k-‐10k

5,780,245

<A

8,547,121

$10-‐60MM

856,634

>10k

2,805,755

A+
&
A

2,485,744

>$60MM

10,928,195

41%
39%
6%

Analytics in B2B
Also, with the old spreadsheet approach, what if a prospect checks boxes
for a couple of attributes but is missing one or two? Are they a viable
target or not? What if a lead is a great fit in one attribute but lacking in
another?
The challenge for the spreadsheet method is these factors as well as
the complexity of relationships. Not all attributes are equally weighted,
and they’re not necessarily equally good or bad. We have to think of
these complex relationships in both multivariate and single-variable (or
univariate) ways. Where our customer sweet spot lies may not be as cut-
and-dry as we think.
For example, if we’re looking at two groups, our prospect universe and
our current customer base, to compare one attribute, say a firm’s average
time in business, we might see that for our prospect universe it’s 12 years
and for our customer base it’s three years. A logical conclusion to draw
is that younger businesses are better. But if you dig in, you might see a
skewed bell-shaped curve. The real sweet spot where we have the best
representation of customers isn’t the absolutely youngest businesses, but
those who have been in business three to six years.
As we do initial exploratory analysis in preparation for building a model,
these are the types of relationships we’ll uncover and harness to make
sure our targets are the best they can be.
Now it’s time to start building our model.
yrs
Lacking "fit in
certain factors
Relative
Importance
Complexity of
Relationship
Time in business1 year 20 yrs
PropensitytoconvertLowHigh
Prospect
universe
Customers
Group
12 years
Average time in business
3 years
So, younger businesses are better….
Right?
Sort of

PART 5
How to Build a Predictive Model (It’s easier than you think.)
In the example of ACME applications, we’re beginning to understand who
our target is. We’re convinced that predictive analytics is a better way, so
we’re ready to build a model to create a top of funnel marketing campaign.
At MarketBridge, we leverage a standard process with our B2B clients to
frame out the business problem all the way through collecting and analyzing
data, delivering insights, and finally taking action on them.
Building a model is easier than you think. First you frame the problem or
objective. In this case, it’s “find prospects who look like and will spend like my
current customers to target them with a marketing impression.”
Next we collect data for this set of customers along with a set of prospects
where we’re going to go fish. We need to ensure a consistent set of
attributes across the two data sets, for example, firmographics. Then we
analyze, organize and cleanse the data—and any analyst will tell you this is
the bulk of the work. We need to identify which attributes are truly related
to being an ACME customer and how those attributes relate to one another.
Once we’ve identified these attributes, we build the model.
Now it’s time for a little math. We promise it won’t hurt.
Frame Collect Analyze Deliver Act

PART 5: How to Build a Predictive Model
Building the initial model and identifying that equation is a relatively short-
term project. You build it and it’s done. How you apply it requires more
ongoing use. Some marketers feel a little intimidated by the idea of using
and scoring the model on a regular basis, but we can break it down into
simple math (it’s easy!).
Below is an example of an equation we would use to predict spend (using
linear regression) with each of the important parts of the equation broken
down.
The “dependent variable” …
or trait you want to identify
– in our case it’s
customer spend
The “coefficient” – think of
this as the “weight” that is
applied to the independent
variable
An “independent variable” … or trait that
relates to your end goal (usually you will
have many different independent or
“predictor” variables in an equation)
y = a + β x + e

Now let’s plug in some numbers to predict customer spend. In the
example below, the weight of 2.9 means that for every increase of one
employee, the firm will spend 2.9 thousand more dollars.
A slightly more complex (and more realistic) example is below. In this
example when trying to predict spend, think of it in a simplistic way: more
employees is very good, a bigger footprint is pretty good, and a bad credit
rating is just that—bad.
customer spend
The weight we apply to the
employee count variable
The predictor variable that is highly
related to customer spend
Customer Spend = 4 + 2.9 (# of employees)
(in thousands)
customer spend
The weights we apply to
each predictor variable
The predictor variables that are
highly related to customer spend
Customer Spend = 2 + 2.5 (# of employees)
(in thousands)
+ 0.003 (square footage)
- 1.6 (credit rating of "C")

If we carry this through we get a predicted spend of $102,000 for Agile
Mobile. Knowing this, Acme Applications can put an appropriate amount
of spend against Agile Mobile as it relates to outbound sales and
marketing touches.
If we as marketers at ACME apply this basic math equation for predicting
customer spend to every account, it’s clear how we can build a cleanly
ranked and ordered list, bundle that rank order into higher level groups,
and suddenly we’ve harnessed the power of predictive analytics at scale.
customer spend
The weights
we apply to each
predictor variable
The predictor variables that are
highly related to customer spend
$102,000 = 2 + 2.5 (34)
+ 0.003 (5,000)
- 1.6 (0)
The weights
we apply to each
predictor variable

There are a number of approaches to building the equation on the
previous page. Some common statistical tools and methodologies are
below. Once we’ve built the model, we’ll need to evaluate it to make sure
it’s a good fit. Testing is key when building an analytical model.
Lastly, we need to take the data generated from our predictive model and
make sure we can execute on it in our marketing campaign or sales play.
From a delivery perspective, there are two key pieces. First, we want to
deliver business-relevant insights about the model. Not many marketers
or sales teams are comfortable ranking their call list based on a black-box
type of model. Organize the information to clearly identify who the model
says is a good customer and who is not. Does it make good business
sense?
Perhaps most important, we want to deliver the actual targets, whether
they are consumed by marketing automation, campaign management,
CRM, or another tool. As with all technology-enabled marketing, the end-
goal of predictive analytics is to have a positive effect on our bottom line,
so a plan and process for execution is critical to our success.
Logistic regression to predict a
binary outcome (0/1)
Survival analysis coupled with
revenue/margin estimation
Logistic regression if binary or
linear regression to predict spend
level (continuous)
Market Basket analysis to identify
associations between products or
product propensity modeling using
logistic, decision trees, or neural network
models
Segmentation driven using techniques
like k-means clustering, latent class,
factor analysis, discriminant analysis
Media and channel propensity
modeling using decision tree, or
logistic regressionSurvival analysis or logistic
regression
Logistic regression to predict a binary
outcome (0/1)
Logistic regression if binary or linear
regression to predict spend level
(continuous)
Survival analysis coupled with revenue/
margin estimation
Survival analysis or logistic regression
Market Basket analysis to identify
associations between products or product
propensity modeling using logistic, decision
trees, or neural network models
Segmentation driven using techniques
like k-means clustering, latent class, factor
analysis, discriminant analysis
Media and channel propensity modeling
using decision tree, or logistic regression

Moving beyond the ability to build a predictive model, there is a broader predictive analytics ecosystem that is important to generate value.
Data
We’ve all heard this said about data, “Garbage in. Garbage out.” For data scientists, the data fueling the predictive analytics ecosystem is the air we breathe.
Clean, well-organized data is great, but not so perfectly organized data doesn’t need to constrain our ability to leverage predictive analytics. At MarketBridge,
we talk to many clients who have pain around this issue of data organization. It’s a huge challenge. But we can typically get more out of our data than we
think. In addition, there are a plethora of third party sources we can use both to source top-of-the-funnel opportunities and to enrich, cleanse, and update
the data we already have.
Systems
The systems we use are the foundation we’re building everything on. We have to deliver predictive analytics into something. The data has to live somewhere,
in a marketing automation, campaign management, or CRM system. The ability for your system to consume predictive analytics is all-important. Some tools
have the ability to score and potentially to build analytics in an embedded way, but if not, it’s very easy to get analytics into these systems through loading of
flat files or SaaS plug-ins.
Effect
Having an effect in market requires that we take action on the insights and outputs of our predictive analytics. A classic example is attrition modeling. It’s
one thing to identify who is likely to attrite and another thing entirely to deploy a program to use those predictions to stem attrition. The predictive analytics
and approach you use to drive the intended effect need to work together.
PART 6
The Predictive Analytics Ecosystem

CONCLUSION
So, get started on leveraging predictive analytics. Your competitors
may already be there, but even if they are there it’s time to
surpass them with better end to end use and application of
predictive analytics. As you get more comfortable with the B2B
uses as outlined in this whitepaper you can start to pave the way
around the more progressive digital applications.
Lastly, while there are great tools out there to put predictive
analytics in the hands of a business user, if you haven’t done it
before please consider hiring a professional. There are a number
of pitfalls in data handling and predictive modeling that you’ll be
well armed to avoid to ensure the best outcome. Ideally they can
also help guide the end to end process: frame the problem (one
you can action on effectively), organize your data, build a strong
model, and deploy and act on it.

ABOUT THE AUTHORS
Stephanie Russell // SVP, Business Analytics
Stephanie leads the Business Analytics practice for MarketBridge. With
over 15 years of experience in CRM strategy, analytics, and enabling
technology, Stephanie leads a team of experts across analytics and
enabling technology disciplines. Through strong client partnerships, the
team delivers relevant, effective and actionable analytic solutions across
industries for clients such as HP, Staples, Humana, and others. These
solutions inform both online and offline sales and marketing decisions
and range from predictive modeling and segmentation solutions
to web analytics and closed loop sales and marketing effectiveness
measurement.
Prior to her role at MarketBridge, Stephanie focused on CRM analytic
strategy, solution development and delivery at Merkle. This included
technology and analytic solutions such as agile BI deployments, media mix
modeling, media attribution, lifetime value modeling, experimental design,
and program analytics for clients such as Dell, MetLife, Under Armour,
Disney, DirecTV, Bristol-Myers Squibb, Ally Bank, GEICO and others.
Stephanie holds a Bachelor of Arts degree in Economics, with a
concentration in Econometrics, from William Smith College and an MBA,
with a concentration in Marketing, from American University.
Peter Guy // SVP, Products
Peter leads MarketBridge’s product strategy and technology development
to address the rapidly changing sales and marketing challenges of our
clients. Peter drives MarketBridge’s RevenueEngines™ digital customer
engagement and S.M.A.R.T.™ Analytics solutions that accelerate revenue
growth and end-to-end marketing and sales productivity for Fortune 1000
clients. As companies make significant investments in sales and marketing
software – CRM, marketing automation, social media, business intelligence
– MarketBridge’s solutions connect these products into scalable and
coherent “digital + data” strategies and programs.
Prior to MarketBridge, Peter was Managing Vice President, Product
Marketing at Gartner. Before Gartner he was Vice President at Lattice
Engines where he led customer development of their predictive analytics
solution for B2B sales productivity delivering nearly a billion dollars of
incremental revenue for customers such as Dell, Staples, EMC, vmWare,
HP, ADP and American Express.
Peter has a BSc in Computing Science and a BEng in Electrical Engineering
from Dalhousie University (Canada) as well as an MBA from INSEAD
(France and Singapore).

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