how predictive analytics can help optimize your sales and marketing efforts from a data scientist who speaks your language (the language of business!). Whether it is to better score leads, become a more effective content marketer, or understand a customer’s lifetime value you may have dabbled in using predictive analytics or just heard about it.

1. Goodbye spreadsheets… hello predictive analytics!
Leveraging predictive analytics in B2B
Stephanie Russell
SVP, Business Analytics
srussell@market-bridge.com

2. ©2014 MarketBridge Corp.– 2 –
MarketBridge –Who We Are
Accelerating Revenue Growth
For more than 20 years, MarketBridge has been delivering technology-enabled solutions for
Fortune 1000 clients combining omni-channel customer engagement and data-driven
analytics solutions to connect marketing and sales, improve marketing effectiveness, and
maximize sales close rates.
Our expertise in the complete direct marketing arena means that our services are
strategically designed to drive conversions and grow revenue.
RevenueEngines™
Digital Engagement Programs
On and offline marketing programs
and tools to increase lead volume,
quality, and conversion while enabling
sales channels to engage customers
SMART™
Predictive Analytics Solutions
Sales and Marketing Analytics,
Reporting and Technology to
optimize activity across the funnel by
prioritizing opportunities and
personalizing interactions

3. ©2014 MarketBridge Corp.– 3 –
Agenda
Context Our consumer experiences B2B applications An example Ecosystem + tips

4. ©2014 MarketBridge Corp.– 4 –
Predictive analytics…
Simply helping us more efficiently identify and harness
patterns in our data

5. ©2014 MarketBridge Corp.– 5 –
Predictive analytics…
Simply helping us more efficiently identify and harness
patterns in our data

6. ©2014 MarketBridge Corp.– 6 –
As consumers, we experience
marketing decisions driven by
predictive analytics almost
every day…

7. ©2014 MarketBridge Corp.– 7 –
Direct Mail | Credit Offers | Shared Mail
income
length of residence
married
home value
geography

8. ©2014 MarketBridge Corp.– 8 –
Product recommendations
rating
family genre
year
popularity

9. ©2014 MarketBridge Corp.– 9 –
Email content
time on category page
purchase recency
last product category purchased
segment

10. ©2014 MarketBridge Corp.– 10 –
Display media
device
time of day
browser
DMA

11. ©2014 MarketBridge Corp.– 11 –
Predictive analytics helps us make a variety of decision types more effectively
PE PBPROPENSITY TO ENGAGE PROPENSITY TO BUY
ARLV ATTRITION RISKLIFETIME VALUE
BPBEST PRODUCT
OFOFFER
MEMEDIA
CHCHANNEL
WHO
WHAT
WHERE
MSMESSAGE

12. ©2014 MarketBridge Corp.– 12 –
Where are B2B marketers leveraging
predictive analytics most?

13. ©2014 MarketBridge Corp.– 13 –
Better decisions across the funnel
Identify and reach potential customers in the marketplace
Prioritize leads and identify who to engage with various
channels and tactics (field sales, inside sales, digital nurturing…)
Close business with the optimal mix of channel, product,
offer, and message
Expand your existing relationships with more relevant cross-sell,
renewal, and proactive retention

14. ©2014 MarketBridge Corp.– 14 –
Reach: Targeted direct marketing
“clone” your current
customers and find them
in the marketplace
revenues
employees
credit rating
services industrysingle site

15. ©2014 MarketBridge Corp.– 15 –
Engage: Optimizing inside sales time and attention
to nurture the right leads
lead channel
priority call lists
engagement recency
industry
firm size
Promotion

16. ©2014 MarketBridge Corp.– 16 –
Convert: Drive initial conversion or add-on
purchases with better content marketing
highlighting the right product, offer, and message
last purchase category
lead source
product
category page
views
download categories
email clicks

17. ©2014 MarketBridge Corp.– 17 –
Expand: Identify, grow, and nurture high lifetime
value customers
first purchase
amount
number of product
categories
payment method
average days
between purchases
usage and adoption

18. ©2014 MarketBridge Corp.– 18 –
Let’s walk through an example
ACME APPLICATIONS
Selling HW and SW solutions to SMB

19. ©2012 MarketBridge Corp.– 19 – ©2014 MarketBridge Corp.– 19 –
ACME’s perfect customer --
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 application development. Run by Elena Stark,
the business has grown reliably and steadily over time. With good
margins and a great financial record their credit rating is strong.
Like others in their industry, Elena is looking forward to a strong
future of growth.

20. ©2012 MarketBridge Corp.– 20 – ©2014 MarketBridge Corp.– 20 –
Employees Target Industry Time in Business Sq. Footage Credit Rating Revenues
Factors we might use to predict spend at the top of the funnel

21. ©2012 MarketBridge Corp.– 21 – ©2014 MarketBridge Corp.– 21 –
Employees Target Industry Time in Business Sq. Footage Credit Rating Revenues
The perfect customer
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

22. ©2012 MarketBridge Corp.– 22 – ©2014 MarketBridge Corp.– 22 –
The perfect target (spreadsheet view)
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 Rating 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%
8,448 total firms meet all of the criteria… and are
these really the BEST targets?

23. ©2012 MarketBridge Corp.– 23 – ©2014 MarketBridge Corp.– 23 –
Lacking ‘fit’ in
certain factors
A few places the spreadsheet breaks down (and predictive analytics shine)
Relative
importance
Complexity of
relationship
√
√
√
√
PropensitytoconvertLowHigh
Time in business1 year 20 yrs
Prospect universe
Customers
Group
12 years
Average time in business
3 years
So, younger businesses are better…. Right? Sort of

24. ©2012 MarketBridge Corp.– 24 – ©2014 MarketBridge Corp.– 24 –
How do you build a predictive model to reach customers??
Frame Collect Analyze Deliver Act
Business Objective:
Find prospects who
“look” like my current
customers to target
them with marketing
impressions
Identify a set of
customers … and a set
of prospects ensuring
consistent data
attributes across the
two data sets (e.g.
firmographics)
1. Organize the data
2. Cleanse the data
3. Identify which
attributes are
related to being a
customer
4. Build the model
5. Evaluate the model
a) Insights:
Business relevant
summary; and
b) Targets – or a score
file to be consumed by
a marketing
automation, campaign
management, or CRM
tool
Execute a sales play or
marketing campaign
drawing on the
predictive
recommendations
ID: 2
Account: Agile Mobile
Decile: 1
Priority: HIGH!

25. ©2012 MarketBridge Corp.– 25 – ©2014 MarketBridge Corp.– 25 –
What the equation means … (p.s. scoring is easier than you think)
(a linear regression example)
y = a + β x + e
The “dependent variable” …
or trait you want to identify
– in our case it’s a customer
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

26. ©2012 MarketBridge Corp.– 26 – ©2014 MarketBridge Corp.– 26 –
A real world example
Customer Spend = 4 + 2.9 (# of employees)
(in thousands)
The “dependent variable” …
or trait you want to identify
– in our case it’s a customer
The weight we apply to the
employee count variable
The predictor variable that is highly
related to customer spend

27. ©2012 MarketBridge Corp.– 27 – ©2014 MarketBridge Corp.– 27 –
A more realistic real world example
Customer Spend = 2 + 2.5 (# of employees)
(in thousands)
The weights we apply to the
employee count variable
The predictor variables that are highly
related to customer spend
+ 0.003 (square footage)
- 1.6 (credit rating of “C”)
The “dependent variable” …
or trait you want to identify
– in our case it’s a customer

28. ©2012 MarketBridge Corp.– 28 – ©2014 MarketBridge Corp.– 28 –
A more realistic real world example
Customer Spend = 2 + 2.5 (34)
(in thousands)
The “dependent variable” …
or trait you want to identify
– in our case it’s a customer
The weights we apply to the
employee count variable
The predictor variables that are highly
related to customer spend
+ 0.003 (5,000)
- 1.6 (0)
The intercept where the
regression line intersects with
the y-axis

29. ©2012 MarketBridge Corp.– 29 – ©2014 MarketBridge Corp.– 29 –
A more realistic real world example
$102,000 = 2 + 2.5 (34)
The weights we apply to the
employee count variable
The “independent variables” that are
highly related to customer spend
+ 0.003 (5,000)
- 1.6 (0)
The “dependent variable” …
or trait you want to identify
– in our case it’s a customer
The intercept where the
regression line intersects with
the y-axis

30. ©2014 MarketBridge Corp.– 30 –
Common Statistical Tools | Methodology
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

31. ©2014 MarketBridge Corp.– 31 –
There are a few key things
that go arm-in-arm with
predictive analytics

32. ©2014 MarketBridge Corp.– 32 –
Data Systems Effect
The predictive analytics ecosystem

33. – 33 –
WEBSITE
MarketBridge
Website
OUR COMMUNITY CHECK OUT
www.market-bridge.com www.digital-bridge.com
BLOG:
“The Optimization
Conundrum: Where Art
Holds a Place Next to
Science”
WEBINAR:
“Is Marketing
Automation Failing You
…or Vice-Versa?”
You Might Also
Like
Thank You !
Stephanie Russell
SVP, Business Analytics
srussell@market-bridge.com