Goodbye spreadsheets… hello predictive analytics!
Leveraging predictive analytics in B2B
Stephanie RussellSVP, Business [email protected]
©2014 MarketBridge Corp.– 2 –
MarketBridge –Who We Are
Accelerating Revenue GrowthFor 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
©2014 MarketBridge Corp.– 3 –
Agenda
Context Our consumer experiences B2B applications An example Ecosystem + tips
©2014 MarketBridge Corp.– 4 –
Predictive analytics…
Simply helping us more efficiently identify and harnesspatterns in our data
©2014 MarketBridge Corp.– 5 –
Predictive analytics…
Simply helping us more efficiently identify and harnesspatterns in our data
©2014 MarketBridge Corp.– 6 –
As consumers, we experience marketing decisions driven by predictive analytics almost every day…
©2014 MarketBridge Corp.– 7 –
Direct Mail | Credit Offers | Shared Mail
income
length of residence
married
home value
geography
©2014 MarketBridge Corp.– 8 –
Product recommendations
rating
family genre
year
popularity
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Email content
time on category page
purchase recency
last product category purchased
segment
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Display media
device
time of day
browser
DMA
©2014 MarketBridge Corp.– 11 –
Predictive analytics helps us make a variety of decision types more effectively
PE PBPROPENSITY TO ENGAGE PROPENSITY TO BUY
ARLVATTRITION RISKLIFETIME VALUE
BPBEST PRODUCT
OFOFFER
MEMEDIA
CHCHANNEL
WHO
WHAT
WHERE
MSMESSAGE
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Where are B2B marketers leveraging predictive analytics most?
©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
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Reach: Targeted direct marketing
“clone” your current customers and find them in the marketplace
revenues
employees
credit ratingservices industry single site
©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
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Convert: Drive initial conversion or add-on purchases with better content marketing highlighting the right product, offer, and message
last purchase categorylead source
product category page views
download categoriesemail clicks
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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
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Let’s walk through an example
ACME APPLICATIONSSelling HW and SW solutions to SMB
©2012 MarketBridge Corp.– 19 – ©2014 MarketBridge Corp.– 19 –
ACME’s perfect customer --
ACCOUNT
AGILE MOBILE, EST. 2011OWNER: ELENA STARK
LOCATION: SAN MATEO, CA AND BANGALORE, INDIA
EMPLOYEES: 34INDUSTRY: PROFESSIONAL, SCIENTIFIC, AND TECHNICAL SERVICES (54)REVENUES: $10.5CREDIT 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.
©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
©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
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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?
©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
√
√
√
√
Pro
pen
sity
to
co
nve
rtLo
wH
igh
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
©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 data2. Cleanse the data3. Identify which
attributes are related to being a customer
4. Build the model5. Evaluate the model
a) Insights:Business relevant summary; andb) 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: 2Account: Agile MobileDecile: 1Priority: HIGH!
©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 + eThe “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
©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
©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
©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
©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
©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 regression Survival analysis or logistic regression
©2014 MarketBridge Corp.– 31 –
There are a few key things that go arm-in-arm with predictive analytics
©2014 MarketBridge Corp.– 32 –
Data Systems Effect
The predictive analytics ecosystem
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Thank You !Stephanie RussellSVP, Business [email protected]