Goodbye spreadsheets hello predictive analytics: How to leverage predictve analytics for business

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


Agenda

Context Our consumer experiences B2B applications An example Ecosystem + tips


Predictive analytics…

Simply helping us more efficiently identify and harnesspatterns in our data


As consumers, we experience marketing decisions driven by predictive analytics almost every day…


Direct Mail | Credit Offers | Shared Mail

income

length of residence

married

home value

geography


Product recommendations

rating

family genre

year

popularity


Email content

time on category page

purchase recency

last product category purchased

segment


Display media

device

time of day

browser

DMA


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


Where are B2B marketers leveraging predictive analytics most?


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


Reach: Targeted direct marketing

“clone” your current customers and find them in the marketplace

revenues

employees

credit ratingservices industry single site


Engage: Optimizing inside sales time and attention to nurture the right leads

lead channel

priority call lists

engagement recency

industry

firm size

Promotion


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


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


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.


Employees Target Industry Time in Business Sq. Footage Credit Rating Revenues

Factors we might use to predict spend at the top of the funnel


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


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?


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


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!


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


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


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



Customer Spend = 2 + 2.5 (34) (in thousands)



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



$102,000 = 2 + 2.5 (34)


The “independent 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


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


There are a few key things that go arm-in-arm with predictive analytics


Data Systems Effect

The predictive analytics ecosystem

– 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 RussellSVP, Business [email protected]

http://www.market-bridge.com/calculators/sales/

http://blog.market-bridge.com/

Goodbye spreadsheets hello predictive analytics: How to leverage predictve analytics for business

Data & Analytics

Transcript of Goodbye spreadsheets hello predictive analytics: How to leverage predictve analytics for business