a case study in b2b prospect scoring: getting to a more targeted prospecting list

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� Click to edit Master subtitle styleA Case Study in B2B Prospect Scoring:Getting to a More Targeted Prospecting List


Problem: How do I find the best prospects to market to?

•Sales volume

•Revenue ($)

•Repeat buyers

•Multiproduct buyers

•Etc

•How do I determine which ones are similar to my customers?

•How do I prioritize a list so that marketing/sales reach out to the best prospects first?

But which ones should I give

to marketing???

I have a list of 19m business sites.

I know who are my best

customers.


Problem: How do I match best customers to a marketing list?


to marketing???



customers.

“Match” customer attributes to those in your marketing list:

1. Firmagraphics: NAICS/SIC

employee size, site sales, site

status (branch, HQ, standalone),

enterprise employees, etc.

2. Sector-Specific Fields: IT spend,

PCs, DP spend, health insurance

spend, MDs, fabrication machines,

etc.

How to Match:

1. Append data to customer list;

2. Select modeling sample of

customer and non-customer

sites;

3. Statistically derive model that

“explains” a best customer in

terms of firmagraphics and

sector-specific fields.


Problem: Which prospects should I give to Marketing?


to marketing???



customers.

Prioritize matched sites:

1. Apply model (“score”) to entire marketing

list;

2. Sort by highest to lowest likelihood that a

site matches the attributes of your best

customers;

3. Give to marketing the top X% of list (e.g.

start with top 10%, then work your way

down the sorted list)

Bus Site ID Rank Index

366043194 2 5.206

893096294 7 2.537

309612334 15 0.215

155334662 16 0.149

8024609 18 0.070

879091777 19 0.069

588339068 20 0.064

112495115 22 0.037

82260450 26 0.017

948960617 27 0.015

48952922 28 0.008

406550483 29 0.007

355478638 34 0.003

347713194 36 0.003

693130478 37 0.003

343722416 41 0.003

8865965 43 0.003

779265997 46 0.002


Problem: But how good is my matching?


to marketing???



customers.

Assess Model “Lift”

1. As part of the modeling/matching process, compare what the model predicts

with what is actually true in the customer sample (“internal” validation);

2. After sorting your sample from highest to lowest predicted likelihood of a

match, you should find that actual customers tend to skew towards the top;

3. The degree to which this is true is called “lift”;

4. Secondary assessment makes use of actual marketing campaign data, a

campaign that made use of the model’s scores (“external” validation).


Case Study

� A client was using an “off the shelf” product to

identify prospects in a B2B marketing list;

� This product matched customer sites using a

limited set of firmagraphics;

� Client wanted to see if this matching could be

improved;

� Improvement metric used = lift

It should be noted that there is a huge difference between matching customer attributes in a marketing list and converting these prospects into customers. The

later depends on the skills of the marketing and sales departments and, hence, is

beyond the control of the modeler.

It should be noted that there is a huge difference between matching best customer attributes to a marketing list and converting these prospects into paying

customers. The later depends on the skills of the marketing and sales departments

and, hence, is beyond the control of the data scientist.


Case Study

� Client provided sample of customers and non-customers;

� The baseline was a simple model using 5 firmagraphic fields in their raw form:� Enterprise employment and revenue;

� Site employment, type (HQ, branch, standalone), NAICS6, Metropolitan Statistical Area (MSA)

� The study looked at:� Whether the addition of sector-specific fields (in this

case, IT fields such as spend, PCs, servers, etc) could appreciably increase lift;

� And whether more careful attention to data preparation and modeling techniques could further incrementally improve lift.


Case Study

� The base line model, using a simple logistic regression model, identified ~75% more customers (as compared to using no model) in the top decile.

� The “lift chart”should exhibit smoothly declining lift as one goes deeper into the sorted file (i.e. lower deciles)=this one does not.

Lift chart created by sorting the scored file from highest

to lowest predicted likelihood of a match, dividing into

deciles, finding the actual occurrence of customers in

each decile and expressing this occurrence in terms of an

index.

Lift chart created by sorting the scored file from highest

to lowest predicted likelihood of a match, dividing into

deciles, finding the actual occurrence of customers in

each decile and expressing this occurrence in terms of an

index.

Firmagraphics Model Performance

(Deciles of Actual Customers)

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25

1 2 3 4 5 6 7 8 9 10

Decile

Lift

Testing

Model

No Model


Case Study

� However, with the addition of sector-specific fields, the discriminatory power of the model improved considerably, with about a 15% gain in lift in the top decile.

Note how the lift chart is now smoother and exhibits a

more pronounced decline in lift from the highest to the

lowest decile (1 to 10).

Note how the lift chart is now smoother and exhibits a

more pronounced decline in lift from the highest to the

lowest decile (1 to 10).

Firmagraphics + Sector-Specific Model Performance


0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25

1 2 3 4 5 6 7 8 9 10

Decile

Lif

t

Testing

Model

No Model


Case Study

� Finally, using a more sophisticated data preparation and logistic modeling methodology, including the consideration of additional firmagraphics, added an additional ~5% to the model’s lift in the top decile.

Methodology Model Performance


0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25

1 2 3 4 5 6 7 8 9 10

Decile

Lift

Testing

Model

No Model

Using the model’s score in decile #1 => more than 2x the

likelihood of identifying a site that is similar to a current

customer (compared to using no model).

Using the model’s score in decile #1 => more than 2x the

likelihood of identifying a site that is similar to a current

customer (compared to using no model).


Case Study

� In sum, both data and methodology matter. In this study:

� Using sector-specific fields in addition to a base set of firmagraphics adds ~15% to lift;

� More careful attention to data preparation and modeling methodology adds another ~5% to lift.

� The result is the potential identification of more prospects that match the attributes of your best customers:

Customers by Decile

0

100

200

300

400

500

600

700

800

1 2 3 4 5 6 7 8 9 10

Decile

Firmagraphics Firmagraphics + Sector Methodology

Across top 3 deciles (30% of the file),

methodology identifies 18% more actual

customers than firmagraphic based

model. Just adding sector-specific

variables to firmagraphics identifies 12%

more customers.


Who are we?

� A team of statisticians, economists, business and

industry subject matter experts;

� Specialists in database marketing analytics (market

sizing; market simulation; segmentation; predictive

prospect, campaign and churn scoring; etc), with

extensive experience in the B2B space;

� Data junkies who love to get their hands dirty

integrating, cleaning, modeling and visualizing client

and 3rd party databases;

� Experienced professionals with particularly deep

experience in the telecom, IT and energy industries.


Who do we serve?

� Our clients range from large providers of B2B

marketing data to small consulting groups;

� We typically wholesale our services but can

and have worked directly with end users.


Contact Info

� Let us know how we can help you:

[email protected]


a case study in b2b prospect scoring: getting to a more targeted prospecting list

Marketing

best customers

customer list

entire marketing list

sample of customers

model lift

b2b marketing

actual occurrence of

best prospects