2. Prospecting & Lead Gen Has Changed
• Today, anywhere from 66-90% of a customer’s
journey is self-directed according to Forrester..
Usually done via Web Research.
• According to Sandler Sales 90% of Trade Show
leads Never get any follow up…..
• Companies that Automate Lead Management see
a 10% growth in revenue in 6 – 10 months
according to Forrester Research
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
4. Marketing & Sales Alignment
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
5. Define Your Sales Process - Model
• Prospects (Scoring & Grading)
• Leads (Scoring & Grading)
• Marketing Qualified Leads (Nurturing)
• Sales Qualified Leads (Sent to Sales)
• Contacted by:
– Inside Sales
– Outside Sales
• Sales Process, B&R, Qualification, Quote/Bid
• Deal Closed Won or Closed Lost
• The Goal is to Generate Revenue , Money, $$$
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
6. Pardot Defines Score vs Grade
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
7. Implicit (Score) Vs Explicit (Grade) Rating
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
8. Types of Lead Scoring & Grading
• Behaviors – Actions (Scoring)
– Implicit: Online Activity
– Explicit: BANT = Budget, Authority, Need, Timeline
• Demographics (Grading)
– Inferred: Geography, Data Quality etc.
– Provided: via Forms, or by Appending
• Matches to your model: Personas, Data
Enrichment, Regression Analysis, Sales Input, Market
Research etc.
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
9. Scoring Straw Poll
• Scoring is based on Implicit Buying Signal
Ten is Top One is Bottom Your Score 1 - 10
– Form Submissions ____
– Page Views ____
– File Downloads ____
– Email Opens & Click Through ____
– Site Searches Product/Service ____
– Joined Community ____
– Subscribed to Blog ____
– Tradeshow Attendance ____
– Online Add Click Through ____
– Webinar Attendance ____
– Others ?? ________________________________
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
10. Results of Our Poll
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
Poll Results - Lead Scoring With 10 Being Best Feb 2019
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
11. Poll Results Comments
• We had 9 respondents
• Most all were B2B
• Various Industries
• Suggestions for Scoring:
– If they ask for a Demo and a Free Trial.
– They contact your for more info
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
16. Location Grade – Census Tract Income
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
17. Lead Grading
• Grading Explicit factors (Personas)
– Company Size matches your target: $1Bill
– Job Title or Level ie: Manager, VP, CEO, COO
– Department: IT, Finance, Sales, Marketing
– Location: Branch, HQ, Reg HQ, Corp HQ
– Revenue $ in your target range
– Industry Matches ie: Finance, Mfg, Mining, SIC, NAICs
– Number of Employees
– Can enrich using Dunn & Bradstreet etc
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
18. Sandler Sales says get to NO Fast
• Negative Scores:
– Email Unsubscribe
– Non Product page visits, ie: Career Page
– No Website visits for a long time
– Short Visits to key pages
– Won’t fill out forms
– Title is: Student, Consultant, etc
– Company Size too Small or Big
– Geography
– No Progression in Buy Cycle
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
19. Advanced
• Product Scoring
– Activity. Downloads, Page visits may suggest the
product for the Rep to suggest to the Prospect
• Account Scoring
– In Big deals in Big Corps there may be a committee
and it may make sense to total that score
• Validate & Change model based on evaluation
what Really Happened …!
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
20. Getting Started - Pardot
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
22. Get Others Involved:
Sales & Marketing & Talk with Customers
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
23. Regression Using Existing Customers
• Run an attribution report to figure out which
marketing efforts lead to conversions.
• Don't only look at the content that converts
leads to customers -- what content did people
view before they become a lead? Award a
higher number of points to people who
download content that's historically converted
people into customers. (HubSpot)
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
24. Manual Scoring Regression
• Manual Lead Scoring
• 1. Calculate the lead-to-customer conversion rate
of all of your leads.
• 2. Pick and choose different attributes customers
who you believe were higher quality leads.
• 3. Calculate the individual close rates of each of
those attributes.
• Note: So, figure out how many people become
qualified leads (and ultimately, customers) based
on the actions they take or who they are in relation
to your core customer. You'll use these close rates
to actually "score" them in the step below.phil@salesforcemaven.com Copyright Phil
Sallaway 2019
25. Manual Scoring Regression
• 4. Compare the close rates of each attribute with your
overall close rate, and assign point values accordingly.
• Look for the attributes with close rates that are
significantly higher than your overall close rate.
• Note: The actual point values will be a little arbitrary,
but try to be as consistent as possible. For example, if
your overall close rate is 1% and your "requested
demo" close rate is 20%, then the close rate of the
"requested demo" attribute is 20X your overall close
rate -- so you could, for example, award 20 points to
leads with those attributes.
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
26. Logistic Regression Lead Scoring
• The most mathematically sound method is one that
employs a data mining technique, such as logistic
regression.
• Logistic regression involves building a formula in Excel
that'll spit out the probability that a lead will close into
a customer. It's a holistic approach that takes into
account how all of the customer attributes -- like
industry, company size, and whether or not someone
requested a trial -- interact with one another.
• Logistic regression in Excel:
http://blog.excelmasterseries.com/2010/04/using-
logistic-regression-in-excel-to.html
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
30. Case Study
The Challenge
• Client Target large US Banks
– How to score leads??
– FDIC Insured Banks 4,909 in 2017 (Stastia.com)
– Number of Bank Employees 1.94 Million (Stastia.com)
– Top 10
• JPMorgan-Chase, Bank of America, Wells Fargo, Citi, US
Bank, Bank of NY Mellon, State Street, Capital One, TD Bank
• Lead Sources: Trade Shows, Data Mining, Trade
Associations, Web to Leads, Sales People’s
Contacts, Prospecting, Purchased Lists, Data.comphil@salesforcemaven.com Copyright Phil
Sallaway 2019
31. Case Study - Resolution
• Model
– Identified top 200 US Banks by Assets
– Bank Location was the one involved in Loan
Processing
– Titles of Leads at that location, Exec Sr VP
– Product targeted based on Trade Assoc
– Web to leads split between:
• Score-Grade / shift to Cust Svc for Small Co. Product
• Score-Grade / shift to Mega Banks Product Nurturingphil@salesforcemaven.com Copyright Phil
Sallaway 2019
32. Case Study - Resolution
• Titles (Regression Approach) Input from Sales
– Identify & use current customer exec titles to Up
Score
– Tie Location of Relevant Division Loan HQ & Up
Score or Down Score
– Down Score Vice Presidents in the wrong
geographic locations ie: Vp in a local branch or
other location
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
33. Case Study - Resolution
• Data Mining & Grooming expensive but good
data 90% good & targeted from the outset
• Created 200 SF Accounts then Used Dunn &
Bradstreet to enrich
• Next used CRM Fusion – Demand Tools to
Match Lead to Data
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
34. Case Study Results
• We had a model that successfully addressed
our markets and customer needs Successfully.
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
46. Basic Model in Trailhead
https://trailhead.salesforce.com/content/learn/modules/pardot-lead-scoring-and-grading/get-
started-with-lead-qualification
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
50. Regression Using Existing Customers
• Run an attribution report to figure out which
marketing efforts lead to conversions.
• Don't only look at the content that converts
leads to customers -- what content did people
view before they become a lead? Award a
higher number of points to people who
download content that's historically converted
people into customers. (HubSpot)
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
51. Manual Scoring Regression
• Manual Lead Scoring
• 1. Calculate the lead-to-customer conversion rate of all of your leads.
• Your lead-to-customer conversion rate is equal to the number of new customers you
acquire, divided by the number of leads you generate. Use this conversion rate as
your benchmark.
• 2. Pick and choose different attributes customers who you believe were higher
quality leads.
• Attributes could be customers who requested a free trial at some point, or customers
in the finance industry, or customers with 10-20 employees.
• There's a certain kind of art to choosing which attributes to include in your model.
You'll choose attributes based on those conversations you had with your sales team,
your analytics, and so on -- but overall, it's a judgment call.
• 3. Calculate the individual close rates of each of those attributes.
• Calculating the close rates of each type of action a person takes on your website -- or
the type of person taking that action -- is important because it dictates the
actions you'll take in response.
• So, figure out how many people become qualified leads (and ultimately, customers)
based on the actions they take or who they are in relation to your core customer.
You'll use these close rates to actually "score" them in the step below.
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
52. Manual Scoring Regression
• 4. Compare the close rates of each attribute with your
overall close rate, and assign point values accordingly.
• Look for the attributes with close rates that are significantly
higher than your overall close rate. Then, choose which
attributes you'll assign points to, and if so, how many
points. Base the point values of each attribute on the
magnitude of their individual close rates.
• The actual point values will be a little arbitrary, but try to be
as consistent as possible. For example, if your overall close
rate is 1% and your "requested demo" close rate is 20%,
then the close rate of the "requested demo" attribute is
20X your overall close rate -- so you could, for example,
award 20 points to leads with those attributes.
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
53. Logistic Regression Lead Scoring
• However, the most mathematically sound method is one
that employs a data mining technique, such as logistic
regression.
• Logistic regression involves building a formula in Excel
that'll spit out the probability that a lead will close into a
customer. It's a holistic approach that takes into account
how all of the customer attributes -- like industry, company
size, and whether or not someone requested a trial --
interact with one another.
• If you'd like to explore logistic regression in Excel, check out
this resource. In the meantime, the manual approach
above this section is a great way to get started.
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
54. Case Study – The Challenge
• Web To leads was mostly small companies
• Some Salespeople shared their data some
didn’t little info about Lead Quality
• Purchased lists only 80+% of e-mails good
• Data.com about 20% of e-mails good
• Data Mining & Grooming expensive but good
data 90% good
phil@salesforcemaven.com Copyright Phil
Sallaway 2019
55. Case Study – The Challenge
• Trade Shows / Trade Association membership
were somewhat Segment – Product Specific
• Titles were a mish mash
– Every Bank branch has several Vice Presidents
• Corp HQ locations not all that helpful
– Most are in New York City and are Corporate
holding Companies
phil@salesforcemaven.com Copyright Phil
Sallaway 2019