Lead Scoring in Salesforce.com Part 2: Advanced Lead Scoring - Integrating Online Behavior

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04/08/23 Manticore Technology, Inc. – Copyright 2009. All rights reserved.

Lead Scoring in Salesforce.comPart Two – Advanced Lead Scoring

February 26, 2009

Manticore Technology, Inc. Confidential – Copyright 2009. All rights reserved.

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Today’s Webinar

Agenda Adding Online Behavior to a Model Scoring Depreciation Multi-Model Lead Scoring Case Study: Intellitactics

Michaela Dempsey – Director of Marketing, Intellitactics Q&A

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Introductions

Christopher DoranVice President, Marketing, Manticore Technology

Member of Founding Manticore Team (2003) Responsible for company’s worldwide marketing

efforts including PR, demand generation and strategic partnerships (including salesforce.com)

Longtime thought leader in the area of demand generation and marketing automation

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Scoring Components

Similarity of Lead to Target Market + Behavior lead has

exhibited to indicate likelihood of purchase

Build in Salesforce.com-OR-

Demand Generation Solution

Need Demand Generation Solution

to Accomplish

“Fit” “Interest”

Last

Wee

kT

his W

eek

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Manticore Technology

Leading provider of demand generation solutions Ideal combination of power and

ease-of-use Unmatched integration with

salesforce.com Industry leader - Founded in

2001

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Build “Fit” Score

Criteria Data Source

Max Points High Qual Mod Qual Low Qual Negative

Qual

Industry Web Form 20

Automotive 20 Auto Parts 5

CPG 20

Size Web Form 10

75k-100k emp 10 40k-50k 5

50k-75k emp 10 30k-40k 5

Region Web Form 10 North America 10

Infrastruct.Web

Form/ Sales

20Salesforce.com 20 Siebel -20

Sugar -20

Example: Expanded Model

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Problem with “Fit” Scoring

QUESTION:

If you purchase a list of leads with perfect score are they ready for sales?

ANSWER:

Obviously not. They may have no idea who you are or what your product or services are.

We remedy by adding a scoring component called “interest” or “behavior”.

This is based on behavior a lead has taken to become a qualified lead.

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STEP I - Build Activity List

List out activities that give indication of a lead’s level of interest/buying propensity

Contact forms – demo, trial, sales contact Download Collateral – datasheets, case studies, whitepapers Webinar attendance Organic search terms used PPC words clicked Number of pages viewed on website Frequency of visit to website Think negative behaviors as well!

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STEP II – Bucket By Indicator

Create three to four buckets of actions based on what you think would be an indicator of level of interest:

High Interest Moderate Interest

These buckets should consider each action independent of other actions. Total “Interest” score will consider aggregate actions.

Low Value Negative Value

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Sample Buckets

High Value Sales contact form Demo/trial request

Moderate Value High value PPC or

Organic search term Whitepaper/Case Study

Low Value Registered for webinar –

didn’t attend Low value PPC word Datasheet

Negative Value Excessive Career page

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STEP III – Identify SQL Threshold

Identify tentative score for “Interest” for lead to become sales qualified lead (SQL) For example – 15 points

Think of this value independent yet relative to “fit” score Can’t have max “fit” as 1,000 and “interest” as 10

KEY POINT: Sales and marketing MUST agree on Score and threshold to become sales qualified lead (SQL)

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STEP IV – Assign Value

Take a first swag assigning “interest” score to each bucket High Value = 20 Mod Value = 10 Low Value = 5 Negative Value = -10

KEY POINT: Point Values don’t matter! – no difference between 10 point max score to 1,000 max score for Interest – what matters is their relativity to one

another – and relationship to “Fit” score.

Proposed SQL “Interest” Score Threshold - 15

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STEP V – Test, Iterate, Test

Run through scenarios If a lead takes two moderate actions each worth 5 points (10

total) is this a sales qualified lead? If so, 10 should be threshold If not change threshold OR score values

High Value = 22

Mod Value = 9

Low Value = 2

Negative Value = -

5

Change Points

SQL = 15 High Value = 20

Mod Value = 10

Low Value = 5

Negative Value = -

10

Change Threshold

SQL = 18

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STEP V – Test, Iterate, Test

No lead scoring model is perfect – accept that Re-visit model as necessary (at least quarterly) Many factors can impact need to modify model:

Change in online actions New PPC words New collateral, etc

Macro Economic Climate Current Pipeline Marketing Mix Organizational Changes

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Adding Score Depreciation

SCENARIO: If a lead downloaded a whitepaper one year ago, what

should be the interest score for that lead today?

ANSWER: 0 To effectively score interest, you must add depreciation

(decay) element to model This reduces the interest score by a certain percentage

over time to correspond with decreasing level of interest

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Depreciation Table

Period of Time % Depreciation of Interest Score for the

period of time

Depreciation should be running against all your leads and contacts to ensure recency of interest is incorporated

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Common Salesforce Sync Mistake

KEY POINT: Relativity of “Interest” and “Fit” Score to one another is critical – deliver it to your sales team!

EXAMPLE:Salesforce.com Field:

QUESTION:What insight does sales have tothe lead’s “fit” and “interest”?

ANSWER:

NONE!

To be effective, sync, “Fit”, “Interest”, & Total Score into individual fields so that sales has insight into how the components create the score

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Scenario

Your company has ten different products in your portfolio Targets different verticals and business sizes Four Different sales groups New Lead enters your system “Joe Hotlead” Which sales group gets the lead? What product is Joe “Hot” for? How does your sales team know what product to sell Joe”?

You don’t know.

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Multi-Model Lead Scoring

SOLUTION: Build Lead Scoring Models for various products in your portfolio

Match inbound leads with their “Fit” with each target market and their “Interest” in each individual product

Six different lead scoring models

match leads with target markets

Create modelto match customers with cross-sell/up-sell opportunities

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Case Study: Intellitactics

Michaela DempseyDirector of Marketing, Intellitactics

Responsible for marketing activities including lead generation, web presence, customer relations programs, marcom and sales support activities.

Prior to Intellitactics, Michaela was the Director of Demand Generation and Customer Advocate at EMC SMARTS.

Michaela’s former positions include Senior Marketing Executive at RiverSoft, Product Marketing Manager at CrowdBurst, Director of Multi-Media at SDA.

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Advanced Lead ScoringImplement, Analyze & Improve!

Michaela DempseyDirector of Marketing, Intellitactics

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Advanced Lead Scoring Overview

About Intellitactics

Why Lead Nurturing

Why Lead Scoring

How to Create a Framework

Sample of Our Framework

What to Look for - Analyzing Data

Refine for Improvement

Results

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Intellitactics, headquartered in Reston, Virginia is a global B2B information security management technology company

Intellitactics’ portfolio of products enable enterprise security managers to effectively and efficiently protect information assets from malicious insiders, fraud, data loss and criminal hackers

3000 companies around the world benefit from Intellitactics products and services either directly or through managed security service providers

About Intellitactics

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Why Lead Nurturing

Thousands of suspects in DB that needed incubation Analytic visibility into suspects patterns/trends Ability to “control” virtual behavior and influence

Allocation of resources Provides marketing greater reach with existing budget/headcount

Align marketing to sales Team is “Riding the Same Bus” Build a repeatable process

Measurable return on investment for marketing dollars Critical knowledge on programs: working / not working Market interest knowledge

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Why Lead Scoring

Suspects are entering the DB from multiple sources daily Suspects are self qualifying their companies for solutions

without human interaction or very minimal interaction Impossible to manually detect the activity level of suspects

and prioritize follow up

With Lead Scoring your organization can: Actively monitor suspects digital body language and

direct them through a controlled nurture process Receive immediate notification when a suspect

reaches interest score Convert uncovered suspect into a qualified lead

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How to Create a Framework

Critical Step 1: sales and marketing team to define key actions, trigger points and score values

Critical Step 2: clean & segment DB before starting process

Items to score Personal or demographic details on suspect Source of suspect Behavioral actions

Items to negatively score Visiting career, management, ect. page No activity in x time Source of subject

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Sample of Our Framework

Suspect Personal/Demographic Data Title Location Etc

Behavioral Data (positive and negative) Registrations Organic and Paid Search Website visits over time Multiple page visits Targeted page visits, targeted PDF

visits Etc

New Suspects

Qualified LeadQualified LeadPipeline Pipeline ProspectProspect

Lead Scoring Lead Scoring ProspectsProspects

Customer Data Behavioral Data (positive and negative)

Targeted/multiple page visits Requested PDF’s Registrations Etc

New CustomersNew Customers& Up Sell & Up Sell

CustomersCustomers

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What to Look For – Analyzing Data

Repeatable overtime – “the Usual Suspects”

You can only improve what you measure

Test groups are critical

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Refine for Improvement

You are now ready for layering in different rule sets Sets for particular verticals Sets for particular products Sets for stages in the sales cycle

One size does not fit all You will always be learning and refiningMarketing Sherpa Year End Survey: Jan 2009

39% of Marketers feel that they are underutilizing their automation system 29% of Marketers say utilization is only fair

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Results

171% increase of sales opportunities accepted into pipeline

Telemarketing follow-up calls 200% higher connect rate due to educated prospects

Shortened sales cycle by 30% due to added efficiency of a coordinated buyers journey

Qualifying about 60% of marketing prospects

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Thank You

To learn more about Intellitactics’ implementation

or other companies having success with Manticore contact: [email protected]

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Q&A

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Contact Information

Christopher DoranVice President, Marketing, Manticore Technologychristopher.doran@manticoretechnology.comwww.manticoretechnology.com

Michaela DempseyDirector of Marketing, [email protected]

Lead Scoring in Salesforce.com Part 2: Advanced Lead Scoring - Integrating Online Behavior

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