Pardot Lead Scoring Feb 2019
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How to Score Leads in Pardot, what value does each Leads Activity have. How to get started and build a Lead Scoring Model for Pardot - Salesforce
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Pardot Lead Scoring Feb 2019
- 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
- 3. Marketing & Sales View Leads Differently 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
- 12. Gathering Info: Explicit Factors phil@salesforcemaven.com Copyright Phil Sallaway 2019
- 13. Gathering Info: Explicit Factors https://www.marketo.com/webinars/predictive-lead-scoring-how-to-turn-data-into-revenue/ phil@salesforcemaven.com Copyright Phil Sallaway 2019
- 14. Scoring Leads phil@salesforcemaven.com Copyright Phil Sallaway 2019
- 15. Grading Leads 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
- 28. Pardot (Compare To our Poll) 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
- 35. The End phil@salesforcemaven.com Copyright Phil Sallaway 2019
- 36. Lead Grading phil@salesforcemaven.com Copyright Phil Sallaway 2019
- 37. Marketo Lead Scoring Worksheet phil@salesforcemaven.com Copyright Phil Sallaway 2019
- 38. Lead Scoring - Pardot 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
- 49. Scoring & Grading https://www.pardot.com/blog/automatically-score-grade-leads-infographic/ 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