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How to Create Cognitive Nurture Programs
This document outlines strategies for creating cognitive nurture programs, emphasizing the need for effective lead segmentation and management to improve conversion rates. It presents various challenges in marketing, such as data quality and unclear definitions of marketing qualified leads, while highlighting the importance of personalized communication in nurturing leads. The results demonstrate significant improvements in engagement and revenue through targeted marketing efforts and the use of predictive modeling.
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How to Create Cognitive Nurture Programs
- 1. How to CreateCognitive Nurture Programs JULY 14TH 10AM PST / 1PM EST
- 2. ● Accomplished StartupMarketer ● Built & Led Global Marketing Ops at Nitro ● Musician-born Marketer ● Podcast & Paleo Aficionado ● 2013 Oracle-Eloqua Markie Winner @szinsmeister Presenters SEAN ZINSMEISTER VP of Product Marketing Infer ● 4+ years at Social Tables ● 100+ person, VC backed startup in D.C. ● Geography buff and serious FIFA player RAY MILLER Sr Marketing Operations Manager Social Tables @touchofray sean@infer.com stackandflow.io ray@socialtables.com
- 3.
- 4. “In 2012, wegenerated 2.5 billion gigabytes of data every single day.” IBM Study
- 5. “About 75% ofthe data we generate is unstructured – in other words, random, and difficult to index.” IBM Study
- 6. “Only 9% ofB2B Marketers say they are getting current customer information, social insights, market shifts, and customer development in real-time.” CMO Council Study
- 8. Too Many Leads Too Few Leads TooFew Leads SPAM Data Storage Missing Data Bad Data Lead Quality Unclear Segmentation Poor Conversion Rates Missing Opportunities
- 9. Too Many Prospects Whichare the best fit to buy? Too Few Prospects Are there more lookalikes? Missing Opportunities Which prospects are most engaged? Unclear Segmentation Who is my ideal customer profile? Go to Market Challenges
- 10. System of Intelligence SYSTEMOF INTELLIGENCE Record Intelligence Engagement
- 11. Natural Language Processing Machine Learning Fuzzy Matching Title Normalization External Data Append Predictive Modeling SPAM Analysis Sales Performance Management Marketing Efficiency Data-Driven Workflows TheKey Components of AI CRM Marketing Automation Product Usage Engagement Data Web Crawl API Google Analytics Proprietary Data Science
- 12. Send to Sales DoNot Work AI is the Line Judge
- 13. Primary Inbound Nurture+ Fit Scoring
- 14. A-LeadsWeb Marketing Automation Sales Development A-Leads B-Leads C-LeadsD-Leads “Contact Me” “Problem Statement” “Contact Me” “Problem Statement” “Customer Credibility” “Contact Me” “Problem Statement” “Customer Credibility” “Contact Me” “Product Education” Recycle Program NURTURE PROGRAMS Primary Inbound Nurture + Fit Scoring
- 15.
- 16. Primary Inbound Nurture+ Fit Scoring A-LeadsWeb Marketing Automation Sales Development A-Leads B-Leads C-Leads D-Leads
- 17. Primary Inbound Nurture+ Fit Scoring A-Leads “Contact Me” Recycle Program
- 18. Primary Inbound Nurture+ Fit Scoring B-Leads “Problem Statement” “Contact Me” Recycle Program
- 19. Primary Inbound Nurture+ Fit Scoring C-Leads “Problem Statement” “Customer Credibility” “Contact Me” Recycle Program
- 20. Primary Inbound Nurture+ Fit Scoring D-Leads “Problem Statement” “Customer Credibility” “Contact Me” “Product Education” Recycle Program NURTURE PROGRAMS
- 21. Primary Inbound Nurture+ Fit Scoring A-Leads B-Leads C-Leads D-Leads “Contact Me” “Problem Statement” “Contact Me” “Problem Statement” “Customer Credibility” “Contact Me” “Problem Statement” “Customer Credibility” “Contact Me” “Product Education” Recycle Program NURTURE PROGRAMS
- 22. Primary Inbound Nurture+ Fit Scoring A-LeadsWeb Marketing Automation Sales Development A-Leads B-Leads C-Leads D-Leads “Contact Me” “Problem Statement” “Contact Me” “Problem Statement” “Customer Credibility” “Contact Me” “Problem Statement” “Customer Credibility” “Contact Me” “Product Education” Recycle Program NURTURE PROGRAMS
- 23. DCB Personalized Who receives? A ACB Trial Ends Standardized Onboarding Who receives? Personalized Who receives? Personalized Customer Journey Trial Ends
- 24. At a Glance +4x engagementfrom top leads +4x more quality leads identified through marketing channels reached impressive conversion rates CUSTOMER SUCCESS SNAPSHOT 5-10%
- 25. “The next bigstep we’re really excited about is getting smarter with our advertising through predictive. Infer lets us look at acquisition channels based on who's coming in, so we can tell exactly which ones are sending us the most good-fit and poor-fit leads. We can now look at more meaningful metrics than volume alone, and have already identified channels that deliver 50% Infer A-Leads versus our standard ratio, which is eight bad leads for every one good lead.” Measuring Marketing Impact NICHOLAS HEIM Director of Inbound Marketing Ads | Campaigns by Lead Grade Campaign 1 A C D A B C D Campaign 2 A C D A B C D A B C D Campaign 4 A B C D A B C D Campaign 3 A B C D
- 26.
- 27.
- 28. Window 1 Window2 Window 3 Window 4 Fit Behavior Time 0 A 3 A 3 A 2 A 1 Fit vs Behavior
- 29.
- 30. A-LeadsWeb Marketing Automation Sales Development Marketing Nurture Programs Soft OfferHard Offer Primary Inbound Nurture + Behavioral Model
- 31. Webinar Registration /Attendee Content Download Industry Report Video Engagement ROI Calculator Web / Email Engagement Soft Offer - Soft Conversion Soft Offer Primary Inbound Nurture + Behavioral Model
- 32. Request a Quote ContactMe Request a Demo Free Trial Hard Offer - Hard Conversion Hard Offer Primary Inbound Nurture + Behavioral Model
- 33. Primary Inbound Nurture+ Behavioral Model A-LeadsWeb Marketing Automation Sales Development Marketing Nurture Programs Soft Offer Hard Offer
- 34. Outbound / ABMNurture
- 35. Zoom Info Data.com Inside View Net-New Accounts Net-New Contacts Good Prospects ToutApp Outreach AdRoll DemandBase Google Twitter PFL MailLift Facebook Evenly Distributed Net-New GoodLeads SEQUENCES/ CADENCES CUSTOM AUDIENCES Sales Queue DIRECT MAIL Outbound / ABM Nurture
- 36. Zoom Info Data.com Inside View Net-New Accounts Net-New Contacts Good Prospects ABM Air Cover Account-Based SalesDevelopment Conversions Outbound / ABM Nurture
- 37.
- 38. CMO Welcome “Problem Statement” Exec Track02 Exec Track 03 MKT OPs Welcome “Problem Statement” MOPS Track 02 MOPS Track 03 Sales Ops Welcome “Problem Statement” SOPS Track 02 SOPS Track 03 MKT Analyst Welcome “Problem Statement” Analyst Product Education Analyst Customer Credibility Analyst “Contact Me” Recycle Program Outbound / ABM Nurture
- 39. ABM Air CoverGood Prospects DemandBase Google Twitter PFL MailLift Facebook Evenly Distributed Net-New Good Leads CUSTOM AUDIENCES Sales Queue DIRECT MAIL AdRoll Outbound / ABM Nurture
- 40. Outbound / ABMNurture Zoom Info Data.com Inside View Net-New Accounts Net-New Contacts Good Prospects ToutApp Outreach AdRoll DemandBase Google Twitter PFL MailLift Facebook Evenly Distributed Net-New Good Leads SEQUENCES/ CADENCES CUSTOM AUDIENCES Sales Queue DIRECT MAIL
- 41. 100+ employees 4,000+customersEstablished 2011
- 42. 100+ million participants 1+ million eventscreated 1.5+ billion ft2 of event space drawn
- 45.
- 46. Challenge: Outline ● Too manyleads with an unclear definition of marketing qualified lead ● Certain channels were lopsided and produced varying levels of conversion ● New channels opening up and no way to prioritize efforts
- 47. Full 14 day freetrial Producing 3000 submissions /month with paid ads Small sales team > 10 dev reps following up 2% conversion rate to customer Steady current channels, diminishing returns in CPL #1 lead channel 80% of our revenue from less than 30% leads, but all had equal focus Inconsistent activity into leads
- 48. Email nurture program A-LeadsWeb Marketing Automation Sales Development A-LeadsB-Leads C-Leads D-Leads “Customer type specific problem” Customer type specific problem” Customer type case study Ecommerce offer Ecommerce offer Monthly Industry/product program NURTURE PROGRAMS Demo - customer specific feature Demo - customer specific feature
- 49. Learnings ● Layering brandedemails and personalized rep emails improves success ● This nurture program decreased conversion time from 62 days to opp to 27 days to opp on average ● Segmenting leads from the start drastically improved sales activity ● Sales had a more targeted offer to follow up on with the offer cadence
- 50. Nurturing is notjust emails A / B LeadsWeb Pardot Sales Marketing Nurture Programs Retargeting ads for “sign-up for a demo of Social Tables” A / B / C Leads Retargeting ads for a customer type specific ebook Wait 5 days
- 51. AT A GLANCE +7% boostedoverall revenue +10% increased average deal size +$500k/mo grew opportunity pipeline +35% increased trial signups +25% Expanded MQL volume
- 52.
Editor's Notes
- #4 Data exhaust refers to the trail of data left by the activities of an Internet user during his/her online activity. An enormous amount of often raw data are created. These data (which can take the form of cookies, temporary files, logfiles etc.) can help to improve the online experience (for example through customized content).
- #5 Cloud computing made it easier to get the data But there’s too much of it All in disparate systems, nothing is centralized making it hard to make actionable
- #6 Cloud computing made it easier to get the data But there’s too much of it All in disparate systems, nothing is centralized making it hard to make actionable
- #7 Cloud computing made it easier to get the data But there’s too much of it All in disparate systems, nothing is centralized making it hard to make actionable
- #8 Exabyte = one billion gigabytes (that’s a lot of data)
- #9 Today’s go-to-market professionals have a lot of problems to solve. And as the data increases for sales and marketing, those problems are growing and getting more complex.
- #13 Nick to speak about MQL definition -- A, B, or C scored leads only.
- #14 So let’s jump into a demo.
- #27 So let’s jump into a demo.
- #35 So let’s jump into a demo.