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Using Account Level Buying Signals & Predictive Analytics To Score Leads
The document discusses how predictive analytics can be used to improve marketing and sales performance. It describes how leading companies are using account-level data and predictive attributes to more accurately score leads and identify those most likely to convert. Specific examples are provided of attributes that can increase conversion likelihood for certain industries. The presentation advocates that marketing organizations embrace these predictive techniques to target prospects selectively and find their unique "trigger" to improve results.
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Using Account Level Buying Signals & Predictive Analytics To Score Leads
- 1. PRESENTED BY! #C2C14! Using Account-LevelBuying Signals and Predictive Analytics to Score Leads! Brian Kardon! CMO! Lattice Engines!
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- 3. #C2C14! Stock price: from$13 to $35!Forrester stock went from $13 to $34 per share
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- 5. #C2C14! About Brian Kardon…! ! CMO ! CMO! CMO ! CMO ! Partner ! ! @bkardon! 5
- 6. #C2C14! From Art to Science … Tradi<onal Marke<ng “Modern” Marke<ng” “Predic<ve” Marke<ng and Selling
- 7. #C2C14! Doing all theright things …! § Marke<ng automa<on § Sales force automa<on § Lead nurturing § Lead scoring § Personas § SLAs in place § Great marke<ng team § Awesome Sales team 94% of your Marke<ng-‐Qualified Leads (MQLs) will never close
- 8. #C2C14! What’s wrong here?! § 94% of all Marketing Qualified Leads will never close1! ! § 52% of sales reps in US did not make quota last year2! ! § Sales reps spend 68% of their time on administration and preparation, not speaking with customers3! ______________________________________ Source: 1 Sirius Decisions; 2 CSO Insights; 3 IDC
- 9. What is thepattern?! Then! Radio! Cable TV! Taxi! Bookstore! Hotels! Thermostat! Now! Pandora! Netflix! Uber! Amazon! Airbnb! Nest!
- 10. #C2C14! § Purchases! § Itemsyou have added to cart, but abandoned! § “Dwell” times! § Product ratings ! § Address! § What your neighbors buy! § Birthday! § Sizes: yours + family + friends! § If you are cheating on your partner!!
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- 15. #C2C14! Source! Selected A,ributes! Marke<ng Automa<on! Contact name, <tle, company, open rates, unsubscribes, web visits, pages visited, lead score, video views, downloads" CRM System! Company, contact informa<on, win/loss, deal value" Product Usage Logs! Features used, logins, session length, collabora<on" Purchase History! Products purchased, prices paid, discounts, contract terms" Customer Support History! Complains, resolu<ons" Public Websites! Job pos<ngs, grants, li<ga<on, patents, contracts, loca<ons. growth" Company Websites! Language(s), products, shopping cart, execu<ve team profiles" Social Websites! Company and personal profiles, likes, comments, updates, friends/connec<ons/followers, usage" Media! News ar<cles and stories, product launches, announcements, press releases, li<ga<on" Private Databases! Credit ra<ngs, financial history, construc<on permits/starts, deployed technologies"
- 16. #C2C14! 16 Proprietary & Confiden<al Algorithmic trading has replaced human trading.
- 17. #C2C14! 17 Proprietary & Confiden<al Who is the Jeopardy player in the middle?
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- 19. #C2C14! What is apredictive attribute?!
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- 21. #C2C14! Finding the Trigger…! Category Predic5ve Trigger Likelihood to Convert from MQL to SQL Foreign Exchange Services New office opened overseas 5x Switches & Routers New lease is signed 3x Marke5ng SoFware Spike in social media ac<vity 3x Financial SoFware New CFO hired who previously bought from you 8x
- 22. 0% 5% 10% 15% 20% 25% 30% 35% 40% 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Purchase Probability Accounts Average 20% 40% 60% 80% 100% Predic5ve Targe5ng 0% 22" Business Banking Example
- 23. 0% 5% 10% 15% 20% 25% 30% 35% 40% 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Purchase Probability Accounts Predicted Average Predic5ve Targe5ng Accounts Likely to Have Specific Financial Service Need in Next 90 Days 20% 40% 60% 80% 100% 0% Highest Probability Segment 23"
- 24. 0% 5% 10% 15% 20% 25% 30% 35% 40% 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Purchase Probability Accounts Predicted Average Predic5ve Targe5ng 20% 40% 60% 80% 100% 0% Companies with the following condi5ons… " Balance of Trade Change Business has experienced >100% increase in balance of trade with Canada, China or Mexico in the past 30 days " Recent Hire of Finance Execu5ve Business has hired a Chief Financial Officer or senior controller within the past ninety (90) days " >30% Increase in Search Adver5sing in the past 30 days " Recent Expansion in Hiring & Recrui5ng 24"
- 25. 0% 5% 10% 15% 20% 25% 30% 35% 40% 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Purchase Probability Accounts Predicted Average Different Contact Strategy by Segment 20% 40% 60% 80% 100% 0% Engage via Front-‐ Line Bankers Mid-‐stage Nurture 25"
- 26. #C2C14! Where is MarketingAutomation? Cumula5ve Adop5on Time A B C D E F 50-70% penetration Source: Sirius Decisions Where is marke5ng automa5on?
- 27. #C2C14! Where is PredictiveMarketing and Selling? Cumula5ve Adop5on Time A B C D E F Source: Lattice Engines Where is predic5ve lead scoring?
- 28. #C2C14! Predictive Analytics forMarketing! § The era of big data and predictive analytics is NOW! ! § There is more information to discover about a prospect than ever before – at the account level! ! § Leading marketing organizations are embracing predictive analytics to dramatically improve performance! ! § Marketing can do more – from lead scoring to predictive lead scoring! ! § Find your trigger … target selectively and quickly!
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- 30. #C2C14! Thank you!! Brian Kardon! CMO,Lattice Engines! ! bkardon@lattice-engines.com! ! @bkardon!