[Webinar] Predictive Marketing: The Science Behind Marketing
 

[Webinar] Predictive Marketing: The Science Behind Marketing

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To watch the entire webinar replay, please visit:
http://www.mintigo.com/predictive-marketing-the-science-behind-marketing/

Title: "Predictive Marketing: The Science Behind Marketing"

Description:

One of the hottest trends in marketing and lead generation is predictive marketing. But what does it mean and how does it really work? Can it be implemented by mere mortals in marketing? Or does it require an army of big data scientists and a black box model?

Implementing predictive data for decision making surrounds us today. The challenge is providing advanced analytics without the need for a team of programmers. Join Tal Segalov from Mintigo as he shows how to quickly build predictive models and how to visualize the results for B2B businesses.

In this webinar you will learn:

- Who is already using predictive marketing all around you
- How 20% of your leads give 80% of your business and we have the proof
- The most efficient way to share predictive scores for optimal engagement
- The importance of clean data for building predictive models and constructing visualizations


About The Speaker:
Tal Segalov, COO and Co-Founder at Mintigo

Tal brings more than 15 years of experience in software development. Prior to Mintigo, Tal was AVP Research and Development for modu, the modular mobile handset company. His previous experience includes developing complex, large scale data analysis systems. He holds a B.Sc. EE and a B.A in Physics from the Technion – Israel’s leading school of technology. He also holds an executive MBA from Tel Aviv University.

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[Webinar] Predictive Marketing: The Science Behind Marketing [Webinar] Predictive Marketing: The Science Behind Marketing Presentation Transcript

  • mintigo Predic've  Marke'ng:     The  Science  Behind  Marke'ng  
  • Host:   Tony  Yang   Director  of  Marke2ng  at  Min2go   Presenter:   Tal  Segalov   COO  &  Co-­‐Founder  at  Min2go  
  • Agenda   •  Why  do  Marketers  need  Science?   •  What  is  Predic2ve  Marke2ng  and  what  can  it   do  for  me?   •  How  to  build  a  Predic2ve  Marke2ng  machine   •  What  do  you  get  from  Predic2ve  Marke2ng?  
  • Netflix is commissioning original content because it knows what people want before they do. “There are 33 million different versions of Netflix” •  Goofy  Comedies   •  Cri2cally-­‐acclaimed  Movies   •  Because  you  watched  Curious  George   •  Foreign  Movies   •  …  
  • Source:  New  York  Times  
  • Predic2ve  Marke2ng   The  ability  to  discover,  target,  and   engage  the  customers  and  prospects   who  are  the  most  likely  to  buy  based   on  your  current  customer  aNributes   including  their  digital  ac2vity.  
  • A  Major  Problem  For  Marketers   You  are  constantly  running   campaigns,  but  not  seeing   desired  sales  results  at  end   of  the  pipeline.  
  • What  If  You  Could…   •  Discover  the  profile  of  your   ideal  customers?   •  Target  the  prospects  who  are   most  likely  to  become   buyers?   •  Respond  to  the  best   opportuni2es  faster?   •  Shorten  your  sales  cycles?   •  Spend  less  on  lead  gen   campaigns?    
  • Predic2ve  Marke2ng   Learn   Enrich  Score  
  • Predic2ve  Marke2ng   Name   Title   Email   MAP   SaaS?   Webinar?   Score   Tal  Segalov   COO   tal@min2go.com   Marketo   Yes   Yes   98   Mickey   Mouse   None  of  your   business   jdoe@gmail.com   0   John  Smith   VP  Marke2ng   js@qualiware.com   None   No   Yes   23   Bob  A   Director   Demand  Gen   boba@zen2st.net   Hubspot   Yes   No   78   Bill  Silver   HR  Specialist   bills@apitech.com   Eloqua   No   Yes   34  
  • Predic2ve  Marke2ng   Name   Title   Email   MAP   SaaS?   Webinar?   Score   Tal  Segalov   COO   tal@min2go.com   Marketo   Yes   Yes   98   Mickey   Mouse   None  of  your   business   jdoe@gmail.com   0   John  Smith   VP  Marke2ng   js@qualiware.com   None   No   Yes   23   Bob  A   Director   Demand  Gen   boba@zen2st.net   Hubspot   Yes   No   78   Bill  Silver   HR  Specialist   bills@apitech.com   Eloqua   No   Yes   34  
  • Predic2ve  Marke2ng   Name   Title   Email   MAP   SaaS?   Webinar?   Score   Tal  Segalov   COO   tal@min2go.com   Marketo   Yes   Yes   98   Mickey   Mouse   None  of  your   business   jdoe@gmail.com   0   John  Smith   VP  Marke2ng   js@qualiware.com   None   No   Yes   23   Bob  A   Director   Demand  Gen   boba@zen2st.net   Hubspot   Yes   No   78   Bill  Silver   HR  Specialist   bills@apitech.com   Eloqua   No   Yes   34  
  • Pareto  –  Your  Hero   Vilfredo  Pareto,  Italian   Economist  observed  that   80%  of  the  land  in  Italy   was  owned  by  20%  of  the   families…and  the  80/20   rule  was  born.  
  • Marke2ng  Science  Process  
  • 20%  of  the  Effort,  80%+  of  the  Impact   Never  Run  a  Bad  Campaign  Again!   20%  Effort   80%+  Impact   80%  of  $   20%  of  Leads  
  • Predictive Marketing Never  Run  a  Bad  Campaign  Again!  
  • Ingredients •  Fast sparse data store •  Company & people MI data •  Input identification & matching to DB •  Jobtitle analysis and clustering •  Input validation (field validity) •  Machine learning engine
  • MI Data •  Rich, accurate company & people data – Coverage vs. Accuracy dilemma – Granular MIs – specific attributes stronger •  Measuring the data quality: – Amount of data – number of “True” in DB – Data accuracy – Measuring data usage in actual models
  • Example of An Indicator - Salesforce User Hiring   Org-­‐chart   Website   Scripts   3rd  Party   Sites   •  Salesforce  2tle?   •  Descrip2on  seman2c   processing   •  Salesforce  admin  in   company?   •  Lead  forms  CRM   connec2on   •  Eco-­‐system  products   (Marketo,  Pardot,  …)   •  Dreamforce  men2on   •  Case  studies   •  PR,  news   ‘Salesforce  User’   Never  Run  a  Bad  Campaign  Again!  
  • Matching Algorithm •  Key element is matching input to DB data •  Identify input fields and map to internal fields •  Match companies and people to MI DB: – Use email, company, name, etc. – Measure match rates on valid input data – Measure match accuracy
  • Jobtitle Classification •  Jobtitles are one of the strongest indicators •  Classify titles by semantic methods: – Interpret jobtitle keywords – Standardization of titles (e.g. dir., Director are the same) – Distance metric between titles
  • Input Validation •  Validations on all available input fields •  Validations used for: – Cleansing customer DB – Modeling input •  Provide a “quality” metric for each lead – Valid input (name, domain, email, etc.) – Contact details validity
  • Machine Learning Model •  Modeling based on: – 100’s of “positive” leads – 10,000’s of “houselist” leads – 1000’s of attributes (MIs) •  Strong feature selection •  Pareto – 80%-20% performance goal
  • Mixing It Together Sparse  Data  Store   MI  Data   Houselist   Input   Connector   Matching  to   DB   iden22es   Input   Valida2on   Machine   learning   model   Predic2on  &   Enrichment   Job2tle   Classifier   Data   Partners’   Leads   New  leads   in  DB  
  • Predictive Marketing Never  Run  a  Bad  Campaign  Again!  
  • The  Marke2ng  Machine:   Knobs  and  Levers   More  fuel  for  sales  
  • The  Marke2ng  Machine:   Knobs  and  Levers   More  fuel  for  sales  Shortcut  through  my  funnel  
  • The  Marke2ng  Machine:   Knobs  and  Levers   More  fuel  for  sales  Shortcut  through  my  funnel   Cross  sell  /  re-­‐sort  my  lists  
  • The  Marke2ng  Machine:   Knobs  and  Levers   More  fuel  for  sales  Shortcut  through  my  funnel   Cross  sell  /  re-­‐sort  my  lists   Cleanse  my  list   ("brush  teeth")  
  • The  Marke2ng  Funnel  
  • The  Marke2ng  Funnel   Cleanse  
  • The  Marke2ng  Funnel   10%  Conversion   2%  Conversion   Cleanse  
  • The  Marke2ng  Funnel   Track  1   Track  2   Track  3   10%  Conversion   2%  Conversion   Cleanse  
  • How  to  Measure  Success?   1.  Qualifica2on:   –  Build  model  based  on  Q4/2013  data   •  Posi2ves  –  Opportuni2es  /  Closed  won   •  Houselist  –  Marke2ng  list   –  Run  predic2on  on  Q1/2014  data   •  Predict  on  Q1/14  Marke2ng  list   –  Test  results:   •  What  percentage  of  posi2ves  are  in  top  scoring  20%?   •  How  many  leads  were  appended  with  relevant  info?   •  What  is  the  amount  of  bad  leads  cleaned  from  the  list?  
  • Predic2ve  Model  –  Results   0%   5%   10%   15%   20%   25%   0%   10%   20%   30%   40%   50%   60%   70%   80%   90%   100%   0%   5%   10%   15%   20%   25%   30%   35%   40%   45%   recall   accuracy   %  Leads   %  of  Posi'ves   Accuracy   To  Sales   Nurture  Tracks   Bad  Fit  
  • Predic2on  -­‐  Results   0%   5%   10%   15%   20%   25%   30%   35%   0   0.1   0.2   0.3   0.4   0.5   0.6   0.7   0.8   0.9   1   Projected  %  Posi'ves   Min'go  Score   Conversion  Rate  by  Score   accuracy  
  • How  to  Measure  Success?   2.  On-­‐going  Usage:   – Enriched  data  based  campaigns   •  2-­‐4x  liu  in  response  rates   – MQL  genera2on  rate  –  top  scores  vs.  the  rest   •  Typical  –  3-­‐5x  liu   – Bullet  train  through  the  funnel  -­‐  reduce  lead  decay   •  Typical  –  15-­‐20%  decay  per  quarter   •  Shortening  the  funnel  by  a  quarter  wins  back  this  decay  
  • With  Predic2ve  Marke2ng  And   Min2go,  DocuSign  Discovered…   •  23.8%  engagement  on  acquired  targets   10X  improvement   •  More  than  35  sales  opportuni2es/live   deals   •  Genera2ng  over  $1M  TCV     •  Higher  %  of  opps  leading  to  Closed  Won   •  Faster  
  • Conclusions   •  Pareto  is  your  Hero!   – 20%  of  your  efforts  generate  80%  of  results   •  Elements  of  successful  predic2ve  marke2ng:   – Good  data  collec2on   – Appending  data  to  your  DB   – Priori2zing  efforts  based  on  scores   – Quick  valida2on  and  qualifica2on   – Apply  across  all  Marke2ng  ac2vi2es  
  • Ques'ons?  
  • Next  Steps   Get  our  free  white  paper  on:     “Marketer’s  Guide  To   Priori'zing  Leads”  
  • Try  Out  Min2go’s  Free  Tool  
  • mintigo Thank  You