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Lead Scoring: How a Stanford Engineer and MIT MBA Approaches It - Ilya Mirman - Onshape - Growth Camp June 2016

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Lead Scoring: How a Stanford Engineer and MIT MBA Approaches It - Ilya Mirman - Onshape - Growth Camp June 2016

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Lead Scoring: How a Stanford Engineer and MIT MBA Approaches It - Ilya Mirman - Onshape - Growth Camp June 2016

  1. 1. Lead Scoring: How a Stanford Engineer and MIT MBA Approaches it Ilya Mirman VP of Marketing, Onshape
  2. 2. (Lead Scoring Tips & Tricks) Ilya Mirman VP of Marketing, Onshape
  3. 3. 1. Why score leads? 2. Multi-factor scoring 3. Implementation 4. Assessing effectiveness Agenda
  4. 4. Why score leads? > Some leads are much better than others > Sales (probably) cannot (and should not) work every lead > Marketing should develop and identify the best leads for Sales to follow up on
  5. 5. Single factor scoring > Type of lead source (or another factor) > Prioritize leads > Identify cut-off (Hopefully, we’re all doing at least this)
  6. 6. Benefits of Using Multiple Factors
  7. 7. > Lots of “signals” regarding lead quality: ● Lead source ● Activity on site ● SaaS app’s usage metrics ● Demographics (user, company) ● Community participation ● Etc. > Taken together, a better way to assess lead quality Benefits of multiple factors
  8. 8. Example: Activity on site
  9. 9. Example: SaaS app usage
  10. 10. Example: Demographics
  11. 11. Example: Community Participation
  12. 12. Implementation
  13. 13. > Outsource to a 3rd party “black box” grading service ● Some good options out there (but I wouldn’t start here) > Use marketing automation software to define scoring criteria ● Requires trial/error for tuning > Develop a mathematical expression that can be easily implemented in CRM tools ● The focus of this talk Implementation options: Pros/Cons
  14. 14. > Could be as simple as a single factor ● Though ideally, find two or more > Need ~100-1,000 leads and 10-50 won deals ● If too early to have enough won deals, could use “MQL” or some other level of qualification (e.g., “Opportunity”) > Start looking for factors EARLY ● Test multiple hypotheses ● Make sure your systems support collecting the data (potentially, record value at time of purchase) How early? How sophisticated?
  15. 15. > Multiple options (a blend of science and art) > I like linear regression ● Linear combination of factors ● Some simplifying assumptions (e.g., factors are independent) ● May not need ALL factors (in fact, might not be able to use all) > Goal: figure out each factor’s weight to maximizes prediction of whether lead will BUY What Type of Model?
  16. 16. Goal: figure out each factor’s weight
  17. 17. Putting it to practice - with Excel
  18. 18. Source Data
  19. 19. Step 1: Find factors that correlate w/purchase
  20. 20. Step 1: Find factors that correlate w/purchase
  21. 21. Step 2: Linear Regression to Establish
  22. 22. Regression Result Statistical measure of how close the data are to the fitted regression line t-stat: Measure of the variable’s relevance. Examples: ● Above 2 (or less than -2) ⇒ 95% confidence that it’s relevant ● Above 3.5 (or less than -3.5) ⇒ 99.96% confidence that it’s relevant May need to iterate: ● Are all variables significant? ● Does the sign of the coefficient make sense?
  23. 23. Predicted vs. Actual What the regression model predicts Difference between “Predicted” and “Actual” Actual (add together “Predicted” and “Residuals”)
  24. 24. Predicted vs. Actual Let’s create a column for “Cumulative Deals”
  25. 25. Now, let’s SORT by lead grade... > This data set: ● Over 30,000 leads ● Less than 600 deals (2%) > Note the high hit rate of deals for the high score leads (over 50%)
  26. 26. Results > Top 10% of leads drive 70% of deals > Top 20% of leads drive 85% of deals
  27. 27. Evaluating & Improving Your Models
  28. 28. Comparing Multiple Models
  29. 29. Pop Quiz: Which Model is Better?
  30. 30. 1. Identify parameters of interest 2. Collect data on LEADS and DEALS 3. Identify factors that correlate w/purchase 4. Create Excel spreadsheet 5. Run linear regression 6. Iterate on which parameters to include in model 7. Tune model to add/remove factors 8. Evaluate quality of model (Rinse & repeat, at the right time) Summary
  31. 31. imirman [at] onshape.com (Thanks!) QUESTIONS?

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