Lead Scoring: How a Stanford Engineer and MIT MBA Approaches It - Ilya Mirman - Onshape - Growth...
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Transcript of Lead Scoring: How a Stanford Engineer and MIT MBA Approaches It - Ilya Mirman - Onshape - Growth...
Lead Scoring:How a Stanford Engineer
and MIT MBA Approaches itIlya Mirman
VP of Marketing, Onshape
(Lead Scoring Tips & Tricks)Ilya Mirman
VP of Marketing, Onshape
1. Why score leads?2. Multi-factor scoring3. Implementation4. Assessing effectiveness
Agenda
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
Single factor scoring> Type of lead source
(or another factor)> Prioritize leads> Identify cut-off
(Hopefully, we’re all doing at least this)
Benefits of Using Multiple Factors
> 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
Example: Activity on site
Example: SaaS app usage
Example: Demographics
Example: Community Participation
Implementation
> 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
> 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?
> 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?
Goal: figure out each factor’s weight
Putting it to practice - with Excel
Source Data
Step 1: Find factors that correlate w/purchase
Step 1: Find factors that correlate w/purchase
Step 2: Linear Regression to Establish
Regression ResultStatistical 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?
Predicted vs. ActualWhat the regression model predicts
Difference between “Predicted” and “Actual”
Actual(add together “Predicted” and “Residuals”)
Predicted vs. ActualLet’s create a column for “Cumulative Deals”
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%)
Results> Top 10% of leads
drive 70% of deals
> Top 20% of leads drive 85% of deals
Evaluating & Improving Your Models
Comparing Multiple Models
Pop Quiz: Which Model is Better?
1. Identify parameters of interest2. Collect data on LEADS and DEALS3. Identify factors that correlate w/purchase4. Create Excel spreadsheet5. Run linear regression6. Iterate on which parameters to include in model7. Tune model to add/remove factors8. Evaluate quality of model
(Rinse & repeat, at the right time)
Summary
imirman [at] onshape.com
(Thanks!)
QUESTIONS?