How do I figure out which actions predict conversion?

To figure out which actions predict conversion, you want to identify behaviors that happen before conversion and are statistically associated with a higher conversion rate. Here’s a practical way to do it:

1) Define “conversion” clearly

Be specific about the outcome:

• Purchase
• Signup
• Demo request
• Upgrade
• Retention after 30 days

Also define the time window:

• Conversion within 7 days of signup?
• Within the same session?
• Within 30 days?

2) List candidate actions

Collect all user actions that might matter:

• Clicked pricing page
• Viewed product demo
• Added item to cart
• Used a key feature
• Opened email
• Returned on multiple days

Make sure these actions occur before the conversion event.

3) Build a user-level dataset

For each user, create rows like:

• Whether they converted
• Whether they performed each action
• Counts/frequency of actions
• Timing relative to conversion
• User attributes if available

Example:

user_id	converted	visited_pricing	started_trial	used_feature_X	emails_opened
1	1	1	1	1	3
2	0	0	1	0	1

4) Start with simple comparisons

For each action, compare conversion rates:

• Conversion rate among users who did the action
• Conversion rate among users who did not

Example:

• Users who viewed pricing: 18% converted
• Users who didn’t: 4% converted

That suggests a strong association.

5) Use statistical models to control for confounding

Some actions may look predictive just because they correlate with other factors. Use:

• Logistic regression for binary conversion
• Survival analysis if time-to-conversion matters
• Decision trees / random forests / gradient boosting for nonlinear patterns

A logistic regression might look like:

• conversion ~ visited_pricing + started_trial + used_feature_X + emails_opened + device_type + traffic_source

This helps estimate which actions predict conversion after controlling for others.

6) Check timing and sequence

An action is more useful if it happens:

• Early enough to act on
• Before conversion
• Consistently across users

Sequence often matters:

• Visiting pricing → starting trial → using feature X may be more predictive than the actions alone.

7) Look for lift, not just correlation

An action is more valuable if it meaningfully changes the odds:

• Odds ratio from a model
• Lift in conversion rate
• Precision/recall if you’re using the action to flag likely converters

8) Validate on holdout data

Don’t trust patterns found only in one dataset. Split data into:

• Training set
• Validation/test set

See if the same actions still predict conversion in unseen data.

9) Be careful: correlation ≠ causation

An action may predict conversion without causing it. For example:

• High-intent users both browse pricing and convert
• The pricing visit is a signal, not necessarily the driver

If you want to know what actions cause conversion, consider:

• A/B tests
• Randomized experiments
• Causal inference methods

10) Find the most useful actions

Usually the best predictive actions are those that are:

• Common enough to measure
• Strongly associated with conversion
• Available early
• Actionable for your team

Quick practical approach

If you want a fast answer:

1. Define conversion and time window
2. Create binary features for key actions
3. Run conversion-rate comparisons
4. Fit logistic regression
5. Rank actions by effect size and stability
6. Validate on a holdout set

If you want, I can also show you:

• how to do this in SQL
• how to do it in Python/pandas
• or how to design a conversion prediction model end to end