Predictive Automation Using Behavioural Segments

Introduction: Automation Works Better When It Predicts

Most marketing automation waits for something to happen. A user signs up. A product is purchased. Then an email or ad is triggered. But by the time that trigger fires, the user may already be gone.

That is why I build predictive automation - flows that trigger before a conversion happens, based on real-time signals that suggest intent or risk. This approach increases conversion rates, lowers bounce and gets ahead of churn. It is not magic. It is about watching what people do - not just what they click.

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Building Behavioural Segments in Real Time

Behavioural segmentation is the foundation. I assign intent scores to users based on patterns like:

• Page sequence and depth
• Referral path (was it branded or not?)
• Scroll depth and interaction time
• Prior cohort behaviour

Using a platform like Segment or a custom middleware built in Python, I send session data to a BigQuery table. Each visitor is tagged with a session ID and rolling context.

Here is a simplified Python example that assigns a score based on product page views and scroll depth:

def score_user(session):
    score = 0
    if session.get("page_type") == "product" and session.get("views") >= 3:
        score += 5
    if session.get("scroll_depth", 0) > 75:
        score += 2
    if session.get("referrer") and "google" not in session["referrer"]:
        score += 3
    return score

This data is streamed into BigQuery or stored in Redis if immediate decisioning is required.

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Triggering Campaigns Based on Pre-Conversion Logic

When a user crosses a threshold (for example, 7 points of intent), I push them into tailored remarketing or lifecycle automation. For one ecommerce client, the key trigger was:

• User visited three product detail pages
• Stayed more than 15 seconds on each
• Did not cart

We assumed they were comparing. So I sent an email:

"Still deciding? Here is a side-by-side comparison of your top picks - and an offer valid for 24 hours."

The open and click rate were both over 40 percent, with a 3.1 times higher conversion rate than the generic cart abandonment flow.

In Google Ads, I push users with this behavioural flag into a custom audience and show carousel ads with dynamic value props.

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Building a Lifecycle Trigger Engine

Over time, I group users by lifecycle behaviour:

• Fast movers (quick to convert, low churn)
• Window shoppers (browse deep, rarely buy)
• Researchers (return often, convert after delay)

Each group gets its own remarketing cadence, email copy and pricing exposure.

I create this using scheduled queries in BigQuery. For example, this SQL logic identifies window shoppers:

SELECT user_id
FROM session_data
WHERE page_type = 'product'
GROUP BY user_id
HAVING COUNT(DISTINCT session_id) >= 4 AND MAX(cart_event) IS NULL;

Then I trigger flows in Iterable or Klaviyo using custom API endpoints. This lets marketing automation reflect real buyer types, not just clicks.

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Final Thought: Automation Is Not About the Past

When automation only reacts to conversions, it misses the buyers who are trying to decide - and need help. Predictive flows close that gap.

I use behavioural scoring, referral signal analysis and session depth logic to trigger smarter flows earlier - whether that is an email, an ad or an offer. The goal is not to stalk. It is to support the buyer in real time.

If your automation only begins after someone converts, I can help you bring the timing forward - and catch more opportunities before they slip away.