> ## Content Index
> Fetch the complete content index at: https://www.techtaco.info/llms.txt
> Use this file to discover other available public pages before exploring further.

# We Taught AI to Spot Buyers Before They Buy
- URL: https://www.techtaco.info/we-taught-ai-to-spot-buyers-before-they-buy/
- Published: 2026-07-20T14:35:00.000Z
- Updated: 2026-07-30T17:27:09.000Z
- Author: Arif Khan

### How behavior sequences reveal hidden conversion intent

Most analytics tools answer one question really well: **“Who already converted?”**

We wanted to answer a harder and more valuable question: **“Who is most likely to convert next, based purely on behavior?”**

So we built an AI algorithm that compares how *converted* users behave versus *non-converted* users, not using demographics or guesswork, but by studying **actual sequences of actions over time**.

### Here’s what we did, in simple terms.

### Data Source

This analysis uses Google’s **GA4 obfuscated sample ecommerce dataset** for the Google Merchandise Store.

The data is publicly available. Details about the dataset can be found at the link below.

**Source:** [https://developers.google.com/analytics/bigquery/web-ecommerce-demo-dataset](https://developers.google.com/analytics/bigquery/web-ecommerce-demo-dataset?ref=techtaco.info)

- **120,000 real users** (scaled from the original dataset)
- Each user performed multiple actions: views, clicks, cart events, checkout steps
- Only **170 users actually purchased**
- The remaining **119,830 users did not**

That imbalance is important.

Conversion is rare. Which makes prediction hard.

![Article content](https://media.licdn.com/dms/image/v2/D5612AQGl6kN0W-DVbw/article-inline_image-shrink_1500_2232/B56Zu.sWnlGkAU-/0/1768430872809?e=1787184000&v=beta&t=npXqrPObg_XPzfDdlHbTz1VHqI6NkP12gz6QnhMg0Is)

### Step 1: Study the Buyers First

Instead of starting with non-buyers, we did the opposite. We deeply studied the **170 converted users** to understand:

- What actions they performed
- In what order those actions happened
- How long they spent between actions
- Which actions repeated
- Which actions mattered and which were just noise

This created a **behavioral fingerprint of conversion**.

Not a funnel. Not a dashboard. A *sequence*.

### Step 2: Clean the Noise

Real user data is messy.

So before comparing anyone, we cleaned the journeys:

- Removed noisy events that don’t show intent
- Removed duplicate actions
- Eliminated loops where users repeat the same step endlessly
- Focused only on meaningful actions

What remained was the **true behavioral path** of each user.

### Step 3: Compare Sequences, Not Just Events

This is the key insight.

Most tools look at **counts**:

- How many users viewed a page
- How many added to cart
- How many dropped off

We looked at **sequences**.

For every non-converted user, we asked:

- How close is their *action sequence* to a converted user’s sequence?
- Do they perform similar actions in a similar order?
- Do they share a large subset of important events?
- Do they spend a similar amount of time progressing?

If a converted user did:

```
A → B → C → D → E → F
```

A non-converted user who did:

```
A → B → C → D
```

Scores **higher** than someone who did:

```
A → C → D
```

Even if the order is not perfect, **subset similarity matters**.

### Step 4: Rank Users by Conversion Likelihood

Once we compared:

- Sequence similarity
- Duration spent
- Shared meaningful events
- Behavioral structure

We ranked **non-converted users by how closely they resemble converted users**.

The result surprised us.

### The Result: Hidden Converters

Out of nearly **120,000 non-converted users**, the algorithm identified:

**130 users with a very high likelihood of converting**

> ***That’s nearly a 76% increase over the number of users who actually converted, identified purely from behavior.***

These users:

- Behaved almost exactly like buyers
- Reached deep checkout steps
- Spent comparable time
- Showed strong intent

They did *everything right*. They just didn’t complete the final purchase.

Most likely reasons:

- Payment failure
- Shipping cost shock
- Distraction
- Technical issues

These are **missed opportunities**, not uninterested users.

### Why This Matters

Traditional analytics treats all non-converters the same.

Our approach proves they’re not.

Some users are noise. Some are browsers. And a **small, valuable group is already “mentally converted.”**

They just need the right nudge.

### What Zinzu Does Differently

Zinzu prioritizes:

- **Behavioral sequences**, not isolated events
- **Duration**, not just clicks
- **Subset similarity**, not rigid funnels
- **Intent patterns**, not averages

Every user journey is treated as a **story over time**. And when you compare stories, patterns emerge.

### Final Thought

Conversion doesn’t happen at the purchase button. It happens much earlier, in behavior. If you can identify users who already *behave like buyers*, you stop guessing and start acting with precision.

That’s what we built. And this is just the beginning.

 If you’re curious, early access is open at [**https://zinzu.io**](https://zinzu.io/?ref=techtaco.info).