Stop Wasting Your Marketing Budget

Drawing from recent industry reports and benchmarks, here's the stark reality:

• Up to 25% of marketing budgets are wasted due to unreliable signals, disconnected tools, and "phantom" metrics that make weak programs appear strong, forcing teams to over-rely on guesswork. Source

  
  

• Without proper attribution models, companies commonly misallocate 30% of their budgets, continuing to fund ineffective channels while underinvesting in high-performers. Source

  
  

• Around 23% of online ad spend (about $20 billion annually) is lost to poor attribution, high costs, and missing data, even making solid campaigns look like failures. Source

  
  

• Small and mid-sized businesses waste up to 60% of their budgets on poor targeting, lack of tracking, or misalignment, with 34% of marketers rarely or never measuring ROI at all. Source

  
  

• In B2B contexts, 73% of companies struggle to link channel activities to revenue, leading to an average annual waste of $847,000 on tactics that generate vanity metrics but no real growth. Source

  
  

• Overall, 30-40% of agency-managed budgets are squandered on ineffective campaigns, unnecessary markups, and untested services that fail to drive results. Source

These aren’t abstract numbers. They represent real dollars disappearing into fragmented data and incomplete views of user behavior.

For example, you might split budget evenly between social ads and email nurtures, only to discover later that email drives 35% of repeat purchases while social contributes just 12%.

The issue isn’t the channel. It’s that the sequence of user actions was invisible.

How teams reduce this waste

Teams that consistently reclaim budget don’t just ask which channel converted. They ask:

• What did users do before converting?

• Where did high-intent users hesitate or drop off?

• Which combinations of actions actually signal intent?

That requires sequential analytics and true multi-touch attribution, not isolated dashboards.

The blind spot most tools leave behind

Most analytics tools show outcomes. They don’t show behavior in order.This is exactly the gap we’re addressing at Zinzu.

By reconstructing full user behavior sequences directly from raw data (like GA4 events), teams can see:

• Where intent builds or breaks

• Which steps correlate with conversion

• Which investments quietly drain budget

No SQL. No predefined funnels. Just behavior, in order.

The real shift: from dashboards to decisions

The biggest change isn’t better charts. It’s moving from static dashboards to behavior-first questions.

When teams can explore data as naturally as they think, they stop defending spend and start deciding with confidence.

If this sounds familiar

If you suspect part of your budget is disappearing into a black hole, you’re probably right.

We’re working with teams who want to see what users actually did, in order, before making their next spend decision.

If that resonates, DM me or reach out at team@zinzu.io. Happy to share what we’re seeing across real datasets.

We Taught AI to Spot Buyers Before They Buy

--author Arif Khan | founder zinzu.io

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

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• 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.

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

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

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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.