"Purchase" is the least useful word in your ad account

Ritesh - founder, Porcellia & Manzuri
November 13, 2026
•
5 min read

‍

Meta and Google optimise on the signals you send them. Today most brands send one word for every sale: "Purchase". This note explains what changes when you send better signals, and the two separate tools that make it possible.
‍

Who this is for

This is written for a brand doing between ₹50 lakh and ₹2 crore in monthly revenue. If that is you, you are probably stuck at a certain scale. Ads still work, but growth has flattened, and more spend no longer buys the same results.
‍

A big lever for unlocking the next stage is looking at your data more granularly. When you are small, there is no point in slicing data finely. Going from 0 to 1 is a creative process: find something people want and say it well. Going from 1 to 10 is different. You need to be scientific, and you need to get deeper into your data.
‍

This is one of the first things in our playbook for taking brands from 1 to 10.
‍

The problem: one word for every order

Every order your store sends to Meta is called Purchase. Two different products look identical to the algorithm. So does an order that is returned a week later. So does a customer who has been on your site for the first time and one who has visited six times.

Meta then learns "find more people like the people who bought", which is a blurry instruction. You cannot see which product, audience or source is really paying back, and the platform cannot either. You get one blended ROAS and one blended instruction.

‍

Same orders, two ways of telling the platform

Order
What the platform hears today
What it could hear
OrderProduct variant Adelivered
→
What the platform hears todayPurchase
→
What it could hearPurchase_VariantA
OrderProduct variant Bdelivered
→
What the platform hears todayPurchase
→
What it could hearPurchase_VariantB
OrderProduct variant Areturned
→
What the platform hears todayPurchase
→
What it could hearNot counted as a net purchase

‍

Two tools, two completely separate jobs

The fix is stronger first-party data, meaning information your customers give you directly or that you collect on your own domain. We use two tools for it. They are not connected to each other. Each has its own data source and its own objective.

‍

Two data sources, no link between them

AliaGoal: understand the customer better
↓
Visitor answers a short popupone question at a time
↓
Zero-party datawhat they tell you: worries, age, intent
↓
Creative strategyads, landing pages, emails, product
CustomerLabsGoal: sharper signals for algorithms
↓
Store, site and CRM eventscaptured server-side, lifetime cookie
↓
One profile per personfirst touch and product kept
↓
Meta, Google, email toolsoptimise on better signals

‍

Alia: understanding the customer

Alia runs the popups on your site. They ask one short question at a time, save every answer as it is given, and tell your story while they do. The answers are zero-party data: what customers choose to tell you. For a creative strategist this is the most powerful way to learn who the consumer really is, what worries them and what they want, at a scale one-to-one calls never reach.

‍

CustomerLabs: sharper signals for every algorithm

CustomerLabs gives Google Ads, Meta Ads and your email marketing tools better data. Wherever a tool optimises with an algorithm, it makes that data sharper and more specific, for example a purchase event per product, or only the orders that were kept.

It also sets a lifetime cookie from your own domain. A normal cookie expires or resets, so when a customer comes back weeks later they look brand new. With a lifetime cookie they stay one person, and their first visit is still remembered.

‍

How it has helped us

Three brands we work with, and what each tool showed us.

‍

Gully Labs: ROAS by size and by design

Gully Labs is a sneaker brand that represents India globally. We use CustomerLabs here. Every pair used to reach Meta as the same Purchase event. Now each order carries its size and its design, so we can read ROAS by size and ROAS by design instead of one blended number.

‍

What we send for each order

Orderone pair, one size, one design
→
Size and design eventsbuilt from the order’s line items
→
ROAS by size, ROAS by designread in reporting

‍

‍

It also shows how each customer first found the brand. We can see whether a first purchase came after discovery through an influencer or through Google Ads. Once several hundred purchases have come through each source, we can assign a different return on investment to each one, instead of giving one blended number to both.

‍

First touch decides which source gets the credit

Discovered through an influencer campaignfirst touch kept, purchase follows
Discovered through Google Adsfirst touch kept, purchase follows
Hundreds of purchases per sourceenough to judge each one
A separate ROI for each sourceso budget follows what pays back

‍

Rara Barefoot: a positioning change from one question

Rara Barefoot makes India's first line of true barefoot lifestyle sneakers. Here Alia was the tool that mattered. By asking buyers why they were buying, we learned that 65% of people buying were buying for comfort, not because it is a barefoot shoe.

‍

Why people buy Rara Barefoot

65%
of buyers buy for comfort, not because it is a barefoot shoe

‍

The Cinnamon Kitchen: who is buying, and what to say to them

The Cinnamon Kitchen makes snacks and cookies. We used Alia, and one of its popup questions was "What makes Cinnamon Kitchen your go to brand?", with options such as gluten free, PCOS friendly and refined sugar free. The answers showed that 65% of the users making a purchase have PCOS themselves. It was a key data point, and one that browsing and ad data could never have told us.

‍

It went straight into creative. People were choosing "PCOS Friendly", so the ad led with exact

‍

From the popup answer to the ad

Popup asking what makes Cinnamon Kitchen your go-to brand
The popup
→
Ad: PCOS-Friendly Snack Brand is making waves
The ad

‍

What the popup told us

65%
of the users making a purchase have PCOS themselves

‍

Another question showed that 70% of users on the website preferred coffee over chai. The next creative followed it: "The best companion for your coffee".

‍

Cinnamon Kitchen ad: The Best Companion for your Coffee
The ad made after the coffee-over-chai answer

‍

Over the same period, blended ROAS went from 4.7 to 5.3 on the same ad spend, we collected about 12,000 more pieces of user data every month, and we went from 25 or more new creatives a month to 10 to 12. We cannot prove that the popup alone caused the ROAS change, but it is the thing that changed what we made.

‍

What Shopify and Meta do not tell you

Shopify will tell you how many repeat customers you have. It will not tell you how many sessions came from repeat users, or how many came from new users. CustomerLabs does, because it recognises the same person across visits.

‍

Who is visiting, not just who is buying

Shopify tells youhow many repeat customers and new customers you have
CustomerLabs also tells youhow many sessions came from repeat users and from new users
Neither tells youhow many sessions came from people who keep visiting but never buy
CustomerLabs doessessions from people with more than three visits who have never purchased

‍

Counting only the orders that stay

Returned-to-origin (RTO) and cancelled orders are counted as purchases by default, so the algorithm keeps learning from buyers who never keep the product. You can send a separate net purchase event only when an order is delivered and kept, and optimise on that.

‍

More signals from the same setup

  • Separate new customers from returning ones, and train on true acquisition.
  • Send high-order-value and prepaid orders as their own signals.
  • Optimise on profit rather than revenue.
  • Send offline and cash-on-delivery sales back to the ad platforms.

‍

One practical limit: each custom event needs enough volume for the platform to learn from it, roughly 50 events a week per ad set is the usual guide. Start with a few groups, not every variant.

‍

Good questions to ask, brand by brand

Every product page can teach you something about the person looking at it. These are questions that could be asked on the pages of other brands.

‍

What a brand could find out on its own product page

The Whole Truth product page
The Whole TruthHow many times a week do you work out? How do you stay fit? How do you consume your protein? What is your ideal serving per scoop?
Cosmix product page
CosmixHow many cups of hot chocolate do you drink a week? Are you buying for yourself, your family or a gift? Do you have tummy troubles: IBS, GERD or none?
K9 Vitality product page
K9 VitalityHow old is your dog? How big? How often do you walk it? What is your biggest fear: running away, biting, poor eyesight, weak joints?

‍

Getting the most out of Alia

  • Make the popup multi-step and tell your story as it goes.
  • Collect zero-party data, including at product level.
  • Use four to six questions: four fixed for at least three months and two experimental ones you change every two to three weeks.
  • Use Reddit to find the biggest pain points first, then size them in the popup, then take the winner into ads, copy and email.
  • Show returning customers a different flow, and use smart exclusions.
  • Have the performance team own the popup.

‍

We wrote more on this in optimising your popup.

‍

If we used this for Bloon Toys

We have not run this for Bloon Toys. This is how we would approach it.

‍

With Alia: what to ask, and what to test

Start with the questions every parent can answer quickly:

  • How old is your child?
  • What matters most to you: less screen time, stronger motor skills, or more cognitive development?
  • Does your child have siblings?

‍

The sibling question lets us test two opposite hypotheses, and we do not know in advance which is true.

‍

Two hypotheses a single question can settle

Hypothesis AParents with more than one child buy more, and spend more per order, because they want the children to play together.
Hypothesis BParents with only one child buy more, because they want that child doing something other than screens.

‍

With CustomerLabs: one event per age

The Pikler comes in versions for different ages. We would pass a different purchase event for each age group, so ROAS can be read by age. If the products for 0 to 2 year olds return a higher ROAS, we would put more money into making creator content for that group.

‍

The obvious worry is accuracy: a parent might see the ad for a smaller Pikler and buy a bigger one. The event is built from the product in the basket, not from the ad clicked, so the credit goes to the age group that was actually bought, and the source is stored next to it.

‍

A mismatch, handled correctly

Ad seenPikler for the youngest age group
→
Order placedPikler for an older age group
→
Event sentthe older age group’s event, source kept

‍

What it costs

Two tools, set up separately. Together they cost about $300 to $500 a month to set up and run. The exact figure depends on your traffic and plan.

‍

Tool
Job
Alia
Understand the customer: popups that collect zero-party data for creative strategy.
CustomerLabs
Sharpen the algorithms: server-side events, lifetime cookie, product and net purchase events for Google, Meta and email.
Both together
About $300 to $500 a month

‍

Want this for your brand?

If you are planning to implement either of these, send us a DM on Instagram (@porcellia_) or email ritesh@porcellia.com.

‍‍

As a thank-you for being part of our newsletter community, we will give you a referral code for the tools.

Read our latest blog

Your business will die because you can’t tell a story.

In a world optimized for speed and dopamine, meaning is the only thing that lasts.

Read the full story
Read our latest blog

Your business will die because you can’t tell a story.

In a world optimized for speed and dopamine, meaning is the only thing that lasts.

Read the full story

Real growth lessons from
brands making the rules up.

Get your hands on the the stuff that actually compounds.
Positioning. Retention. Content. Community.

Full documentation in Finsweet's Attributes docs.