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Every rupee you spend on marketing should give you clear results. But if you're a brand selling on quick delivery apps like Blinkit or Zepto, there's a big problem that's wasting your money.
Here's what's happening: You run ads on Instagram, Google, or work with influencers to get people to buy your products on these apps. But once someone clicks your ad and goes to the app, you lose track of them completely.
You can't tell which of your ads actually led to sales. This means you don't know what's working and what's not. Without this information, you can't improve your marketing or stop wasting money on ads that don't work.
It's like shooting arrows in the dark - you hear some hit the target, but you don't know which arrows those were.
The technical infrastructure behind quick commerce platforms creates fundamental barriers to attribution. Here's what makes measurement nearly impossible:
Unlike your own website where Google Analytics can track every user journey, quick commerce platforms operate as closed ecosystems.
When you run a Facebook campaign for your brand, you can see clicks and traffic to Blinkit, but that's where the trail goes cold.
Blinkit doesn't provide the granular attribution data you'd get from Shopify or WooCommerce. You know 10,000 people clicked your ad, but have no idea if those clicks translated into actual purchases of your product.
Your Google Ads dashboard shows great click-through rates, while Blinkit's brand portal shows sales spikes. When you have to connect these dots, it requires manual guesswork. There's no automated data flow between Meta's campaign insights and Zepto's sales reports either.
This creates information silos where marketing teams celebrate campaign engagement while sales teams wonder why certain products aren't moving. The sales team is completely unaware that they're looking at the same customer journey from different angles.
Without proper attribution, brands face budget misallocation and blind spots that compound over time. The inability to connect campaign performance with actual conversions creates operational inefficiencies.
Without proper tracking, marketing teams look at things like clicks and likes to judge if an ad is working. But these numbers can be misleading - an ad might get lots of clicks but zero sales.
The problem gets worse because Facebook and Google optimize your ads for clicks, not sales. So you end up spending more money on ads that look good but don't actually make money.
The opposite problem also happens. Some ads might drive lots of sales on Blinkit or Zepto, but don't get many clicks or likes. Since these ads look "unsuccessful," you stop investing in them.
This means you're missing out on your best opportunities while competitors who track better take your customers.
ROAS calculations require connecting advertising costs with attributed revenue, but quick commerce attribution gaps make this fundamental metric unreliable.
Teams resort to blended ROAS calculations that obscure individual campaign performance, making optimization decisions based on incomplete or misleading data.
Budget planning becomes a guessing game, preventing data-driven scaling decisions and creating uncertainty around marketing investments.
Standard web attribution, such as UTM parameters and promo codes, provides limited visibility in quick commerce environments. UTM data gets lost in platform handoffs, while promo code adoption rates remain low due to friction in mobile checkout experiences.
These legacy attribution methods weren't designed for the rapid, app-centric purchasing behavior typical of quick commerce platforms, leaving brands with incomplete conversion tracking despite implementing standard measurement protocols.
When quick delivery brands can't track which ads lead to sales, the damage goes far beyond wasting money on bad ads. These problems grow bigger over time and hurt every part of your business.
Think about it this way: if you're wasting 20% of your ₹10 lakh monthly ad budget, that's ₹2 lakhs down the drain every month. Over a year, that becomes ₹24 lakhs in wasted money.
But it gets worse. While you're throwing money at ads that don't work, you're also missing out on the ads that do work. Your competitors who track better are finding these winning strategies and stealing your customers.
Let's say your Instagram ads actually drive great sales on Blinkit, but you can't see this connection. You might cut your Instagram budget, thinking it doesn't work, while your competitor doubles down on Instagram and captures your market share.
The timing makes it worse too. By the time you realize a campaign isn't working (usually weeks later), you've already burned through thousands of rupees.
When your marketing team can't show clear results, leadership starts questioning every marketing decision. This creates several problems:
Without proper tracking, you might run ads on Google, Facebook, and Instagram that all target the same customers. This drives up your costs because you're essentially bidding against yourself.
For example, a customer might see your Google ad, then your Facebook ad, then finally buy through your Instagram ad. You're paying for three ads when one would have worked.
While you're guessing, your competitors with better tracking are:
Here's what this actually costs brands:
And this quick-commerce attribution problems compound because:
Effective quick commerce measurement requires granular tracking across the complete customer journey, from initial campaign exposure through final transaction completion.
You need to connect every step of the customer journey - from seeing your ad to making the final purchase on Blinkit or Zepto.
This means tracking each customer as they move from your Instagram ad, click through to the app, and complete their order. You need to keep track of the same person across all these different platforms.
Your tracking should work at two levels:
You need to know where your ads work best and which products they're selling.
This means matching your ad spending in different cities with actual sales in those areas. For example, if you spend ₹50,000 on Mumbai ads, you should see exactly how much you sold in Mumbai.
You also need product-specific tracking to see which ads work for different categories. Your snack ads might work great, but your beverage ads might not.
Your system should let you break down results by:
Don't just wait for final purchases to know if your ads are working. Track the smaller actions that occur before someone makes a purchase - these provide early clues about campaign success.
Key signals to track: Product views, Adding to cart, Time spent browsing, Checkout attempts, Wishlist additions, Product comparisons, etc.
These early signals help you spot problems and opportunities quickly. For example, if lots of people are adding your products to cart but not buying, maybe your pricing is off or there's a technical issue. If people spend a long time on your product pages, it shows your ads are attracting genuinely interested customers.
You can also use these signals to retarget customers who showed interest but didn't buy. Someone who added your product to cart is much more likely to buy than a random person.
Instead of waiting weeks to see if a campaign worked, these micro-conversions give you feedback within days, so you can adjust your ads faster.
Comprehensive attribution requires understanding user behavior patterns across advertising touchpoints and quick commerce platform interactions.
This involves analyzing session depth, repeat purchase patterns, and cross-category shopping behavior to evaluate long-term campaign impact beyond immediate conversions.
The system should track customer lifetime value attribution to understand which campaigns drive not just initial purchases but sustained platform engagement and repeat transactions.
Attribution blindness doesn't have to be permanent. Graas' Extract provides the missing infrastructure layer that quick commerce brands need to connect their marketing efforts with actual sales outcomes.
Extract creates custom data flows that seamlessly integrate advertising campaign data with quick commerce sales and order information, while incorporating real-time inventory and product-level performance metrics.
This unified approach eliminates data silos and provides the granular attribution visibility that allows confident optimization decisions. Instead of guessing which campaigns drive conversions, brands gain precise insight into channel efficiency, geographic performance, and product-specific attribution patterns.
The platform transforms disconnected data streams into actionable intelligence, enabling brands to optimize campaigns based on actual conversion data rather than proxy metrics. With Extract, marketing teams can finally demonstrate clear ROAS calculations while identifying the highest-performing channels for strategic budget allocation.
Ready to close your attribution gap? Customize and unify your data with Graas' Extract.
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