What’s the backstory behind Littledata? Where did the idea come from?

Ed Upton: My backstory is that I was working as an analytics consultant. Littledata is my second company; my first was in the ed tech space, helping teachers create and share curriculum resources. We had one hundred thousand teachers using the product but struggled with finding a good business model. So I switched to analytics consulting, working for UK-based brands like MADE.com and JD Williams group. I noticed a recurring problem: brands wanted to be data-driven but were unsure about trusting their data setup.

I saw many big brands struggling daily to base decisions on reliable data. With Shopify, we could productize what we did manually for larger brands—setting up Google Tag Manager, running audits and creating tagging plans. Shopify’s standard data layer allowed us to automate the data pipeline for any store. Brands no longer needed to worry about finding agencies or maintaining it themselves. This shift from service to product was our journey, starting fully in 2018. We left behind customers not on Shopify and focused entirely on helping DTC brands with our app.

As a data-driven person, I understand the impact reliable data can have on scaling a business quickly. It’s crucial not to doubt the data points you rely on, which can otherwise undermine management teams. Recently, a brand founder I spoke to shared how discrepancies in their Google Analytics reports lost them credibility during board meetings. Such issues erode trust and hinder growth. That’s why accurate data is transformational—it enables brands to grow faster.

Why do brands need a tool like Littledata in their stack nowadays?

Ed Upton: As a brand owner, you might not be aware of the underlying issues, but as a data expert, I can pinpoint what’s wrong with the numbers collected in Google Analytics and other marketing platforms. They’re primarily gathered by pixels or tracking scripts on your storefront, combined with cookies in browsers to identify visits and page views. However, over the past five years, the accuracy of data collected through these pixels has steadily declined.

This discrepancy manifests as revenue mismatches between platforms. For instance, Shopify might report $100,000 in sales, while Google Analytics shows only $80,000. Such revenue discrepancies signal broader issues with your data integrity.

Why does this matter for brands? Firstly, accurate data is crucial for analyzing which campaigns attract high lifetime value customers, ensuring profitable customer acquisition. Secondly, precise data enables better targeting within advertising platforms like Google, Meta, or Klaviyo. These platforms rely on accurate information about customer touchpoints and purchase behavior to optimize ad campaigns effectively.

To address this challenge, we combine traditional browser pixel tracking with server-side tracking, which we’ve pioneered. Unlike standard tracking provided via Shopify’s Google and Facebook sales channels, our approach captures real-time event data directly from Shopify—such as add-to-cart, checkout, and purchases—linking them accurately to web sessions and marketing campaigns. This advanced tracking technology enhances your ability to understand and optimize your online marketing performance.

Server-side tracking and better identity resolution can also have a big impact on Klaviyo email flows, allowing brands to build bigger high-intent audiences for retargeting in abandoned cart or browse flows.

How has the shift towards privacy-first impacted the trust brands have in their marketing data?

Ed Upton: In 2021 with the release of iOS 14, there was a significant shift in Facebook marketing that marked a turning point for many brands. Suddenly, there was a noticeable drop in performance, prompting brands to reconsider their reliance on Facebook for customer acquisition. This decline wasn’t in fact a single event but rather a series of updates over several years by Apple aimed at tightening user tracking restrictions. Apple prioritizes customer privacy over ad companies’ profitability, which led to changes that affected how data could be tracked via cookies.

In response, Facebook (now Meta) introduced their Conversions API, allowing businesses to transmit conversion data like purchases directly to Facebook’s servers. This method bypasses restrictions imposed by Apple and other browsers on cookie-based tracking, enabling platforms like Meta (formerly Facebook) to link specific conversions not just to cookie identifiers but to individual customers using data points such as email, phone number, physical address, and IP address. This approach allows them to maintain attribution across devices and even after cookie clearing.

Similarly, Google Ads has introduced Enhanced Conversions, leveraging first-party data from customer databases to match and attribute conversions accurately within their ecosystems.

Regarding cookie deprecation, I hold slightly contrarian views. Much has been made about the ‘cookie apocalypse,’ but in reality, Google’s phasing out of third-party cookies this year mostly impacts advertisers using DSPs and other ad networks. For those advertising directly on Google and Meta, the impact is minimal. First-party cookies, which are set and stored by your own site, remain unaffected and are crucial for tracking user interactions and behaviors.

Although Apple has also discussed restricting first-party cookies, this hasn’t received as much attention recently compared to their actions regarding third-party tracking. The landscape is evolving, but it’s clear that leveraging first-party data and adapting to new tracking technologies will be key for marketers moving forward.