Liv Up

Using data to adapt to changing consumer trends in the midst of a global pandemic

Indicative helps Liv Up make real-time data accessible to non-technical team members - so they can lean on customer data to power decision-making and weather uncertain circumstances.


Liv Up is a direct-to-customer company in the food-tech space. The digital native vertical brand (DNVB) launched in 2017 with a mission to reconnect consumers with the agriculture industry. Based in Brazil, Liv Up partners with farmers and producers to deliver healthy, organic, convenient food directly to consumers.

The team at Liv Up have understood and leveraged the power of data from the very beginning. “Data is the core of our strategy,” Luiz said.

In the 3 years since the company’s founding, the team has grown to more than 500 people, they’ve expanded to serve more than 30 cities, and they’ve gone from generating a few megabytes of data to several terabytes a day.

Quickly and easily turning that data into customer insights, actionable analyses, and product decisions is vital to Liv Up’s core strategy. They use Indicative to connect directly with their Snowplow event data, allowing everyone on the team (including non-technical members) to run quick, powerful analyses that inform their decision-making.

The COVID-19 pandemic accelerated the need for essential services like Liv Up and changed everything about how, when, and where people eat their meals. Customer data became an even more important tool for Liv Up to help inform the development of their product.

  • Allow non-technical users to create and make simple analyses about the product
  • Reduce time to complete a deep dive analysis of the product
  • Keep pace with changing consumer trends created by the COVID-19 pandemic

“Our team grew from 35 to 500+ people, we expanded from two cities to thirty, we evolved from processing a few megabytes to terabytes of data every day, we raised three funding rounds. As a result of continuous change, our platform had to evolve accordingly.”

Luiz Arakaki

Head of Product, Consumer Products


The Liv Up team knew from the time they launched that data would be an integral part of how they make decisions and improve their product. They wanted that data to come directly from their customers, and they had plenty of it. The challenge was operationalizing all that data. With a small data team and a rapidly growing customer base, data accessibility for non-technical teams was key.

As the company grew, they cobbled together a few data setups, but none that could scale with the business and allow non-technical team members to access and make sense of the data on their own, without the help of the data team.

“We would use Looker and BigQuery for most of the events, but building queries from scratch was the only way to do event-level analysis,” Luiz said.

Despite the potential of a tool like Looker, it’s complexity and SQL requirements meant it wasn’t a tenable option for most of the people in the company. It was “slow and costly for event analysis, hard to build funnels or cohorts, and required deep SQL and LookML knowledge,” Luiz Arakaki highlighted.

Those difficulties limited who could access and analyze key data—inhibiting how well the rest of the team could truly understand Liv Up customers, their behavior, and the product improvements they should make. That limitation would have been particularly problematic during a time of rapid change like the COVID-19 pandemic ushered in, blocking the team from real-time customer insights that could inform product development. The trends were obvious: fewer trips out to the grocery store and more meals eaten at home. To adapt to that change, the Liv Up team needed to understand how product changes and new features were adopted (or not).

  • Increase usability of data for decision making across non-technical teams
  • Simplify the process of doing product analysis
  • Better understand the way users use our platform and look for improvements

“We had two main problems: (1) How to allow non-technical users to create and make simple analysis about the product and (2) how to reduce the time it takes to do a deep dive analysis of our product.”


Indicative has helped Liv Up turn their customer and event data into accessible analyses and actionable knowledge. The team collects event data using Snowplow and pipes that data into Indicative for visualization and analysis.

“To empower Product Managers, Analysts, and Tech Leads with event data,” Luiz said, “we built a pipeline to load data to a product analytics tool. Leveraging the Snowplow loosely coupled architecture, we subscribed for its event processing pipeline and loaded data to Indicative API.”

On the Indicative side, the team primarily uses customer journey funnels to better understand how users navigate through their platform. Doing so enables the entire Liv Up team to analyze customer behavior and identify potential improvements they can implement throughout the funnel.

One improvement the team is eyeing is a grocery delivery option, in addition to the ready-to-eat meals the company already delivers. Indicative was instrumental in helping the team to identify this potential opportunity.

Once launched, the team will be able to use Indicative to see which customer segments a feature like that appeals to, identify user behavior and points of friction that correlate with adoption of the new feature, and find levers they can pull to encourage and speed adoption.

  • Deep dive analysis about users in the product
  • Understanding results of newly implemented features
  • Allow non-technical teams to understand their products, without needing a data analyst


After implementing Indicative, the Liv Up team has seen a huge change in how they can analyze and operationalize data across the organization. Without the need for technical and SQL knowledge, Indicative has helped to democratize access to customer data. That’s resulted in some key changes for Liv Up.

Being able to analyze real-time customer data enabled them to adapt quickly to the changes wrought by the COVID-19 pandemic, for one. The company previously offered only ready-to-eat meals, for example. As the product team looked at their data in Indicative, they identified an opportunity to expand the product to support online grocery delivery, too.

According to Luiz, that’s just the tip of the iceberg. The team plans to increase their usage of Indicative—by integrating with additional data sources they use, broadening access to additional teams like production, marketing, and logistics teams, and more.

“We’re working on a portfolio expansion strategy, so we really would love to understand how to measure the performance of our items in our platforms,” Luiz said.

  • Designers and product managers have been able to make simple and quick analyses
  • Better understanding users allows the team to improve decision-making
  • Adapt to shifting trends and consumer expectations during the COVID-19 pandemic

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