Tinybird Customer Story
Smartme Analytics builds complete consumer insights with Tinybird
Read how the leading consumer behavior platform builds comprehensive Consumer 360 software with real-time, zero-party data and analytics powered by Tinybird.
Data Stack
"When we started building with Tinybird, the data team was no longer the bottleneck. For the first time, we were building new data pipelines faster than our frontend developers could integrate them into the product."
~6.6 TB
processed per month
+80
requests per month
Smartme Analytics is the leading platform in real-time data collection from markets, consumers, advertising, and media.
Through its innovative observational technology, it provides continuous and unified measurement, directly from people, allowing complete knowledge of the population: the sizing of each target, the tracking of their habits and interests, the various digital services they use, the measurement of the media they consume, and the effectiveness of the advertising campaigns that impact them.
With Smartme, brands have a single point of contact for direct and agile access to the latest information about the consumer, based on real people without relying on third-party cookies and improving the enrichment of first-party data.
For over six years, Smartme Analytics has provided deep insights into consumer behavior to hundreds of companies. Smartme's customers value the comprehensive view of consumer behavior that Smartme provides, powered by zero-party data that Smartme collects through safe, secure, and privacy-oriented means.
Until recently, Smartme operated primarily as a consultancy, developing custom data products for their customers upon request. As the company has grown, however, they've begun shifting their core offering to a SaaS model, providing out-of-the-box data APIs and Dashboards that their customers can utilize to gain a comprehensive understanding of how their customers behave.
Smartme has developed a proprietary method to capture zero-party consumer data. They monitor almost 150,000 consenting consumers, over 20,000 business applications, and over 35,000 leading websites.
In the past, Smartme's data teams would utilize batch ETL pipelines run on on-premises architecture. They would calculate consumer KPIs using R, export the results to CSV files, and visualize the data in ad hoc Tableau reports and dashboards for their customers.
As they evolved, they moved this infrastructure to the cloud, making it easier to monitor and maintain the data extraction and preparation processes.
However, as Smartme shifted away from their consultancy and towards their SaaS, they realized that their current data processing methods were both costly and inflexible. Mapping user behaviors is incredibly complex, involving large amounts of time series data from many sources that must be enriched with user dimensions.
With their legacy architecture, Smartme's analytics would take forever to run. The antiquated process of dumping results into CSV files and visualizing them in Tableau did not scale, and it wouldn't support their growing SaaS business.
They knew that to build a successful SaaS product, they would need to move faster, be more flexible, and - most importantly - empower their analysts and software engineers to self-serve the analytics they needed to build new features.
Smartme's team of data engineers began assessing data warehousing technology to improve both the speed of their queries over analytical data and make it easier to develop and deploy complex analytical data pipelines.
Having moved their infrastructure to AWS, the team first evaluated Redshift but found it too expensive and too complex for their team to maintain.
"We have a small data engineering team, and we couldn't ask them to both support our product team's data needs and maintain a complex infrastructure. We needed a data platform technology that would unburden our data engineers and empower our organization to build new things with the data we are collecting."
Smartme's data team wanted to simplify their stack and streamline their team. They didn't have time to manage complex infrastructure, and they were keen to minimize overhead so that they could iterate their SaaS product quickly and reliably.
After discovering Tinybird, Smartme's Data Engineers ran an initial pilot, developing a proof of concept for a complex use case in less than two weeks with the help of Tinybird's support engineers. Impressed by the speed at which they could develop new data products and the performance of the platform itself, they began developing more features for their SaaS within Tinybird.
To date, Smartme's engineering teams have developed at least 7 user-facing data products using Tinybird. Using Tinybird's managed data connectors, Smartme's data engineers can quickly and easily import consumer data from various sources into Tinybird.
Thanks to Tinybird's single-click API generation, Smartme software developers can now quickly build and integrate new data products into user-facing applications. Powered by Tinybird, Smartme's SaaS product is now faster, more reliable, easier to iterate, and much less costly to maintain.
Tinybird gives Smartme the real-time data platform that they've needed to more quickly build and ship new real-time data features to production. With Tinybird, the data engineers can support a large team of both data analysts and software engineers without having to worry about infrastructure.
"We weren't surprised that Tinybird made our data pipelines faster and more flexible. But we were amazed by how quickly our broader organization was able to build new use cases with it. Over 60% of our workforce are data analysts, and they're using Tinybird directly - with just SQL - to bring new features and data products to production."
With Tinybird as its real-time data platform and Tinybird's staff supporting them along the way, Smartme has evolved from consultancy to premiere Consumer 360 SaaS, providing highly accurate real-time consumer behavior insights to customers on demand.
With Tinybird, Smartme's data engineers have accelerated their development speed, empowered a broader group of internal teams to build new data features, and delivered faster, more reliable user-facing applications.
"We have the very best consumer data measurement in the world. Now with Tinybird, we're able to get that data and the insights it generates into the hands of more customers faster and with fewer resources than before."
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