Trax Gives Top Retail Brands an Edge with Sisense and Google BigQuery

In a sea of crowded supermarkets and stiff retail competition, top brands are always on the hunt for a solution that can provide a single source of shelf truth. Many of the best-known international brands have found that source of truth in Trax’s computer vision platform. Trusted by brands like Coca-Cola, Nestlé, Heineken, and Molson Coors, the Trax platform allows retailers to understand how their products look, perform, and compete on the shelf in real time. 

Trax’s customers are early adopters. They understand that it isn’t efficient nor scalable to rely on manual audits and surveys to assess where and how their products are stocked in-store. With access to real-time data, Trax customers are making decisions faster than ever before, tweaking campaigns instantaneously to maximize performance and profits. So, when it was time to enhance the analytics experience within the product, Trax knew their customers’ expectations would be high. To deliver the level of service their customers expected, the Trax team selected a combination of Sisense as a BI and analytics platform and Google BigQuery as a cloud data warehouse.

Replacing legacy analytics solutions

The team at Trax knew that without reliable and intuitive access to their data, customers were only capitalizing on a fraction of the insights generated by the Trax platform. Its legacy analytics solutions were two-fold: an in-house solution built and customized for each customer and a white-labelled version based on the platform of a competing analytics vendor. But neither solution provided the flexibility and ease of use the Trax team and customers needed.

Our legacy in-house analytics solution was great in theory. It looked nice and performed well in prospect demos, but in practice, our customers didn’t use it.

Doron Mutsafi
Doron Mutsafi, Director of Sales Engineering

The team’s in-house solution was elegant but rigid. A customer request for a change to the data model required attention from the product team and multiple cycles of development — time better spent developing new features for the Trax platform. And the analytics solution they had white-labelled wasn’t working for the opposite reason — customers were overwhelmed by too many options and quickly got lost in the dated product design. The Trax team even discovered that some customers were exporting the data from pivot tables to Microsoft Excel and only then digging into the data.

With an eye toward the future and a desire to reduce impact on both the product and professional services teams, it was time to find a better solution.

Building the future of retail analytics with a modern data strategy

In Sisense, Trax found a BI and analytics platform that was flexible and intuitive, providing their customers with a wide range of customization options, while keeping the dashboards clean and digestible for the average business user. But the team also had plans to modernize their data strategy by remodeling their data and migrating to the cloud. Sisense’s native data connector for Google BigQuery made the decision even easier. 

With our previous solution, we were paying for a huge server — the costs for both the infrastructure and the application were enormous. Once we moved to Sisense with BigQuery, this cost reduced dramatically. Now, we pay as we go, and scale as our customer base — and our data — expands.

Ilan Pinto
Ilan Pinto, Director of Software Development

The Trax team started with a complete review of the current data strategy and data models. The original data architecture was complex and oftentimes even internal associates found it complicated to understand. Most importantly, Trax customers said it wasn’t easy to build dashboards, and the data structure wasn’t intuitive. It was extremely difficult for customers to access data with the correct correlations.

To ensure good query performance moving forward, the Trax team did a data architecture review and opted to model the data in BigQuery so it could be easily leveraged by analytical platforms and business users. The remodel allowed the team to leverage the BigQuery platform to flatten variants and create analytical structures and aggregates that could optimize query performance against BigQuery and allow the Sisense platform to quickly visualize the data. The entire process was completed within one month, delivering a model the team could then use to create a demo dashboard. 

With Sisense and BigQuery, the Trax data team has been able to use Sisense’s live connector to BigQuery to stream data into Sisense, utilizing batch querying for faster performance on a data set that averages 50 million rows. With the live connector, powerful real-time queries and dashboards are now possible. Paired with Sisense, the Trax team also has the option to manage high-query, slow-moving data by easily caching it using Sisense’s proprietary performance acceleration technology. And if there is data coming from additional sources outside BigQuery, Trax can leverage Sisense’s cloud-native architecture to seamlessly connect to these sources, creating a much more holistic snapshot of their data.

Everybody wins with real-time data and insights

As Trax introduces more customers to their new data-powered approach, the team expects to see even more improvements in the customer experience and greater empowerment for internal teams.

We had a meeting with a Japanese customer. We did a demo, starting with a blank canvas. We wanted to show them what it was like to be a designer. After 40 minutes, they had a dashboard they could start working with. This was very impressive for the customers, and to be honest, for us.

Doron Mutsafi, Director of Sales Engineering

According to Doron, “70% of the data model can be reused for every customer. This is a big win for taking the pressure off our professional services team.” The professional services team used to spend an incredible amount of time and resources customizing dashboards for each customer, though many of their top customers have very similar needs. Now, with a standardized data model, the team can tackle some of their customers’ other high priority needs.

“Sisense and Google BigQuery allows us to give the power back to our customers,” says Ilan. “Now, our customers can build amazing data experiences on their own within a single platform. We can encourage them to be dashboard designers without forcing them to be data architects.”