Overcoming the emerging data quality challenges with GVRN | Articles

In the next five years, customer analytics is estimated as the most crucial factor of driving customer experience improvement. High-quality data is one of the essential drivers of digital marketer’s efforts: create customer personas, deliver better-targeted campaigns, personalised offerings and to be more flexible. But what happens when your data is polluted? Poor customer data quality affects marketing efforts significantly: affecting marketing forecasts, misunderstanding customer needs and AI-powered programs being ill-informed. There are several ways for poor quality data to appear: human mistakes or technology issues.

Carglass has always been on the forefront of innovation, monetizing data-driven insights across many areas: dynamic creatives in audio, video and display, personalised landing pages, automated audience creation, data-driven customer journey flows. Customer data flawed with errors can ruin your business. MIT Sloan Management Review estimates that correcting data errors and dealing with the business problems caused by data costs companies 15% to 25% of their annual revenue. Carglass was in need of regular data quality assessments to measure the quality of data sets. A methodology for such assessment, specified to the data marketing needs and at an affordable price, is non-existent in the MarTech industry.

Almost two years ago, Semetis and Carglass started reflecting together on what could be a better alternative to the extremely expensive tools on the market. It turned out that building a tool that answers those needs was the best option. Atlas, Semetis R&D centre, seized the opportunity and engineered GVRN.

GVRN can emulate every user interaction on a website, from a simple click to filling in a form or even making a fake transaction. The tool is able to retrieve all the tags and dataLayer variables along the emulated user journey. More importantly, the tags and dataLayer values are not actually triggered, GVRN prevents the data from being sent to the different platforms, hence keeping your data clean. But the true purpose of this tool is to evaluate the quality of data pipelines. How? By creating and automating tests to validate tags and dataLayer values.

This method allows marketers to assess their data quality sent to their marketing stack. Real data values are validated against the tracking plan. The tool can match records, validate new data, establish remediation policies and identify possible outlier values. If a data error occurs, an email notification is sent. Every automated test completion generates a report of passing and failing results. On top of that, the software has a collaboration feature. Marketers and data engineers have the option to leave comments when consulting a report to ease the data quality improvement processes designed among team members.

Carglass was really impressed by the user experience proposed. “The user friendliness of the scenario creation was beyond my expectations.” said Steven Audoor, digital customer journey manager at Carglass. For Carglass accurate data is key for their DCO and Smart Bidding campaigns. GVRN can spot data issues immediately, ensuring the DCO campaign is showing the right ad and smart-bidding algorithms aren’t cluttered by false data points.

GVRN, the data quality management tool, has become a core component in every MarTech stack.

GVRN won a Golden Award for "Best Belgian Marketing Tool" at the IAB Mixx Awards 2022


publication auteur Maxime Denis
AUTEUR
Maxime Denis

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