Supermetrics integrates with BigQuery to automate data warehousing for marketers

Dubbed Supermetrics for BigQuery, the integration was unveiled as part of a keynote address at the Google Cloud Next conference in San Francisco, and it allows users to effectively store and analyze massively large datasets stored in Google’s data warehousing service.

Supermetrics’s marketing and web analytics offerings allow marketers to pull data from various sources such as advertising networks and social media channels and to generate customized reports and visualizations without writing code or formulating API calls. The company’s various solutions have become invaluable to marketers seeking to merge signals from siloed channels for custom, comprehensive reporting in spreadsheets and business intelligence tools.

With the new BigQuery integration, users can now perform these tasks and track activity over long periods of time, using truly big data.

Modern marketing requires big data

This new solution comes as a welcome development for marketers. Enterprises are now generating and collecting vast amounts of information about customers through their digital tools and channels. All of these pieces of information can be analyzed to reveal trends and opportunities that businesses can use to their advantage.

For example, correlating ad creative with conversion rates is best done at scale, with experimentation over time, and taking a complete, 360-degree view of the customer lifecycle into consideration.

“Combining data across business functions and platforms is definitely the direction businesses need to go to in order to get a truly comprehensive view of their return on investments,” said Mikael Thuneberg, CEO of Supermetrics. “Marketing and advertising agencies, for example, are already combining their customers’ marketing data from online channels with information collected from offline channels.”

Moreover, by tracking user behaviour on ecommerce sites, businesses are able to check which elements of the user experience are causing friction with users. Fixing these issues can lead to smoother transactions and more satisfied customers. By monitoring social media, marketers can also determine prevailing market sentiment. Knowing can could help them create new campaigns to engage customers better.

“Combining web analytics data with marketing metrics and CRM information, for example, helps build a more comprehensive view of customer behaviour and journey, and helps identify gaps and inefficiencies that would be difficult to identify recognise in individual platforms,” Thuneberg continues.

“Some specific examples of combining data across platforms could be linking advertising data to web analytics data for post-click analysis and a holistic view of the user journey. Or linking web analytics data to CRM data to analyse how website user behavior is correlated with purchases and different customer types.”

Unfortunately, much of the information gathered by companies go to waste. Forrester estimates that between to 60 to 73 percent of all data within an enterprise aren’t even used for analytics. Companies simply lose out on discovering potential game-changing insights by failing to act on data.

As such, marketers must look for ways to put their data to good use. And what they need are better tools to help them make sense of the available information.

Enterprise-grade warehouses for all

Previously, only large enterprises were able to embark on big data projects due to the high cost of the computing power and infrastructure needed to process large volumes of data.

Fortunately, cloud computing has changed this by making infrastructure and platforms available as a service. Capabilities like data warehousing are now available at a fraction of what they used to cost. Organisations have been steadily adopting such services. The database-as-a-service (DBaaS) segment is expected to grow at a compounded annual growth rate (CAGR) of 65.5 percent from 2019 to 2025.

“In order to make it possible for organisations to utilise data in meaningful ways, data management needs to become more accessible to non-technical professionals,” says Thuneberg. “That access is going to improve, user interfaces of data solutions will develop, and no-code solutions will become available in growing numbers.”

BigQuery is an emerging leader in the cloud-based data storage space, providing users with scalable and on-demand access to analytics-focused data warehouse capabilities. Thanks to Google’s infrastructure, BigQuery is capable of handling petabytes of data. This makes it ideal for digital marketing purposes, since it can be used to consolidate records auto-imported from various tools and channels.

However, storing and accessing large volumes of data is just one part of the big data equation. Generating actionable insights is another. By placing a code-free analytics exporting layer on top of BigQuery, Supermetrics lets users to readily query and analyse large and varied datasets.

The advantages of self-service

Another key advantage to tools like Supermetrics is its ease of use. It lets users quickly setup marketing data warehouses on their own with just a few clicks. Data is also presented as readable rows and columns that are labelled intuitively, allowing even those unfamiliar with code and databases to readily perform analyses.

With this self-service element, companies can save on both time and resources, since marketing teams could work on data without needing dedicated support or external expertise.
The accurate intelligence and insights that could be generated from such analyses could be used to promptly solve various business issues as soon as they become apparent. Businesses can effectively close the gap between possessing data and actually benefiting from it.

Conventional tools such as spreadsheets, and even Google Analytics, are typically not capable of processing big data. While Microsoft Excel can be used to accomplish small-scale analyses, it is only capable of storing up to 1 million rows of data. Big data involves much more information than this.

“Supermetrics for BigQuery solves many data issues for any organisation – big or small – working with larger amounts of data or data points. It really comes down to making the workflow fluent and automated and getting rid of slow data transfers, human error, and manual data transfer work,” notes Thuneberg. “Google Sheets allows 2 million rows of data but slows down significantly at roughly 1 million rows. If you combine data from multiple sources, it does not take that many dimensions or segments to run into limitations. When you hit these types of limitations and issues, it is time to look at more robust options, like Supermetrics for BigQuery.”

Big data improves competitiveness

For modern enterprises, big data adoption is not something to delay or ignore anymore. There is now a growing list of stories documenting returns of investment from big data projects. Companies would do well to explore these tools to boost their capabilities.

Just by automating data importation, Supermetrics can free up 10 to 30 hours of marketers’ time per month.

Supermetrics users have already reported their own successes. Marketing agency Dentsu Aegis, for instance, streamlined its data pipeline management for client reporting through the BigQuery integration.

Competition across industries is only getting tougher, so businesses must create every advantage they can. Realising the potential of big data through these powerful tools should greatly help.

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