Using Google Analytics Parallel Tracking and a custom data pipeline for Shopify, we managed to get all necessary data in BigQuery for more advanced analysis and reporting.
Case Study: Solving The Discrepancy Between A/B Testing Tool, Google Analytics and Backend Data For a Large E-Commerce Business
Working with skewed data can be worse than having no data at all. This is why we’re always promoting all sorts of analytics audits and making sure all data sources agree with each other. At the very least, you should know why the numbers in different tools don’t match (i.e. analytics doesn’t include offline sales but backend does).
Our client in this case study contacted us with a quite specific problem. They were running a decent CRO program with 4-6 A/B experiments running every month. The problem they had with the program, though, was that the numbers they saw in their testing tool Optimizely, Google Analytics and backend didn’t match. In fact, there was a ~35% discrepancy overall.
Over the years, we have helped companies of all sizes and from various industries to collect, process and make use of digital data.
Something we should have started doing a long back is sharing the success stories of our clients. We’ve done so many cool things together and for our clients that these stories are definitely worth sharing, and reading.
SQL is the most popular language for professionals to communicate with databases and query data. Google Analytics is the most popular tool for digital analytics. How come there’s no way to query Google Analytics data using SQL? In this article, we’ll explore the solutions.
Google Analytics, while being by far the most popular tool in its segment, does have a few limitations that can make this, otherwise nearly perfect tool, unsuitable for a large number of companies.
The main limitations of Google Analytics are related to sampling and data collection limits. Most affected are companies that can’t afford the premium 360 version of Google Analytics (~150k/year) but still have a good amount of traffic visiting their websites. In general, Google Analytics properties with >1M sessions/month or >10M hits/month are being affected by some heavy sampling and data collection limits.
In this article, we’re going to cover the different types of limitations present in the free version of Google Analytics and provide solutions/workarounds to all of them. Oh, and the solution, in most cases, does not include buying the 360 version.
Is your marketing data currently under the control of multiple vendors and platforms? Do you have to mix together siloed data and reporting tools to answer questions about marketing ROI and your customer’s journey? It’s time to take control of your data!
Cloud databases (i.e. BigQuery) and various data management tools have made it possible for marketers to build data pipelines without a big investment in hardware, software, and custom development.
In this article, we will walk through the steps of gathering data from Google Analytics, Google Ads, Google Search Console, Facebook, CRM and several other sources into Google BigQuery data warehouse and making it the single source of truth for all marketing data.
The number of marketing tools an average business uses has grown rapidly. Besides one or two analytics platforms there’re a few ads platforms, CRM, CMS, several social media platforms, an email system and probably a few more tools and platforms.
All of those tools are supposed to make our work as marketers, business owners or data analysts easier and more effective. In reality, though, you will end up with a bunch of silos – systems that don’t really communicate well with each other and almost never agree on any of the important metrics.
Data silos create confusion and disagreement between teams, leading to a situation where, at the end of the day, no-one knows which tool or numbers to trust.
BigQuery is an extremely powerful tool for analyzing massive sets of data. It’s serverless, highly scalable and integrates seamlessly with most popular BI and data visualization tools like Data Studio, Tableau and Looker.
Working with Google Analytics data in BigQuery has mostly been a privilege of those having a 360 version of Google Analytics. Its hefty price tag, though, has made that list quite short.
Google Analytics is a really good tool for marketing-focused digital analytics. And by far the most popular one in this segment. With some custom setup, you can also use Google Analytics for tracking SaaS and other web apps & products.
Two of the most common shortcomings of Google Analytics that most of the more advanced users experience, though, are the lack of hit-level granularity and sampling. In this article, we are taking a look at some of the ways you can overcome these shortcomings without spending a fortune on Google Analytics 360.
This blog post is aimed for anyone planning to build a data pipeline or upgrade their current setup.
An end-to-end analytics data pipeline is a secure and reliable mechanism that is responsible for feeding your business with valuable data that can be used for reporting, analysis, machine learning or any other activity that requires accurate data about your business.