Size: 40 employees
Markets: North America
Product: Supermetrics for BigQuery
Inseev Interactive is a boutique SEO & performance agency based in San Diego, California. Inseev was founded by Jimmy Page in 2013, but powered by excellent results and word of mouth, Inseev has grown incredibly quickly and was rated by the San Diego Business Journal as one of the fastest growing companies in San Diego. A key factor behind their growth is one of the company’s core values that champions “results over optics”.
But with incredible growth comes new challenges. For Inseev, to keep up with their growing business, they needed to rapidly modernize the way they managed and worked with data. The company realized that Excel spreadsheets and saving files on local computers was not going to cut it if they were to meet the increased demand for business and the sophisticated expectations of clients.
To solve this challenge, Inseev brought in Eddie Zaldivar as Senior Director of Paid Media & Analytics. Eddie, who was previously a Senior Analyst for Criteo and Performance Manager for Resolution Media, worked with Jason Melman, Inseev’s Associate Director of SEO – and all-around WebTech wiz kid – to figure out how Inseev could best tackle these challenges and go even further to deliver superior results for their clients at scale.
For Eddie and Jason to take Inseev to the next level, they had to answer these three questions:
1. Where do we store data that’s inexpensive and easy to use?
2. How do we transfer the data from the different platforms to our data warehouse?
3. How do we connect the data and make it accessible & easy to use for both the internal team & clients?
In this case we’ll explain how Eddie and Jason took Inseev from Excel jockeys to a database and vizualisation powerhouse.
Moving to BigQuery
Eddie and Jason’s first task was deciding where to store their marketing data. Excel can only hold a couple hundred thousand lines of data and with a growing list of clients, Inseev had to upgrade their data storage. Given Inseev was only a few years old, they didn’t have any in-house IT staff and investing tens of thousands to hundreds of thousands on a server was something they had to think long and hard about. So instead, they started looking at possible cloud solutions.
Deciding on a data warehouse was an easy decision and the team quickly decided on BigQuery, part of the Google Cloud Platform. On the decision, Eddie says that, “BigQuery’s setup was easy, they have flexible pricing models, and are highly secure, eliminating the need for a large initial investment as BigQuery is serverless and can be accessed from anywhere with an internet connection. We no longer had to worry about infrastructure or maintenance and we could just focus on diving deeper into the data.”
Building a marketing data warehouse
The second task was getting the data they needed data from Google Analytics, Google Ads, Facebook, LinkedIn, and Google Search Console into their marketing data warehouse in BigQuery. They had previously used UpWork to hire data engineers to create API connections to different platforms, but these pipelines would often break whenever pushing an update to the API. This would leave the data pipelines down for days, if not weeks, until the API connections could be updated.
Inseev tried different pipeline vendors, and Eddie says that, “We tried using Stitch Data, Blendo, and others, but we found that the schemas being offered did not have connectors to all the platforms we needed. And when they did have the connectors we wanted, they didn’t have the granularity we were looking for. At this point, our founder Jimmy suggested we look into Supermetrics.”
Inseev had been using the Supermetrics connector for Excel the year prior as the Inseev team found Supermetrics was the only product that scored high on data quality. When they heard the announcement of Supermetrics for BigQuery, the team were delighted. Eddie says that, “We were really excited when Supermetrics released a native BigQuery transfer product. It had connections to all the platforms we needed and offered easy to use schema’s right out of the box.”
Eliminating thousands of hours of work
After using Supermetrics for BigQuery, Eddie and his team noticed a difference immediately. He says, “With other vendors, we had to spend weeks doing backend work, transforming the raw tables into something we could easily use, whether it meant unnesting tables from Facebook or creating multiple joins to pair account, campaign names, adgroup names, and keywords to their corresponding performance data. The data from Supermetrics was complete and easy to use. We could connect Tableau directly to these tables, avoiding hundreds, if not thousands of hours of backend work, and avoiding any discrepancy in data due to campaign name changes or using the wrong type of join.”
Eddie, Jason, and the Inseev team then began using Supermetrics for BigQuery in multiple different ways. Here are four different use cases showcasing what the Inseev team have been able to achieve.
1. Eagle-eye view of all accounts for all platforms
The first project Inseev worked on was creating an all-client Tableau dashboard where they could quickly see all of their clients’ performance across the different platforms for multiple periods of time. This dashboard used Google Analytics as the point-of-truth and combined it with spend data from their various ad platforms.
Using Supermetrics to connect BigQuery to these platforms, and then connect Tableau to BigQuery to create easy to read dashboards, the whole team could quickly identify performance on both a microscopic and macroscopic level. This report is automatically emailed to the performance team every morning.
2. SEO dashboard
Another use case Inseev had was to create dashboards for SEO reporting. Using Supermetrics to pull the needed data into BigQuery, Inseev created visualizations in Tableau that helped their analyst quickly identify any anomalies in traffic. They were able to track across queries, pages, search engines and devices, and then compare across different periods whether it was the same time last year, the prior period, or a different holiday.
Eddie says, “Creating this report using Excel or Google Sheets would be nearly impossible as some of our clients have over 100K queries per day and up to 50M rows per year, far exceeding traditional technology limitations. In addition, the process to compile the needed data to go from seeing a traffic or revenue change from one year to the next, to drilling down on the exact pages and queries driving the change would take hours due to the limitations of both the Google Analytics and Google Search Console UIs, for example due to the thousand row limit on GSC datapulls. But now with the new SEO Dashboard, we can now complete these sorts of diagnostics in a matter of minutes. This gives our whole SEO team exponentially more time to diligently craft strategies for clients, rather than burning hours on simple diagnostic work.”
3. Device bid adjustment recommendation dashboard
Another use case Inseev have for Supermetrics and BigQuery is to optimize their bids for different devices based on a recommendation dashboard.
In Google Ads, device bid adjustments are a crucial part of improving performance. Depending on the industry, users can substantially favor desktop over mobile or vice versa. At Inseev, they aim for the mean CPA when determining ideal device bids as a low CPA on one device means a missed opportunity and needs more support. The same applies the other way around. “It can get a little messy when you have to factor previous bid adjustments and their impact on current performance. It’s nearly impossible to keep track of previous bid adjustments and how they affected performance over time using only the Google Ads interface,” Eddie says.
Using BigQuery and the integrated tools available in the Google Cloud Platform, Eddie created a dashboard that takes the guessing out of bid adjustments and then tracks the standard deviation between the current devices, all in an effort to reach the mean—or as Inseev likes to call it, equilibrium.
4. Budget tracker
Another use case is for tracking budgets on paid campaigns, including both paid search and social. This helps the Inseev team get both an overview to monthly and daily budgets to ensure they’re able to pace campaigns over time.
Summarizing the impact Supermetrics & BigQuery has had for Inseev, Eddie concludes that, “Using Supermetrics for BigQuery has freed up thousands of hours for our team, which means we can focus on strategy and execution at scale. No longer are our days spent exporting and pivoting excel sheets, nor manipulating raw tables on the backend into a form our strategists can use. Supermetrics now does most of the dirty work, which helps us deliver on our core value of results over optics.”
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