Chances are that around the corner from the analytics project you’re working on that’s using SaaS data piped-into your data warehouse using a service from Stitch or FIvetran, there’s a marketing analyst using Google Sheets and an add-in from Supermetrics to analyze their channel advertising spend.
Supermetrics are the biggest analytics tool vendor company you’ve never heard, and that’s because their products are aimed squarely at marketers, not data engineers or data analysts, and within that particular market they’re massive; what they sell is easy-to-setup connectivity to all the major advertising and social networks directly from within Google Sheets in the form of the Supermetrics Google Sheets Add-In, a product we’ve used in the past when looking to to understand how well we place in Google search results for keywords relevant to our business.
Supermetrics do two things well; they provide connectivity to all the major ad networks that marketers’ use (and there’s lots of them beyond the basic Adwords, Facebook Ads and Snapchat Ads that we’re most familiar with), and they focus exclusively on the non-technical, marketing user rather than data engineers and data analysts, providing in-addition to their Google Sheets add-in a connector for Google Data Studio, for example.
But although you can use Google Sheets as federated sources for Google BigQuery tables and theoretically you could then use the scheduled data refresh feature in Supermetrics’ add-in to regularly download new data and thereby create a sort-of data pipeline into your warehouse, in-practice this probably requires a bit more knowledge of how BigQuery works than the average marketer is likely to have.
So I was particularly interested to see that one of the new BigQuery features announced at Google Cloud Next’19 last week was a number of Supermetrics-provided data connectors for BigQuery in the Google Cloud Marketplace, providing data pipeline-as-a-service connectivity from ad network sources such as Bing Ads, LinkedIn ads, AdRoll and others and with data being fed directly into Google BigQuery using their Data Transfer service.
Some of these sources are also covered by new connectors from Fivetran also in this same Google Cloud Marketplace, so what’s the difference between a data pipeline provided by Supermetrics compared to one from Fivetran or Stitch? Setting up a Supermetrics data pipeline using Google BigQuery’s data transfer service is pretty straightforward and not that different from setting up a data pipeline using Fivetran or Google’s own underlying data transfer technology, as shown in the screenshots below:
Its when you look at how Supermetrics land data into BigQuery though that you see their difference in approach; instead of presenting you with several tables of data mapped to the structure of the API calls for each service, the Supermetrics BigQuery tables are typically denormalized (flattened into one big table of data) and come with extensive and verbose column descriptions and metadata, making it much easier for marketers to know what each column is used for and the precise definition of all the metrics and dimension attributes.
Pricing is per data source with a 14-day free trial at the start, to me it looks like Supermetrics are looking to position their pipeline-as-a-service in-between Stitch and Fivetran’s in-terms of pricing but with the service aimed at marketers, not data engineers. Details of pricing and pricing metrics are on the Supermetrics support page and full details of the service are on the Supermetrics for BigQuery product homepage.