Google Analytics 4, the latest update to Google’s free web analytics service does away with the old Universal Analytics measurement model of users, sessions and hits and instead replaces it with the same type of visitor event-based model used by Segment, Snowplow Analytics and Mixpanel.
So how does Google Analytics 4 work and why has it changed, how do you query its event data in Google BigQuery and perhaps most importantly … what’s the catch?
I blogged about using Google Analytics (GA) for event-based visitor analytics back in 2018 but in-practice using the Universal Analytics release of GA available back…
One of the simplest but most actionable ways that you can analyse your customer base is to build an RFM (Recency, Frequency, Monetary Value) model and make that analysis actionable using a Looker dashboard such as the one in the screenshot below, used within our business to help us better understand the client base within our consulting business.
Commonly used by retail, marketplace, professional services and eCommerce businesses an RFM model scores your customer base by three factors:
We’ve been users of the Looker testing tool Spectacles since its days as an open-source project started by Josh Temple and Dylan Baker, and regularly make use of it on client projects to ensure that the SQL content within our LookML views and models is tested along with the LookML code.
Most Looker developers will be familiar with the Validate LookML button that appears when you’re editing the LookML definition of your project, but this only checks that you’ve used the right syntax and property definitions for your dimensions and measures.
What it doesn’t check is whether the SQL you’ve…
A couple of weeks ago I posted an article on this blog on Customer 360-Degree Analysis using BigQuery, dbt and new “reverse ETL” tools such as Hightouch and Rudderstack. In this article I’m going to talk a bit more about we’ve transformed our customer data warehouse into the core of our new customer data platform, adding segmentation, interest scoring and marketing audiences that we then sync to services such as Facebook Custom Audiences and Intercom.
We do this by creating a set of additional derived customer and contact tables that take data from all parts of our business, both offline…
One of the dashboards I find most useful for understanding the direction of our business is the Customer Cohort Performance dashboard I’ve created using Looker, shown with demo numbers in the screenshot below.
Across a number of key areas for our business such as efficiency of delivery, project profitability and client retention it tells me whether the customers we manage to attract and retain now are in-fact more, or less, valuable than the ones we acquired back in the past.
Why is this information important? Because on the one hand it tells me whether our spend on marketing and sales…
Rittman Analytics are a Segment Implementation Partner and we use Segment Personas together with Segment Connections to connect all of our digital marketing touchpoints and create a single view of client and visitor interactions across those digital channels.
Customer Data Platform services such as Segment Personas are great for collecting these granular interaction histories for clients and prospects, data that can be combined with transaction and other information we hold for those clients when they identify themselves to us when they login to our site or register their interest in a new product or service we’re offering.
One of the most common tasks in a data centralization project is to create single, deduplicated records for each of the companies, contacts, products and other entities the business interacts with.
Doing this allows you to connect sales activity from Salesforce and Hubspot to project delivery and invoicing data from Jira and Xero, for example:
Doing this in-practice can however get pretty confusing and complicated quickly as a recent thread on the dbt Slack forum discussed. To summarise the complications this adds to a typical data centralization project:
Last year I blogged about our RA Data Warehouse for dbt, a set of data models, data transformations and data warehousing design patterns for dbt (“Data Build Tool”), Fivetran, Stitch, Segment, Google BigQuery that we use to rapidly build-out the data warehouse layer for analytics solutions and data platforms we build for our clients.
By pre-building data source modules for popular SaaS applications such as Xero, HubSpot, Salesforce, Google Ads and Stripe , connecting them to a common set of routines for combining, deduplicating and integrating each of their datasets we:
One of our most popular services is helping Looker customers update and refresh their Looker dashboards, especially if they’ve been using Looker for a while now and the team who put it all together has since moved on.
Back when Looker first came out it’s fair to say that it was back-end features such as direct SQL querying of column-store cloud data warehouses together with LookML that got the product noticed, not its data visualization and dashboarding features which lagged well behind desktop BI tools such as Tableau and Qlikview.
What many customers don’t realise though is that Looker’s front-end…
First published on the Rittman Analytics blog on 20th September 2020.
You’ve probably heard of the saying “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.”, and digital marketing was supposed to fix this problem. If only it were that easy.
You spend money on online ads and know precisely who clicked on the advert, how many then went on to buy something (or “convert” in eCommerce terminology) and how many then went on to buy again and again and become your best customers.
CEO of Rittman Analytics, host of the Drill to Detail Podcast, ex-product manager and twice company founder.