If you’re struggling to create a comprehensive 360° view of your customers in order to drive sales or reduce costs… and you are already a Google BigQuery (GBQ) user, this blog is for you.
Quality in, quality out
Marketing teams are focused on driving results towards two key goals: acquiring new customers and improving customer lifetime value (growth) with existing customers. However, to do this at scale is complex and time-heavy. Most marketers rely on data-driven strategies to support advertising or marketing campaigns, but crucially the value of data inputs and analysis influences the value of the output. Like the old phrase goes: quality in, quality out.
In addition, the marketing world is experiencing a data tsunami, with businesses having access to more information about their customers’ preferences and behaviours than ever before. But, without the right tools, it can quickly overwhelm a conventional on-premise data warehouse and cause delays in data processing or even crash the system. Without the right tools, it is almost impossible to use this data effectively to deliver personalised marketing experiences at scale that drive topline and bottomline business outcomes. Siloed systems and outdated data often leave companies unable to take full advantage of their own data. More than two-thirds of the ingested data go unused. But thanks to the power of Google BigQuery (GBQ) and Zeotap CDP, it doesn’t have to be that way.
Introducing Google BigQuery
BigQuery is a completely serverless and cost-effective enterprise data warehouse. It has built-in machine learning and BI that works across clouds, and scales with your data. (1)
Integrating with a data warehouse, like GBQ can be a game-changer for your marketing efforts. It allows you to assemble all of your separate marketing pieces and gain visibility into your overall performance and how it contributes to the success of your business. But, I know what you might be thinking: setting up a data warehouse can be time-consuming and resource-intensive. The good news is that if you’re a user of GBQ already, we’ve got you covered.
How does Zeotap CDP’s integration with GBQ help you?
Data warehouses act as the single source of truth where companies store actionable data that gives a complete picture of their user’s profiles. BigQuery is one of the most used data warehouses by companies and with Zeotap’s integration, we get tightly coupled with the Google ecosystem. This provides you with the best way to quickly and easily get your marketing data warehouse up and running. Through this integration, you can ingest all your customer data residing in the GBQ tables into the Zeotap CDP; thus simplifying the onboarding process within just a few clicks. This will not only optimise your campaigns but ease the planning and forecast process for the entire marketing team.
Using our direct integration with the GBQ tables, you can ingest the customer data securely to Zeotap, streamline it into a single source of truth and derive actionable insights. In no time, the data is ready for business intelligence, visualisation, or any other tools you want to use to create the right kind of marketing campaign.
As a marketer, how can I use GBQ?
Typically, an e-commerce business has data scattered across many platforms: paid platforms – like Facebook, Instagram and TikTok Ads, web analytics platforms – like Google Analytics, Adobe Analytics or e-commerce platforms – like Shopify. Bringing this data together is integral to fully maximise the value of your customer data and optimise the return on ad spend (ROAS) of campaigns and also improve the efficiency of campaigns.
Let us take the example of a Head of Marketing Operations that needs to find out the number of leads coming from Instagram in January. By using the pre-built reports from these platforms or by using a tool like HubSpot you can easily find out the number of leads or how many purchases were made in a given month. However, you may want to go a layer deeper and find out the cause of a 20% drop in purchases in January by your most valued customers compared to the previous month. In order to answer this question, the system needs to understand customer traits and website behaviour over that time period. This is the value of Zeotap CDP’s integration with GBQ can help you with. It gives you the capability to identify user events and track them in bulk for decision-making to create actionable insights.
A data flow diagram of Zeotap CDP to show GBQ as a data source:
What does Zeotap CDP bring in addition to GBQ?
- Single source of truth – Capability to analyse data from web, mobile and other data sources in a single place that can be treated as the holy grail for marketers
- Hassle-free integration – Can be integrated in a few clicks with minimal training or oversight
- Secured and compliant data onboarding – Data is centralised and properly secured, reducing the risk of a data breach
- Automated sync frequency – Option to set the frequency with which you want to auto-update the data to Zeotap CDP
- Right message, right time – Omnichannel view of customers across multiple touchpoints allows marketers to personalise engagements
- Increase in ROI – Overall increase in marketing returns by harnessing and analysing the effectiveness of the ingested data
In summary, marketing teams are responsibility for attracting, engaging, converting and retaining prospects and customers throughout the customer lifecycle and the various touchpoints along the way. Zeotap CDP’s integration with GBQ, provides a centralised platform which your team can derive powerful customer insights and evaluate marketing performance. These insights lead to more effective marketing and outcomes.
What’s next?
If you want to learn more about how Zeotap CDP uses Google BigQuery, read a recent blog post written by Dr. Ali Arsanjani, Director, Cloud Partner Engineering, Google and Sathish K S, CTO, Zeotap: Built with BigQuery: Zeotap uses Google BigQuery to build highly customized audiences at scale
Or get in touch us and we’ll be happy to help: [email protected]
(1) https://cloud.google.com/bigquery
