Organizations need to process large volumes of both structured and low-density unstructured data on a daily basis. Referred to as Big Data, this massive collection of information becomes the key to improving marketing efficiency. There are a number of marketing tools used for data analysis at the ready for business owners.
These tools and platforms can make the work of digital analysts a lot easier. But in reality, data silos, although very effective in measuring important KPIs or metrics, can be a source of confusion. In order to get the numbers to make more sense and see results you can trust, you need a scalable, faster, and fully-managed data warehouse like Google BigQuery.
Benefits of BigQuery visualization tools
BigQuery and data visualization help marketers with:
About 57% of consumers are willing to provide companies with their personal information to get notified about upcoming discounts, deals, promotions, etc. With marketing data analysis using BigQuery, you will have access to limitless data, which you can utilize to increase leads.
Collect relevant user information from various platforms, transfer to one centralized place and create a single, trustworthy source and improve your marketing performance via data analysis.
Some native platforms may limit your access to past data, and others built on top of an API extend it a few more months, but you may still not have a complete picture of the past performance. But BigQuery visualization tools help gather and store all your historical data.
You can dig into several metrics and dimensions and analyze big data at granular levels. This kind of higher granularity may not be possible with native interfaces since you don’t have access to raw events data.
For an eCommerce business, you can perform in-depth path analysis and evaluate the common page paths taken by users who made purchases and those who were here for just browsing.
Visualizing your data in BigQuery
Moving the data
The first on the BigQuery journey is to build your data schema, plan projects, data sets, tables and views.
You can move data from Google Search Console, Google My Business and other services that are not integrated with BigQuery and even from non-Google marketing platforms like Bing, Twitter, Instagram, Facebook, etc.
The next step is to export it to BI and visualization tools. Google Cloud Functions can automate simple processes. Use Google Data Studio connector to export data to BigQuery tables with API documentation, directly or via Cloud Storage.
The BigQuery interface
You can pin a project, find registered queries, job histories and past queries. Here are some best practices for practical data analysis.
Denormalized tables: By grouping your data for a high number of fields, you can create fewer tables.
Query validator. Under the query table, red or green circles in the bottom right corner help you detect errors and fix the issue before running a query.
Preview mode. The preview tab lets you understand the different types of data at your disposal before you start creating a table.
Open your current dashboard and create a Data Studio dashboard by choosing click new data source and BigQuery. Connect to a table or view and add to the report to make a connection. If using GSuite, connect using Google Sheets by choosing a BigQuery project. You can also use non-Google BigQuery and data visualization tools like Power BI, QlikView or Tableau.
Cross-channel PPC analysis
Digital marketing campaigns involve SEO, PPC and omnichannel marketing. In order to identify the platforms performing the best and what are the pain points in each campaign, BigQuery allows you to pull data across multiple channels.
Besides having access to crucial blended big data in a central location and generating all-inclusive visual reports, you can measure key indicators. Check the performance of your SEO efforts, improve site rankings and boost organic traffic.
CRM and web analytics
Digital marketers use visitor behavior data to improve conversions. You can analyze several content pieces and how users interact with the pages, pre-conversion. Collect the user data tied to an anonymous cookie ID and export it to Google BigQuery. Converted prospects have a unique CRM ID each, which you can combine with enriched data for a deeper insight.
Buying behavior analysis
Attribution analysis becomes challenging for modern buying behaviors, as conversions do not connect with a single channel or specific touchpoint. By gathering the data across all touch points, you can get a solid grip on your marketing funnel and better understand the customer journey.
Taking advantage of large data sets
Even smaller teams can use BigQuery and data visualization tools without many resources by:
GA 360 Integration: You get a Google Analytics 360 license by shelling out some bucks for a non-free version, which lets you perform SQL-style queries on the Analytics UI in BigQuery. You get higher acceleration in connecting Google Data Studio BigQuery tables as you visualize millions of rows at lightning speeds.
Flat File Integration: Upload large CSV files and export them as tables with unwieldy files and data from vendors or stakeholders. Perform a meaningful analysis in BigQuery by uploading your flat files and using the advanced options. Build the schema correctly for any data analysis.
Firebase Integration: For mid-size to large companies, Firebase integration offers more comprehensive data analysis. If you have one or more apps, you can ship the data to BigQuery for more flexibility. Choose whether you want to include advertising IDs or match download ad campaigns.
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If your company has an effective marketing data warehouse working on a modern cloud infrastructure and can gather and analyze data from every marketing tool you are doing, great! If you don’t use one as of now and want to transform into a forward-thinking company, you need to use BigQuery and data visualization.
Contact Otaliems Academy to learn how to leverage big data for your organization. We also offer a complete site audit and deliver successful digital marketing strategies for businesses of all sizes.