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Customer 360 Data Programs and the Emergence of the Cloud Connector

As enterprises embrace the cloud, some are disaggregating the architecture that supports their customer 360 data programs.

Rather than relying on a monolithic customer data platform, their marketing and data teams now make the cloud data warehouse their primary source of customer truth. Cloud data warehouses such as Snowflake and Google BigQuery offer a flexible platform to integrate fast-rising volumes of multi structured customer data right alongside data for related business functions. The customer data platform can still provide the logic needed to profile customers.

This shift marks a new architectural approach in which cloud connectors can deliver customer profiles from the data warehouse straight to targets such as advertising platforms. This blog examines what this new architectural approach means to a customer 360 data program, with digital advertising as a case study. Blog 2 in the series will offer evaluation criteria for cloud connectors, and blog 3 will recommend guiding principles to succeed with this new approach.

Digital Advertising 

So, how does digital advertising work? Marketing teams devise content and offers that engage prospective buyers through their phones, tablets, laptops, and other digital devices. They define their audiences, then create content and publish it on advertising platforms such as Google Ads, LinkedIn, and Amazon DSP. Prospects that respond and engage—e.g., by visiting a web page and/or providing their contact information—become sales leads. Businesses will spend $602 billion globally on digital ads this year and $681 billion in 2023, according to Statistica.

The Customer 360 Data Program

Like other lead-generation programs, digital ads depend on a customer 360 data program that curates rich profiles of customers—i.e., who they are, and what, why, and how they buy. These profiles enable enterprises to make sense of target buyers. They win their business and loyalty based on intimate knowledge of requirements, influences, and behavior.

The C360 program has five stages. Marketing and data teams integrate customer data, profile customers, and activate those profiles to generate sales leads. All the while, they synchronize profiles based on new data points and analyze findings. Let’s walk through these stages in the context of a sports equipment retailer that manages a digital advertising campaign.

Customer 360 Data Lifecycle

Integrate. Data engineers and marketing operations managers start by integrating multi-structured customer data from various sources into BigQuery. They ingest clickstreams from their website, sales records from their Salesforce customer-relationship management (CRM) platform, and product references from the Twitter social-media platform. They merge and reformat all these elements, then create a data model that standardizes how they relate to one another. They also configure streaming pipelines that continuously ingest, merge, and reformat updates from these data sources.

Profile. Next the marketing operations manager uses Lytics Decision Engine to build a profile of each customer based on analytics of their digital activities. They configure native machine learning models to cluster similar profiles—for example, fathers that shop for multiple children’s sports each August and March—into “audiences.” These profiles and audiences become the source of truth for customers’ identities, desires, behavior, intentions, and influences. The marketing operations manager also creates lookalike profiles—i.e., aggregate profiles of prospects that share characteristics with existing audiences. It then stores all those profiles in BigQuery.

Activate. The marketing operations manager and/or DevOps manager activates profiles by feeding them into the digital advertising platforms for ad campaigns. They use Lytics Cloud Connect to browse their audiences within BigQuery and select the audience for seasonal father shoppers. Then they configure basic structured query language (SQL) commands to extract those audience records and load them into Google ads, Facebook, and Amazon. This “reverse ETL” operation applies the same commands to all three targets, saving configuration time. The marketing operations manager starts publishing their ad content on these platforms.

Synchronize. Data engineers, marketing operations managers, and DevOps managers continuously synchronize data across their architecture. They extract results from ad platforms—for example, click-throughs and purchases by existing customers—and load those updates to their profiles and audiences in BigQuery. This gives them a live, real-time view of the truth for analytics.

Analyze. Marketing operations managers and data analysts create dashboards and reports to track key performance indicators (KPIs) for their ads. They use business intelligence (BI) tools such as ThoughtSpot, Qlik, and Tableau to visualize and communicate their findings to executives or other stakeholders. These teams might find, for example, that their ads generated strong results among existing buyers on Google Ads, but not the others. Perhaps their lookalike audience had poor results. Such findings prompt them to check assumptions, change profiles, or devise new ads. They might also enlist a data scientist to build or update ML models that improve results.

To make this approach work, marketing and data teams need to evaluate whether the benefits of the cloud connectors outweigh their costs. The next blog in our series will tackle product selection.

Kevin Petrie

Kevin's passion is to decipher what technology means to business leaders and practitioners.  He has invested 25 years in technology, as an industry analyst, writer, instructor, product marketer, and services...

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