Machine Learning Marketing Case Study: Real-Time Campaign Analytics

What Is a Machine Learning Marketing Platform?

A machine learning marketing platform ingests campaign data from every channel a brand spends on, then uses models to work out what is actually driving return. The hard part is rarely the modelling. It is that the underlying data sources change constantly.

Ad platforms revise their APIs and audience interfaces on their own schedule. Agencies sit between the brand and the spend, each with their own reporting. Real-time bidding adds another layer of signal that has to be reconciled against everything else. A platform that measures marketing ROI has to absorb all of that without breaking every time a vendor ships a change.

That is why the engineering problem here is a data integration problem first and an analytics problem second. Pre-built connectors reduce the cost of adding a new source. Quality rules and alerts catch a feed that has started returning bad data before it reaches a dashboard a client is looking at. Flexible storage keeps raw and processed data side by side, so a new question can be answered against history rather than only against what was collected for the original use case.

Get that foundation right and the machine learning becomes tractable: models can look for optimisation opportunities across channels, and the resulting dashboards give marketers something they can act on rather than another report to reconcile.

The measure of success is whether a marketer can answer a question they did not anticipate when the data was collected. If every new question needs a new pipeline, the platform has not solved the problem.

The data-driven world of online ads and promotions is fast-changing, fragmented and ultra-competitive. Origami Logic provides real-time insights into the ROI of digital marketing campaigns for media agencies and the global brands who pay for them. Origami’s interactive dashboards speed and simplify the detailed performance analytics give marketers the power to optimize campaign ROI and make the most of budgets by eliminating wasteful spending.

The Challenge

Distinguishing signal from noise in ad spending is a huge data headache for marketers. The data sources are constantly changing because Facebook, Twitter, Youtube, Google are constantly changing the mix of ad/audience interfaces. High-speed bidding signals – across a deep web of agencies – complicate data analytics even further. Continuous transformation of data by Origami into unified campaign performance dashboards across brands, brokers, and media required a nimble, end-to-end approach.

The Solution

CloudGeometry provided Origami a stable, predictable, end-to-end data integration infrastructure to create, test, and deploy new data flows. Thanks to its microservices architecture, Origami was no longer constrained by a conventional data platform strategy, moving instead to an agile data pipeline fabric better suited to the analytic velocity of broad-spectrum AdTech data. Business analysts and data scientists alike benefited from dozens of ready-to-go connectors preconfigured for popular APIs and data feeds. Versatile data quality rules and alerts ensured the robustness and consistency feeding dataflows to downstream analytic processes.

Amazon S3 data storage was a key success factor in achieving cost-effective operations without sacrificing performances. S3 could retain data in either its raw processed formats, to apply these streams in an endless variety of use cases. AWS Athena provided industry-standard SQL access data access and manipulation right off of S3 buckets. The versatility of Athena also made it easier to build machine learning (ML) models, unearthing campaign optimization opportunities from the latest digital media feeds. Dashboards built with AWS QuickSight completed the picture with visual analytics.

The Benefits

Origami Logic provided its customers a deep and flexible data integration fabric that delivered continuous, reliable transparency across all dimensions of the marketing supply chain.

<div class="case__txt--cols"><div><h4>Assembly Line for Data</h4><p>Drag-and-drop pipeline canvas to wire SaaS APIs, and Social Media feeds, IoT and more to AWS Aurora, RDS, Redshift, and Athena on S3.</p></div><div><h4>Continuous Incremental Change</h4><p>Acquire new data sources easily, with dozens of ready-to-go connectors preconfigured for popular APIs and data feeds.</p></div><div><h4>End-to-end Data Versatility</h4><p>Flexible data topologies to flow data across many-to-many origins and destinations.</p></div></div>

Frequently Asked Questions

What is machine learning marketing?

Machine learning marketing is the use of models to find patterns in campaign data that a human analyst would miss or take too long to spot. In practice that means attributing return across channels, identifying which audience segments respond, and flagging spend that is not working. It depends entirely on the quality and completeness of the underlying data, which is why most of the engineering effort goes into integration rather than modelling.

How do you measure marketing ROI across multiple channels?

Measuring marketing ROI across channels requires unifying data from platforms that each report differently, then normalising it so that a conversion means the same thing everywhere. The difficulty is that ad platforms change their reporting interfaces frequently, so a pipeline built against today's format will break. A connector-based architecture with continuous quality checks absorbs that change without a rebuild each time.

Why is marketing data integration so difficult?

Marketing data integration is difficult because the sources are numerous, inconsistent and outside your control. Social platforms, search engines, ad exchanges and agency reporting systems all change independently. Add real-time bidding signals across a chain of intermediaries and the reconciliation problem compounds. The practical answer is a pipeline designed for change rather than one tuned to the sources as they exist today.

What does a real-time campaign analytics platform need to handle?

A real-time campaign analytics platform needs to ingest asynchronously from many sources, enrich events with context as they arrive, validate data quality continuously, and store both raw and processed forms so new questions can be asked of historical data. It also needs to scale with burst traffic, since campaign activity is uneven by nature.