machine learning marketing attribution

Machine Learning Is the Key to Cracking Marketing Attribution

From the moment we open our eyes and check our notifications until to the time we fall asleep with our devices carefully placed within arm’s reach, we are bombarded by a daily barrage of about 4,000 marketing messages. Each email, banner ad, social media post, direct mail, product placement, and other marketing message, is fighting a war for our attention—and the competition is fierce. The technology in the battle for our eyeballs is constantly advancing in a perpetual competition to push through the noise.

Related Webinar: Using Machine Learning & Spark to Power Data-Driven Marketing

With all of these messages competing for attention, how would a marketer know which touch points or certain combinations of touch points are most effective? Marketers need data frameworks that effectively gather data and deliver actionable insights to increase revenue, reduce costs and gain market share. Successful organizations are  turning to outside technology consultants to transform their data intelligence strategies and foster a culture of data science.

In 2017 a staggering $206.77 billion was spent on media advertising in the United States. With such a hefty marketing-message price tag, business users would want to optimize their spends and know which touch points are most effective in the customer journey, and which ones squander precious resources. Despite massive marketing budgets, the majority of marketers still struggle to attribute which touch-points or combinations of touch-points are most effective.

Many organizations are still using simplistic attribution models that hinder their ability to make data-driven decisions in the competition for converting customers. Organizations may lack the framework and technology needed to properly gather large quantities of data, stitch together each touch-point in a customer journey, and understand and appreciate the contribution of each message. Many marketers may not even act on insights, despite marketing attribution, according to 2017 State of Marketing Attribution report.

Adopting a Culture of Measurement and Accuracy

Data champions who foster a culture of measurement and accuracy inside an organization are crucial to effective marketing attribution. According to the 2017 State of Marketing Attribution report, 80% of brands and 71% of agencies rate “creating a culture of measurement and accuracy” as a top-three marketing attribution issue.

However, even those organizations that are already gathering data and feeding it to analytics and business intelligence tools are still missing a powerful weapon in the marketing attribution arsenal: Machine Learning.

Use Case: Implementing Machine Learning with Spark for Marketing Attribution

An organization based in the U.S. wanted to develop an infrastructure that would enable them to gather, store, cleanse, consolidate and distribute data in order to propel them closer to their business goals. Their data was comprised of offline, online and third-party sources. The organization approached Caserta, a technology consulting and implementation firm, with their data challenges and marketing goals. Full disclosure, I’m the VP, Marketing at Caserta and I’m blown away by our bold tech solutions.

Marketers beware, tech talk ahead.

In order for the organization to perform advanced analytics and Machine Learning, Caserta built an all-purpose data lake with Spark, an analytics framework, to conduct the big data transformations. Spark can power not only Business Intelligence queries, which organizations are already accustomed to, but also Machine Learning processes effectively. Spark comes with a machine learning toolkit, MLlib, which creates trained models for predictive analytics. However, these algorithms need vast computational power to do so.  Caserta opted for using Databricks to manage the computational infrastructure and propel discovery on the data lake using their Spark managed service. Databricks’ technology can simply ask more machines from AWS to power Spark for the required scale of the data, giving nearly infinite processing ability.

Now that all offline, online and third-party data sources are inside a single all-purpose data lake with a managed Spark service, the organization’s data scientists are free to analyze, transform and model data without the constraints of scaling tools. Removing this roadblock increases the room to innovate and will help marketers discover their optimum marketing mix. Organizations that promote a data culture will celebrate marketers that know how to derive insights from Machine Learning and take action.

As long as organizations continue to use simplistic data attribution models, or fail to use attribution at all, they will cede success and market share to those organizations who have the tech to understand what actually works in their marketing channels. Marketers will increasingly depend on Machine Learning to provide them with deep insights into the customer journey. The data may reveal perhaps that three direct mailings, one email open and seeing five social media posts is your organization’s perfect recipe for converting a first-time customer. Aren’t you curious about what your yet-to-be discovered combination of marketing touches is your magic mix for customer acquisition?

For more information, check out this webinar: Using Machine Learning & Spark to Power Data-Driven Marketing.