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How Machine Learning Can Help Marketers Measure Multi-Touch Attribution

How Machine Learning Can Help Marketers Measure Multi-Touch Attribution

Alex Geer

By 2019, according to the Harvard Business Review, it’s estimated that global spending on media is expected to reach $2.1 trillion. But is all that money effective in producing improved ROI? Without knowing which channels are driving sales, or more specifically, which individual efforts are working, marketing spending is like a black box.

In today’s world of digital commerce it is not uncommon for a transaction to involve as many as thirty marketing activities, or “touchpoints.” Yet many marketers take a convenient short cut and credit each sale to the last touchpoint before the sale. To properly attribute the influence of all marketing activities, businesses need an enterprise-grade methodology capable of quantifying each touchpoint’s impact on the sale.


Why Worry About Marketing Attribution?

You may know in aggregate that your marketing “is working,” but without true attribution, you don’t know which marketing activities have the greatest impact and which are simply eating into limited marketing budgets. Attribution allows you to:

  • Justify marketing budgets and optimize marketing activities
  • Make smarter bids for digital campaigns
  • Effectively measure key performance indicators

Proper Attribution Solves Common Marketing Inefficiencies

As the saying goes, “you can’t improve what you don’t measure.” Marketing with little or no information and analysis leads to marketing dollars spent inefficiently and ineffectively. Proper marketing attribution solves at least four major problems.

  • Cluster analysis can result in flawed targeting
  • Attributing sales to the last touchpoint is flawed 
  • Separating the effects of multiple touchpoints is difficult
  • Knowing which ads reached a specific consumer

To learn more, download the report or watch the on-demand webinar.

Machine Learning and Mapping the Path to Purchase

For effective marketing attribution, marketers need to develop highly accurate predictive models.

  • Statistical methods, which add a score for each separate personal characteristic, are not up to the task of modelling complex human behavior.
  • Machine learning models capture and analyze the complexity of human behavior, analyze the impact of many touchpoints, and identify which marketing activitiesmost influence a sale.

Traditionally, data scientists build machine learning algorithms manually. This process can be frustratingly time consuming, with some projects taking months to deliver. By the time the algorithm is ready, it may already be obsolete.

Marketing needs a faster way to build the algorithms – a process that isn’t so manual. The answer is Automated Machine Learning (AML), a technology that automatically constructs algorithms from historical data, sometimes in as little as a few hours instead of days or months.

Marketing Attribution with Automated Machine Learning (AML)

AML empowers users of all skill levels – including marketers – to make better predictions faster. By automating many of the skills traditionally applied only by data scientists, AML provides
the fastest path to data science success for users who understand the business and the data.

AML allows you to create sophisticated marketing attribution models to perform complex “what-if” analyses that quantify the effectiveness of different kinds of marketing activities and different combinations of marketing touchpoints:

  • Start with a Baseline – To begin, you need to determine a baseline – the sales that would naturally occur without any marketing activity. You can use AML to analyze the impact on sales if you remove all the marketing touchpoints.
  • Determine the Marketing Contribution to Sales – This is the difference between actual sales and the calculated baseline sales. The more effective your marketing activities, the more sales are boosted above this baseline.
  • Assign a Contribution for Each Touchpoint – AML performs a variety of what-if calculations on the impact to sales if you remove one, or multiple, touchpoints.

By using historical touchpoints and outcomes, AML automatically finds patterns, creating a model that predicts sales depending upon the touchpoints that apply to each lead. Using the model, you will run a number of “what if” scenarios using different touchpoints to predict how different combinations of touchpoints impact sales.

Attribution performed in this way “lights up” your sales funnel, giving you insights not possible only a few short years ago. Attribution creates a clear guide to show you which marketing programs are worth spending money on, and which are not. With this information, you do more of what works, and reduce or eliminate what doesn’t.

Download the new report Multichannel Marketing Attribution and discover:

  • The need for multichannel attribution in the age of multiple touchpoints
  • How proper attribution solves common marketing inefficiencies
  • How machine learning provides the best way to measure marketing effectiveness 

Watch this free webinar on Multichannel Marketing Attribution and discover:  Data Scientist, Colin Priest of DataRobot explains the importance of multichannel, multi-touch attribution to accurately measure the success of your marketing efforts – and how automated machine learning offers the shortest path to success.