shiwaneeg

I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots. ...

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I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots.

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How to enhance multichannel marketing attribution with ML

By shiwaneeg |Email | Mar 14, 2018 | 6258 Views

In today's world of digital commerce, it is common for a transaction to involve in many touchpoints. Many marketers take a convenient shortcut towards the last touchpoint before sale. Businesses need an enterprise-grade technique to quantify each touchpoint's impact on the sale.

Attribution allows to justify marketing budgets and optimize marketing activities. Further, it also makes smarter bids for digital campaigns and measure key performance indicators (KPIs) effectively. Marketing with little or no information and analysis leads to irrelevant spending of huge sum of marketing budget. Proper marketing attribution solves major business problems.

Marketing practices of a firm majorly includes using cluster analysis. Cluster analysis is a statistical technique that assigns prospects and customers to groups, or clusters, based on characteristics that are common to individuals within that group. Each individual within a cluster may be grouped in the same cluster, but their purchasing behaviors may vary. 

But, cluster analysis is simple and practical while calculating clusters using commonly available software running on the computing power at the time. With the more competitive market and customer's innumerable demands, cluster analysis is not sufficient. And, Modern customers expect to be treated as individuals.

Standard practice in the industry has been to attribute a sale to the most recent marketing touchpoint. But by creating brand awareness, earlier touchpoints may have had a significant impact on the consumer's decision to purchase. In fact, a consumer may have been planning to purchase regardless of whether he or she saw the latest online ad.

In today's digital world, the average sale results from more than 30 touchpoints. Without better predictive models, marketers simply don't know which activities were most effective in driving the purchase decision.
In a perfect world, we would know exactly which customers saw a particular online or TV ad. But, to date, this information has been difficult or impossible to come by. The nature of digital marketing and media placements can often be at odds with data-minded marketing professionals. Views and clicks in the digital world are aggregated and anonymized, and television and print media show only broad demographic information about potential influence.

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 modeling complex human behavior. But machine-learning models capture the complexity of human behavior, analyze the impact of many touchpoints, and identify which marketing activities most influence a sale.

Traditionally, data scientists used to build machine-learning algorithms manually. That 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.

AML empowers users of all skill levels 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 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. 

By using historical touchpoints and outcomes, AML automatically finds patterns, creating a model that predicts sales depending on 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 increases revenue, gives insights for a long-term purpose. Attribution creates a clear guide to show you which marketing programs are worth spending money on and which are not. With this information, marketing can be easier because this knowledge can help in segregating between what's important and what's not and act accordingly. 


Source: HOB