Handling Marketing Attribution Challenges with ML

By Martech Outlook | Wednesday, September 18, 2019

The advancements in machine learning and natural language processing is assisting marketers with more effective and reliable attribution models.

FREMONT, CA: Struggling with attribution is not a new challenge. According to a report, only one out of every four marketers can attribute revenue to their digital endeavors. Attribution is a pressing issue that can be a serious challenge for the sales and marketing teams. Initiating cross-channel campaigns via different platforms result in siloed data across various systems. The continuous growth of the martech stack can lead to significant challenges that include attribution accuracy. Despite such challenges, it’s crucial that the team concerned, such as a digital marketing team is able to confidently track ROI and share those numbers with the organizational leaders.

The Attribution Issue

Various marketers experience frustration on a daily basis, and attribution disparities often unveil larger organizational challenges. Furthermore, if marketers can’t provide their leadership the ROI behind their digital marketing, how can they demand further martech investment?

Single-touch is possibly the most-leveraged model, where attribution is credited to the final touchpoint that changes. On the other hand, multi-touch attribution models use a methodology that offers various weighted values depending on how likely marketers assume each touchpoint influences the conversion across the entire customer journey.

The primary challenge that the marketers face is the determination of attribution model and where to assign the weight. Undermining one source in the model can present a challenge when leadership buy-in in continued investment in that source is required. Part of the problem is the standard attribution techniques that result in complex challenges without clean and good data sets. Chain-based models must also be used going forward.

The practice of allotting weight to channels throughout customer journeys depends heavily on human bias. The channels that are assumed of driving heavy conversions are assigned the maximum weight. However, machine learning (ML) can assist in tracking the lead and revers-engineer the consumer journey, thereby eliminating the chances of human bias. Here are the major potentials of ML:

ML and Chain-Based Attribution

The advancements in ML and natural language processing are getting much more tangible to marketers in the past few years. However, many of them are still facing difficulty in understanding on how to use the advancements for marketing purposes. ML model can remove human bias and will learn with the data to find various outcomes- pipeline, revenue, lead generation, and others.

The ML model analyzes buying patterns over time and records the patterns that trigger or influence a chain of events. The chain model begins with the outcome and reiterating the steps back and forth across the journey, which is in the interest of the end result.

Marketing Intelligence for Leadership

ML and automation have a strong impact on the martech landscape. However, the way they are used by many fails to grasp the holistic outcomes of each step of the customer journey. Organizations need to leverage ML tools in a way that improves the quality of marketing insights and intelligence. Equipping oneself with accurate knowledge of the digital performance will result in well-informed discussions with decision-makers. Further, adopting this approach can significantly push marketers who require leadership buy-in for continued investment in undermined sources. 



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