Could machine learning solve attribution challenges?         

According to reports by Nielsen, only one out of every four marketers can confidently attribute revenue growth to their digital efforts. This may be true for even more people. It is a serious challenge to find the correct methodology to attribute revenue to particular digital initiatives- for marketing and sales teams.

The reason is that cross channel campaigning gives rise to data that is in silos, and relevant only to their part of the systems. This creates disconnected systems. Certainly, it will be a challenge to understand which part of the campaign can be attributed to what proportion of revenue growth. As the Martech stack grows, this lack of measurability could become a significant challenge and more so, the attribution accuracy would be unclear.  This question becomes then an issue for proper and fair marketing budgets, and especially so for the digital marketing part of the monies. If the marketers are unable to attribute growth to their digital efforts, how would they justify asking for budgets?

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Today, the most used models for attribution measurement are the Single-touch (where attribution is credited to the final touch point that converts) and the Multi-touch attribution models. The latter uses a methodology that assigns different weighted values based on how likely marketers believe each touch point influences the conversion through the customer journey. However, the practice of assigning weight to channels has a heavy and clear human bias.

A modern solution seems to be one based on Machine learning and chain-based attribution. It helps remove the human bias and learning with the given scenarios, it gatherings learning from varying data to look at different outcomes — revenue, pipeline, lead generation, etc. From these it identifies the successful touch points across the customer journey. The ML model analyses buying patterns over time and identifies the patterns that influence a chain of events. This way, the ML driven chain-based model starts with the outcome, then looks back at the steps across the journey taken to drive the result.

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Essentially the tool connects siloed platforms. It allows assesability of a digital initiative in marketing by tracking its RoI, with the outcome as a starting point. It then works backward to understand the holistic impact of each step of the customer journey. This method would provide marketers with accurate knowledge of their digital performance, thus assuring quality of marketing intelligence and insights, giving them the power to take well-informed decisions on investments for best marketing outcomes.

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