The current economic climate is putting advertisers and marketers under tremendous pressure to demonstrate ROI from their marketing dollars. Gone are the Mad Men days of advertising when the art of storytelling was all it took. Today, advertisers are spending in an omnichannel environment and must account for every ad dollar. It’s no longer all about reach – or clicks on online ads. The entire focus of advertising has evolved from reach or clicks to the desired outcome – usually a purchase – either online or offline.
How do marketers know which of their multitude of channels and touchpoints are performing on par and responsible for consumers’ desired actions?
Enter marketing attribution
As the marketing ecosystem gets more complex, the platforms that need marketing dollars to increase in number, and the demand for justifiable ROI from marketing reaches unprecedented decibels, sound measurement, and attribution have become the holy grail for brands and CMOs. They are seeing an immediate need to link different marketing touchpoints in their attribution and decision-making models to truly understand the new consumers’ purchasing path.
Marketers have been following simplistic attribution models such as a first click or last click or attributing credit to the first or last touchpoint in the path to purchase. However, these attribution models are hardly comprehensive or data-driven, and they rarely say anything about the consumers’ intent. AI-based algorithms now play a significant role in building sound attribution models for the new complex, non-linear buying journeys and multitude of marketing channels, platforms, and touchpoints.
Custom attribution models with AI at the core
We have developed four key models for AI-based attribution that are truly data-driven and cutting-edge. AI-based custom attribution models are truly compelling in the new-age marketing ecosystem. Our four attribution methods are based on all events and channels where customer touchpoints exist and can predict – to a large degree of accuracy – whether a touchpoint led to conversion or not.
What are these four methods? Here’s a quick summary:
- Logistical Regression – It is a well-established statistical model that takes inputs from existing touchpoint data and predicts which class the data should belong to. A non-linear function is applied to each touchpoint. Smaller the value, smaller the weightage assigned to it. Using these touchpoints, the model is trained over time. Each touchpoint becomes a variable in the logistical regression model, predicting conversion based on historical data to a reasonable degree of accuracy.
- Shapley Value – The Shapley Value model takes a game-theoretic approach to multi-touch attribution. The core idea is to keep or remove a channel and then ascertain the outcome. This naturally tells you your highest performing and lowest performing channels and is a fair and transparent way to attribute credit to each channel or combination of channels.
- Markov Chains – This model considers the sequence of the customer journey, i.e., the likelihood of each customer being exposed to some marketing tactic and the potential next step in the journey. In summary, the Markov Chains model focuses on the probability of each consumer transitioning from one exposure to the next marketing exposure and taking a desirable action in the process, such as a website visit or a purchase. The model considers all possible conversion paths. It gives appropriate weightage to each exposure on the customer’s journey to conversion. We then take away one of the channels and see the impact on conversion and subsequently ascertain the value of that channel in the attribution model.
- Hidden Markov Model – Hidden Markov Model, although new, is one of the most effective attribution models in marketing. It attempts to determine the state of mind of each consumer when they perform any action during the path to purchase. For example, what state of mind is the consumer in when he or she visits the website, searches, clicks on an ad, etc. This determines whether the action will lead to purchase or not.
- The Hidden Markov Model has had a significant impact on ML, and its impact on marketing and advertising is only beginning. It is safe to say that in the world of clicks – sometimes even inadvertent – the Hidden Markov Model can truly predict the role of each channel in bringing the consumer closer to desired actions like purchase.
The Bottom Line:
AI-based custom models are the future of marketing attribution. Evidently, AI-based attribution models can track each consumer at each stage of the buying journey and understand the importance of each “moment” and “action” in the customer journey. This helps advertisers truly understand the performance of each touchpoint and channel in the buying journey and optimize media spend continuously during campaigns. AI-based attribution models are driving the next generation of ROI-focused marketing.
Are you ready to up your measurement game with AI? If yes, then reach out to [email protected] or visit us here to know more.
