Predictive vs Data-Driven marketing attribution models
In today’s marketing landscape, attribution models play a crucial role as they help marketers understand the touchpoints that lead to conversions and allocate credit to the appropriate marketing channels.
One popular approach to attribution modelling is data-driven attribution, which uses vast amounts of data about customers to provide insights into the most effective marketing channels. 60% of marketers still consider data-driven attribution essential for understanding the customer journeys of high-value clients.
However, in the modern cookieless environment, data-driven attribution struggles to provide good results. This is why predictive modelling in marketing attribution is emerging as a promising alternative approach, especially in the cookieless world.
In this article, we will explore the differences between data-driven attribution and predictive attribution. We’ll delve into the different types of predictive and data-driven attribution models and discuss how Conversion Modeling can be used as a predictive attribution tool.
Predictive attribution VS Data-driven attribution: what’s the difference?
Let’s see what’s the actual difference between data-driven attribution (DDA) models and predictive attribution.
What is data-driven attribution?
Data-driven attribution takes customer data and subjects it to advanced analytics to determine the most effective marketing touchpoints or channels that led to a desired outcome.
This approach relies on data about actual conversions that have happened. One of the issues with data-driven attribution is that it cannot track down the initial touchpoints that led to the conversion. The challenges that cause this issue are:
- Cross-device journeys. Customers often use multiple devices during their path to purchase. Tracking these cross-device journeys is nearly impossible, making it difficult to accurately attribute conversions to the correct marketing touchpoints.
- Long sales cycles. In industries with long sales cycles, it can take way more than a couple of days for a customer to convert. This can make it difficult to track and attribute conversions accurately, especially when the customer interacts with multiple touchpoints over an extended period of time.
- Tracking restrictions. Many tracking restrictions and privacy regulations, such as GDPR, limit the amount of data that can be collected about customers. This can make it challenging to get a complete picture of the customer journey.
- Cookie use limitations. With the rise of privacy concerns, many web browsers are now limiting the use of first-party cookies and the amount of data they can store. This can make it difficult to track customer behaviour and accurately attribute conversions to specific marketing touchpoints.
Despite these challenges, data-driven attribution remains a popular method for determining the most effective marketing channels and touchpoints. Yet, businesses are starting to see that it doesn’t provide as much actionable information as it could before.
Types of data-driven attribution models
Basically, any retrospective attribution model is a DDA model, because both these groups need a conversion to happen first to have the required data for further analysis.
For this article, we will review two data-driven attribution models, the Markov chains attribution model and the Shapley Value attribution model.
The Markov chains model assigns credit based on the probability of each touchpoint leading to a conversion. This model uses historical data to estimate the probability of a customer moving from one touchpoint to the next and assigns credit accordingly. This model is useful for identifying which touchpoints are most likely to lead to a conversion and for optimising marketing campaigns accordingly.
The Shapley Value attribution model uses a concept from cooperative game theory. It considers all possible combinations of touchpoints and calculates the average marginal contribution of each touchpoint to the conversion.
This approach ensures that credit is distributed fairly among all touchpoints. The Shapley Value model is an improvement over traditional attribution models that use heuristic rules to allocate credit, as it takes into account the interaction and cooperation between different touchpoints in the journey to conversion.
What is predictive attribution?
Predictive attribution involves using statistical models and algorithms to predict which marketing touchpoints or channels will lead to a desired outcome, be it a conversion or a lead. This approach relies on historical data and can be useful in situations where there is limited data available, just like in the current cookieless world.
Machine Learning is used extensively for predictive attribution. Typically, marketers feed user behavioural data from their website to the algorithm to detect the behaviour that typically leads to a conversion. With this, detecting the most promising channels becomes possible.
Types of predictive attribution models
There are several custom predictive attribution models available on the market today, they are typically built using Machine Learning algorithms or AI. These technologies are used to analyse data and predict which channels and marketing tactics lead to conversions. One of the best examples of predictive attribution is Conversion Modeling.
It uses Machine Learning algorithms to assess the impact of all marketing activities and predict the probability to convert for all website visits, even when actual conversions are impossible to observe. This is particularly helpful in situations where conversion paths involve cross-device interactions, or when initial cookies expire.
Conversion Modeling is a great example of a predictive tool that does a good job when other attribution models and marketing measurement tools give up.
Conversion Modelling as a predictive attribution tool
SegmentStream’s Conversion Modelling is also a predictive attribution tool.
It uses Machine Learning algorithms to analyse data from micro-conversions on a website, and predict the probability of conversion for each website session. This gives marketers the ability to do real-time decision-making, as every website visit is assessed immediately. The tool also helps marketers to assess all marketing efforts and optimise them accordingly, as well as make wise budget allocation decisions.
In addition to powerful prediction abilities, the platform offers a toolset for reporting and data visualisation. With its customisable reports, marketers to create custom attribution models or use preset ones to compare data. This helps to ensure that marketing efforts are assessed properly and that marketers make the right decisions to optimise advertising campaigns.
Another benefit of Conversion Modelling is its ability to send feedback signals back to advertising platforms. Using these signals, advertising platforms optimise ad delivery, which in turn improves the overall effectiveness of marketing campaigns.
Overall, the tool beats data-driven attribution models in terms of it’s ability to operate in the modern cookieless world. For Conversion Modelling, it’s not crucial to be able to observe the whole customer journey, as well as wait for the actual conversion to happen. Thanks to it’s ability to predict the probability of conversion for each website visit and assess the performance of marketing channels, this tool is way more effective than data-driven marketing attribution.
Pros of predictive attribution
Predictive attribution offers several benefits compared to data-driven or multi-touch attribution models.
- Predictive attribution doesn’t wait for an actual conversion to happen. Instead, it estimates the likelihood of a conversion occurring. This means that marketers can get insights into the effectiveness of their campaigns right away and make adjustments on the go.
- Predictive attribution can also provide more precise analysis results compared to DDA models, as it can take into account a wider range of data points and factors. This leads to attributing conversion credit more accurately to different marketing touchpoints and channels.
As a predictive attribution tool, Conversion Modelling can overcome all the challenges associated with marketing attribution that we’ve mentioned before: long sales cycles, tracking restrictions, and cross-device journeys.
It can be used for both marketing analysis and ad campaign optimisation. This means that marketers can gain insights into the effectiveness of their campaigns as well as use that information to optimise their advertising spend and help ad platforms enhance ad targeting and bidding.
Conclusion: DDA or predictive attribution?
Data-driven attribution has been considered the most sophisticated and accurate approach to attribution. However, this is no longer true — with the rise of privacy regulations and cross-device journeys, DDA faces limitations.
And predictive attribution provides a solution to these exact challenges that DDA can no longer overcome. It can operate in a cookieless world and doesn’t need the conversion to happen prior to providing valuable insights.
Conversion Modelling is an excellent example of a predictive attribution tool that analyses data about tons of micro-conversions on the website with the help of an ML algorithm, which in turn assesses each website visit and calculates the probability to convert in the future. The tool helps marketers with real-time decision making and boasts the ability to send feedback signals back to the ad platforms.
As you can see, predictive attribution is a better solution for marketers who want to overcome the challenges of data-driven attribution models. If you want to stay ahead of the curve it is better to start exploring predictive attribution models like Conversion Modelling and take advantage of their benefits.
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