Future of marketing attribution: how AI changes the game
The rise of cookie restrictions and tracking limitations has made traditional rule-based methods of marketing attribution increasingly difficult to implement. This is where Machine Learning (ML) comes in, as it provides an alternative way for businesses to accurately attribute conversions to the marketing channels that are driving them.
Now, Machine Learning and Artificial Intelligence (AI) change the game for marketing attribution completely and shape its future.
Introduction to Machine Learning and marketing attribution
What is marketing attribution?
Marketing attribution is the identification of user actions that contribute to a desired outcome, and then the assignment of value to each of these actions. This means that marketing attribution involves looking at all touchpoints or interactions a customer has with a brand before making converting and assigning value to each of these touchpoints based on their contribution to the final conversion.
As a result, marketing attribution helps identify the most effective marketing channels, campaigns, and touchpoints that lead to conversions, and most importantly calculate their value.
Many marketers and analysts become trapped in the idea of tracking customer journeys and want to know which traffic sources or touchpoints a user interacted with before converting, and then distribute the value from this conversion accordingly. This is the biggest misconception about marketing attribution.
With the rise of a cookieless world, attribution faces a set of challenges that prevent it from showing adequate results.
Why marketing attribution needs Machine Learning?
There are major challenges that affect marketing attribution, and it turns to Machine Learning for a solution.
With the increasing use of cookie restrictions and tracking limitations, businesses are finding it challenging to accurately attribute conversions. For example, observing the complete customer journey from the first touchpoint to the conversion is impossible in today’s privacy-focused digital environment. With users using multiple devices and taking breaks between visits, tracking their entire journey becomes difficult. Even when trying to complete the same task, users often switch between devices, which makes it impossible to assign fair credit to the initial touchpoints. As a result, these touchpoints are usually highly undervalued, and all credit goes to closing channels.
This happens because cross-device and cross-browser journeys are difficult to link to a customer who has converted. Tracking user behaviour becomes difficult when they switch between devices without logging in, making it impossible to determine which channels are driving conversions.
Moreover, tracking restrictions and cookie use limitations add to the challenge. In recent years, browsers like Safari, Chrome, and Firefox have introduced new features and technologies to protect user privacy by limiting the lifespan and amount of data that a first-party cookie can store on a user’s device. This prevents long-term tracking of users’ browsing activities.
This is when marketing attribution turns to Machine Learning algorithms. They can analyse vast amounts of data, including all customer interactions with a website, to accurately attribute value to marketing campaigns and channels.
For example, this is how SegmentStream uses Machine Learning. First, it collects tons of behavioural data from the website and feeds it to the ML algorithm. The algorithm then spots particular behaviour and micro-conversions that typically lead to a conversion, and assigns fair value to every website visit using this data. As a result, Machine Learning does tons of work under the hood to compensate for the unavailability of tracking data and is able to produce better results than traditional attribution does.
By adopting Machine Learning for marketing attribution, businesses can overcome the challenges posed by cookie restrictions and tracking limitations, while still gaining valuable insights into their marketing performance.
Yet, many businesses still use traditional rule-based marketing attribution.
Rule-based VS Machine Learning marketing attribution
The most adopted rule-based marketing attribution model is multi-touch attribution. It is supposed to divide the value from conversion between multiple touchpoints in the user journey that led to that conversion.
There are multiple types of multi-touch attribution:
- Linear attribution evenly distributes credit across all touchpoints that have contributed to a conversion.
- Time decay attribution gives more credit to touchpoints that are closer in time to the conversion than those closer to the beginning of the customer journey.
- U-shaped attribution gives equal weight to the first and last touchpoints in the customer journey while dividing the remaining credit equally among all other touchpoints.
- Data-driven attribution involves analysing data from various marketing channels to identify touchpoints that contributed to conversion and assigning weights to each touchpoint based on its contribution.
- Algorithmic attribution uses an algorithm to assign credit to each touchpoint based on factors such as the type of touchpoint, the position in the customer journey, and the time since the touchpoint occurred. This model requires a significant amount of data to train the algorithm.
Despite having a variety of multi-touch attribution models, marketers still have no real choice. Since there are many tracking restrictions and privacy regulations, even the most sophisticated multi-touch attribution tools show results similar to single-touch models.
On the other hand, there is ML-based marketing attribution. It is an innovative approach that solves all challenges that traditional attribution faces, in particular the lack of data due to tracking and cookie use regulations.
SegmentStream is a perfect example of how revolutionary ML is for the marketing attribution industry. As you already know, SegmentStream collects behavioural data from the website and feeds it to the ML algorithms. Then, it uses this data to assess each website visit in real time, assign a monetary value to each visit, and calculate the probability of future conversion.
If the probability is high enough, SegmentStream creates a Modelled Conversion. These conversions can be used for advanced marketing analysis and reporting on the performance of all campaigns and channels. What is more, Modelled Conversions can be sent to advertising platforms as feedback signals to help optimise ads’ performance and enhance bidding and targeting algorithms.
As you can see, SegmentStream is a perfect example of an AI-driven marketing attribution tool. It utilises Machine Learning to not only enhance marketing attribution and reporting but also to help elevate the digital advertising game.
There are multiple benefits of ML and marketing attribution synergy which we will discuss below.
Benefits of Machine Learning in marketing attribution
Using Machine Learning in marketing attribution has a number of significant benefits for businesses, especially in the face of the cookieless world.
Improved accuracy of analysis
Machine Learning algorithms can quickly analyse vast amounts of both behavioural and cost data from the website and ad platforms respectively. The algorithms can see data patterns and trends that no human can spot, making analysis way more accurate.
All this data can then be used for thorough reporting and to accurately attribute conversion value to marketing campaigns.
Real-time insights into marketing performance
Using all analysed data, ML algorithms can provide real-time insights into marketing performance. This helps marketers quickly adjust campaigns and optimize their marketing spend to achieve better results.
For example, using ML SegmentStream evaluates each website visit immediately and calculates the probability to convert in the future. It doesn’t need to wait for the actual conversion to happen to analyse it retrospectively.
Better targeting and bidding
By analyzing customer data, Machine Learning can help to identify patterns and preferences, enabling marketers to create more targeted campaigns that better fit their audience. With advanced tools, this process can even be automated.
When SegmentStream evaluates website visits, it calculates the conversion probability for each of them. If it’s high enough, the platform creates a Modelled Conversion which can be sent back to advertising platforms as a feedback signal. Then, ad platforms use these signals to automatically adjust targeting and bidding algorithms, ensuring higher ad campaign efficiency.
Increased efficiency of marketing teams
AI in marketing attribution can automate many of the processes, freeing up time and resources for marketers to focus on other areas of their business.
SegmentStream automatically collects and analyses customer data and ad-related data, creates comprehensive customisable reports with multiple metrics and visualises data, and offers multiple attribution models to compare the results of marketing campaigns. All these features save tons of time and effort for marketers who want insights into the performance of all marketing activities, hassle-free.
Compliance with privacy regulations
With the increasing focus on data privacy and security, ML-based tools can help to ensure compliance with regulations such as GDPR by minimizing the use of personal data.
As a Machine Learning marketing attribution tool, SegmentStream uses only first-party data and has access to it if only the user has accepted cookies on the website. All other private data stays safe and isn’t used in any marketing activities.
And thanks to the use of AI, marketing attribution in SegmentStream can still deliver adequate results with Conversion Modelling. Even when the whole customer journey cannot be observed, SegmentStream still attributes conversion value properly with the help of Machine Learning.
How AI and ML change marketing attribution
In conclusion, the rise of cookie restrictions and tracking limitations has made traditional rule-based methods of marketing attribution outdated and inefficient.
Machine Learning provides an alternative way for businesses to accurately attribute conversions to the marketing channels that are driving them. The challenges of tracking customer journeys and attributing value to touchpoints can be overcome by Machine Learning algorithms.
These algorithms can analyze vast amounts of data to accurately attribute value to marketing campaigns and channels. ML-based marketing attribution solves all challenges that traditional attribution faces, in particular, the lack of data due to tracking and cookie use regulations.
There are multiple benefits of the synergy between Machine Learning and marketing attribution, including better insights, advanced marketing analysis and reporting, and elevating the digital advertising game. With Machine Learning and Artificial Intelligence changing the game for marketing attribution completely, it is safe to say that the future of marketing attribution is bright.
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