Benefits of Machine Learning-Based Advanced Attribution Models

Digital advertising has remained undeterred despite the adverse effects of the Coronavirus pandemic. This sector has traced an upward trajectory for several years exceeding a global turnover of 300 million dollars.  

Your organization’s ability to estimate advertising ROI is compounded by the presence of multi-channel advertising in the absence of advanced attribution models augmented by Machine Learning (ML) capabilities. 

How Do Traditional Attribution Models Fail Your Organization?  

An attribution model is a group of rules that an organization implements to gauge the value of every brand interaction at the final conversion stage. When considering omnichannel models, these laws encompass advertising impact and various touchpoints on the page and brick-and-mortar. 

Typically followed attribution models include: 

Data-driven Attribution: This model tweaks the ratio of attribution for each impact variably. This model is cut-short because it solely relies on analyzing the pathway taken by users outside of the website. As such, they do not consider user activity on the main website itself.  

Last-click Attribution: This model positions the whole conversion on one point of impact – The last click at the end of the funnel. This model only allows a single cross-sectional view into the state of affairs. With the complex customer journeys of today, this model is highly restrictive.  

Multi-channel Attribution: This model considers many segments and feeds and disperses a set conversion ratio for various impressions. It is limited in being a closed model, meaning the ratios are fixed and are not dynamic irrespective of user activity.  

Advanced attribution models set themselves apart by examining user channels as well as user activity across the website. They also verify data to indicate complete purchase cycles.  

How Can the Application of Machine Learning Benefit Advanced Attribution Models? 

Self-taught ML algorithm’s functions are based on previously segmented information. This means the outcome is outlined to the system and generates parameters based on data fed.  

This also implies that ML-based advanced attribution models are not pre-formatted but consistently modify themselves to become efficient in due course. In real-time, this means that any changes on the website need not be relayed to the attribution model. Instead, the algorithm adjusts on its own to accommodate modifications.  

ML can also expose bot-generated counterfeit traffic whose volumes do not match up with website activity.  

How Does an Advanced Attribution Model Work with Machine Learning?  

There are three core steps: 

Gathering Data: This is done through JavaScript code used to save the information or Google Analytica 360. Additional channels could be models that interpret the UTM, Adservers, or CRM. 

Creating a Data Flow with Data Segmentation:  Platforms like Snowflake, Azure, Amazon, or BigQuery helps with creating a data flow through data segmentation. 

Applying ML Augmented Attribution Model to Data Lake: This stage allocates a specific value for every session and externally generated impact received by the user. Such a measure allows for the accurate measurement of conversions. This algorithm conducts a superior level calculation then converts them into global ratios to estimate the total impact of each activity on end sales.     

Restructure Strategies Based on Real-time Data and Dynamic Allocation 

ML attribution models enable the measurement of overall advertising campaign impacts and deliver a global picture of data in real-time. With such insight, it becomes easier for your enterprise to modify campaign strategies on the go. Advanced attribution models can also predict plausible scenarios and allow you to choose the most beneficial option.  

If you’re interested in exploring ML augmented advanced attribution models to enhance your advertising efforts, the SprigHub team would be glad to help. SprigHub can help you with marketing campaign planning and budgeting tools, plus more with our revolutionary, AI-powered marketing investment optimizer. With a strong foundation in AI-powered data analytics, our platform provides a closed-loop process for marketers to plan and measure campaigns. With multiple levels of real-time data visualization, your campaign leaders can make smarter, informed decisions driven by automated AI data analytics. If you’d like to know more, book a demo to see how you can leverage enhanced marketing performance visibility to increase marketing ROI.    

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