What’s the right attribution model for measuring and optimizing campaign performance? How does it help you understand where to allocate advertising budget and focus marketing resources? Find out what the analytics experts recommend.
Keeping track of every single step of your customer’s journeys is not an easy task. Even after this, analysis becomes the next problem. It might seem incredibly difficult to follow your customers’ full experience, starting from initial contact and ending at point of sale.
But really, once you have the data it becomes easy with the proper tool. There are powerful attribution models that can help you to develop a highly effectice marketing strategy based on analysis of your data through multi-touch attribution modelling.
It is thanks to these attribution models that you can achieve a meaningful and concrete big picture view. And, with it, the proper distribution of investment to the correct touch points along the conversion path becomes clear.
We’re getting ahead of ourselves though. To take it back to square one, it all boils down to trying to assign answers to questions like:
Remember, at the end of the day, the number one goal is trying to improve your marketing ROI. That’s the metric you should be focusing on. The real challenge is choosing the right tool to do so. Of course, you need one that fits your budget, but one that provides accurate data to improve your marketing strategy is the real game changer.
Let’s look at two different approaches for assigning value to your touch points: rule-based and data-driven attribution models.
These are simple attribution models, based on predefined formulas. On these models, the touch points are defined by preexisting rules. The most common rules-based attribution models are the single-touch, linear and custom attribution models.
Here are a few examples:
Rule-based attribution models are simple and predefined. They cannot be customized around your specific business. The determent here is the inability to develop a real marketing strategy based on the rudimentary insights that such models give you. When real money is on the table, these models are far too simplistic.
Data-driven modeling is a sophisticated algorithmic method of attribution. Models, for example, can be based on recurrent neural networks, the Shapley value (sometimes called game theory) and Markov chains. In general, the technique is based on scientific approaches that provide output predictions built on the data that is feed into it. The more you can give it, the better they become.
Data-driven attribution can be customized for any type of business and produces dynamic output. Its advantage lies in assigning credit to different touch points based on both customer paths that end in conversions, and ones that don’t.
It determines which touch points are the most influential in the customer journey and provides more accurate conversion data. In short, it values all steps on the conversion path. This model relies on high-quality data and requires a high degree of human interaction for analyzing its data.
An algorithmic attribution model allows you to meticulously measure the most relevant KPIs for highly integrated cross-channel campaigns, including profit, ROI, and sales.
Rule-based attribution offers predefined simplistic models with basic formulas, and its attribution credit goes to the first or last click. However, “keeping it simple” does not offer the complete and high quality perspective that a professional marketing strategy needs to be succeed.
Data-driven attribution has a specific quality that can engage with all types of business: it can be fully customized to meet any requirements. This is the recommended model to measure and optimize campaign performance. Using data-driven attribution can help you fully understand where to allocate resources and spend your budget.
ROIVENUE has actually developed its own data-driven attribution model for use in its application. Want to see how it works? Sign up for a 1 to 1 live demo.