Attribution models have benefitted marketing in a great way. As a marketer, you are able to report on how well different touch-points are in achieving the business’ goal. There are simplistic attribution models such as first and last touch which only assign credit to one channel. Multi-touch models are more complex than the single-touch ones. There are different ones to choose from. The effectiveness of each model is based largely on trial and error. Additionally, each attribution model has to be actionable if you are to draw conclusions and use the information to increase conversions. One of the most effective models is algorithmic attribution.

What Is Algorithmic Marketing Attribution?

As mentioned, multi-touch attribution models are relatively more complex. Each channel or campaign in the customer’s journey plays a role. Credit is assigned to each of these touch-points based on various rules. However, these rules can be limiting since they are not data-based. Algorithmic attribution steps in to remedy the situation.

Algorithmic-Attribution

Source: LinkedIn https://www.slideshare.net/SparkSummit/digital-atribution-chen-yanpptx

Algorithmic marketing attribution uses data science to assign credit to each touch-point. It analyzes past touch-points that have led to conversions and those that haven’t as well. This data is then computed in a formula based on logistical regression. With this, you get to know the impact of each channel or campaign. This model uses machine learning which means the information is updated into the algorithm in real time. Thus, your model is actionable and constantly updated.

Advantages of Algorithmic Marketing Attribution

The mark of an effective and reliable attribution model is how actionable it is. You want to have the most accurate results so you can optimize your marketing. This helps to efficiently allocate resources as well as maximize on conversions. This is the true mark of marketing attribution value addition.

Whenever you use custom multi-touch attribution models, you need to come up with rules on how to assign percentage contribution by each touch-point. In order to come up with numbers that reflect actual contribution, you need to do some heavy lifting. This includes monthly, weekly, daily or even hourly monitoring of your channels. Eventually, this will result in a trend that you can confidently use to come up with your custom model. Note that this monitoring has to be constant in order to reflect any changes and adjust the model over time. The great thing about algorithmic marketing attribution is that it involves machine learning. Your model picks up all the data, constantly analyzes it, and updates the information in real time. Not only is this conveniently easier but also more reliable.

Algorithmic Attribution vs. Multi-touch Attribution Models

Now that you have an idea about how algorithmic ad attribution models work, the next step is to decide whether the model will work for you. If you’re using single touch attribution models, then a change to algorithmic touch-point attribution will yield reliable and actionable results. On the other hand, if you’re using a fairly complex multi-touch attribution model, you need to figure out if making the move to algorithmic modeling will have significant impact.

How do you do this? You first need to analyze how complex your marketing and sales process is. If it’s fairly simple, then you don’t need a complex model. You can comfortably use a rule-based multi-touch model that works for your business. On the other hand, if you have different channels and campaigns, it makes the situation more complicated. A rule-based system won’t work as well as algorithmic modeling.

The second thing that you need to look at is your current model. If you have a fairly complex multi-touch model that yields actionable and beneficial results, you may not need to move to algorithmic marketing attribution. However, you would need to see whether including algorithmic modeling makes a significant change in your process. If it needs rechanneling of thousands of dollars, you might be better off switching models. If the percentage change is high but the reallocation of funds is fairly low or negligible, then your multi-touch model would still work for you.

Conclusion

Algorithmic attribution models are a step higher than complex multi-touch models. The biggest advantage of the model is that it automates everything and yields actionable intel. This way, you are able to optimize your marketing and sales process.

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