Multi Touch Attribution — Data driven Marketer’s Guide
In today’s digital age, businesses have a plethora of data at their disposal. The challenge lies in making sense of this data and deriving insights that can help improve marketing strategies. Multi-touch attribution is a technique that can help with this. It allows businesses to track and analyze multiple touchpoints in a customer’s journey to conversion, enabling them to identify the channels that are driving the most conversions. However, as the number of touchpoints increases, so does the complexity of analysis. That’s where machine learning models come in.
Multi-touch attribution is a marketing technique that allows you to analyze and assign credit to multiple touchpoints in a customer’s journey to conversion. Instead of attributing all the credit to the last touchpoint, multi-touch attribution gives you a more comprehensive view of the entire customer journey. There are several types of multi-touch attribution models, including:
- First-touch attribution: gives all the credit to the first touchpoint in the customer journey.
- Last-touch attribution: gives all the credit to the last touchpoint in the customer journey.
- Linear attribution: distributes the credit equally among all touchpoints in the customer journey.
- Time-decay attribution: gives more credit to touchpoints that are closer to the conversion.
Each model has its advantages and disadvantages, and you need to choose the right model based on your business goals and customer behavior.
One of the recent advancements in multi-touch attribution is the use of machine learning models. Machine learning is a subset of artificial intelligence that enables machines to learn from data and improve over time without being explicitly programmed.
Machine learning is a subset of artificial intelligence that enables machines to learn from data and improve over time without being explicitly programmed. Machine learning models can analyze vast amounts of data, identify patterns, and make predictions based on historical data. In multi-touch attribution marketing, machine learning models are used to automate the attribution process and make it more accurate.
The first step in using machine learning models for multi-touch attribution is data preparation. The data must be cleaned, formatted, and organized before being fed into the machine learning model. This process involves removing duplicates, filling in missing values, and converting the data into a format that the machine learning model can understand.
Once the data is prepared, the next step is to choose a machine learning algorithm. There are several algorithms to choose from, each with its own strengths and weaknesses. The most commonly used algorithms in multi-touch attribution are decision trees, random forests, and neural networks.
Logistic regression is a statistical technique used in multi-touch attribution to model the probability of a conversion based on different touchpoints.
Shapley value is a game theory concept used in multi-touch attribution to allocate credit to each touchpoint based on its contribution to the conversion.
Markov chain model is a statistical model used in multi-touch attribution to analyze the sequence of touchpoints and their probabilities of leading to a conversion.
Decision trees are a type of machine learning algorithm that uses a tree-like structure to make decisions based on the data. In multi-touch attribution, decision trees can be used to identify the touchpoints that are most likely to lead to a conversion.
Random forests are similar to decision trees but are more complex. They are made up of multiple decision trees and can provide more accurate predictions than a single decision tree. Random forests can be used in multi-touch attribution to analyze the data and identify the touchpoints that are driving the most conversions.
Neural networks are a type of machine learning algorithm that is inspired by the structure of the human brain. They consist of interconnected nodes that process and transmit information. In multi-touch attribution, neural networks can be used to identify complex patterns in the data and make predictions based on those patterns.
Once the machine learning algorithm is chosen, the next step is to train the model using historical data. This involves feeding the model with a large dataset of historical customer journeys and their corresponding conversions. The model then learns from this data and uses it to make predictions about new customer journeys.
The final step is to evaluate the performance of the model. This involves comparing the model’s predictions with the actual conversions and measuring the accuracy of the model. If the model is not accurate enough, it can be retrained with new data or a different algorithm can be chosen.
Machine learning models have several advantages over traditional multi-touch attribution methods. They can analyze large datasets quickly, identify complex patterns in the data, and make accurate predictions. Machine learning models can also automate the attribution process, saving time and increasing accuracy.
In conclusion, machine learning models are a powerful tool in multi-touch attribution marketing. They can help businesses to track and analyze multiple touchpoints in a customer’s journey to conversion, enabling them to identify the channels that are driving the most conversions. Machine learning models can also automate the attribution process and make it more accurate. As the amount of data continues to grow, machine learning models will become increasingly important in multi-touch attribution marketing.
Stay tuned for more articles.
If you liked my article, give it a few claps and share it with your friends or colleagues.
Follow for more : Linkedin | Github | Website
© Copyright 2023 pavannaik.com. All Rights Reserved.
