The role of machine learning in multitouch attribution
As customer journeys have become more complex, marketers often struggle to determine which touchpoints lead to sales. Explore how machine learning can help.
In the fast-moving world of digital marketing, the customer journey has become increasingly complex.
Consumers interact with brands across multiple touchpoints before making a purchase. To evaluate each touchpoint's role in driving conversions, marketers must attribute credit to various channels. Multitouch attribution models assign value to each touchpoint in a customer's journey to help marketing teams identify the most effective channels. However, traditional MTA models can oversimplify omnichannel marketing. Machine learning (ML) offers advanced analysis to enhance the precision of these models.
To create an effective attribution strategy, marketing leaders with complex customer journeys should understand how ML can improve MTA.
What is machine learning?
ML is a subset of AI that lets computers learn from data and make predictions or decisions without explicit programming. It uses self-changing algorithms to identify patterns within data and make informed decisions based on them. The algorithms can improve themselves and become increasingly accurate in their predictions over time.
In the context of marketing, ML can analyze vast amounts of data from customer interactions, purchase history and demographic information, to predict future behaviors. This predictive capability can enhance MTA models, which aim to understand the effect each marketing touchpoint has on a customer's decision to convert.
ML's role in MTA
MTA is inherently complex due to the many touchpoints involved in a modern customer journey. Traditional attribution models, such as linear attribution, time decay and last-touch attribution, apply fixed rules to assign credit to each touchpoint. However, these models often oversimplify the customer journey, failing to account for the nuanced interactions between different channels.
ML introduces a level of sophistication that traditional attribution methods lack, which can enhance MTA models. ML improves MTA in the following ways:
Handles complexity. ML algorithms can process and analyze large data sets of customer interactions across channels. This offers a more detailed understanding of how various touchpoints interact and influence each other throughout the customer journey.
Improves accuracy. ML models continuously learn from new data and can adjust their predictions and attributions to reflect real-time changes in consumer behavior. This adjustment maintains accuracy in attribution models, particularly in omnichannel environments where customer behavior can shift rapidly.
Identifies nonlinear journeys. Unlike traditional models, ML can uncover nonlinear customer journeys, which are unexpected, or complex sequences of touchpoints that customers take before they convert. This is essential for understanding the full scope of the customer journey, which often involves nonlinear paths.
Enhances personalization. ML can help marketers create personalized attribution models that cater to individual customer segments. It recognizes patterns specific to different demographics and purchasing behaviors, offers relevant insights and helps marketers tailor their strategies to various audience segments.
Use cases of ML in MTA
ML can enhance various MTA models, such as Markov chain and survival analysis, to offer deeper insights into customer behavior.
ML introduces a level of sophistication that traditional attribution methods lack.
1. Markov chain attribution
Markov chain attribution models use probabilities to predict the likelihood that a customer will transition from one touchpoint to another before conversion. ML enhances this model, as it can more accurately predict transitions between touchpoints and account for complex interactions that simpler models miss. ML algorithms can also handle larger data sets and offer a more comprehensive view of the customer journey across channels.
2. Survival analysis attribution
Survival analysis originated in the medical field to analyze the time until an event, such as the onset of a disease, took place. Marketers often use it to predict the time it takes for a customer to convert after a specific touchpoint interaction.
ML enhances survival analysis because it incorporates more variables, such as time between touchpoints and the sequence of interactions, into the model. This leads to a more nuanced understanding of how different marketing efforts contribute to conversion over time -- particularly for long sales cycles.
3. Deep learning attribution
Deep learning, a subset of ML, uses neural networks to model complex relationships in data. In MTA, deep learning can analyze intricate patterns in customer behavior that simpler models might miss. This approach can capture the effect of upper-funnel activities, such as brand awareness campaigns, which may indirectly influence conversions.
4. Shapley value attribution
Shapley value is a concept borrowed from cooperative game theory, which assigns credit to each player, or touchpoint, based on their contribution to the total outcome, or conversion. This method analyzes all possible combinations of touchpoints and determines how much each one influences sales based on the presence or sequence of other points in the journey.
Marketers can use ML to calculate each touchpoint's contribution more efficiently, especially when dealing with large data sets and numerous channels. This method offers a fairer distribution of credit across touchpoints and considers how each touchpoint contributes to customer acquisition.
5. Bayesian attribution
Based on Bayes' Theorem -- a mathematical formula to calculate conditional probabilities -- Bayesian models incorporate prior knowledge and update predictions as new data comes in. This approach lets marketers continuously update the estimation of how each touchpoint affects sales.
To enhance these models, ML can refine an organization's existing knowledge and improve Bayesian models' ability to learn from new data. This approach can benefit marketers in dynamic environments where consumer behavior constantly evolves, such as e-commerce, social media and tech.
Key takeaways
ML has emerged as an effective MTA tool, because it helps marketers analyze complex interactions in customer journeys. Its advanced algorithms move beyond traditional, static attribution models, unlocking a previously unattainable level of precision and insight. These advanced models offer a more accurate reflection of each touchpoint's true value and let marketers develop highly personalized attribution strategies tailored to specific audiences.
As marketing channels proliferate and consumer behaviors evolve, customer journeys will continue to become more complex. Therefore, ML will likely play an increasingly important role in MTA.
Marketers that embrace ML can stay ahead of the curve, make data-driven decisions, drive higher ROI and foster deeper connections with customers. The future of marketing is data-driven, and ML is at the heart of this transformation.
Robert Peledie is an enterprise architect, solution architect and director of CRM consultancy 365Knowledge Ltd. He has several years of consulting experience in global organizations.
