At XPON, we understand the challenge of marketing spend optimisation. Traditional attribution models often struggle to capture the complexities of today’s customer journeys.

This can lead to wasted budget and difficulty in identifying which marketing activities are truly driving conversions.

In this post, we’ll explore how regression-based attribution, powered by artificial intelligence (AI), can revolutionise your approach to marketing spend optimisation.

The Challenge of Marketing Attribution

Many businesses rely on traditional attribution models like “last click” or “first click” to assign credit for conversions.

However, these models have significant limitations:

  • Inability to Capture the Complex Customer Journey: Today’s customers interact with brands across multiple touchpoints – websites, social media, email marketing, mobile apps, and more. Traditional models struggle to account for this multi-touch attribution.
  • Difficulty in Assigning Credit for Multi-Touchpoint Interactions: Traditional models often give full credit to a single touchpoint, neglecting the influence of earlier or parallel interactions that may have nurtured the customer towards conversion.

These limitations can lead to:

  • Wasted Budget: Resources allocated to channels that appear successful based on last-click activity, may not be the true driver of conversions.
  • Difficulty in Identifying High-Performing Channels: Inability to accurately measure the effectiveness of different marketing activities.

Either of these outcomes could have a negative impact on the your marketing strategy and effectiveness.

Regression-Based Attribution: A Data-Driven Solution

Regression-based attribution offers a more sophisticated approach to marketing spend optimisation. It leverages statistical techniques to analyse customer journey data, including website visits, ad clicks, email opens, and more.

By analysing this data, the model can:

  • Identify patterns in customer behaviour that traditional models might miss.
  • Predict the conversion probability for each customer interaction.
  • Assign credit to each touchpoint based on its predicted impact on conversion.

This data-driven approach provides a more holistic view of the customer journey, allowing you to see the true contribution of each marketing activity.

regression attribution graphic

AI and Machine Learning: Powering Regression-Based Attribution

At XPON, AI/ML plays a vital role in enhancing the scale, accuracy and effectiveness of all of our marketing analytics solutions. AI can enhance the power of your regression-based attribution by:

  • Identifying Complex Patterns: AI algorithms can identify subtle patterns in customer data that would be difficult for you to detect. This allows for a more nuanced understanding of customer behaviour.
  • Continuous Learning and Improvement: AI models can continuously learn and improve over time. As they are exposed to more data, they become better at predicting conversion probability and attributing credit accurately.

By implementing an AI-powered regression-based attribution model, using powerful AI technology like those available within Google Cloud, you can gain valuable insights to optimise your marketing spend:

  • Identify High-Performing Channels and Touchpoints: Regression analysis highlights the marketing activities that have the greatest impact on conversions. This allows you to focus your budget on the channels that deliver the most value.
  • Data-Driven Decision-Making for Marketing Campaigns: Regression-based attribution provides you with the data you need to measure the true effectiveness of your campaigns and tactics. This empowers you to make informed decisions about campaign optimisation and budget allocation.

Here’s an example:

Imagine you discover that your social media marketing efforts are driving a significant portion of website traffic, but very few conversions.

Regression analysis might reveal that your email marketing campaigns, which nurture leads after they visit your website, are actually playing a crucial role in driving conversions.

This data can help you adjust your strategy, potentially increasing your budget for email marketing and refining your social media approach to focus on lead generation rather than just traffic acquisition.

The traditional methods of marketing attribution are no longer sufficient in today’s complex marketing landscape.

Regression-based attribution, powered by AI, offers a powerful solution for businesses seeking to optimise their marketing spend and achieve a better return on investment.

By leveraging this technology and the insights it provides, you can gain a deeper understanding of your customer journey, identify the marketing activities that are truly driving conversions, and ultimately achieve your marketing goals.

Start small, unlock value quickly

To implement regression-based attribution models or start powering them with AI, it’s essential to define clear objectives and gather clean data from various customer touch points.

Implementing AI-powered regression-based attribution models can be complex and resource-intensive, but they don’t have to start that way.

Start small by conducting pilot projects focused on specific channels or campaigns.

This will allow you to test the effectiveness of the model and refine your approach before scaling up.

Following these steps can lay the foundation for leveraging advanced analytics to optimise marketing spend and gain valuable insights into customer behaviour.