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Custom Attribution in Digital Marketing: Tailoring Data for Precise Insights

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Custom attribution in digital marketing

Marketers have always used traditional attribution models (think, last-click attribution) in digital marketing to measure the effectiveness of their campaigns, ads, and marketing channels.

With the increasing complexity of the customer journey and the massive variety of touchpoints in today’s digital landscape, though, more than relying on traditional attribution models is needed.

That’s where custom attribution comes into play.

What is custom attribution?

In digital marketing, custom attribution creates a unique model for associating marketing channels and touchpoints with their proportional contribution to a sale or lead conversion. Unlike standard attribution models (like first-click, last-click, linear, and time-decay), it helps companies tailor the process to their business and marketing strategies.

It models are particularly useful for businesses with complex customer journeys and multiple touchpoints (e.g., B2B SaaS companies). They provide an accurate and tailored understanding of the effectiveness of different marketing efforts. In that way, they facilitate better decision-making and more strategic resource allocation.

How does custom attribution work?

Although every business tailors its attribution models based on its goals, the type of marketing collateral they’re measuring, and the complexity of its customer journey, there are some standard steps to building a this model.

It’s much simpler than it sounds — fundamentally, all you’re doing is weighting the touchpoints based on their impact.

Identifying key touchpoints

The next step involves identifying and understanding the various touchpoints a customer interacts with throughout their journey. Focus on every stage of your customer’s journey toward a purchase (Awareness, Consideration, and Decision).

Examples of touchpoints include:

  • Your website
  • Social media channels
  • Email campaigns
  • Blog posts
  • Ebooks
  • YouTube videos
  • Influencer partnerships
Different sales channels
Different sales channels

Data collection and integration

The first step is gathering comprehensive data from all marketing channels and customer touchpoints. This includes online (like social media, PPC ads, email campaigns, and website interactions) and offline channels (like TV ads, events, or in-store promotions).

For example:

  • Clicks and impressions from Google Ads.
  • Engagement metrics (likes, shares, comments) from Facebook posts.
  • Open and click-through rates from email newsletters.
  • Traffic and engagement on blog posts.

To centralize this data, use a marketing attribution tool to integrate and analyze your marketing data in one place. This way, you can get a clear picture of each customer’s touchpoint on their journey and how these touchpoints interact.

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Setting up attribution models and rules

Depending on the business objectives and the nature of customer interactions, specific rules or models are set up to attribute credit to these touchpoints. Unlike standard models like first-click or last-click, a custom attribution model is tailored to the unique aspects of your business. It gives more weight to touchpoints deemed more influential in the customer journey.

To identify which ones to weigh more heavily, you’ll need to consider data points like:

  • The content and timing of each touchpoint
  • The engagement level of each touchpoint
  • The customer’s behavior before and after the touchpoint

Suppose, hypothetically, you’re an online retailer selling fitness equipment. Your marketing channels include Google Ads, Facebook, email newsletters, and a blog with fitness tips.

When you look closely at your customer’s decision-making process, you notice that:

  • They often first encounter your brand through Google Ads.
  • They engage with your brand on Facebook, sharing and commenting on posts.
  • Many conversions occur after customers receive an email newsletter with a discount code.
  • The blog plays a key role in educating customers, as evidenced by the time spent on these pages.

Based on this analysis, you decide to assign preliminary weights:

  • Google Ads (first touch): 20% weight, as it’s often the first interaction.
  • Facebook engagement: 15% weight for building brand awareness and engagement.
  • Email newsletters: 40% weight due to their high conversion rate.
  • Blog engagement: 25% weight for educating and influencing purchase decisions.

Continuous refinement

You’ll observe the outcomes when you implement weights in your model. In the hypothetical above, pretend you notice that:

  • Sales increase when the email newsletter is sent after a customer reads a blog post.
  • Customers who engage on Facebook are likelier to click on Google Ads later.
Sales optimization
Sales optimization

Based on these insights, you adjust the weights. You might increase the weight of blog engagement because of its significance as an influencer. You may also slightly increase the weight of Facebook, acknowledging its role in reinforcing other channels.

Applying advanced analytics and machine learning

Advanced analytics and machine learning algorithms are often used to handle the complexity and volume of data. These technologies can analyze patterns in the data, providing insights into how different touchpoints contribute to conversions. They can also help refine the attribution model and make it more accurate over time.

For example, they can identify which touchpoints are most impactful at each stage of the buying process and automatically adjust the weights. They can also predict which touchpoints will lead to a conversion, which can help your marketing team optimize their strategies and channels.

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Final thoughts

Custom attribution is a powerful tool for businesses looking to understand better how their marketing efforts impact customer behavior. By implementing a this model, companies can make more informed decisions about resource allocation and optimize their marketing strategies for maximum ROI.

The best models have an AI-driven component. When you leverage machine learning, you can create a dynamic model that refines itself over time with limited human intervention.

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