Attribution Modeling and Measuring the Performance of Your Digital Marketing Channels

Marketing divisions are routinely charged with driving revenue and business growth for retail organizations, providing information and insight for assessing performance, and with informing other departments for budgetary and business planning.

For retailers, understanding the steps that a customer takes before making a purchase can be just as valuable as the sale itself. Being able to identify these steps empowers marketers to create environments where these stages can be recreated with other consumers, leading to further sales.

Achieving this insight and monitoring marketing performance requires skills and specialist techniques — one of which is attribution modeling.

What Attribution Modelling Is — And Why It Matters

From first learning about the existence of a product to researching its specs, comparing prices, choosing brands, and finally making a purchase, customers may tap into any number of on and offline sources (some more than once) before making the decision to buy.

An omnichannel approach to marketing acknowledges this. But knowing which source or touchpoints were critical to the buying decision may not be easy to determine. That's where attribution modeling comes into the picture.

Attribution modeling provides an organized set of rules for assigning credits to different touchpoints throughout the customer journey. This credit-based system enables marketers to measure the effect that each touchpoint is having and assess the overall impact of their marketing efforts.

There are numerous attribution model variants, each designed for a particular set of circumstances. In the rest of this article, attribution for the screenshots and the basis for the simplified descriptions that follow goes to the team at

First Click Attribution Model

In this model, credit for a conversion is given to the first source that referred a buyer to your website. Marketers can use this information to identify channels that bring in the most leads or customers and the best channel for creating awareness of your brand.

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This method is ideal for retailers with very few marketing channels. Since it assigns no value to other touchpoints, it's unsuitable for organizations with multiple channels or organizations with a sophisticated customer journey. In addition, with a customer buying cycle of 30 to 90 days, information may become unavailable, as Google Analytics cookies expire.

Last Click Attribution Model

The marketing touchpoint that resulted in conversion gets credit here. This enables marketers to discover which channels are driving the most conversions and identify high-impact touchpoints near the end of the sales funnel.

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As with the first click model, other touchpoints are ignored, making this method unsuitable for more complex customer journeys.

Last Non-Direct Click Attribution Model

With this model, all credit goes to the touchpoint that came before the direct visit that resulted in a conversion. This knowledge allows marketers to assess the effectiveness of a particular strategy without taking direct traffic into account.

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Retailers with offline campaigns that could lead a user to their website to convert should not use this method.

Linear Attribution Model

This model gives equal credit to every channel across the entire customer journey — even if the same channel is used more than once. The result of this is that linear attribution enables marketers to track both the sources of conversion and channels that create brand awareness.

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Retailers with a comprehensive knowledge of their existing customer journey or those wishing to identify critical channels will not benefit from this model, as it assigns equal value to every touchpoint.

Position-Based or U-Shaped Attribution Model

Here, the first and last touchpoints contributing towards a conversion are assigned 40% of the credit, with the remaining 20% shared among all the others. This kind of segmentation can help marketers identify which channels are best for acquiring an audience and which ones drive conversion.

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Marketers who rely heavily on nurturing tactics or eCommerce retailers with many seasonal campaigns should not use this method. Journeys with a long decision-making cycle may also suffer due to a loss of data with cookie expiration.

W-Shaped Attribution Model

Here, the First Touch, Lead Creation, and Conversion touch points get an equal share of 90% of the credit (i.e., 30%), with other channels on the customer journey sharing the remaining 10%.

This approach helps marketers identify their audience builder, lead generator, and conversion channels, and is especially useful for B2B marketers with clear funnel stages for their consumers.

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B2C marketers with short sales cycles and user journeys may not have a lead generation stage and likely won't benefit from this method.

Time Decay Attribution Model

Channels closer to the conversion point receive higher credit in this model, which enables B2C marketers and B2B marketers with short customer journeys to establish which channels are assisting and driving conversions.

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Marketers with long B2B sales cycles or retailers focusing on driving brand awareness should not use this model.

Click Attribution or Last Google Ads Model

Marketers on a paid advertising platform like Google Ads or Facebook can use this model for tracking performance. Credit is assigned to the last Google Ads campaign or specific Google ad that led directly to a conversion.

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Marketers using multiple paid channels or retailers with a complicated customer journey containing valuable touchpoints won't benefit from this method.

Lead Conversion Touch Attribution Model

Useful in assessing lead performance and identifying their sources, this model gives credit to the channel through which a lead was generated.

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Marketers with a long nurturing process featuring multiple activities may not directly benefit from this approach.

Full-Path Or Z-Shaped Attribution Model

Here, the First Touch, Lead Generation, Opportunity Creation, and Customer Close channels each receive an equal 22.5% share of the first 90% of credits. The remaining 10% is distributed among the other channels.

Organizations with a strong alignment between their marketing and sales teams and a more complex customer journey can use this model to identify which marketing channels are driving actions that lead to conversion.

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Retailers with a simple customer journey or a sales team that works without marketing support should not use this model.

Custom, Algorithmic, or Data-Driven Attribution Model

This is a variable process that uses data analytics to identify all marketing channels that play a significant role in bringing visitors to your website and converting them into customers.

Intelligent algorithms can assign the most credit to your best channels and eliminate those that don't perform, making it ideal for users of all kinds — assuming they have the resources to pay for the software and analytics expertise.

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The Machine Learning Advantage in Measuring Marketing Performance

In the digital economy, marketers may be called upon to manage several different marketing applications at a time and predict with confidence the return on investment (ROI) and return on ad spend (ROAS) from all the data that they capture. Software, automation, and external data handling expertise can make this much easier.

Attribution modeling systems with AI and machine learning capabilities can bring audience, campaign, content, and revenue information into a unified data layer. This provides greater visibility into past, current, and future marketing activities for measuring performance and business planning.

Attribution modeling, machine learning, and performance optimization for marketing channels are all set to be hot topics at eTail Europe 2020, which takes place from 23 - 24 June, 2020, at the Queen Elizabeth II Conference Centre, London.

Download the agenda today for more information and insights.

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