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Retail Business Review | Monday, October 17, 2022
To optimize ROI, marketers need to identify their campaigns' effectiveness and determine which channels drive revenue.
FREMONT, CA: Marketing attribution is one of the toughest challenges we face as marketers. An outlook can have anywhere from one to hundreds of touchpoints before purchasing. To optimize ROI, marketers need to identify their campaigns' effectiveness and determine which channels drive revenue. Here we explain the premise behind attribution and some tips further below.
WHAT IS ATTRIBUTION?
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Attribution measures the effectiveness of a prospect's marketing interactions with a brand. In short, the goal of marketing is to convert prospects into customers. Doing so needs a marketing mix of various channels, like paid ads, content creation, email campaigns, in-person events, and social media.
After a marketing mix is extended, marketers must look back and evaluate what is working, what is not, and what is driving customer acquisition. Here is where attribution comes into play.
Today, attribution continues to enhance complexity as the digital marketing landscape promotes more fluid prospect-brand interactions within an individual buyer's journey.
ATTRIBUTION MODELS
• First Interaction
Credit is provided for the first interaction a brand has with a customer. This model goes better for companies with high lead-to-customer conversion rates. Suppose the marketing team identifies that a purchase will likely be made once a lead is generated. In that case, the emphasis of the marketing mix should focus on which channels are generating traffic and leads.
• Last Interaction
Last interaction modeling gives credit to a customer's last marketing touchpoint before converting. For instance, a prospect clicks on a google ad which leads to a form fill. If that prospect remains a lead until lastly converting through a nurture track email, the majority of the credit will go to the transforming email. This function properly for businesses that generate consistent leads but have low lead-to-customer conversion rates.
• Linear
Linear models provide equal credit to all touchpoints. For example, the credit will be split equally if a customer has 20 interactions with a brand before converting. Linear models are beneficial for simplifying attribution; they can also be misleading.
• Time Decay
Like the last interaction and linear model, time decay attribution gives increasing credit to touchpoints closer to the conversion.
• U-Shaped
U-Shaped modeling values the first and last transforming touchpoints. This model acts well, considering the type of content utilized to acquire a lead often varies from the content used to transform a lead into a customer.
• W-Shaped
Akin to a U-shaped model but also values the touchpoint that causes a conversion within the buyer's journey. This model imputes revenue to the first and last touchpoints and any touchpoint that changes the lifecycle stage (i.e., leads to MQL). The percentage distribution can vary, but generally, 25% or 33% goes to the first, last, and converting touchpoint (s).
• Custom/Algorithmic Model
Algorithmic modeling is a customs procedure for a specific business. It is ultimately the most advanced attribution model that employs the concepts of the previous models into one method that aims for the greatest level of accuracy. Algorithms work best when connecting multiple touchpoints with more specific ratios.
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