Ecommerce and Retail • Maropost Commerce Cloud
In today’s ecommerce landscape, every buying cycle is different. Shoppers take multiple paths to find your store and purchase, and measuring the impact of each step in the buyer journey is becoming increasingly challenging.
Understanding the impact of each of these steps, or touchpoints that a customer has with your business is critical though, because it helps you do two things:
Luckily, attribution modelling can help us.
Attribution modeling is the process of determining how credit for sales and conversions is assigned to various touch-points in the customer journey. An attribution model is based on a rule or set of rules that helps you measure and assign value to each of your marketing channels.
There’s a number of different attribution models you can use, depending on the buying cycle, goals of your business and which ecommerce metrics you are reporting on. Let’s take a look at five of the most common models.
In the First Click attribution model, the first touch point receives 100% of the credit for the conversion.
Example: A shopper clicks on a Facebook ad for a new hair product and signs up for your email list but doesn’t buy. They interact with you over the coming months through organic search, display advertising and direct before finally purchasing. The Facebook ad is allocated a value of $100 for this interaction because it was the shopper’s ‘first touch’ with your business.
First Click is most effective if your advertising is limited to one or two channels. Because you know a customer has limited options for finding your store.
In the Last Click attribution model, the final touch point before the sale receives 100% of the credit for the conversion. Use this model to most effectively measure the decision factor that results in sales.Example: if you run an email campaign offering a 15% off promo code to your current customers that generates $5k in sales, all of that revenue would be accurately attributed to the email using Last Click attribution.
In the Linear attribution model, each touch point on the conversion path shares equal credit for a conversion. Use this model to measure overall brand awareness and see which channels are consistently influential during a customer journey.
Example: A shopper finds your store organically while searching on Google for outdoor recreation gear. They subscribe to your email list and browse your store again after receiving a promotional email. Later, they are retargeted by a Facebook ad for camping, and a week later they see a Display Ad that is the final kicker to make them purchase a tent. In this example, each of the channels are assigned a value of $20.
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The Time Decay model gives more weight to channels closer to the conversion point and less to channels earlier in the funnel. Because this model credits every touch point, you can use it to help determine what is driving repeat purchases
The Positional Based model favours both the first and last touch - the channel that first brought the customer in and the one that converted them—typically giving them each 40% of the credit—while dividing the remaining 20% amongst the middle touchpoints. This helps you figure out which channels work best for bringing in new customers and which work best for converting them.
First Order Attribution helps retailers to see the true return on ad spend through a customer centric lens. This model credits sales and conversions to the channel that brought customers to your site for their first purchase (as opposed to the channel where they first interacted with your business). All orders after that are attributed to that channel.
Example: if a shopper clicked on one of your Paid Ads, landed on your online store and made a purchase, Paid Search would get credit for that purchase.If any future purchases were made via organic search or direct entry, these orders would be attributed to Paid Search since that’s what brought the buyer to your site in the first place. It would give due credit to the channel that first exposed a shopper to your store and eventually led to future purchases.
When it comes to accurately measuring the ecommerce ROI of your digital channels, which is particularly important for omni-channel marketers, many of the traditional attribution models fail to properly account for the lifetime value of the customer. First Order attribution, however, is designed specifically for online sellers as it’s based strictly on profitability, a formula that makes sense to retailers and deliver the most value.
This model is based strictly on profitability, a formula that makes sense to retailers and delivers the most value. Neto merchants using this model in Neto Analytics Studio tell us that they have a much better understanding of how each marketing channel is performing, which helps them make informed decisions about future marketing spend.
Another tool that can help you take the guesswork out of attribution modeling and add to your bottom line is Google.
You can find the attribution models above (except for First Order attribution) in conversion reports in Google Analytics, and last year Google released Google Attribution (beta), which reduces the use of ‘last click attribution’ by using machine learning to assign a weighted value to every touchpoint along the path to purchase. The goal is to make sense of ad dollars’ effectiveness across different channels and devices.
While multi-touch attribution has been around for years, Google Attribution will be more accurate and faster than existing solutions. It's also free in the version designed for small and medium-sized businesses.
An informed attribution strategy helps ecommerce marketers decide when and where to best spend their dollars across the customer journey. While choosing the right attribution model for your business comes with a variety of challenges, it can be a very rewarding project to undertake. By moving beyond a basic attribution model marketers and business owners gain valuable insights about the efficacy of their marketing channels and where increased effort should be applied to drive more revenue.
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