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Mohammad Ashraf

Engagement Lead

Attribution Basics

Introduction to Attribution Modeling

Saras Analytics builds bespoke data solutions for eCommerce brands. Their products Daton and Pulse enable brands to build a single source of truth for marketing, operations and finance teams across DTC, Amazon, and retail channels.

In This Article:

In the ever-evolving landscape of direct-to-consumer (DTC) brands, acquiring new customers is critical for growth. Particularly for subscription-based models, the acquisition of a high-quantity of customers isn’t sufficient. They would need to acquire high-quality customers as well, which would lay the bricks for future sustained success.

To optimize their marketing budget, marketers need to know 3 main things: which platforms to spend on, which content or themes to spend on, and where one can spend aggressively versus conservatively.

Yet, optimizing budgets to reach these objectives requires a deep understanding of customer behavior and the effectiveness of marketing efforts. This is where attribution and attribution modeling comes into play.

Attribution is the process of identifying the sources that influenced each customer to make their initial purchase with the brand.

Why understanding acquisition sources is critical

For DTC brands, especially those operating on a subscription basis, not all customers hold the same value. Some customers contribute significantly more to the brand's lifetime value and profitability as compared to others.

Attribution isn’t the only spending optimization solution. Traditional spend optimization solutions also include the likes of Marketing Mix Modeling (MMM) which have been widely used. MMM is ​a technique which helps brands quantify the impact of numerous marketing inputs on their sales or Market Share.

However, its flaw lies in its ability to provide only aggregated answers. In contrast, attribution offers a more granular, customer-level perspective, making it particularly effective for subscription-based DTC brands.

With attribution, brands can measure and track various important metrics at both an aggregate and channel level. This is critical as it gives brands the necessary macro and micro level of insights they need to guide their growth.

These metrics include Customer Acquisition Cost (CAC), Customer Lifetime Value (LTV), Return on Ad Spend (ROAS), and more. Using these metrics, brands can allocate their spending efficiently, and also accurately identify the campaigns that are and are not working.

Data sources for implementing attribution modeling

To effectively implement attribution modeling, brands rely on a combination of online and offline data sources.

Online data sources

Online data sources such as Google Analytics 4 (GA4) and the Shopify Customer Journey API offer valuable touchpoint data. This data allows marketers to stitch together the customer journey leading up to acquisition. This way, they will have a clear understanding of the entire process.

Using that data and information, businesses can build different types of attribution models. These include First-Click (FC), Last-Click (LC), Last Non-Direct Click (LNDC), Linear (L), Time-Decay (TD), U-Shaped (US), W-Shaped (WS), and more. This is important because it enables brands to build them out accurately, and be able to tap onto the insights offered by these models.

The limitations of online data sources

However, online sources have limitations.

Firstly, data is only available for click-based attribution and not view-based attribution.

View-based attribution is also often referred to as impression tracking. It is the framework for measuring impressions that lead to conversions, or impressions that play a role in an eventual conversion. This means that brands can attribute customers who clicked on an Ad and visited the site, but not those customers who didn’t click on the Ad but visited the site directly or through other means such as a Google search.

There are also data capture issues when it comes to users who employ ad blockers. In such cases, touchpoint data may not get captured and hence impedes the accuracy and vastness of data. Additionally, there is also the need for assumptions when identifying the true influencing touchpoint. Despite all the data provided, it doesn’t give a clear indication of who and how much your spend can be attributed to.

Not only that, for brands with a GA4 implementation on the client-side, close to 15% of the data isn’t captured. This is taken from our observation across 20+ engagements. It shows that a client-side implementation of GA4 tends to capture 80% - 90% of all conversions that happen on the website. It thus tends to miss that 10% - 20% due to Ad Blockers, Network failures, etc.

How offline attribution sources fill that gap

Here is where offline sources like post-purchase surveys (PPS) or pixel-based tracking fill that gap. They offer a direct insight into customer influences without the guesswork associated with online data sources.

Offline sources provide a golden source of truth and help give brands a clearer overall picture. However, this may also be limited as it may only capture data from a minority of customers who fill out the survey.

The approach DTC brands should follow for attribution modeling

A robust attribution modeling approach for DTC brands involves leveraging data from both online and offline sources. Online data will form the base dataset, and gets supplemented by offline data, such as PPS survey data, to enhance accuracy.

When talking about PPS data, it serves as a source of truth for surveyed customers. They aid in evaluating the validity of various multi-touch attribution (MTA) models built using online data sources. Combining the data from multiple sources, brands can thus create a custom attribution model that suits their business model.

In conclusion, attribution modeling plays a crucial role in maximizing marketing efficiency for DTC brands, particularly those operating on subscription-based models. By combining data from online and offline sources and building custom attribution models, brands can gain valuable insights into customer behavior, optimize marketing budgets, and drive sustainable growth in competitive markets.

What businesses need to build an attribution model

Building an effective attribution model is not easy. There are prerequisites and data foundations in place that are needed for your brand to successfully build one out. Here, we’ll take you through what is needed and how best to do it.

The prerequisites to building an attribution model

Centralizing your data

At the core of it all, brands must first centralize data from various sources into a single, self-owned data warehouse (DWH). This is ideally done using platforms like Google Cloud Platform (GCP), Snowflake, or Microsoft Azure. GCP is generally recommended for brands due to its flexibility and cost-effectiveness.

By centralizing your data, it allows your brand to bring various data sources together. This would enable better data integration and make it easier to gain insights from a comprehensive dataset.

Using an ETL tool to automate data centralization

From there, brands can use an commerce-focused Extract, Transform, and Load (ETL) tool like Daton to automatically replicate data to the DWH. This can be done on a daily basis from Shopify, GA4, Fairing, and more.

Such tools can tap into the APIs of almost any e-commerce related platform and reliably replicate the data without any manual effort. This streamlines the process, making it less labor-intensive, while still ensuring that your database is up to date.

Joining datasets on a common customer key

Customer keys allow you to identify and search for an asset by a data value known only by you. A customer key is a unique value across your business that is typically stored in your database. Brands would need to join datasets on a common customer key, typically the customer email or Shopify customer ID. This can be done internally with your team’s data engineer, or via an external product such as ‘Daton Insights’.

While it may not be a viable option for all, having a data engineer can also help with customization and further iterations of the attribution model. Nonetheless, external services will suffice as well.

Making sense of the data

From there, when combining and summarizing the data, you will need to spend time to understand the format and granularity of the data coming from each source. This is a necessary step in order to draw meaningful insights, as you need to have the right context to be able to put it to use effectively.

To highlight this, with regards to granularity, touchpoint data from Shopify and GA4 will include the source or medium, and also the campaign details.

However, it is likely that the PPS data from Fairing would only provide data as per the options included in the survey questions. As such, one will need to summarize all inputs into a common format and level of granularity before drawing insights from them.

Similarly, for formating, one needs to be cognisant of the different data types of each field and join them accordingly.

Approach to building a custom attribution model

With all these prerequisites fulfilled, your brand is now ready to build out an attribution model to elevate your marketing.

As such, a typical attribution modeling approach that we would recommend could look something like this:

a. Use PPS data to attribute customers who filled the survey

b. Use Shopify Customer Journey API data to attribute the rest of the customers using one of the above mentioned models

c. Use GA4 data to attribute customers who are still not attributed to any paid channel using one of the above mentioned models

d. Brands can pick which model to use, based on each model’s accuracy levels when compared against PPS data

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