Maximizing ROI Through Data-Driven Attribution Modeling

There’s no doubt that marketers need better performance metrics to enhance their decision-making abilities. As the digital marketing landscape evolves, touchpoints are becoming increasingly varied and dispersed, making it harder for marketers to measure the ROI of each marketing activity accurately.

Recent advances in machine learning have paved the way for data-driven attribution models. A powerful tool for marketers to start making genuinely fact-based budget and campaign decisions. Here’s how they can help.

What is attribution modeling and what does ‘data-driven’ mean?

As marketers, you’ll already know the importance of attribution modeling. Tracking and valuing customer touchpoints that drive a specific business goal is key to informing marketing strategy and demonstrating ROI. The core challenge is that customers will likely see various digital ads, social media posts, and email newsletters before they click to buy. Each one is doing its bit in the customer journey, so how much credit should you allocate to each touchpoint?

There are a range of attribution models to consider, each giving different levels of ‘credit’ to different touchpoints. Until recently, Last Click was the most commonly used attribution, giving sole credit for each conversion to the last place before the conversion. But it’s easy to see the flaws in this model.

Data-driven attribution modeling has now arrived to give marketers a clearer view of which touchpoints have the most impact through machine learning. This algorithmic system combines predictive modeling, historical data, and integrated analytics to appropriately credit each touchpoint. Becoming data-driven allows marketers to see the true value of each marketing campaign element, platform, and channel.

Should I put a data-driven attribution model in place?

For the right organizations, data-driven attribution can be a powerful tool to increase the efficiency of marketing budgets and increase conversions. According to Google, advertisers switching to data-driven attribution from another model have seen a 6% average rise in conversions. It’s now Google’s recommended attribution model as it assigns credit more evenly, re-evaluating touchpoints that may have been previously undervalued.

Data-driven attribution will particularly benefit larger organizations with a high level of quality data. It’s an intensive data-modeling exercise that seeks to find and analyze statistically relevant patterns. And this requires a healthy volume of data and an organization-wide interest in actioning insights. If undertaken successfully, the benefits can be transformative.

How to maximize the ROI of data-driven attribution modeling

Cleanse and organize your data

To aid business decision-making, data-driven marketing techniques must work with high-quality customer data – and lots of it. Before implementing any attribution modeling that factors in historical data, you’ll need to make sure that data is fit for purpose.

Unblock data siloes and unify into one central location, remove outdated and duplicate information to achieve maximum identity resolution. This process in itself will create cross-departmental efficiencies and cohesion that will benefit in the long term. The right attribution provider can assist you in this process and insert solutions seamlessly into existing martech stacks.

Define your goals

Before taking on any new tools or strategies, it’s best to consider your current business pain points and what constitutes success. Define key objectives and KPIs that are consistent across channels and departments then set a common, unified metric of success. Take a holistic view of customer touchpoints to determine how attributing credit will contribute towards meeting this metric.

Knowing what success looks like will keep disparate departments on the same track. Attribution is not an overnight process and will struggle if all departments have different measurements of success. The goal doesn’t have to be revenue, but it will need to be something actionable.

Get key stakeholders on board

To successfully implement a data-driven attribution model, you’ll need the buy-in of key stakeholders from across the business. They will need to contribute to the implementation and have a willingness to make process changes that break down data siloes and action insights. Once you have defined the goals and metrics for success, this will be easier to do.

If there is a fairly high appetite for change, the next hurdle is how easy it will be to action insights. A common sticking point is often budget – or more specifically whose budget is it all coming out of. Allocating a cross-departmental budget is the best recipe for success.

Select your provider carefully

There are a range of considerations to make when choosing your attribution partner, but the most basic one is: can they ingest and unify your data quickly and easily? Organizing the vast volumes of data will be it’s own project, and your attribution partner should be able to provide flexible options for data collection. It also helps if they are media agnostic and will share unbiased recommendations for your business.

Carefully consider the support and requirements your business will need to get the best results. Will you need consultations to analyze the data? Do they provide end-to-end analytics? Can they handle multi-screen journeys? Ultimately, can they make data ingestion, transformation, visualization, discovery, and fulfillment straightforward?

Looking to implement a data-driven attribution model? We can help…

Allant Group can simplify the chaos of your customer data quickly and easily. Our powerful Audience Management Platform+ (AMP+) is a streamlined end-to-end analytics platform that can attach data-driven attribution models – and accelerate their execution. With a hyper-fast query engine designed for ‘no-code’ reporting, we can help unlock efficiency and better decision-making.