If you’re using a single tool for measurement, think again

Which channels should get credit for turning potential customers into purchasers — and how much credit should each channel get? As the customer journey grows more complex, understanding each touchpoint’s contribution can mean the difference between business growth and marketing efforts that fail to deliver.

Once you correctly determine which touchpoints have the greatest influence on a purchase decision, you can optimize campaigns to drive the most revenue. But you also need to determine who converted as a result of a marketing message, and who was going to buy anyway.

No single measurement tool can perfectly capture all media-exposure, conversion, and sales data and give you actionable insights on an ongoing basis. To be successful, you’ll need to use a blended approach to measuring media impact across channels.

Evolve beyond a marketing mix model

A marketing mix model (MMM) is a powerful tool to evaluate media performance and optimize budgets across each media type. MMMs, however, are not always set up to provide actionable insights for digital spend. The insights you get out are only as good as the data you put in.

Digital granularity is critical to reflect the different impacts of impressions across platforms and ad formats relative to their cost. This means, at the very least, you have to break down online video by platform and search by branded and nonbranded keywords.

Many brands have historically relied on MMMs as the primary source to guide their resource allocation decisions. But because they’re based on past results and typically run annually, they’re unable to keep pace with the rapidly changing marketplace. This is why savvy brands employ additional tools, like multitouch attribution and experiments, that can provide more granular insights in real time.

Get granular and actionable insights with multitouch attribution

Modeling has always been important for effective measurement. Data-driven attribution (DDA), a type of multitouch attribution (MTA), uses models to provide more actionable real-time insights than MMMs for digital media channels by continuously valuing the relative impact of different media channels.

Data-driven attribution uses machine learning to determine how much credit to assign to each click in the customer journey, from the first time a customer engages with your brand to their final interaction before taking a desired action. It analyzes unique conversion patterns, comparing the path of customers who completed a desired action against those who did not, to determine the most effective touchpoints for each business.

Marketers have traditionally used a variety of methods to determine attribution for digital ads, such as last click and other rules-based approaches. In recent years, however, DDA models have proved to be the most effective.

Use experiments to validate and implement findings

Attribution is best for day-to-day, always-on measurement and is effective for setting ad budgets and informing bid strategies on a campaign or channel level. But more sophisticated performance analysis requires the use of randomized controlled experiments to determine what drove each conversion — what’s known as incrementality or lift.

In other words, while attribution helps you correlate consumer behavior with sales and conversions and is a helpful tool in steering your marketing investments to get the biggest return, incrementality experiments use causal methodology to determine whether an ad actually changed consumer behavior. While experiments can take many forms, incrementality specifically relates to causality, which is not to be confused with measuring the impact of increases on an ad campaign’s budget, or A/B creative testing.

Incrementality is the gold standard for measurement, helping you understand the true causal impact of your media through rigorous controlled experiments. But, like mining for gold, it can be a costly and time-intensive process, and it may require an additional investment in resources and people. As a result, incrementality is not suitable for every brand in every situation. It's best deployed by businesses that are prepared to move beyond DDA and understand the commitment that running experiments requires. These businesses can use incrementality experiments to set channel-level budgets or to measure lift to optimize future campaigns.

Blend multiple methods for best results

Given that there is no one-size-fits-all solution to measuring marketing impact by channel, it is best to employ a blended approach tailored to your specific situation and objectives.

• Use MMM for a high-level view of the efficacy of all your marketing spend (online and offline)
• Complement the guidance from MMM with real-time, DDA to inform online media bidding and budgeting decisions.
• If appropriate for your business, run incrementality experiments to unlock causal insights that can inform channel-level budgeting and validate results from both MMM and attribution.

Ultimately, marketers have a number of ways to measure the impact of each touchpoint along the customer journey and to optimize campaigns to get the most value for their investments. Understanding the benefits and drawbacks of each allows you to have a plan that gets you the insights you need to grow your business.