ABM Basics: Using Campaign Attribution to Tell the Full Story

May 24, 2018

ABM Basics: Attribution

Editor's Note: This guest post is authored by Gil Allouche, CEO at Metadata.

Some marketing campaign attribution models may be easier to understand than others, but they all require a large set of data pulled directly from the customer journey.

First and last-touch attribution are fairly simple in design: They attribute the ROI for conversions based on either the first touch or the last touch with a converted lead. If you close a deal with a B2B buyer who discovered your business through LinkedIn, it doesn’t matter that future engagements crossed Facebook, email, and a website referral from a Google search before the buyer closed the deal.

In first-touch attribution, LinkedIn gets credited with the conversion. In last-touch, Google earns the ROI. While these are relatively easy attribution models to execute, neither tells the full story.

That’s why multi-touch attribution has become a popular approach for marketers. In recognition that the buyer journey can be long, winding, and shaped by multiple points of content, multi-touch campaign attribution attempts to paint a complete picture of that journey, dividing credit fairly among channels that played a part.

For account-based marketing, this type of attribution is critical to understanding and improving your marketing and sales strategy.

The Importance of Multi-Touch Attribution

First- and last-touch attribution are predicated on the fallacy that a single point of contact leads to a conversion. But research from the Data & Marketing Association has found that it takes, on average, seven to 13 touches before a buyer becomes a qualified sales lead.

Each of those points of contact play a small but important role in moving prospects closer to a conversion. Multi-touch attribution aims to carefully consider these roles. Over time, this attribution model can combine data from across your different sales and marketing efforts to identify which channels are performing well, and which ones may be impeding your ability to convert prospects into conversions for your company.

But there’s a reason some marketing teams prefer simpler attribution models to a true multi-touch approach: Creating a model for your company isn’t easy, and it often requires a period of trial and error before that model offers reliable ROI attribution.

Setting Up an Attribution Model

While it’s possible to build your own multi-touch attribution model from scratch, there’s a risk in doing this if you aren’t already well-acquainted with attribution model strategy. In most cases, it’s best to start out by using a general template for attribution models, and then making adjustments over time as you learn more about your strategy.

A linear attribution model, for example, is simple in its design: It divides ROI equally among every point of contact. So if a prospect goes through seven points of contact on their way to a conversion, each point earns one-seventh of that conversion’s ROI. If one channel serves as two points, that channel receives two-sevenths.

There are obvious limitations to this model, since most marketers would agree that not every channel plays an equal role in driving a conversion. Bizible has highlighted the most common alternative multi-touch attribution models, all of which are worth considering as you explore what works best for your company. Keep in mind that whatever you choose isn’t set in stone: You can switch attribution models over time or simply make small tweaks to whichever one you settle on.

Taking Time to Get Your ROI Right

One of the biggest misconceptions and frustrations about multi-touch campaign attribution is that the work isn’t done when you set up your first attribution model. Marketers and executive leaders need to understand that any model only represents a possible way of interpreting data. However you decide to attribute value and measure ROI, there’s a level of subjectivity to this work that will make it prone to inaccuracies and distortions.

Those distortions can be corrected over time, but it will take patience and close attention to identify aspects of the attribution model that aren’t interpreting data as well as you expected. Since the model is built specifically for your business and your marketing goals, correcting its fallacies isn’t as simple as borrowing a model template from another company. You need to figure out how your data interpretation is succeeding or failing at giving you the information you need.

Once you’re happy with your multi-touch attribution model, you can use it to optimize your spend across channels, direct prospect interactions toward higher-performing communication channels, and overhaul your channel-specific campaigns to make them more effective on the path to conversion. Your account-based marketing strategy will become far more efficient in the process, and you’ll be able to use these insights to improve other facets of your brand marketing strategy.

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