Attribution is sometimes perceived as being too complex, too technical, and too cold. However, when you look beyond the advanced-level math that goes into attribution algorithms and consider consumer behavior and buying tendencies, the importance of attributing revenue across the customer purchase path becomes very apparent.

Consider your own purchase behavior. How many steps did it take to get you from awareness to purchase the last time you bought something? Chances are it took you at least a few steps to get from “I think I could use a new lawn mower” to deciding “This 200cc self-driving, side-discharge robot lawnmower is the exact one that I need, and I’m going to purchase it this weekend because there’s going to be a seasonal sale.”

With that in mind, the next logical question is, based on your customer journey, do you think it makes sense for the last advertisement you saw prior to buying to get 100% of the credit for your purchase? If your answer is “no, that doesn’t make sense,” then you’ve uncovered the problem that attribution is trying to address.

On the analysis end, attribution modeling platforms like Adometry are tackling one of the most grounded-in-reality problems marketers face: “Is my multi-channel marketing budget being spent on the right channels?” On the execution end, revenue management platforms like Marin Software enable marketers to optimize their campaigns based on their advanced attribution data and answer the question, “How can I take that attribution data and improve my future ROI?”

As a recent Google study showed, consumer purchase paths are rarely straightforward; 60% of purchases take multiple steps, and depending on the industry you’re in, up to 84% of total revenue can come from purchases that required multiple steps across several days. Advanced attribution models can quantify each step of the customer purchase pathway. Armed with this knowledge, marketers can begin to associate ROI with specific marketing channels, understand the time lag for customer decision-making, and optimize spending across different marketing channels.

The multi-step customer purchase path may not be an issue if every step occurred within a single channel, but alas that’s not the case. The path from discovery to purchase typically involves multiple disparate marketing channels, each playing a slightly different role.

In order to optimize your marketing spend, you first need to understand the interplay amongst the various channels. Data-driven attribution allows marketers to assign proper credit to each touch point along the buyer journey. This allows the marketer to understand the proper valuation of channel and budget, and bid and tailor creative more effectively.

For example, within the travel vertical, Social acts as an assistive interaction and is many steps displaced from the actual purchase decision. In contrast, Display is almost as close to the customer’s purchase decision as the paid Search channel. In this case, direct marketers may optimize their campaigns to weigh the Display and Search channels more significantly. In many other cases, Display plays much more of an assistive role, and direct marketers may optimize their campaigns towards Search or another channel that is closer to the customer’s purchase decision.

As the saying goes, “knowing is half the battle.” But knowing is only half the battle. The value of attribution is only realized once marketers can act upon their data. Adometry’s Programmatic Connector enables marketers to seamlessly incorporate attribution data into day-to-day decision-making workflows. Additionally, this is where an open stack execution partner like Marin Software helps complete the circle. Marin’s Revenue Connect is an open, flexible platform that enables advertisers to integrate data from any of their sources, including advanced attribution data, to improve campaign performance.

Activating your attribution data can help achieve real results. MoneySuperMarket, the UK’s leading price comparison site, partnered with Marin Software and Adometry to activate their attribution data in their marketing campaigns. By marrying their search intent and first-party audience data and then applying an algorithmic multi-click attribution model, MoneySuperMarket increased CTR by 12% and reduced CPC 7% across their motor insurance campaign, and increased profit margins 14% across all insurance campaigns.