The Fundamentals of the Funnel — Getting to the Bottom of It
Enterprise marketers are under constant pressure to show results — to prove how the ad dollars they request and spend are bearing fruit and affecting the bottom line in a positive way. It comes down to return on ad spend (ROAS), a clear indicator of what is working to drive buyers through the bottom of the sales funnel.
Let’s visualize that funnel. The goal is to collect customers (call them marbles) in the top of the funnel, push as many of those marbles through the middle and, ultimately, direct as many marbles as possible through and out the narrow, bottom part of the funnel with a purchase. But not every marble follows the same path from top to bottom, or at the same speed.
Marketing Attribution’s Impartial Approach
Enter marketing attribution, specifically multi-touch attribution (MTA). MTA can provide a third-party, impartial view of how each marble made it through the funnel. Maybe the buying journey looked like this:
· A potential customer heard a broadcast ad (radio or television);
· Next saw a banner ad;
· Then read a blog;
· Submitted a form online;
· Were contacted by a salesperson due to the form submission;
· Eventually physically visited a showroom and, finally, made a purchase.
That’s a specific journey tied to a specific consumer, and each portion of their journey deserves some weight.
So How Does Attribution Work?
Attribution analyzes that journey, and the journeys of all other buyers, to definitively identify the specific touchpoints that influenced those buyers. The resulting report compares marketing dollars spent to the revenue brought in — the ROAS. This helps optimize ad dollars and eliminate wasted or misdirected ad spend.
One method employed by marketing attribution companies is to place a tracking pixel on a business website. The pixel begins watching for existing marketing campaigns from common sources such as Google, Facebook, Twitter, Instagram, Pinterest, UTM parameters, referrals, and more. Data from these sources is automatically added to a dashboard. Events are triggered when prospects visit web pages, participate in marketing campaigns, view impressions of ads, fill out a form, and act on any other source event the business would like to examine. Online, as well as offline, efforts are tracked. The pixel gives impartial credit to all marketing programs.
A Case Study — Garnering More Sales Through Attribution
With a total ad spend of $613,000 for the first quarter, a direct-to-consumer eCommerce company was doing its best to gain traction, and sales, for its products. Much of its ad spend was divided between Facebook and Google. Based on click and conversion data from these two “walled gardens,” it was impossible to know which advertising source was performing better at generating new sales.
Attribution was used to track 9,000 purchases to impartially define customer journeys. Data was collected on 100 percent of the company’s marketing touchpoints. At first glance, the initial analysis showed that Facebook received a modest 41 percent credit for product purchases, while Google received only a measly 7 percent credit. Direct visits, referred visits, and other marketing efforts — including discount and email marketing campaigns — accounted for the rest. Moreover, the cost analysis indicated a higher average cost of acquisition for Google at $73.28, versus only $71.58 for Facebook.
Initially, before attribution, the data showed advertising on Facebook was clearly the best channel for the D2C company. That was wrong.
While performing a complete ROAS analysis over the period, it was clear that although a greater number of purchases were attributed to Facebook, the numbers were skewed by a disproportionate share of the overall ad spend. The company was allocating 83 percent of its budget to Facebook, and only 17 percent to Google. This resulted in a significantly larger reach with Facebook ads, and thus explained why basic attribution models showed Facebook as the clear winner in terms of sales volume. However, this was not a complete picture and didn’t facilitate optimization across channels nor consider the shared costs of delivering those conversions.
The vast majority of customer journeys contained both a Facebook ad and a Google ad touchpoint in the path to conversion. Not sharing credit between these two was the problem.
Taking a Course of Action
In the end, the attribution data suggested moving a percentage of its ad spend away from Facebook and reallocating it toward Google to achieve higher ROAS. By monitoring customer journeys, and ROAS, the company was able to continue to optimize its ad spend, adjust its marketing campaigns, and produce maximum revenue from each advertising dollar spent. The company was able to iterate its marketing campaigns over the course of several weeks, leading to an improvement in results.
And because attribution identifies what is working and not working in real time, companies can pivot — as in the case above — multiple times as necessary. This allows enterprise marketers to steer their ad spend in the right direction. In the end, the company in the case study saw slight increase in revenue year over year, but saw a substantial decrease in ad spend — from $613,000 in Q1 of year one to $281,000 in Q1 in year two. It’s ROAS — the main Key Performance Indicator — increased dramatically.
Attribution does its job by examining the entire funnel journey, identifying who the marbles are and their paths into and through the funnel. The data shows what actually led them to exit the funnel by making a purchase (converting) and add to the bottom line. That’s getting to the bottom of it.
