How Brands Go Wrong With Digital Marketing Metrics

How Brands Go Wrong With Digital Marketing Metrics

Is your digital marketing data actually providing insights to fuel customer experiences? Definitely not if you're busy with these tactics.

Numbers may not lie, but they certainly mislead and withhold the whole truth on a regular basis, if you’re not careful. And in today’s digital marketing world, it’s easier than ever to be fooled by data that’s masquerading as insights. 

That’s because the end of third-party cookies, the introduction of Mail Privacy Protection (MPP) by Apple, the rise of omnichannel shopping, and other developments have made it harder to see the insights hiding in your data. All of those changes are highlighting the deficiencies in how digital marketers are currently using their metrics.

Let’s talk about four such deficiencies.

In email marketing, for instance, opens have been the easy-to-see metric that’s been abused, wrongly used as a victory metric for subject line A/B testing and given way too much weight in reporting. It took MPP to shift marketers’ focus to better metrics like clicks. And in the web world, traffic is the plentiful, easily accessible metric that’s overused.

The problem with metrics like opens and web traffic is that they’re surface metrics. They’re at the top of the interaction funnel. And for most brands, driving bottom-of-the-funnel activity is the true business goal.

The prevailing myth has been that if you maximize the number of people entering the top of your funnel, then that will naturally trickle down and maximize the number of people at the bottom of the funnel. That myth is responsible for openbait subject lines, clickbait headlines and other misguided gamesmanship that ignore the fact that getting the right people — not just any person — to engage is the key to success.

In contrast, optimizing top-of-the-funnel elements by how they drive mid-funnel and bottom-of-the-funnel activities, helps marketers steer clear of trust-sacrificing tactics and drive customer loyalty and real business growth.

Measuring audience behavior is so much easier when they follow the Golden Path we lay out for them, where they receive a campaign and click through it and then convert. However, in today’s omnichannel world, consumers do that less and less.

Instead of clicking through an email, for example, some subscribers will open their browser and type in the brand’s URL and then buy. Others will see that email and then hop in their car and drive to the brand’s store and buy. Still others will tell their partner about the email, and they’ll buy. And that’s without getting into call centers, social buying and other fulfillment channels and influencing behaviors.

Admittedly, connecting all of those dots can be hard, especially if you don’t have a customer data platform (CDP) that unifies customer information and activity across channels. However, the need has been made clear over the past couple of years. For example, after the huge jump in ecommerce sales in 2020 led to a massive jump in email marketing revenue, the rebound in in-store shopping caused declines during the 2021 holiday season.

However, that decline was purely a symptom of poor visibility into customer behavior across channels. It wasn’t that email marketing was suddenly less effective, but rather that it was driving more subscribers to go to stores, which is unequivocally a great outcome. That was evident in the fact that 2021 holiday retail sales increased more than 14%, according to the National Retail Federation.

In the early 2000s, the average customer purchase involved two touchpoints. However, consumers today use an average of almost six touchpoints, according to research by Aberdeen, Oracle and Relationship One.

So, while using a single-touch attribution model was completely reasonable and appropriate two decades ago, it rarely is today. Yet, plenty of brands are still using last-click or first-click attribution models to assign credit to their channels and marketing efforts, when they should be using a multi-touch attribution model, such as a time decay or U-shaped model.

Of course, switching attribution models is easier said than done. Not only are there data availability issues, but intense internal political issues. The channels and touchpoints that have benefitted from the use of a single-touch attribution model will likely be hesitant to change models. There will probably be fierce debates about the right model to use and just how representative it is of how your audience actually behaves.

I encourage brands to do some analysis and talk to all of the channel stakeholders…and then make the best decision for today. Recognize that there’s no perfect model and that you can always make changes later. Also, recognize that whichever multi-touch model you thoughtfully pick will be drastically more accurate than any single-touch model you were using before.

At far too many companies, marketers are in charge of managing campaigns instead of managing their audiences. So, they spend their days creating the next campaign and maximizing the results of that, instead of creating customer experiences that maximize customer lifetime value.

Similar to the focus on surface metrics, the underlying problem in this approach is the flawed belief that maximizing campaign results naturally maximizes audience results. It doesn’t. This focus on campaigns instead of customer experiences creates a variety of problems, including: 

All of that tends to lead to higher list churn and shorter list tenures, which drives up subscriber and customer acquisition costs and drives down customer lifetime values. At its core, campaign-centric thinking is short-term thinking, while subscriber-centric thinking is long-term thinking. 

Extracting insights from digital marketing metrics is harder than ever. But it’s also more vital than ever, too. Find the insights in your data by focusing on deep metrics, cross-channel behaviors, multi-touch attribution and customer-centric metrics like customer lifetime value.

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