How Carter’s juggles an overhaul of marketing measures

There’s a disconnect between those ad buyers in the trenches of online advertising and their own corporate executives down to the C-suite, who are used to determining marketing groups’ budgets based on return on investment and other basic advertising KPIs that, frankly, are going haywire right now.

One way to handle this disconnect is to add someone who speaks both languages, so to speak. To bridge the gap between finance and marketing for children’s clothing and footwear brand Carter’s, Jeffrey Coleman, a former financial analyst and director of agency analytics, made the jump a year ago to the brand, beginning a leadership role in the science of digital marketing.

Coleman serves as a finance-trained intermediary for the marketing and finance teams, as well as senior management, as the brand works through its budgeting and analytics process, Coleman said.

“Every day is a new challenge and it feels like the sand beneath our feet is shifting day by day,” Coleman said. “Last year was not like this year; and even since then there have been major changes in Apple iOS, Google and Facebook. We all learn on the fly as we go.

AdExchanger has caught up with Coleman, as Carter’s negotiates a delicate overhaul of marketing metrics that actually gives credit (and budgets) to media outlets that deserve it. It also explains how the retailer should strengthen their first-party data set.


AdExchanger: What is Carter’s current setup for attribution and analytics?

JEFFREY COLEMAN: We think we’re as specific as possible, but it makes sense to do that kind of attribution.

Right now we have a media mix model that allows us to look at it from that angle as well. We had a multi-touch attribution model (MTA), but we are in the process of finding another vendor to go with MTA at this time.

Are you abandoning the MTA model, redesigning it, or just adding another provider?

We rely heavily on media mix modeling. The reason for this is that, at least on the social media side, it is increasingly becoming a black box. But with media mix modeling, we can examine [social media] from the prism of incrementality.

There is always a factor of error in any attribution model. Modeling the media mix allows us to ask ourselves, “What are the things that our model says are driving incremental gains?” Whether it’s traffic, sales or new customer acquisitions. »

From there, we can manage the portfolio accordingly. If you’re watching a video online, for example, someone might watch a video and not necessarily click. How do you measure this? How does this translate into an attribution model?

Then we start introducing the world of digital audio – all those non-clickable or non-interactive channels. But digital marketing measurement is built around interactivity and events that can be tracked by user engagements. How do we assess the impact of these channels on results?

Media mix modeling allows us to do this when other models, whether last-click or multi-touch, are going to have holes.

How does the media buying team handle attribution changes that could cause wild swings in their budgets or reported performance?

The focus on privacy has changed the landscape of what digital marketers access from platforms. Google and Apple are of course huge players in this ecosystem. And some of the things they introduced to protect user privacy played a big part in what we can and can’t get.

Now it’s up to you, the marketer, to develop first-party data. Media mix modeling gives us a way to always see and report back to our management an indication of the value of marketing efforts where data doesn’t allow for the transparency it once did.

But when you get down to the channel level and your paid search or paid social network manager looks at the weeds on the measurement reports, it becomes an effort of art and science.

Arts and sciences?

For example, campaign reports suggest at first glance that maybe you shouldn’t invest in digital audio, or opt out of it and spend more in our direct channel and search keywords because it’s is efficient. Well, that’s a false positive.

What happens is people hear the audio ad and type “Carters.com”. This is where it comes down to an exercise in art and science, to figure out what works and what just pays off.

Online video has one of the highest ROIs for us. The media mix model also indicates that digital audio ads have a high incremental ROI. But you would never know from an attribution perspective, because you can’t click on an audio stream.

There’s a lot of art that goes into interpreting what you see on self-reporting platforms, as well as what your media mix model tells you is happening.

Have you added (or subtracted) vendors as you work through these advertising and measurement issues?

When I came here, there were a lot of manual reports. We deal with a ton of disparate platforms, as you surely know from digital marketing. So the first thing I needed to do was to be able to extract data with sufficient speed. I added a provider called Adverity, which gathers this data so we can start doing more advanced types of analytics.

Make changes and updates made by Google Analytics affect your work closely?

We are not a Google Analytics store per se. We use Adobe Analytics. But we have to be careful and consider these changes because we use many other Google products, whether it’s GA360 (Google’s business analytics service), Google’s display advertising network, and the campaign manager.

The reason GA has an impact on this is that IP addresses have been used, or even have been the primary means of understanding geolocation analysis. So when they pull out those data points, it further crystallizes in our minds why we need to have as robust a first-party data set as possible. We need to rely less and less on third-party data or platform-reported data and focus more and more on building our first-party data warehouse.

Some retailers have launched ad ventures or marketed first-party data for outdoor advertising. Is this planned for Carter’s?

Where Carter’s is, for example, in children’s clothing, you can see interesting intersections and business opportunities with other companies that serve the children’s space or young families. At some point, we might be a good partner for that. Frankly, our first-party data is in the early stages of that maturation.

We have an application and a loyalty program that allow us to know our customers and their buying habits. And once we’ve strengthened our first-party data set, we may be able to partner with non-threatening, non-competitive companies that need data and information about kids and parents of kids who buy. with Carter’s.

What are your proprietary data sources and potential ways to “enhance” this data set?

One thing we want to do is create a way to track our own customers’ shopping habits and behaviors outside of what Facebook and Google can offer us.

We will also be launching our updated CRM this summer. This is going to allow us to really increase functionality in a way that we just weren’t able to do in our old CRM. It will be a cloud-based solution built on top of the Snowflake environment. This CRM will allow us to start developing our ability to collect data and track our customer IDs across different channels and digital activities.

This interview has been condensed and edited.

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