Design To Convert with Matt Bahr: The Evolution of The Attribution Ecosystem
Here are a few of the points discussed:
- How has the attribution ecosystem evolved since 2020?
- Does Fairing provide default questions to get customers started?
- What are some Fairing use cases merchants should be thinking about?
- How do you help merchants handle bias?
- How do different sized merchants use Fairing differently?
- Can you explain zero party data and how it’s evolving?
- What should merchants internalize to get the most value out of Fairing?
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How has the attribution ecosystem evolved since 2020?
I'd like to say that we had somewhat of a crystal ball, but it's really just a matter of timing. I got into e-commerce in 2010, so I've been in this space for a bit. Back then, there wasn't such a simple arbitrage opportunity with Facebook, and it was never a matter of just putting money in and getting money out at scale. Facebook came along, got very good at targeting, and everyone started putting all their eggs in one basket. That's typically never a good idea. Obviously, you want to scale what's working and diversify your strategy if your goal is to build a sustainable business. What iOS 14 woke everyone up to is that we should actually think about diversifying where our customers are coming from. We shouldn't put all our ad spend into the Meta platform. We should also consider things like Word of Mouth and try to understand the actual scope of where customers come from. It was a good wake-up call for everyone, and I'm sure we'll see more channels repeat themselves when there's an awesome arbitrage opportunity. We like to think that Fairing can help brands identify those opportunities early just by listening to customers.
Does Fairing provide default questions to get customers started?
We currently have 23 questions in our Question Bank . Our methodologist advisor, who has worked on research teams at Facebook and Airbnb, has helped us craft these questions and filter how we word them to avoid bias. The order of the questions is also important. We think surveys are antiquated and haven't been innovated in the last 50 to 75 years. Instead, we have a feature called a Question Stream™ where questions can live on their own.
With Question Stream™, it's super simple to get started from A to B. In fact, we have a video on YouTube showing me installing Fairing, accepting payments, and getting an attribution question live in just 53 seconds. Our Northstars focus on asking the right question at the right time, which allows us to dynamically serve a question based on what we already know about the customer. So let's ask a different question that's going to deliver more business value.
What are some Fairing use cases merchants should be thinking about?
We grouped our use cases into four categories. The first category is attribution, and there are three different attribution questions. We don't recommend asking all of them at once to avoid overwhelming the user.
The first question is, "How did you hear about us?" This works for brands that are fairly new. However, for legacy brands, the second question is more appropriate: "What led you to the store today or the brand name today?" We have a case study with Playboy coming out soon about how they used this question to understand attribution. We had to help refactor their question to improve it, and now they have an option that simply asks, "Are you a legacy Playboy person?" This change led to better responses. The third question is, "How long ago did you hear about us?" This helps brands understand the time lag and attribution window. In Google Analytics, the time lag page is not very helpful since users cross devices, and many customers purchase the first time they land on the site.The second category is personalization, where you can push anything to Klaviyo to help segment and personalize responses. Our favorite question is, "How would you describe yourself?" This simple question helped a customer understand that over half of their customers were hobbyists, which changed their approach.
The third category is CRO, or conversion rate optimization. This includes anything that you can ask a customer to improve their experience. Questions like, "How was your shopping experience today?" can create a tap list of things to improve.The fourth category is research, which includes questions about age, gender, or if the customer is switching from another brand. These questions help better understand the customer.
How do you help merchants handle bias?
Bias isn't necessarily a bad thing if you acknowledge there is bias. We lean on our Question Bank. When building it, our advisor told us not to allow customers to change any questions, but we disagreed because it could open up a can of worms with everyone wanting to make changes to fit their brand. This is step one. Our focus at Fairing is to be the pipes for zero party and survey data, not to be in the research corner. For deep research, we suggest using Survey Monkey or Qualtrics to poll a segment of your audience, build a PDF and get a 360-degree view of your customer.
What we do is capture data in real-time, asking the right question at the right time, which is our Northstar. We will start getting towards this later this year. Then we allow you to take action on that data in real-time, just like how we integrated Klaviyo, so now customers receive personalized order confirmation emails. We don't lean too far into the research side of things but focus on capturing data to allow you to make better decisions, create a more personalized experience, and allocate your ad spend.
How do different sized merchants use Fairing differently?
It's interesting that our tool isn't that different for various customers, which is a good thing for us. Our ideal customer profile covers a wide range of verticals and sizes. We usually recommend our tool for customers who do more than 500 orders a month. However, we do have customers who do about a half-million orders a month, and they go through their responses manually to clean them up and tag every open-ended response. We're working on automating this process to improve it, but it shows how important this data is to them, that they're willing to allocate resources to improve it. It's a hard thing to solve on the service side of things since we need to categorize open-ended responses without forcing users to submit something that might not reflect what they intend to convey.
Can you explain zero party data and how it’s evolving?
Yeah, zero party data is essentially survey data or form data. Giving your email address is an example of zero party data. Forrester gave it a name a few years ago, but it really should be considered first party data. Third party data will always be there, depending on where privacy goes. However, we believe that zero party data is very foolproof. Consumers don't seem to care about privacy as much as we think, as evidenced by the number of people using password managers. The government, on the other hand, is concerned about privacy.
As we move in that direction, Google and Chrome have been testing various ways to target and attribute consumers on the internet without knowing exactly who they are. Zero party data will always be there, and companies like Mars Candy Bar rely on it. Its Head of Data Analytics was discussing zero-party data at Shoptalk this morning, as third party data becomes harder to purchase or obtain. This is why zero party data will be the key to truly understanding what's going on.
What should merchants internalize to get the most value out of Fairing?
Yeah, our focus is on our Question Stream™ product and the concept of asking questions. We don't want our customers to feel overwhelmed with building surveys. It's really about what you want to learn and how we can help you capture that data as efficiently as possible while also considering scalability, privacy, security, and other important factors for most brands. So you'll hear us emphasizing the value of our Question Stream™ product more this summer, as it can offer much more compared to using a more outdated survey tool.