In this week’s interview, we talk customer insights with Eric Tsai, Vice President of Marketing and Analytics at Joybird.
June 29, 2018
Can you tell me about your role?
I am the VP of Marketing & Analytics at Joybird – we leverage technology to manufacture, source, and distribute quality furniture directly to consumers.
A big part of my role is growing the business leveraging data and technology to elevate the brand experience, reduce inefficiency and driving top line revenue.
What’s your biggest priority right now?
At the moment we’re working on building out our customer insights program and updating our martech stack to enable more transparency and broader access to data across teams.
We’re really focused on abstracting insight to fill in gaps around the furniture buying experience so we can better cater to the needs of our customers. It’s really about building a smart feedback loop not just for the marketing team but also supports merchandising, products, user experience, and logistics in a pursue to provide a more holistic, 360 view of the customer to our team.
So how are you actually improving this data flow?
We started having meeting with internal business stake holders on their data needs surrounding our customers. What we’ve learned was that there were many data silos that led to inefficiency and reduced visibility into customer data. And since we rely heavily on data to make decisions, our focus on improving the data flow has been on the integrity and quality of the data as priority so having a solid “perspective” of our database and define how we’ll be using the data is the key to improve everything. Once these business rules were defined more technical optimization could be had via data engineering to make the process faster and more efficient.
What did you learn?
Data is a double-edged sword. So handle with care :)
Building a data pipeline to aggregate both the quantitative and qualitative data has already start to empower our team to gain better insights so we can focus on the overall brand experience, not just marketing handing off a prospect to sale but really make every touch point friction-less.
First, we had to really identify the context behind the numbers and how they fit into the entire picture of our customers and their journeys.
Second we had to really ask ourselves if we could trust what the data points were saying; if what we’re tracking doesn’t make sense or somehow connect to bring value to more insights about the customer’s over experience/journey, we move on.
Our goal is to build a culture of objectivity and merits so naturally everything will need to be challenged on integrity when it comes to tracking and measurement of data as well.
Do you trust your data more now than you did at the start of your customer insights program?
Yes and as it should since every day that goes by, we’re collecting more data and it’s increasing the integrity of what the data is indicating.
As we saw more benefits from improving our marketing data, we started expanding our customer insights program and has started to further the insights via customer interviews and in-person meetings to further enriched our data and learning of the customers. It showed what our customers really think of us and why beyond just click data from analytics platforms.
How important is resourcing?
It’s more about having access to proven expertise and trusted partners since resources may not always be available. We had to learn enough to understand how and why just so we can make better informed decisions. As a technology company, our leadership strives to stay on top and act quickly to align with strategies to improve the brand experience, not just the buying experience. Ultimately being resourceful simple means having the ability to acquire the necessary outcome fast. We see that as one of the key difference makers in opportunity costs.
How important is Machine Learning in all of this?
ML is being and will continue to be commoditized as it progresses through it’s natural “hype cycle”. if your data has integrity and is of good quality, that value will stay with your business/brand regardless of what the future looks like. I think its important to really understand the ecosystem you’re playing in from both marketplace, marketing, technology, as well as resources and then formulate your hypothesis of how you see ML can help you in terms of applications. For example, ML to us is about explaining cross-sectional data differences to improve our ability to explain our forecasts/predictive models that’s already built.
Any final advice on improving customer insights?
Be obsessed with your customers. Challenge assumptions and standardize your KPIs (and change when/where make senses) so everyone’s align on goals and the reasoning behind them.
Identify and obtain all data sources, bring a level of attention to the quality and integrity of your data, ultimately what you get out of your data is what you put in.
In this week’s interview, we talk customer insights with Eric Tsai, Vice President of Marketing and Analytics at Joybird.
June 29, 2018
Can you tell me about your role?
I am the VP of Marketing & Analytics at Joybird – we leverage technology to manufacture, source, and distribute quality furniture directly to consumers.
A big part of my role is growing the business leveraging data and technology to elevate the brand experience, reduce inefficiency and driving top line revenue.
What’s your biggest priority right now?
At the moment we’re working on building out our customer insights program and updating our martech stack to enable more transparency and broader access to data across teams.
We’re really focused on abstracting insight to fill in gaps around the furniture buying experience so we can better cater to the needs of our customers. It’s really about building a smart feedback loop not just for the marketing team but also supports merchandising, products, user experience, and logistics in a pursue to provide a more holistic, 360 view of the customer to our team.
So how are you actually improving this data flow?
We started having meeting with internal business stake holders on their data needs surrounding our customers. What we’ve learned was that there were many data silos that led to inefficiency and reduced visibility into customer data. And since we rely heavily on data to make decisions, our focus on improving the data flow has been on the integrity and quality of the data as priority so having a solid “perspective” of our database and define how we’ll be using the data is the key to improve everything. Once these business rules were defined more technical optimization could be had via data engineering to make the process faster and more efficient.
What did you learn?
Data is a double-edged sword. So handle with care :)
Building a data pipeline to aggregate both the quantitative and qualitative data has already start to empower our team to gain better insights so we can focus on the overall brand experience, not just marketing handing off a prospect to sale but really make every touch point friction-less.
First, we had to really identify the context behind the numbers and how they fit into the entire picture of our customers and their journeys.
Second we had to really ask ourselves if we could trust what the data points were saying; if what we’re tracking doesn’t make sense or somehow connect to bring value to more insights about the customer’s over experience/journey, we move on.
Our goal is to build a culture of objectivity and merits so naturally everything will need to be challenged on integrity when it comes to tracking and measurement of data as well.
Do you trust your data more now than you did at the start of your customer insights program?
Yes and as it should since every day that goes by, we’re collecting more data and it’s increasing the integrity of what the data is indicating.
As we saw more benefits from improving our marketing data, we started expanding our customer insights program and has started to further the insights via customer interviews and in-person meetings to further enriched our data and learning of the customers. It showed what our customers really think of us and why beyond just click data from analytics platforms.
How important is resourcing?
It’s more about having access to proven expertise and trusted partners since resources may not always be available. We had to learn enough to understand how and why just so we can make better informed decisions. As a technology company, our leadership strives to stay on top and act quickly to align with strategies to improve the brand experience, not just the buying experience. Ultimately being resourceful simple means having the ability to acquire the necessary outcome fast. We see that as one of the key difference makers in opportunity costs.
How important is Machine Learning in all of this?
ML is being and will continue to be commoditized as it progresses through it’s natural “hype cycle”. if your data has integrity and is of good quality, that value will stay with your business/brand regardless of what the future looks like. I think its important to really understand the ecosystem you’re playing in from both marketplace, marketing, technology, as well as resources and then formulate your hypothesis of how you see ML can help you in terms of applications. For example, ML to us is about explaining cross-sectional data differences to improve our ability to explain our forecasts/predictive models that’s already built.
Any final advice on improving customer insights?
Be obsessed with your customers. Challenge assumptions and standardize your KPIs (and change when/where make senses) so everyone’s align on goals and the reasoning behind them.
Identify and obtain all data sources, bring a level of attention to the quality and integrity of your data, ultimately what you get out of your data is what you put in.