Sam Minassian

Fjallraven

Feb 7, 2017

How do you create online shopping experiences that encourage users to discover products they didn’t yet know they needed or wanted?

This was the challenge that Fjallraven faced. Though the Swedish outdoors clothing and gear company was built on the back of a very durable, comfortable-to-wear backpack for the hiking type, the brand had also become popular with the metropolitan, fashionable set. Fjallraven had users coming to the site with different purposes and had to figure out how to, in essence, serve two masters at the same time.

For Fjallraven, the answer was personalization. We spoke with Sam Minassian, the Digital Marketing Manager for Fjallraven North America about the challenges of personalizing web experiences for consumers who come to the brand with different needs and wants.

Dynamic Yield: Why did you feel you needed to start personalizing web experiences for your customers?

Sam Minassian: Our brand is pretty unique and we are well known for our bags. We have some really popular bags. But our bag consumer is very different from our hiking/trekking/backpacking customer. One is a fashion consumer; one is an outdoor consumer. The fashion consumer is usually female between the ages of 18-35 and our outdoors consumer is typically male.

So we have different customer segments. When they come to our site, we want to offer the appropriate products and deals to the relevant customer segment. If we wanted to reduce the number of clicks that it takes each customer to get to relevant products, we had to personalize.

But we also want to get the backpack customer to consider our outdoor products. So how do we get a 22 year customer who wants to a backpack to look at a down jacket? We are doing gender specific experiences based on past on website behavior. We can really pinpoint customers much more easily.

We also wanted to segment based on other factors. If they’re coming from snowy locations, we want to offer them different stuff than if they’re coming from a rainy location. With Dynamic Yield, we can mirror what’s happening right outside their window. It has done amazing things for us in getting the right products in front of the right people.

DY: Can you offer an example of how personalization helped you with a specific marketing campaign?

SM: We had a program for the holidays where we had different free shipping thresholds. In a short amount of time, we were able to implement them and test them.

To encourage customers to reach the “free shipping” threshold, we sent in-cart notifications such as, “Add another $50 to get free overnight shipping.” We were unable to do this before we started using Dynamic Yield.

We also played around with low inventory messaging to create urgency and exit intent messaging to keep people from leaving the page before making a purchase.

DY: One of your experiments shows a 45% uplift. What were your expectations going in?

SM: This is the first year with DY so we were testing a lot. We didn’t have a clear expectation for uplift. So, certain cases that we did had amazing results — like similar items on product pages.

DY: How do you think Machine Learning plays a role in product recommendations?

SM: Product recs have been huge. We have 150% click through on recommendation engine on our product pages. So we went from something that was completely ineffective to something that was crucial in our product pages and our cart pages. We also moved that section to optimize. This is a case in point in machine learning. So, we are able to take a customer based on what they are shopping for — and show other products, in the correct size, that might go with their jacket. So, we have seen big increases in Average Order Value. In the future, we want to focus on quantity of products per order too.

DY: How have notifications helped you?

SM: Low in stock notifications have been huge for us, especially during the holiday season. We saw 38% increase in conversions. If you found a perfect jacket and then you receive a message says that you shouldn’t wait another day because it’s about to sell out, the consumer has an incentive to buy. The customer is thinking, “If I don’t buy it now, I have to wait until next winter to get it.”

DY: There are a lot of platforms and products out there that tout personalization. Why did you choose Dynamic Yield?

SM: I met them at a mobile commerce companies conference. We had previously talked to a few different customers but Dynamic Yield was the best solution we came across for key use cases like homepage optimization, getting users to product, and audience segmentations.

It was also incredibly easy to implement and run. We are a small team and we need to be able to conceptualize, strategize, and implement quickly. We weren’t able to do that before we implemented DY.

DY: What was it like working with the Dynamic Yield team?

SM: It was really smooth. I don’t do any development myself but we started with a training session and they taught me how to use the platform. The DY team is really good at helping me. It’s been a really great process overall.

What are your goals for 2017?

SM: We are still trying to improve our audience segmentation practices. We want to dial in even more to the kind of person who comes to the site. We want to get more granular with the male/female audiences and better understand what products they are looking at. We want to know how to make our website experience faster and more convenient for them [the consumers]. All of that stems from Dynamic Yield’s audience segmentation engine.

If you want to learn more about Fjallraven’s success using Dynamic Yield, read the Retail Touchpoints’ feature.

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